Global inter-comparison of microwave and infrared LST from multiple sensors (AMSR-E, MODIS, SEVIRI, GOES, and MTSAT-2)

NASA Astrophysics Data System (ADS)

Ermida, Sofia L.; Jiménez, Carlos; Prigent, Catherine; Trigo, Isabel F.; DaCamara, Carlos C.

2017-04-01

Land Surface Temperature (LST) is an important diagnostic parameter of land surface conditions. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, which only allows clear sky estimates. Microwave (MW) observations can alternatively be used to derive an all-weather LST. Here we present an inter-comparison between LST derived from the Advanced Microwave Scanning Radiometer - Earth observation system (AMSR-E), the MODerate resolution Imaging Spectroradiometer (MODIS) on-board Aqua, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board Meteosat Second Generation (MSG) satellites, the Geostationary Operational Environmental Satellite (GOES) and the Japanese Meteorological Imager (JAMI) on-board the Multifunction Transport SATellite (MTSAT-2). The higher discrepancies between MW and IR products are observed over snow covered areas. MW emissivity is highly variable for snow-covered ground and not always properly accounted for by the climatological emissivity used in the retrieval. There is a conspicuous bias between MODIS and AMSR-E over desert areas, which is most likely related to the underestimation of LST by MODIS as previously reported in other studies. Inter-comparison between all IR and MW retrievals shows that the STD of the differences between MW and IR LST is generally higher than between IR retrievals. However, the biases between MW and IR LST are, in some cases, of the same order as the ones observed among infrared products. In particular, GOES presents a daytime bias with respect to AMSR-E of 0.45 K whereas the bias with respect to MODIS is 0.60 K. Given that AMSR-E can provide LST under cloudy conditions, the use of microwaves, considering simultaneous overpasses with IR, represents an increase of more than 250% of the number of available LST estimates over equatorial regions. With the MW products of a comparable quality to the IR ones, the MW LST is a very powerful complement of the IR estimates.

Estimating rice yield from MODIS-Landsat fusion data in Taiwan

NASA Astrophysics Data System (ADS)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.

2017-12-01

Rice production monitoring with remote sensing is an important activity in Taiwan due to official initiatives. Yield estimation is a challenge in Taiwan because rice fields are small and fragmental. High spatiotemporal satellite data providing phenological information of rice crops is thus required for this monitoring purpose. This research aims to develop data fusion approaches to integrate daily Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat data for rice yield estimation in Taiwan. In this study, the low-resolution MODIS LST and emissivity data are used as reference data sources to obtain the high-resolution LST from Landsat data using the mixed-pixel analysis technique, and the time-series EVI data were derived the fusion of MODIS and Landsat spectral band data using STARFM method. The LST and EVI simulated results showed the close agreement between the LST and EVI obtained by the proposed methods with the reference data. The rice-yield model was established using EVI and LST data based on information of rice crop phenology collected from 371 ground survey sites across the country in 2014. The results achieved from the fusion datasets compared with the reference data indicated the close relationship between the two datasets with the correlation coefficient (R2) of 0.75 and root mean square error (RMSE) of 338.7 kgs, which were more accurate than those using the coarse-resolution MODIS LST data (R2 = 0.71 and RMSE = 623.82 kgs). For the comparison of total production, 64 towns located in the west part of Taiwan were used. The results also confirmed that the model using fusion datasets produced more accurate results (R2 = 0.95 and RMSE = 1,243 tons) than that using the course-resolution MODIS data (R2 = 0.91 and RMSE = 1,749 tons). This study demonstrates the application of MODIS-Landsat fusion data for rice yield estimation at the township level in Taiwan. The results obtained from the methods used in this study could be useful to policymakers; and thus, the methods can be transferable to other regions in the world for rice yield estimation.

Different combination of MODIS land surface temperature data for daily air surface temperature estimation in North West Vietnam

NASA Astrophysics Data System (ADS)

Noi Phan, Thanh; Kappas, Martin; Degener, Jan

2017-04-01

Land air temperature (Ta) with high spatial and temporal resolution plays an important role in various applications, such as: crop growth monitoring and simulations, environmental risk models, weather forecasting, land use cover change, urban heat islands, etc. Daily Ta (including Ta-max, Ta-min, and Ta-mean) is usually measured by weather stations (often at 2 m above the ground); thus, Ta is limited in spatial coverage. Satellite data, especially MODIS land surface temperature (LST) data at 1 kilometre and high temporal resolution (4 times per day, combining TERRA and AQUA) are free available and easily to access. However, there is a difference between Ta and LST because of the complex surface energy budget and multiple related variables between them. Several researches states that the Ta could be estimated using MODIS LST data with accurate of 2-4oC. However, there are only a handful of studies using dynamically combining of four MODIS LST data for Ta estimation. In this study, we evaluated all 15 - possible - combinations of four MODIS LST using support vector machine (SVM) and random forests (RFs) models. MODIS LST and Ta data was extracted from 4 weather stations in rural area in North West Vietnam from 2010 to 2012 (three years). Our results indicated that the accuracy of Ta estimation was affected by the different combination and the combined data (multiple variables) gave better results than those of single LST (solely variable), the best result was achieved (coefficient of determination (R2) = 0.95, 0.97, 0.97; root mean square error (RMSE) =1.7, 1.4, 1.2 oC for Ta-min, Ta-max, Ta-mean respectively) when all four LSTs were combined and RFs performed better than SVM.

ESA DUE GlobTemperature project: Infrared-based LST Product

NASA Astrophysics Data System (ADS)

Ermida, Sofia; Pires, Ana; Ghent, Darren; Trigo, Isabel; DaCamara, Carlos; Remedios, John

2016-04-01

One of the purposes of the GlobTemperature project is to provide a product of global Land Surface Temperature (LST) based on Geostationary Earth Orbit (GEO) and Low Earth polar Orbit (LEO) satellite data. The objective is to use existing LST products, which are obtained from different sensors/platforms, combining them into a harmonized product for a reference view angle. In a first approach, only infra-red based retrievals are considered, and LEO LSTs will be used as a common denominator among geostationary sensors. LST data is provided by a wide range of sensors to optimize spatial coverage, namely: (i) 2 LEO sensors - the Advanced Along Track Scanning Radiometer (AATSR) series of instruments on-board ESA's Envisat, and the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's TERRA and AQUA; and (ii) 3 GEO sensors - the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG), the Japanese Meteorological Imager (JAMI) on-board the Japanese Meteorological Association (JMA) Multifunction Transport SATellite (MTSAT-2), and NASA's Geostationary Operational Environmental Satellites (GOES). The merged LST product is generated in two steps: 1) calibration between each LEO and each GEO that consists in the removal of systematic differences (associated to sensor type and LST algorithms, including calibration, atmospheric and surface emissivity corrections, amongst others) represented by linear regressions; 2) angular correction that consists in bringing all LST data to reference (nadir) view. Angular effects on LST are estimated by means of a kernel model of the surface thermal emission, which describes the angular dependence of LST as function of viewing and illumination geometry. The model is adjusted to MODIS and SEVIRI/MSG LST estimates and validated against LST retrievals from those sensors obtained for other years (not used in the calibration). It is shown that the model leads to a reduction of LST differences between the two sensors, indicating that it may be used to effectively estimate/correct angular dependence in LST. A global set of kernel model parameters is finally obtained by adjusting the model to either a GEO and a LEO or the two LEOs (poles). A first version of the merged product will be released in 2016, available for download through the GlobTemperature portal. This includes only the calibration process (step 1), incorporating LST data from SEVIRI, GOES, MTSAT and MODIS; information on directional effects added as an extra layer of information. A second version of the dataset with a better incorporation of the angular correction is currently in preparation.

Does quality control matter? Surface urban heat island intensity variations estimated by satellite-derived land surface temperature products

NASA Astrophysics Data System (ADS)

Lai, Jiameng; Zhan, Wenfeng; Huang, Fan; Quan, Jinling; Hu, Leiqiu; Gao, Lun; Ju, Weimin

2018-05-01

The temporally regular and spatially comprehensive monitoring of surface urban heat islands (SUHIs) have been extremely difficult, until the advent of satellite-based land surface temperature (LST) products. However, these LST products have relatively higher errors compared to in situ measurements. This has resulted in comparatively inaccurate estimations of SUHI indicators and, consequently, may have distorted interpretations of SUHIs. Although reports have shown that LST qualities are important for SUHI interpretations, systematic investigations of the response of SUHI indicators to LST qualities across cities with dissimilar bioclimates are rare. To address this issue, we chose eighty-six major cities across mainland China and analyzed SUHI intensity (SUHII) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The LST-based SUHII differences due to inclusion or exclusion of MODIS quality control (QC) flags (i.e., ΔSUHII) were evaluated. Our major findings included, but are not limited to, the following four aspects: (1) SUHIIs can be significantly impacted by MODIS QC flags, and the associated QC-induced ΔSUHIIs generally accounted for 24.3% (29.9%) of the total SUHII value during the day (night); (2) the ΔSUHIIs differed between seasons, with considerable differences between transitional (spring and autumn) and extreme (summer and winter) seasons; (3) significant discrepancies also appeared among cities located in northern and southern regions, with northern cities often possessing higher annual mean ΔSUHIIs. The internal variations of ΔSUHIIs within individual cities also showed high heterogeneity, with ΔSUHII variations that generally exceeded 5.0 K (3.0 K) in northern (southern) cities; (4) ΔSUHIIs were negatively related to SUHIIs and cloud cover percentages (mostly in transitional seasons). No significant relationship was found in the extreme seasons. Our findings highlight the need to be extremely cautious when using LST product-based SUHIIs to interpret SUHIs.

Evaluation of MODIS Land Surface Temperature with In Situ Snow Surface Temperature from CREST-SAFE

NASA Astrophysics Data System (ADS)

Perez Diaz, C. L.; Lakhankar, T.; Romanov, P.; Munoz, J.; Khanbilvardi, R.; Yu, Y.

2016-12-01

This paper presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) product provided by the National Aeronautics and Space Administration (NASA) Terra and Aqua Earth Observing System satellites using in situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center - Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January-April) and 2014 (February-April). A total of 314 day and night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. Additionally, this investigation incorporated supplementary analyses using meteorological CREST-SAFE in situ variables (i.e. wind speed, cloud cover, incoming solar radiation) to study their effects on in situ snow surface temperature (T-skin) and T-air. Furthermore, a single pixel (1km2) and several spatially averaged pixels were used for satellite LST validation by increasing the MODIS window size to 5x5, 9x9, and 25x25 windows for comparison. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and nighttime values. Results indicate that, although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy ( 1°C), both suggesting that MODIS LST retrievals are reliable for similar land cover classes and atmospheric conditions. Results from the CREST-SAFE in situ variables' analyses indicate that T-air is commonly higher than T-skin, and that a lack of cloud cover results in: lower T-skin and higher T-air minus T-skin difference (T-diff). Additionally, the study revealed that T-diff is inversely proportional to cloud cover, wind speed, and incoming solar radiation. Increasing the MODIS window size showed an overestimation of in situ LST and some improvement in the daytime Terra and nighttime Aqua biases, with the highest accuracy achieved with the 5x5 window. A comparison between MODIS emmisivity from bands 31, 32, and in situ emissivity showed that emissivity errors (Relative error = -.003) were insignificant.

Detection of geothermal anomalies in Tengchong, Yunnan Province, China from MODIS multi-temporal night LST imagery

NASA Astrophysics Data System (ADS)

Li, H.; Kusky, T. M.; Peng, S.; Zhu, M.

2012-12-01

Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using multi-temporal MODIS LST (Land Surface Temperature). The monthly night MODIS LST data from Mar. 2000 to Mar. 2011 of the study area were collected and analyzed. The 132 month average LST map was derived and three geothermal anomalies were identified. The findings of this study agree well with the results from relative geothermal gradient measurements. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect geothermal anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.

Land Surface Temperature Measurements from EOD MODIS Data

NASA Technical Reports Server (NTRS)

Wan, Zheng-Ming

1998-01-01

We made more tests of the version 2.0 daily Level 2 and Level 3 Land-Surface Temperature (LST) code (PGE 16) jointly with the MODIS Science Data Support Team (SDST). After making minor changes a few times, the PGE16 code has been successfully integrated and tested by MODIS SDST, and recently has passed the inspection at the Goddard Distributed Active Archive Center (DAAC). We conducted a field campaign in the area of Mono Lake, California on March 10, 1998, in order to validate the MODIS LST algorithm in cold and dry conditions. Two MODIS Airborne Simulator (MAS) flights were completed during the field campaign, one before noon, and another around 10 pm PST. The weather condition for the daytime flight was perfect: clear sky, the column water vapor measured by radiosonde around 0.3 cm, and wind speed less than a half meter per second. The quality of MAS data is good for both day and night flights. We analyzed the noise equivalent temperature difference (NE(delta)T) and the calibration accuracy of the seven MAS thermal infrared (TIR) bands, that are used in the MODIS day/night LST algorithm, with daytime MAS data over four flat homogeneous study areas: two on Grant Lake (covered with ice and snow, respectively), one on Mono Lake, and another on the snow field site where we made field measurements. NE(delta)T ranges from 0.2 to 0.6 k for bands 42, 45, 46, and 48. It ranges from 0.8 to 1.1 K for bands 30-32. The day and night MAS data have been used to retrieve surface temperature and emissivities in these bands. A simple method to correct the effect of night thin cirrus has been incorporated into the day/night LST algorithm in dry atmospheric conditions. We compared the retrieved surface temperatures with those measured with TIR spectrometer, radiometers and thermistors in the snow test site, and the retrieved emissivity images with topographic map. The daytime LST values match well within 1 K. The night LST retrieved from MAS data is 3.3 K colder than those from field measurements most likely because of the effect of haze at night. The good agreement among the regional averaged surface temperatures obtained from LST values retrieved at different resolutions increased our confidence in the MODIS day/night LST algorithm.

All-weather Land Surface Temperature Estimation from Satellite Data

NASA Astrophysics Data System (ADS)

Zhou, J.; Zhang, X.

2017-12-01

Satellite remote sensing, including the thermal infrared (TIR) and passive microwave (MW), provides the possibility to observe LST at large scales. For better modeling the land surface processes with high temporal resolutions, all-weather LST from satellite data is desirable. However, estimation of all-weather LST faces great challenges. On the one hand, TIR remote sensing is limited to clear-sky situations; this drawback reduces its usefulness under cloudy conditions considerably, especially in regions with frequent and/or permanent clouds. On the other hand, MW remote sensing suffers from much greater thermal sampling depth (TSD) and coarser spatial resolution than TIR; thus, MW LST is generally lower than TIR LST, especially at daytime. Two case studies addressing the challenges mentioned previously are presented here. The first study is for the development of a novel thermal sampling depth correction method (TSDC) to estimate the MW LST over barren land; this second study is for the development of a feasible method to merge the TIR and MW LSTs by addressing the coarse resolution of the latter one. In the first study, the core of the TSDC method is a new formulation of the passive microwave radiation balance equation, which allows linking bulk MW radiation to the soil temperature at a specific depth, i.e. the representative temperature: this temperature is then converted to LST through an adapted soil heat conduction equation. The TSDC method is applied to the 6.9 GHz channel in vertical polarization of AMSR-E. Evaluation shows that LST estimated by the TSDC method agrees well with the MODIS LST. Validation is based on in-situ LSTs measured at the Gobabeb site in western Namibia. The results demonstrate the high accuracy of the TSDC method: it yields a root-mean squared error (RMSE) of 2 K and ignorable systematic error over barren land. In the second study, the method consists of two core processes: (1) estimation of MW LST from MW brightness temperature and (2) three-time-scale decomposition of LST. The method is applied to two MW sensors (i.e. AMSR-E and AMSR2) and MODIS in northeast China and its surrounding area, with dominating land covers of forest and cropland. By comparing against the in-situ LST and surface air temperature, we find the merged LST has similar accuracy to the MODIS LST in version 6 and good image quality.

Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST) is a critical parameter in environmental studies and resource management. The MODIS LST data product has been widely used in various studies, such as drought monitoring, evapotranspiration mapping, soil moisture estimation and forest fire detection. However, cloud cont...

MEaSUReS Land Surface Temperature and Emissivity data records

NASA Astrophysics Data System (ADS)

Cawse-Nicholson, K.; Hook, S. J.; Gulley, G.; Borbas, E. E.; Knuteson, R. O.

2017-12-01

The NASA MEaSUReS program was put into place to produce long-term, well calibrated and validated data records for Earth Science research. As part of this program, we have developed three Earth System Data Records (ESDR) to measure Land Surface Temperature (LST) and emissivity: a high spatial resolution (1km) LST product using Low Earth Orbiting (LEO) satellites; a high temporal resolution (hourly over North America) LST product using Geostationary (GEO) satellites; and a Combined ASTER MODIS Emissivity for Land (CAMEL) ESDR. CAMEL was produced by merging two state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UWIREMIS), and the JPL ASTER Global Emissivity Dataset v4 (GEDv4). The CAMEL ESDR is currently available for download, and is being tested in sounder retrieval schemes (e.g. CrIS, IASI, AIRS) to reduce uncertainties in water vapor retrievals, and has already been implemented in the radiative transfer software RTTOV v12 for immediate use in numerical weather modeling and data assimilation systems. The LEO-LST product combines two existing MODIS products, using an uncertainty analysis approach to optimize accuracy over different landcover classes. Validation of these approaches for retrieving LST have shown that they are complementary, with the split-window approach (MxD11) being more stable over heavily vegetated regions and the physics-based approach (MxD21) demonstrating higher accuracy in semi-arid and arid regions where the largest variations in emissivity exist, both spatially and spectrally. The GEO LST-ESDR product uses CAMEL ESDR for improved temperature-emissivity separation, and the same atmospheric correction as the LEO LST product to ensure consistency across all three data records.

Comparison of MODIS-derived land surface temperature with air temperature measurements

NASA Astrophysics Data System (ADS)

Georgiou, Andreas; Akçit, Nuhcan

2017-09-01

Air surface temperature is an important parameter for a wide range of applications such as agriculture, hydrology and climate change studies. Air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of air surface temperature at both regional and global scales. Land Surface (skin) Temperatures (LST) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to groundbased near surface air (Tair) measurements obtained from 14 observational stations during 2011 to 2015, covering coastal, mountainous and urban areas over Cyprus. Combining Terra and Aqua LST-8 Day and Night acquisitions into a mean monthly value, provide a large number of LST observations and a better overall agreement with Tair. Comparison between mean monthly LSTs and mean monthly Tair for all sites and all seasons pooled together yields a very high correlation and biases. In addition, the presented high standard deviation can be explained by the influence of surface heterogeneity within MODIS 1km2 grid cells, the presence of undetected clouds and the inherent difference between LST and Tair. However, MODIS LST data proved to be a reliable proxy for surface temperature and mostly for studies requiring temperature reconstruction in areas with lack of observational stations.

Analysis Of The Land Surface Temperature And NDVI Using MODIS Data On The Arctic Tundra During The Last Decade

NASA Astrophysics Data System (ADS)

Mattar, C.; Duran-Alarcon, C.; Jimenez-Munoz, J. C.; Sobrino, J. A.

2013-12-01

The arctic tundra is one of the most sensible biome to climate conditions which has experienced important changes in the spatial distribution of temperature and vegetation in the last decades. In this paper we analyzed the spatio-temporal trend of the Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI) over the arctic tundra biome during the last decade (2001-2012) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) land products MOD11C3 (LST) and MOD13C2 (NDVI) were used. Anomalies for each variable were analyzed at monthly level, and the magnitude and statistical significance of the trends were computed using the non-parametric tests of Sen's Slope and Mann-Kendal respectively. The results obtained from MODIS LST data showed a significant increase (p-value < 0.05) on surface temperature over the arctic tundra in the last decade. In the case of the NDVI, the trend was positive (increase on NDVI) but statistically not significant (p-value < 0.05). All tundra regions defined in the Circumpolar Arctic Vegetation Map have presented positive and statistically significant trends in NDVI and LST. Values of trends obtained from MODIS data over all the tundra regions were +1.10 [°C/dec] in the case of LST and +0.005 [NDVI value/dec] in the case of NDVI.

Land surface temperature measurements from EOS MODIS data

NASA Technical Reports Server (NTRS)

Wan, Zhengming

1994-01-01

A generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data has been developed. Accurate radiative transfer simulations show that the coefficients in the split-window algorithm for LST must depend on the viewing angle, if we are to achieve a LST accuracy of about 1 K for the whole scan swath range (+/-55.4 deg and +/-55 deg from nadir for AVHRR and MODIS, respectively) and for the ranges of surface temperature and atmospheric conditions over land, which are much wider than those over oceans. We obtain these coefficients from regression analysis of radiative transfer simulations, and we analyze sensitivity and error by using results from systematic radiative transfer simulations over wide ranges of surface temperatures and emissivities, and atmospheric water vapor abundance and temperatures. Simulations indicated that as atmospheric column water vapor increases and viewing angle is larger than 45 deg it is necessary to optimize the split-window method by separating the ranges of the atmospheric column water vapor and lower boundary temperature, and the surface temperature into tractable sub-ranges. The atmospheric lower boundary temperature and (vertical) column water vapor values retrieved from HIRS/2 or MODIS atmospheric sounding channels can be used to determine the range where the optimum coefficients of the split-window method are given. This new LST algorithm not only retrieves LST more accurately but also is less sensitive than viewing-angle independent LST algorithms to the uncertainty in the band emissivities of the land-surface in the split-window and to the instrument noise.

Multi-scales and multi-satellites estimates of evapotranspiration with a residual energy balance model in the Muzza agricultural district in Northern Italy

NASA Astrophysics Data System (ADS)

Corbari, C.; Bissolati, M.; Mancini, M.

2015-05-01

Evapotranspiration estimates were performed with a residual energy balance model (REB) over an agricultural area using remote sensing data. REB uses land surface temperature (LST) as main input parameter so that energy fluxes were computed instantaneously at the time of data acquisition. Data from MODIS and SEVIRI sensors were used and downscaling techniques were implemented to improve their spatial resolutions. Energy fluxes at the original spatial resolutions (1000 m for MODIS and 5000 m for SEVIRI) as well as at the downscaled resolutions (250 m for MODIS and 1000 m for SEVIRI) were calculated with the REB model. Ground eddy covariance data and remote sensing information from the Muzza agricultural irrigation district in Italy from 2010 to 2012 gave the opportunity to validate and compare spatially distributed energy fluxes. The model outputs matched quite well ground observations when ground LST data were used, while differences increased when MODIS and SEVIRI LST were used. The spatial analysis revealed significant differences between the two sensors both in term of LST (around 2.8 °C) and of latent heat fluxes with values (around 100 W m-2).

Assessment of urban heat Island for Craiova from satellite-based LST

NASA Astrophysics Data System (ADS)

Udristioiu, Mihaela Tinca; Velea, Liliana; Bojariu, Roxana; Sararu, Silviu Constantin

2017-12-01

The urban heat island is defined as an excess of heating in urban areas compared with surrounding rural zones which is illustrated by higher surface and air temperatures in the inner part of the cities. The aim of this study is to identify the UHI effect for Craiova - the largest city in the South-Western part of Romania - and to assess its intensity during summer. To this end, MODIS Land surface temperature (LST) for day and night for summer months (June, July, August), in the interval 2002-2017, as well as yearly Land Cover Type (LCT) data also from MODIS were employed. Furthermore, measurements of air and soil temperature from meteorological station Craiova, available from the National Meteorological Administration database, were used to investigate their relation with LST. The analysis shows that in the urban area of Craiova the long-term summer mean LST is about 4 °C (2 °C), higher than in the rural area during daytime (nighttime). During high temperatures episodes, the mean daytime LST reaches 45-47 °C in the city, while the difference from the rural surrounding area is of 2-3 °C. A high correlation (0.77-0.83) is found between LST and air temperature for all land-use types in the area considered. Both LST and 2m-air temperature time-series manifest an increasing linear tendency over the period considered, being more pronounced during the day.

Land surface temperature measurements from EOS MODIS data

NASA Technical Reports Server (NTRS)

Wan, Zhengming

1995-01-01

A significant progress has been made in TIR instrumentation which is required to establish the spectral BRDF/emissivity knowledge base of land-surface materials and to validate the land-surface temperature (LST) algorithms. The SIBRE (spectral Infrared Bidirectional Reflectance and Emissivity) system and a TIR system for measuring spectral directional-hemispherical emissivity have been completed and tested successfully. Optical properties and performance features of key components (including spectrometer, and TIR source) of these systems have been characterized by integrated use of local standards (blackbody and reference plates). The stabilization of the spectrometer performance was improved by a custom designed and built liquid cooling system. Methods and procedures for measuring spectral TIR BRDF and directional-hemispheric emissivity with these two systems have been verified in sample measurements. These TIR instruments have been used in the laboratory and the field, giving very promising results. The measured spectral emissivities of water surface are very close to the calculated values based on well established water refractive index values in published papers. Preliminary results show that the TIR instruments can be used for validation of the MODIS LST algorithm in homogeneous test sites. The beta-3 version of the MODIS LST software is being prepared for its delivery scheduled in the early second half of this year.

Thermal anomaly mapping from night MODIS imagery of USA, a tool for environmental assessment.

PubMed

Miliaresis, George Ch

2013-02-01

A method is presented for elevation, latitude and longitude decorrelation stretch of multi-temporal MODIS MYD11C3 imagery (monthly average night land surface temperature (LST) across USA and Mexico). Multiple linear regression analysis of principal components images (PCAs) quantifies the variance explained by elevation (H), latitude (LAT), and longitude (LON). The multi-temporal LST imagery is reconstructed from the residual images and selected PCAs by taking into account the portion of variance that is not related to H, LAT, LON. The reconstructed imagery presents the magnitude the standardized LST value per pixel deviates from the H, LAT, LON predicted. LST anomaly is defined as a region that presents either positive or negative reconstructed LST value. The environmental assessment of USA indicated that only for the 25 % of the study area (Mississippi drainage basin), the LST is predicted by the H, LAT, LON. Regions with milled climatic pattern were identified in the West Coast while the coldest climatic pattern is observed for Mid USA. Positive season invariant LST anomalies are identified in SW (Arizona, Sierra Nevada, etc.) and NE USA.

Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring.

PubMed

Wu, Mingquan; Li, Hua; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-08-01

There is a shortage of daily high spatial land surface temperature (LST) data for use in high spatial and temporal resolution environmental process monitoring. To address this shortage, this work used the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Spatial and Temporal Data Fusion Approach (STDFA) to estimate high spatial and temporal resolution LST by combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST and Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The actual ASTER LST products were used to evaluate the precision of the combined LST images using the correlation analysis method. This method was tested and validated in study areas located in Gansu Province, China. The results show that all the models can generate daily synthetic LST image with a high correlation coefficient (r) of 0.92 between the synthetic image and the actual ASTER LST observations. The ESTARFM has the best performance, followed by the STDFA and the STARFM. Those models had better performance in desert areas than in cropland. The STDFA had better noise immunity than the other two models.

Using Landsat, MODIS, and a Biophysical Model to Evaluate LST in Urban Centers

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad Z.; Quattrochi, Dale A.; Bounoua, Lahouari; Lachir, Asia; Zhang, Ping

2016-01-01

In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the models LST to different land cover types, fractions (percentages), and emissivities compared to reference points derived from Landsat thermal data. This was demonstrated in three climatologically- and morphologically-different cities of Atlanta, GA, New York, NY, and Washington, DC. Our results showed that in these cities SiB2 was sensitive to both the emissivity and the land cover type and fraction, but much more sensitive to the latter. The practical implications of these results are rather significant since they imply that the SiB2 model can be used to run different scenarios for evaluating urban heat island (UHI) mitigation strategies. This study also showed that using detailed emissivities per land cover type and fractions from Landsat-derived data caused a convergence of the model results towards the Landsat-derived LST for most of the studied cases. This study also showed that SiB2 LSTs are closer in magnitude to Landsat-derived LSTs than MODIS-derived LSTs. It is important, however, to emphasize that both Landsat and MODIS LSTs are not direct observations and, as such, do not represent a ground truth. More studies will be needed to compare these results to in situ LST data and provide further validation.

Heat waves measured with MODIS land surface temperature data predict changes in avian community structure

Treesearch

Thomas P. Albright; Anna M. Pidgeon; Chadwick D. Rittenhouse; Murray K. Clayton; Curtis H. Flather; Patrick D. Culbert; Volker C. Radeloff

2011-01-01

Heat waves are expected to become more frequent and severe as climate changes, with unknown consequences for biodiversity. We sought to identify ecologically-relevant broad-scale indicators of heat waves based on MODIS land surface temperature (LST) and interpolated air temperature data and assess their associations with avian community structure. Specifically, we...

Simultaneous inversion of multiple land surface parameters from MODIS optical-thermal observations

NASA Astrophysics Data System (ADS)

Ma, Han; Liang, Shunlin; Xiao, Zhiqiang; Shi, Hanyu

2017-06-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface variables usually focus on individual parameters separately even from the same satellite observations, resulting in inconsistent products. Moreover, no efforts have been made to generate global products from integrated observations from the optical to Thermal InfraRed (TIR) spectrum. Particularly, Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal, which contains both reflected and emitted radiation. In this paper, we propose a unified algorithm for simultaneously retrieving six land surface parameters - Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Emissivity (LSE), Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) by exploiting MODIS visible-to-TIR observations. We incorporate a unified physical radiative transfer model into a data assimilation framework. The MODIS visible-to-TIR time series datasets include the daily surface reflectance product and MIR-to-TIR surface radiance, which are atmospherically corrected from the MODIS data using the Moderate Resolution Transmittance program (MODTRAN, ver. 5.0). LAI was first estimated using a data assimilation method that combines MODIS daily reflectance data and a LAI phenology model, and then the LAI was input to the unified radiative transfer model to simulate spectral surface reflectance and surface emissivity for calculating surface broadband albedo and emissivity, and FAPAR. LST was estimated from the MIR-TIR surface radiance data and the simulated emissivity, using an iterative optimization procedure. Lastly, LWUP was estimated using the LST and surface emissivity. The retrieved six parameters were extensively validated across six representative sites with different biome types, and compared with MODIS, GLASS, and GlobAlbedo land surface products. The results demonstrate that the unified inversion algorithm can retrieve temporally complete and physically consistent land surface parameters, and provides more accurate estimates of surface albedo, LST, and LWUP than existing products, with R2 values of 0.93 and 0.62, RMSE of 0.029 and 0.037, and BIAS values of 0.016 and 0.012 for the retrieved and MODIS albedo products, respectively, compared with field albedo measurements; R2 values of 0.95 and 0.93, RMSE of 2.7 and 4.2 K, and BIAS values of -0.6 and -2.7 K for the retrieved and MODIS LST products, respectively, compared with field LST measurements; and R2 values of 0.93 and 0.94, RMSE of 18.2 and 22.8 W/m2, and BIAS values of -2.7 and -14.6 W/m2 for the retrieved and MODIS LWUP products, respectively, compared with field LWUP measurements.

Construction and Analysis of Long-Term Surface Temperature Dataset in Fujian Province

NASA Astrophysics Data System (ADS)

Li, W. E.; Wang, X. Q.; Su, H.

2017-09-01

Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.

Quantifying the clear-sky bias of satellite-derived infrared LST

NASA Astrophysics Data System (ADS)

Ermida, S. L.; Trigo, I. F.; DaCamara, C.

2017-12-01

Land surface temperature (LST) is one of the most relevant parameters when addressing the physical processes that take place at the surface of the Earth. Satellite data are particularly appropriate for measuring LST over the globe with high temporal resolution. Remote-sensed LST estimation from space-borne sensors has been systematically performed over the Globe for nearly 3 decades and geostationary LST climate data records are now available. The retrieval of LST from satellite observations generally relies on measurements in the thermal infrared (IR) window. Although there is a large number of IR sensors on-board geostationary satellites and polar orbiters suitable for LST retrievals with different temporal and spatial resolutions, the use of IR observations limits LST estimates to clear sky conditions. As a consequence, climate studies based on IR LST are likely to be affected by the restriction of LST data to cloudless conditions. However, such "clear sky bias" has never been quantified and, therefore, the actual impact of relying only on clear sky data is still to be determined. On the other hand, an "all-weather" global LST database may be set up based on passive microwave (MW) measurements which are much less affected by clouds. An 8-year record of all-weather MW LST is here used to quantify the clear-sky bias of IR LST at global scale based on MW observations performed by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) onboard NASA's Aqua satellite. Selection of clear-sky and cloudy pixels is based on information derived from measurements performed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the same satellite.

Reconstructing spatial-temporal continuous MODIS land surface temperature using the DINEOF method

NASA Astrophysics Data System (ADS)

Zhou, Wang; Peng, Bin; Shi, Jiancheng

2017-10-01

Land surface temperature (LST) is one of the key states of the Earth surface system. Remote sensing has the capability to obtain high-frequency LST observations with global coverage. However, mainly due to cloud cover, there are always gaps in the remotely sensed LST product, which hampers the application of satellite-based LST in data-driven modeling of surface energy and water exchange processes. We explored the suitability of the data interpolating empirical orthogonal functions (DINEOF) method in moderate resolution imaging spectroradiometer LST reconstruction around Ali on the Tibetan Plateau. To validate the reconstruction accuracy, synthetic clouds during both daytime and nighttime are created. With DINEOF reconstruction, the root mean square error and bias under synthetic clouds in daytime are 4.57 and -0.0472 K, respectively, and during the nighttime are 2.30 and 0.0045 K, respectively. The DINEOF method can well recover the spatial pattern of LST. Time-series analysis of LST before and after DINEOF reconstruction from 2002 to 2016 shows that the annual and interannual variabilities of LST can be well reconstructed by the DINEOF method.

Optimal use of land surface temperature data to detect changes in tropical forest cover

NASA Astrophysics Data System (ADS)

van Leeuwen, Thijs T.; Frank, Andrew J.; Jin, Yufang; Smyth, Padhraic; Goulden, Michael L.; van der Werf, Guido R.; Randerson, James T.

2011-06-01

Rapid and accurate assessment of global forest cover change is needed to focus conservation efforts and to better understand how deforestation is contributing to the buildup of atmospheric CO2. Here we examined different ways to use land surface temperature (LST) to detect changes in tropical forest cover. In our analysis we used monthly 0.05° × 0.05° Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations of LST and Program for the Estimation of Deforestation in the Brazilian Amazon (PRODES) estimates of forest cover change. We also compared MODIS LST observations with an independent estimate of forest cover loss derived from MODIS and Landsat observations. Our study domain of approximately 10° × 10° included the Brazilian state of Mato Grosso. For optimal use of LST data to detect changes in tropical forest cover in our study area, we found that using data sampled during the end of the dry season (˜1-2 months after minimum monthly precipitation) had the greatest predictive skill. During this part of the year, precipitation was low, surface humidity was at a minimum, and the difference between day and night LST was the largest. We used this information to develop a simple temporal sampling algorithm appropriate for use in pantropical deforestation classifiers. Combined with the normalized difference vegetation index, a logistic regression model using day-night LST did moderately well at predicting forest cover change. Annual changes in day-night LST decreased during 2006-2009 relative to 2001-2005 in many regions within the Amazon, providing independent confirmation of lower deforestation levels during the latter part of this decade as reported by PRODES.

Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

NASA Astrophysics Data System (ADS)

Hengl, Tomislav; Heuvelink, Gerard B. M.; Perčec Tadić, Melita; Pebesma, Edzer J.

2012-01-01

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval, resampling and filtering of MODIS images—are anticipated.

Comparison of Satellite-Derived and In-Situ Observations of Ice and Snow Surface Temperatures over Greenland

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Box, Jason E.; Casey, Kimberly A.; Hook, Simon J.; Shuman, Christopher A.; Steffen, Konrad

2008-01-01

The most practical way to get a spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land-surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived "clear-sky" LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air-temperature observations from Greenland Climate Network (GC-Net) automatic-weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from -40 to 0 C. The satellite-derived LSTs agree within a relative RMS uncertainty of approx.0.5 C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a "point" while the satellite instruments record data over an area varying in size from: 57 X 57 m (ETM+), 90 X 90 m (ASTER), or to 1 X 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty approx.2 C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.

Optimal use of land surface temperature data to detect changes in tropical forest cover

NASA Astrophysics Data System (ADS)

Van Leeuwen, T. T.; Frank, A. J.; Jin, Y.; Smyth, P.; Goulden, M.; van der Werf, G.; Randerson, J. T.

2011-12-01

Rapid and accurate assessment of global forest cover change is needed to focus conservation efforts and to better understand how deforestation is contributing to the build up of atmospheric CO2. Here we examined different ways to use remotely sensed land surface temperature (LST) to detect changes in tropical forest cover. In our analysis we used monthly 0.05×0.05 degree Terra MODerate Resolution Imaging Spectroradiometer (MODIS) observations of LST and PRODES (Program for the Estimation of Deforestation in the Brazilian Amazon) estimates of forest cover change. We also compared MODIS LST observations with an independent estimate of forest cover loss derived from MODIS and Landsat observations. Our study domain of approximately 10×10 degree included most of the Brazilian state of Mato Grosso. For optimal use of LST data to detect changes in tropical forest cover in our study area, we found that using data sampled during the end of the dry season (~1-2 months after minimum monthly precipitation) had the greatest predictive skill. During this part of the year, precipitation was low, surface humidity was at a minimum, and the difference between day and night LST was the largest. We used this information to develop a simple temporal sampling algorithm appropriate for use in pan-tropical deforestation classifiers. Combined with the normalized difference vegetation index (NDVI), a logistic regression model using day-night LST did moderately well at predicting forest cover change. Annual changes in day-night LST difference decreased during 2006-2009 relative to 2001-2005 in many regions within the Amazon, providing independent confirmation of lower deforestation levels during the latter part of this decade as reported by PRODES. The use of day-night LST differences may be particularly valuable for use with satellites that do not have spectral bands that allow for the estimation of NDVI or other vegetation indices.

Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control

NASA Technical Reports Server (NTRS)

Moncet, Jean-Luc; Liang, Pan; Galantowicz, John F.; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher

2011-01-01

A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by including a water fraction correction. Also note that current reliance on the MODIS day-night algorithm as a source of LST limits the coverage of the database in the Polar Regions. We will consider relaxing the current restriction as part of future development.

Simultaneous Assimilation of AMSR-E Brightness Temperature and MODIS LST to Improve Soil Moisture with Dual Ensemble Kalman Smoother

NASA Astrophysics Data System (ADS)

Huang, Chunlin; Chen, Weijin; Wang, Weizhen; Gu, Juan

2017-04-01

Uncertainties in model parameters can easily cause systematic differences between model states and observations from ground or satellites, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this paper, a novel soil moisture assimilation scheme is developed to simultaneously assimilate AMSR-E brightness temperature (TB) and MODIS Land Surface Temperature (LST), which can correct model bias by simultaneously updating model states and parameters with dual ensemble Kalman filter (DEnKS). The Common Land Model (CoLM) and a Q-h Radiative Transfer Model (RTM) are adopted as model operator and observation operator, respectively. The assimilation experiment is conducted in Naqu, Tibet Plateau, from May 31 to September 27, 2011. Compared with in-situ measurements, the accuracy of soil moisture estimation is tremendously improved in terms of a variety of scales. The updated soil temperature by assimilating MODIS LST as input of RTM can reduce the differences between the simulated and observed brightness temperatures to a certain degree, which helps to improve the estimation of soil moisture and model parameters. The updated parameters show large discrepancy with the default ones and the former effectively reduces the states bias of CoLM. Results demonstrate the potential of assimilating both microwave TB and MODIS LST to improve the estimation of soil moisture and related parameters. Furthermore, this study also indicates that the developed scheme is an effective soil moisture downscaling approach for coarse-scale microwave TB.

The Impact of Temporal Aggregation of Land Surface Temperature Data for Urban Heat Island Monitoring

NASA Astrophysics Data System (ADS)

Hu, L.; Brunsell, N. A.

2012-12-01

Temporally composited remote sensing products are widely used in monitoring the urban heat island (UHI). In order to quantify the impact of temporal aggregation for assessing the UHI, we examined MODIS land surface temperature (LST) products for 11 years focusing on Houston, Texas and its surroundings. By using the daily LST from 2000 to 2010, the urban and rural daily LST were presented for the 8-day period and annual comparisons for both day and night. Statistics based on the rural-urban LST differences show that the 8-day composite mean UHI effects are generally more intensive than that calculated by daily UHI images. Moreover, the seasonal pattern shows that the summer daytime UHI has the largest magnitude and variation while nighttime UHI magnitudes are much smaller and less variable. Regression analyses enhance the results showing an apparently higher UHI derived from 8-day composite dataset. The summer mean UHI maps were compared, indicating a land cover related pattern. We introduced yearly MODIS land cover type product to explore the spatial differences caused by temporal aggression of LST product. The mean bias caused by land cover types are calculated about 0.5 ~ 0.7K during the daytime, and less than 0.1K at night. The potential causes of the higher UHI are discussed. The analysis shows that the land-atmosphere interactions, which result in the regional cloud formation, are the primary reason.

Evaluation of Surface and Near-Surface Melt Characteristics on the Greenland Ice Sheet using MODIS and QuikSCAT Data

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Nghiem, Son V.; Schaaf, Crystal B.; DiGirolamo, Nicolo E.

2009-01-01

The Greenland Ice Sheet has been the focus of much attention recently because of increasing melt in response to regional climate warming. To improve our ability to measure surface melt, we use remote-sensing data products to study surface and near-surface melt characteristics of the Greenland Ice Sheet for the 2007 melt season when record melt extent and runoff occurred. Moderate Resolution Imaging Spectroradiometer (MODIS) daily land-surface temperature (LST), MODIS daily snow albedo, and a special diurnal melt product derived from QuikSCAT (QS) scatterometer data, are all effective in measuring the evolution of melt on the ice sheet. These daily products, produced from different parts of the electromagnetic spectrum, are sensitive to different geophysical features, though QS- and MODIS-derived melt generally show excellent correspondence when surface melt is present on the ice sheet. Values derived from the daily MODIS snow albedo product drop in response to melt, and change with apparent grain-size changes. For the 2007 melt season, the QS and MODIS LST products detect 862,769 square kilometers and 766,184 square kilometers of melt, respectively. The QS product detects about 11% greater melt extent than is detected by the MODIS LST product probably because QS is more sensitive to surface melt, and can detect subsurface melt. The consistency of the response of the different products demonstrates unequivocally that physically-meaningful melt/freeze boundaries can be detected. We have demonstrated that these products, used together, can improve the precision in mapping surface and near-surface melt extent on the Greenland Ice Sheet.

Remote sensing of forest dynamics and land use in Amazonia

NASA Astrophysics Data System (ADS)

Toomey, Michael Paul

The rich, vast Amazonian ecosystem is directly and indirectly threatened by human activities; remote sensing serves as an essential tool for monitoring, understanding and mitigating these threats. A multi-faceted body of work is described here, addressing three major issues that employ and advance remote sensing techniques for the study of Amazonia and other tropical rainforest regions. In Chapter 2, canopy reflectance modeling and satellite observations were used to quantify the effect of epiphylls on remote sensing of humid forests. Modeling simulations demonstrated sensitivity of canopy-level near infrared and green reflectance to epiphylls on leaves. Time series of Moderate Resolution Imaging Spectrometer (MODIS) data corroborated the modeling results, suggesting a degree of coupling between epiphyll cover and vegetation indices which must be accounted for when using optical remote sensing in humid forests. In Chapter 4, 11 years (2000--2010) of MODIS land surface temperature (LST) data covering the entire Amazon basin were used to ascertain the role of heat stress during droughts in 2005 and 2010. Preliminary accuracy assessments showed that LST data provided reasonably accurate estimates of daytime air temperatures (RMSE = 1.45°C; Chapter 3). There were moderate to strong correlations between LST-based air temperature estimates and tower measurements (mean r = 0.64), illustrating a sensitivity to temporal variability. During both droughts, MODIS LST data detected anomalously high daytime and nighttime canopy temperatures throughout drought-affected regions. Multivariate linear models of LST and precipitation anomalies explained 65.1% of the variability in forest biomass losses, as determined from a wide network of forest inventory plots. These results suggest that models should incorporate both heat and moisture to predict drought effects on tropical forests. In Chapter 5, I performed high spatial and temporal resolution modeling of carbon stocks and fluxes in the state of Rondonia, Brazil for the period 1985--2009. Based on this analysis, Rondonia contributed ˜4% of pan-tropical humid forest deforestation emissions while carbon uptake by secondary forest was negligible due to limited spatial extent and high turnover rates. Spatial analysis of land cover change demonstrated the necessity for fine resolution carbon monitoring in tropical regions dominated by non-mechanized, smallholder land uses.

Estimating Morning Change in Land Surface Temperature from MODIS Day/Night Observations: Applications for Surface Energy Balance Modeling.

PubMed

Hain, Christopher R; Anderson, Martha C

2017-10-16

Observations of land surface temperature (LST) are crucial for the monitoring of surface energy fluxes from satellite. Methods that require high temporal resolution LST observations (e.g., from geostationary orbit) can be difficult to apply globally because several geostationary sensors are required to attain near-global coverage (60°N to 60°S). While these LST observations are available from polar-orbiting sensors, providing global coverage at higher spatial resolutions, the temporal sampling (twice daily observations) can pose significant limitations. For example, the Atmosphere Land Exchange Inverse (ALEXI) surface energy balance model, used for monitoring evapotranspiration and drought, requires an observation of the morning change in LST - a quantity not directly observable from polar-orbiting sensors. Therefore, we have developed and evaluated a data-mining approach to estimate the mid-morning rise in LST from a single sensor (2 observations per day) of LST from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Aqua platform. In general, the data-mining approach produced estimates with low relative error (5 to 10%) and statistically significant correlations when compared against geostationary observations. This approach will facilitate global, near real-time applications of ALEXI at higher spatial and temporal coverage from a single sensor than currently achievable with current geostationary datasets.

Determination of annual and seasonal daytime and nighttime trends of MODIS LST over Greece - climate change implications.

PubMed

Eleftheriou, Dimitrios; Kiachidis, Kyriakos; Kalmintzis, Georgios; Kalea, Argiro; Bantasis, Christos; Koumadoraki, Paraskevi; Spathara, Maria Eleni; Tsolaki, Angeliki; Tzampazidou, Maria Irini; Gemitzi, Alexandra

2018-03-01

Climate change is one of the most challenging research topics during the last few decades, as temperature rise has already posed a significant impact on the earth's functions thus affecting all life of the planet. Land Surface Temperature (LST) is identified as a key variable in environmental and climate studies. The present study investigates the distribution of daytime and nighttime LST trends over Greece, a country in the Mediterranean area which is identified as one of the main "hot-spots" of climate change projections. Remotely sensed LST data were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) sensor in the form of 8-day composites of day and night values at a resolution of 1km for a 17-year period, i.e. from 2000 to 2017. Spatial aggregates of 10km×10km were computed and the annual and seasonal temporal trends were determined for each one of those sub-areas. Results showed that annual trends of daily LST in the majority of areas demonstrated decrease ranging from -1∗10 -2 °C to -1.3∗10 -3 °C, with some sporadic parts showing a slight increase. A totally different outcome is observed in the fate of night LST, with all areas over Greece demonstrating increasing annual trends ranging from 4.6∗10 -5 °C to 3.1∗10 -3 °C, with highest values in the South-East parts of the country. Seasonal trends in day and night LST showed the same pattern, i.e., a general decrease in the day LST and a definite increase in night. An interesting finding is the increase in winter LST trends observed both for day and night LST, indicating that the absolute minimum annual LST observed during winter in Greece increases. Our results also indicate that the annual diurnal LST range is decreasing. Copyright © 2017 Elsevier B.V. All rights reserved.

Parametrization of Land Surface Temperature Fields with Optical and Microwave Remote Sensing in Brazil's Atlantic Forest

NASA Astrophysics Data System (ADS)

McDonald, K. C.; Khan, A.; Carnaval, A. C.

2016-12-01

Brazil is home to two of the largest and most biodiverse ecosystems in the world, primarily encompassed in forests and wetlands. A main region of interest in this project is Brazil's Atlantic Forest (AF). Although this forest is only a fraction of the size of the Amazon rainforest, it harbors significant biological richness, making it one of the world's major hotspots for biodiversity. The AF is located on the East to Southeast region of Brazil, bordering the Atlantic Ocean. As luscious and biologically rich as this region is, the area covered by the Atlantic Forest has been diminishing over past decades, mainly due to human influences and effects of climate change. We examine 1 km resolution Land Surface Temperature (LST) data from NASA's Moderate-resolution Imaging Spectroradiometer (MODIS) combined with 25 km resolution radiometric temperature derived from NASA's Advanced Microwave Scanning Radiometer on EOS (AMSR-E) to develop a capability employing both in combination to assess LST. Since AMSR-E is a microwave remote sensing instrument, products derived from its measurements are minimally effected by cloud cover. On the other hand, MODIS data are heavily influenced by cloud cover. We employ a statistical downscaling technique to the coarse-resolution AMSR-E datasets to enhance its spatial resolution to match that of MODIS. Our approach employs 16-day composite MODIS LST data in combination with synergistic ASMR-E radiometric brightness temperature data to develop a combined, downscaled dataset. Our goal is to use this integrated LST retrieval with complementary in situ station data to examine associated influences on regional biodiversity

A Simple Downscaling Algorithm for Remotely Sensed Land Surface Temperature

NASA Astrophysics Data System (ADS)

Sandholt, I.; Nielsen, C.; Stisen, S.

2009-05-01

The method is illustrated using a combination of MODIS NDVI data with a spatial resolution of 250m and 3 Km Meteosat Second Generation SEVIRI LST data. Geostationary Earth Observation data carry a large potential for assessment of surface state variables. Not the least the European Meteosat Second Generation platform with its SEVIRI sensor is well suited for studies of the dynamics of land surfaces due to its high temporal frequency (15 minutes) and its red, Near Infrared (NIR) channels that provides vegetation indices, and its two split window channels in the thermal infrared for assessment of Land Surface Temperature (LST). For some applications the spatial resolution in geostationary data is too coarse. Due to the low statial resolution of 4.8 km at nadir for the SEVIRI sensor, a means of providing sub pixel information is sought for. By combining and properly scaling two types of satellite images, namely data from the MODIS sensor onboard the polar orbiting platforms TERRA and AQUA and the coarse resolution MSG-SEVIRI, we exploit the best from two worlds. The vegetation index/surface temperature space has been used in a vast number of studies for assessment of air temperature, soil moisture, dryness indices, evapotranspiration and for studies of land use change. In this paper, we present an improved method to derive a finer resolution Land Surface Temperature (LST). A new, deterministic scaling method has been applied, and is compared to existing deterministic downscaling methods based on LST and NDVI. We also compare our results from in situ measurements of LST from the Dahra test site in West Africa.

Estimation of the Total Atmospheric Water Vapor Content and Land Surface Temperature Based on AATSR Thermal Data

PubMed Central

Zhang, Tangtang; Wen, Jun; van der Velde, Rogier; Meng, Xianhong; Li, Zhenchao; Liu, Yuanyong; Liu, Rong

2008-01-01

The total atmospheric water vapor content (TAWV) and land surface temperature (LST) play important roles in meteorology, hydrology, ecology and some other disciplines. In this paper, the ENVISAT/AATSR (The Advanced Along-Track Scanning Radiometer) thermal data are used to estimate the TAWV and LST over the Loess Plateau in China by using a practical split window algorithm. The distribution of the TAWV is accord with that of the MODIS TAWV products, which indicates that the estimation of the total atmospheric water vapor content is reliable. Validations of the LST by comparing with the ground measurements indicate that the maximum absolute derivation, the maximum relative error and the average relative error is 4.0K, 11.8% and 5.0% respectively, which shows that the retrievals are believable; this algorithm can provide a new way to estimate the LST from AATSR data. PMID:27879795

Analysis of Relationship Between Urban Heat Island Effect and Land Use/cover Type Using Landsat 7 ETM+ and Landsat 8 Oli Images

NASA Astrophysics Data System (ADS)

Aslan, N.; Koc-San, D.

2016-06-01

The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88 % for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6 °C for 2001 and 6.8 °C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r2 = 0.7 and r2 = 0.9 for 2001 and 2014, respectively).

Derivation and evaluation of land surface temperature from the geostationary operational environmental satellite series

NASA Astrophysics Data System (ADS)

Fang, Li

The Geostationary Operational Environmental Satellites (GOES) have been continuously monitoring the earth surface since 1970, providing valuable and intensive data from a very broad range of wavelengths, day and night. The National Oceanic and Atmospheric Administration's (NOAA's) National Environmental Satellite, Data, and Information Service (NESDIS) is currently operating GOES-15 and GOES-13. The design of the GOES series is now heading to the 4 th generation. GOES-R, as a representative of the new generation of the GOES series, is scheduled to be launched in 2015 with higher spatial and temporal resolution images and full-time soundings. These frequent observations provided by GOES Image make them attractive for deriving information on the diurnal land surface temperature (LST) cycle and diurnal temperature range (DTR). These parameters are of great value for research on the Earth's diurnal variability and climate change. Accurate derivation of satellite-based LSTs from thermal infrared data has long been an interesting and challenging research area. To better support the research on climate change, the generation of consistent GOES LST products for both GOES-East and GOES-West from operational dataset as well as historical archive is in great demand. The derivation of GOES LST products and the evaluation of proposed retrieval methods are two major objectives of this study. Literature relevant to satellite-based LST retrieval techniques was reviewed. Specifically, the evolution of two LST algorithm families and LST retrieval methods for geostationary satellites were summarized in this dissertation. Literature relevant to the evaluation of satellite-based LSTs was also reviewed. All the existing methods are a valuable reference to develop the GOES LST product. The primary objective of this dissertation is the development of models for deriving consistent GOES LSTs with high spatial and high temporal coverage. Proper LST retrieval algorithms were studied according to the characteristics of the imager onboard the GOES series. For the GOES 8-11 and GOES R series with split window (SW) channels, a new temperature and emissivity separation (TES) approach was proposed for deriving LST and LSE simultaneously by using multiple-temporal satellite observations. Two split-window regression formulas were selected for this approach, and two satellite observations over the same geo-location within a certain time interval were utilized. This method is particularly applicable to geostationary satellite missions from which qualified multiple-temporal observations are available. For the GOES M(12)-Q series without SW channels, the dual-window LST algorithm was adopted to derive LST. Instead of using the conventional training method to generate coefficients for the LST regression algorithms, a machine training technique was introduced to automatically select the criteria and the boundary of the sub-ranges for generating algorithm coefficients under different conditions. A software package was developed to produce a brand new GOES LST product from both operational GOES measurements and historical archive. The system layers of the software and related system input and output were illustrated in this work. Comprehensive evaluation of GOES LST products was conducted by validating products against multiple ground-based LST observations, LST products from fine-resolution satellites (e.g. MODIS) and GSIP LST products. The key issues relevant to the cloud diffraction effect were studied as well. GOES measurements as well as ancillary data, including satellite and solar geometry, water vapor, cloud mask, land emissivity etc., were collected to generate GOES LST products. In addition, multiple in situ temperature measurements were collected to test the performance of the proposed GOES LST retrieval algorithms. The ground-based dataset included direct surface temperature measurements from the Atmospheric Radiation Measurement program (ARM), and indirect measurements (surface long-wave radiation observations) from the SURFace RADiation Budget (SURFRAD) Network. A simulated dataset was created to analyse the sensitivity of the proposed retrieval algorithms. In addition, the MODIS LST and GSIP LST products were adopted to cross-evaluate the accuracy of the GOES LST products. Evaluation results demonstrate that the proposed GOES LST system is capable of deriving consistent land surface temperatures with good retrieval precision. Consistent GOES LST products with high spatial/temporal coverage and reliable accuracy will better support detections and observations of meteorological over land surfaces.

Estimation of the Land Surface Temperature over the Tibetan Plateau by Using Chinese FY-2C Geostationary Satellite Data

PubMed Central

Hu, Yuanyuan; Zhong, Lei; Ma, Yaoming; Zou, Mijun; Xu, Kepiao; Huang, Ziyu; Feng, Lu

2018-01-01

During the process of land–atmosphere interaction, one of the essential parameters is the land surface temperature (LST). The LST has high temporal variability, especially in its diurnal cycle, which cannot be acquired by polar-orbiting satellites. Therefore, it is of great practical significance to retrieve LST data using geostationary satellites. According to the data of FengYun 2C (FY-2C) satellite and the measurements from the Enhanced Observing Period (CEOP) of the Asia–Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet), a regression approach was utilized in this research to optimize the split window algorithm (SWA). The thermal infrared data obtained by the Chinese geostationary satellite FY-2C over the Tibetan Plateau (TP) was used to estimate the hourly LST time series. To decrease the effects of cloud, the 10-day composite hourly LST data were obtained through the approach of maximal value composite (MVC). The derived LST was used to compare with the product of MODIS LST and was also validated by the field observation. The results show that the LST retrieved through the optimized SWA and in situ data has a better consistency (with correlation coefficient (R), mean absolute error (MAE), mean bias (MB), and root mean square error (RMSE) values of 0.987, 1.91 K, 0.83 K and 2.26 K, respectively) than that derived from Becker and Li’s SWA and MODIS LST product, which means that the modified SWA can be applied to achieve plateau-scale LST. The diurnal variation of the LST and the hourly time series of the LST over the Tibetan Plateau were also obtained. The diurnal range of LST was found to be clearly affected by the influence of the thawing and freezing process of soil and the summer monsoon evolution. The comparison between the seasonal and diurnal variations of LST at four typical underlying surfaces over the TP indicate that the variation of LST is closely connected with the underlying surface types as well. The diurnal variation of LST is the smallest at the water (5.12 K), second at the snow and ice (5.45 K), third at the grasslands (19.82 K) and largest at the barren or sparsely vegetated (22.83 K). PMID:29382089

Estimation of the Land Surface Temperature over the Tibetan Plateau by Using Chinese FY-2C Geostationary Satellite Data.

PubMed

Hu, Yuanyuan; Zhong, Lei; Ma, Yaoming; Zou, Mijun; Xu, Kepiao; Huang, Ziyu; Feng, Lu

2018-01-28

During the process of land-atmosphere interaction, one of the essential parameters is the land surface temperature (LST). The LST has high temporal variability, especially in its diurnal cycle, which cannot be acquired by polar-orbiting satellites. Therefore, it is of great practical significance to retrieve LST data using geostationary satellites. According to the data of FengYun 2C (FY-2C) satellite and the measurements from the Enhanced Observing Period (CEOP) of the Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet), a regression approach was utilized in this research to optimize the split window algorithm (SWA). The thermal infrared data obtained by the Chinese geostationary satellite FY-2C over the Tibetan Plateau (TP) was used to estimate the hourly LST time series. To decrease the effects of cloud, the 10-day composite hourly LST data were obtained through the approach of maximal value composite (MVC). The derived LST was used to compare with the product of MODIS LST and was also validated by the field observation. The results show that the LST retrieved through the optimized SWA and in situ data has a better consistency (with correlation coefficient (R), mean absolute error (MAE), mean bias (MB), and root mean square error (RMSE) values of 0.987, 1.91 K, 0.83 K and 2.26 K, respectively) than that derived from Becker and Li's SWA and MODIS LST product, which means that the modified SWA can be applied to achieve plateau-scale LST. The diurnal variation of the LST and the hourly time series of the LST over the Tibetan Plateau were also obtained. The diurnal range of LST was found to be clearly affected by the influence of the thawing and freezing process of soil and the summer monsoon evolution. The comparison between the seasonal and diurnal variations of LST at four typical underlying surfaces over the TP indicate that the variation of LST is closely connected with the underlying surface types as well. The diurnal variation of LST is the smallest at the water (5.12 K), second at the snow and ice (5.45 K), third at the grasslands (19.82 K) and largest at the barren or sparsely vegetated (22.83 K).

Estimating morning changes in land surface temperature from MODIS day/night land surface temperature: Applications for surface energy balance modeling

USDA-ARS?s Scientific Manuscript database

Observations of land surface temperature (LST) are crucial for the monitoring of surface energy fluxes from satellite. Methods that require high temporal resolution LST observations (e.g., from geostationary orbit) can be difficult to apply globally because several geostationary sensors are required...

Frost monitoring of fruit tree with satellite data

NASA Astrophysics Data System (ADS)

Fan, Jinlong; Zhang, Mingwei; Cao, Guangzheng; Zhang, Xiaoyu; Liu, Chenchen; Niu, Xinzan; Xu, Wengbo

2012-09-01

The orchards are developing very fast in the northern China in recent years with the increasing demands on fruits in China. In most parts of the northern China, the risk of frost damage to fruit tree in early spring is potentially high under the background of global warming. The growing season comes earlier than it does in normal year due to the warm weather in earlier spring and the risk will be higher in this case. According to the reports, frost event in spring happens almost every year in Ningxia Region, China. In bad cases, late frosts in spring can be devastating all fruit. So lots of attention has been given to the study in monitoring, evaluating, preventing and mitigating frost. Two orchards in Ningxia, Taole and Jiaozishan orchards were selected as the study areas. MODIS data were used to monitor frost events in combination with minimum air temperature recorded at weather station. The paper presents the findings. The very good correlation was found between MODIS LST and minimum air temperature in Ningxia. Light, middle and severe frosts were captured in the study area by MODIS LST. The MODIS LST shows the spatial differences of temperature in the orchards. 10 frost events in April from 2000 to 2010 were captured by the satellite data. The monitoring information may be hours ahead circulated to the fruit farmers to prevent the damage and loss of fruit trees.

Antarctic Temperature Extremes from MODIS Land Surface Temperatures: New Processing Methods Reveal Data Quality Puzzles

NASA Astrophysics Data System (ADS)

Grant, G.; Gallaher, D. W.

2017-12-01

New methods for processing massive remotely sensed datasets are used to evaluate Antarctic land surface temperature (LST) extremes. Data from the MODIS/Terra sensor (Collection 6) provides a twice-daily look at Antarctic LSTs over a 17 year period, at a higher spatiotemporal resolution than past studies. Using a data condensation process that creates databases of anomalous values, our processes create statistical images of Antarctic LSTs. In general, the results find few significant trends in extremes; however, they do reveal a puzzling picture of inconsistent cloud detection and possible systemic errors, perhaps due to viewing geometry. Cloud discrimination shows a distinct jump in clear-sky detections starting in 2011, and LSTs around the South Pole exhibit a circular cooling pattern, which may also be related to cloud contamination. Possible root causes are discussed. Ongoing investigations seek to determine whether the results are a natural phenomenon or, as seems likely, the results of sensor degradation or processing artefacts. If the unusual LST patterns or cloud detection discontinuities are natural, they point to new, interesting processes on the Antarctic continent. If the data artefacts are artificial, MODIS LST users should be alerted to the potential issues.

Pre-Launch Performance Assessment of the VIIRS Land Surface Temperature Environmental Data Record

NASA Astrophysics Data System (ADS)

Hauss, B.; Ip, J.; Agravante, H.

2009-12-01

The Visible/Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) provides the surface temperature of land surface including coastal and inland-water pixels at VIIRS moderate resolution (750m) during both day and night. To predict the LST under optimal conditions, the retrieval algorithm utilizes a dual split-window approach with both Short-wave Infrared (SWIR) channels at 3.70 µm (M12) and 4.05 µm (M13), and Long-wave Infrared (LWIR) channels at 10.76 µm (M15) and 12.01 µm (M16) to correct for atmospheric water vapor. Under less optimal conditions, the algorithm uses a fallback split-window approach with M15 and M16 channels. By comparison, the MODIS generalized split-window algorithm only uses the LWIR bands in the retrieval of surface temperature because of the concern for both solar contamination and large emissivity variations in the SWIR bands. In this paper, we assess whether these concerns are real and whether there is an impact on the precision and accuracy of the LST retrieval. The algorithm relies on the VIIRS Cloud Mask IP for identifying cloudy and ocean pixels, the VIIRS Surface Type EDR for identifying the IGBP land cover type for the pixels, and the VIIRS Aerosol Optical Thickness (AOT) IP for excluding pixels with AOT greater than 1.0. In this paper, we will report the pre-launch performance assessment of the LST EDR based on global synthetic data and proxy data from Terra MODIS. Results of both the split-window and dual split-window algorithms will be assessed by comparison either to synthetic "truth" or results of the MODIS retrieval. We will also show that the results of the assessment with proxy data are consistent with those obtained using the global synthetic data.

Evaluation and sensitivity testing of a coupled Landsat-MODIS downscaling method for land surface temperature and vegetation indices in semi-arid regions

NASA Astrophysics Data System (ADS)

Kim, Jongyoun; Hogue, Terri S.

2012-01-01

The current study investigates a method to provide land surface parameters [i.e., land surface temperature (LST) and normalized difference vegetation index (NDVI)] at a high spatial (˜30 and 60 m) and temporal (daily and 8-day) resolution by combining advantages from Landsat and moderate-resolution imaging spectroradiometer (MODIS) satellites. We adopt a previously developed subtraction method that merges the spatial detail of higher-resolution imagery (Landsat) with the temporal change observed in coarser or moderate-resolution imagery (MODIS). Applying the temporal difference between MODIS images observed at two different dates to a higher-resolution Landsat image allows prediction of a combined or fused image (Landsat+MODIS) at a future date. Evaluation of the resultant merged products is undertaken within the Southeastern Arizona region where data is available from a range of flux tower sites. The Landsat+MODIS fused products capture the raw Landsat values and also reflect the MODIS temporal variation. The predicted Landsat+MODIS LST improves mean absolute error around 5°C at the more heterogeneous sites compared to the original satellite products. The fused Landsat+MODIS NDVI product also shows good correlation to ground-based data and is relatively consistent except during the acute (monsoon) growing season. The sensitivity of the fused product relative to temporal gaps in Landsat data appears to be more affected by uncertainty associated with regional precipitation and green-up, than the length of the gap associated with Landsat viewing, suggesting the potential to use a minimal number of original Landsat images during relatively stable land surface and climate conditions. Our extensive validation yields insight on the ability of the proposed method to integrate multiscale platforms and the potential for reducing costs associated with high-resolution satellite systems (e.g., SPOT, QuickBird, IKONOS).

Using Machine learning method to estimate Air Temperature from MODIS over Berlin

NASA Astrophysics Data System (ADS)

Marzban, F.; Preusker, R.; Sodoudi, S.; Taheri, H.; Allahbakhshi, M.

2015-12-01

Land Surface Temperature (LST) is defined as the temperature of the interface between the Earth's surface and its atmosphere and thus it is a critical variable to understand land-atmosphere interactions and a key parameter in meteorological and hydrological studies, which is involved in energy fluxes. Air temperature (Tair) is one of the most important input variables in different spatially distributed hydrological, ecological models. The estimation of near surface air temperature is useful for a wide range of applications. Some applications from traffic or energy management, require Tair data in high spatial and temporal resolution at two meters height above the ground (T2m), sometimes in near-real-time. Thus, a parameterization based on boundary layer physical principles was developed that determines the air temperature from remote sensing data (MODIS). Tair is commonly obtained from synoptic measurements in weather stations. However, the derivation of near surface air temperature from the LST derived from satellite is far from straight forward. T2m is not driven directly by the sun, but indirectly by LST, thus T2m can be parameterized from the LST and other variables such as Albedo, NDVI, Water vapor and etc. Most of the previous studies have focused on estimating T2m based on simple and advanced statistical approaches, Temperature-Vegetation index and energy-balance approaches but the main objective of this research is to explore the relationships between T2m and LST in Berlin by using Artificial intelligence method with the aim of studying key variables to allow us establishing suitable techniques to obtain Tair from satellite Products and ground data. Secondly, an attempt was explored to identify an individual mix of attributes that reveals a particular pattern to better understanding variation of T2m during day and nighttime over the different area of Berlin. For this reason, a three layer Feedforward neural networks is considered with LMA algorithm. Considering the different relationships between T2m and LST for different land types enable us to improve better parameterization for determination of the best non-linear relation between LST and T2m over Berlin during day and nighttime. The results of the study will be presented and discussed.

MEaSUREs Land Surface Temperature from GOES Satellites

NASA Astrophysics Data System (ADS)

Pinker, Rachel T.; Chen, Wen; Ma, Yingtao; Islam, Tanvir; Borbas, Eva; Hain, Chris; Hulley, Glynn; Hook, Simon

2017-04-01

Information on Land Surface Temperature (LST) can be generated from observations made from satellites in low Earth orbit (LEO) such as MODIS and ASTER and by sensors in geostationary Earth orbit (GEO) such as GOES. Under a project titled: "A Unified and Coherent Land Surface Temperature and Emissivity Earth System Data Record for Earth Science" led by Jet Propulsion Laboratory, an effort is underway to develop long term consistent information from both such systems. In this presentation we will describe an effort to derive LST information from GOES satellites. Results will be presented from two approaches: 1) based on regression developed from a wide range of simulations using MODTRAN, SeeBor Version 5.0 global atmospheric profiles and the CAMEL (Combined ASTER and MODIS Emissivity for Land) product based on the standard University of Wisconsin 5 km emissivity values (UWIREMIS) and the ASTER Global Emissivity Database (GED) product; 2) RTTOV radiative transfer model driven with MERRA-2 reanalysis fields. We will present results of evaluation of these two methods against various products, such as MOD11, and ground observations for the five year period of (2004-2008).

Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xiaoma; Zhou, Yuyu; Asrar, Ghassem R.

High spatiotemporal land surface temperature (LST) datasets are increasingly needed in a variety of fields such as ecology, hydrology, meteorology, epidemiology, and energy systems. Moderate Resolution Imaging Spectroradiometer (MODIS) LST is one of such high spatiotemporal datasets that are widely used. But, it has large amount of missing values primarily because of clouds. Gapfilling the missing values is an important approach to create high spatiotemporal LST datasets. However current gapfilling methods have limitations in terms of accuracy and time required to assemble the data over large areas (e.g., national and continental levels). In this study, we developed a 3-step hybridmore » method by integrating a combination of daily merging, spatiotemporal gapfilling, and temporal interpolation methods, to create a high spatiotemporal LST dataset using the four daily LST observations from the two MODIS instruments on Terra and Aqua satellites. We applied this method in urban and surrounding areas for the conterminous U.S. in 2010. The evaluation of the gapfilled LST product indicates that its root mean squared error (RMSE) to be 3.3K for mid-daytime (1:30 pm) and 2.7K for mid-13 nighttime (1:30 am) observations. The method can be easily extended to other years and regions and is also applicable to other satellite products. This seamless daily (mid-daytime and mid-nighttime) LST product with 1 km spatial resolution is of great value for studying effects of urbanization (e.g., urban heat island) and the related impacts on people, ecosystems, energy systems and other infrastructure for cities.« less

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

NASA Astrophysics Data System (ADS)

Li, Nana; Jia, Li; Lu, Jing; Menenti, Massimo; Zhou, Jie

2017-01-01

The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as "HM model") and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situ G0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from -7 to -0.5 K in LST amplitude and from -300 to 300 J m-2 K-1 s-0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

Correction of the angular dependence of satellite retrieved LST at global scale using parametric models

NASA Astrophysics Data System (ADS)

Ermida, S. L.; Trigo, I. F.; DaCamara, C.; Ghent, D.

2017-12-01

Land surface temperature (LST) values retrieved from satellite measurements in the thermal infrared (TIR) may be strongly affected by spatial anisotropy. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a parametric model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. Two models are consistently analyzed to evaluate their performance of and to assess their respective potential to correct directional effects on LST for a wide range of surface conditions, in terms of tree coverage, vegetation density, surface emissivity. We also propose an optimization of the correction of directional effects through a synergistic use of both models. The models are calibrated using LST data as provided by two sensors: MODIS on-board NASA's TERRA and AQUA; and SEVIRI on-board EUMETSAT's MSG. As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is stratified by means of a cluster analysis using information on land cover type, fraction of vegetation cover and topography. The models are then adjusted to LST data corresponding to each cluster. It is shown that the quality of the cluster based models is very close to the pixel based ones. Furthermore, the reduced number of parameters allows improving the model trough the incorporation of a seasonal component. The application of the procedure discussed here towards the harmonization of LST products from multi-sensors has been tested within the framework of the ESA DUE GlobTemperature project. It is also expected to help the characterization of directional effects of LST products generated within the EUMETSAT LSA SAF.

Investigating warming trends and spatial patterns of Land Surface Temperatures over the Greater Los Angeles Area using new MODIS and VIIRS LST products

NASA Astrophysics Data System (ADS)

Malakar, N. K.; Hulley, G. C.

2016-12-01

The Los Angeles (LA) metropolitan area is one of the fastest growing urban centers in the United States, and home to roughly 18 million people. Understanding the trends and impacts of warming temperatures in urban environments is an increasingly important issue in our changing climate. We used thermal infrared data from Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors to retrieve Land Surface Temperature using a new Temperature Emissivity Separation algorithm adapted for these sensors. We analyzed day and night LST retrievals to study the warming trends of LST for the greater LA region from 2002-2015. The average warming trend over LA for summer days and nights over this period for MODIS Aqua data was 1.1 °C per decade, while a more rapid warming is observed for the years 2012-2016 for both MODIS and VIIRS observations. We have also found that inland LA regions are warming more rapidly than the other regions. We further investigate the underlying cause of the warming by looking into the physical factors such as changes in net radiation, cloud cover, and evapotranspiration. The results will help to understand how indicators of climate change are evolving in the beginning of the 21st century, and how they compare with global climate model projections. Identification of potential impacts, and underlying causes of warming trends in various LA regions will help decision makers to develop policies to help mitigate the effects of rising temperatures.

Monitoring Thermal Status of Ecosystems with MODIS Land-Surface Temperature and Vegetation Index Products

NASA Technical Reports Server (NTRS)

Wan, Zhengming

2002-01-01

The global land-surface temperature (LST) and normalized difference vegetation index (NDVI) products retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data in 2001 were used in this study. The yearly peak values of NDVI data at 5km grids were used to define six NDVI peak zones from -0.2 to 1 in steps of 0.2, and the monthly NDVI values at each grid were sorted in decreasing order, resulting in 12 layers of NDVI images for each of the NDVI peak zones. The mean and standard deviation of daytime LSTs and day-night LST differences at the grids corresponding to the first layer of NDVI images characterize the thermal status of terrestrial ecosystems in the NDVI peak zones. For the ecosystems in the 0.8-1 NDVI peak zone, daytime LSTs distribute from 0-35 C and day-night LST differences distribute from -2 to 22 C. The daytime LSTs and day-night LST differences corresponding to the remaining layers of NDVI images show that the growth of vegetation is limited at low and high LSTs. LSTs and NDVI may be used to monitor photosynthetic activity and drought, as shown in their applications to a flood-irrigated grassland in California and an unirrigated grassland in Nevada.

Downscaling Satellite Land Surface Temperatures in Urban Regions for Surface Energy Balance Study and Heat Index Development

NASA Astrophysics Data System (ADS)

Norouzi, H.; Bah, A.; Prakash, S.; Nouri, N.; Blake, R.

2017-12-01

A great percentage of the world's population reside in urban areas that are exposed to the threats of global and regional climate changes and associated extreme weather events. Among them, urban heat islands have significant health and economic impacts due to higher thermal gradients of impermeable surfaces in urban regions compared to their surrounding rural areas. Therefore, accurate characterization of the surface energy balance in urban regions are required to predict these extreme events. High spatial resolution Land surface temperature (LST) in the scale of street level in the cities can provide wealth of information to study surface energy balance and eventually providing a reliable heat index. In this study, we estimate high-resolution LST maps using combination of LandSat 8 and infrared based satellite products such as Moderate Resolution Imaging Spectroradiometer (MODIS) and newly launched Geostationary Operational Environmental Satellite-R Series (GOES-R). Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, MODIS and GOES-R have lower spatial resolution (1km and 4km respectively) with much higher temporal resolution. Several statistical downscaling methods were investigated to provide high spatiotemporal LST maps in urban regions. The results reveal that statistical methods such as Principal Component Analysis (PCA) can provide reliable estimations of LST downscaling with 2K accuracy. Other methods also were tried including aggregating (up-scaling) the high-resolution data to a coarse one to examine the limitations and to build the model. Additionally, we deployed flux towers over distinct materials such as concrete, asphalt, and rooftops in New York City to monitor the sensible and latent heat fluxes through eddy covariance method. To account for the incoming and outgoing radiation, a 4-component radiometer is used that can observe both incoming and outgoing longwave and shortwave radiation. This enables us to accurately build the relationship between LST, air temperature, and the heat index in the future.

Comparisons of MODIS vegetation index products with biophysical and flux tower measurements

NASA Astrophysics Data System (ADS)

Sirikul, Natthanich

Vegetation indices (VI) play an important role in studies of global climate and biogeochemical cycles, and are also positively related to many biophysical parameters and satellite products, such as leaf area index (LAI), gross primary production (GPP), land surface water index (LSWI) and land surface temperature (LST). In this study we found that VI's had strong relationships with some biophysical products, such as gross primary production, yet were less well correlated with biophysical structural parameters, such as leaf area index. The relationships between MODIS VI's and biophysical field measured LAI showed poor correlation at semi-arid land and broadleaf forest land cover type whereas cropland showed stronger correlations than the other vegetation types. In addition, the relationship between the enhanced vegetation index (EVI)-LAI and normalized difference vegetation index (NDVI)-LAI did not show significant differences. Comparisons of the relationships between the EVI and NDVI with tower-measured GPP from 11 flux towers in North America, showed that MODIS EVI had much stronger relationships with tower-GPP than did NDVI, and EVI was better correlated with the seasonal dynamics of GPP than was NDVI. In addition, there were no significant differences among the 1x1, 3x3 and 7x7 pixel sample sizes. The comparisons of VIs from the 3 MODIS products from which VI's are generated (Standard VI (MOD13)), Nadir Adjusted Surface Reflectance (NBAR (MOD43)), and Surface Reflectance (MOD09)), showed that MODIS NBAR-EVI (MOD43) was best correlated with GPP compared with the other VI products. In addition, the MODIS VI - tower GPP relationships were significantly improved using NBAR-EVI over the more complex canopy structures, such as the broadleaf and needleleaf forests. The relationship of tower-GPP with other MODIS products would be useful in more thorough characterization of some land cover types in which the VI's have encountered problems. The land surface temperature (LST) product were found useful for empirical estimations of GPP in needleleaf forests, but were not useful for the other land cover types, whereas the land surface water index (LSWI) was more sensitive to noise from snowmelt, ground water table levels, and wet soils than to the canopy moisture levels. Also the MODIS EVI was better correlated with LST than was NDVI. Finally, the cross-site comparisons of GPP and multi-products from MODIS showed that the relationships between EVI and GPP were the strongest while LST and GPP was the weakest. EVI may thus be useful in scaling across landscapes, including heterogeneous ones, for regional estimations of GPP, especially if BRDF effects have been taken into account (such as with the NBAR product). Thus, the relationships of EVI-GPP over space and time would potentially provide much useful information for studies of the global carbon cycle.

Characterizing Urban Heat Islands of Global Settlements Using MODIS and Nighttime Lights Products

NASA Technical Reports Server (NTRS)

Zhang, Ping; Imhoff, Marc L.; Wolfe, Robert E.; Bounoua, Lahouari

2010-01-01

Impervious surface area (ISA) from the National Geophysical Data Center (NGDC) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) signature on LST amplitude and its relationship with development intensity, size, and ecological setting for more than 3000 urban settlements globally. Development intensity zones based on fractional ISA are defined for each urban area emanating outward from the urban core to the nearby nonurban rural areas and used to stratify sampling for LST. Sampling is further constrained by biome type and elevation data to ensure objective intercomparisons between zones and between cities in different biomes. We find that the ecological context and settlement size significantly influence the amplitude of summer daytime UHI. Globally, an average of 3.8 C UHI is found in cities built in biomes dominated by forests; 1.9 C UHI in cities embedded in grass shrubs biomes; and only a weak UHI or sometimes an urban heat sink (UHS) in cities in arid and semi-arid biomes. Overall, the amplitude of the UHI is negatively correlated (R = -0.66) with the difference in vegetation density between urban and rural zones represented by the MODIS normalized difference vegetation index (NDVI). Globally averaged, the daytime UHI amplitude for all settlements is 2.6 C in summer and 1.4 C in winter. Globally, the average summer daytime UHI is 4.7 C for settlements larger than 500 square kilometers compared with 2.5 C for settlements smaller than 50 square kilometers and larger than 10 square kilometers. The stratification of cities by size indicates that the aggregated amount of ISA is the primary driver of UHI amplitude, with variations between ecological contexts and latitudinal zones. More than 60% of the total LST variance is explained by ISA for urban settlements within forests at mid to high latitudes. This percentage will increase to more than 80% when only settlements in the US are examined.

Characterizing Urban Heat Islands of Global Settlements Using MODIS and Nighttime Lights Products

NASA Technical Reports Server (NTRS)

Zhang, Ping; Imhoff, Marc L.; Wolfe, Robert E.; Bounoua, Lahouari

2010-01-01

Impervious surface area (ISA) from the National Geophysical Data Center (NGDC) and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) signature on LST amplitude and its relationship to development intensity, size, and ecological setting for more than 3000 urban settlements over the globe. Development intensity zones based on fractional ISA are defined for each urban area emanating outward from the urban core to the nearby non-urban rural areas and used to stratify sampling for LST. Sampling is further constrained by biome type and elevation data to insure objective inter-comparisons between zones and between cities in different biomes. We find that the ecological context and settlement size significantly influence the amplitude of summer daytime UHI. Globally, an average of 3.8 C UHI is found in cities built in biomes dominated by forests; 1.9 C UHI in cities embedded in grass/shrub biomes, and only a weak UHI or sometimes an Urban Heat Sink (UHS) in cities in and and semi-arid biomes. Overall, the amplitude of the UHI is negatively correlated (R = -0.66) to the difference in vegetation density between urban and rural zones represented by MODIS Normalized Difference Vegetation Index (NDVI). Globally averaged, the daytime UHI amplitude for all settlement is 2.6 C in summer and 1.4 C in winter. Globally, the average summer daytime UHI is 4.7 C for settlements larger than 500 square kilometers, compared to 2.5 C for settlements smaller than 50 square kilometers and larger than 10 square kilometers. The stratification of cities by size indicates that the aggregated amount of ISA is the primary driver of UHI amplitude with variations between ecological contexts and latitudinal zones. More than 60% of the total LST variances is explained by ISA for urban settlements within forests at mid-to-high latitudes. This percentage will increase to more than 80% when only USA settlements are examined.

Investigation of Cyprus thermal tenancy using nine year MODIS LST data and Fourier analysis

NASA Astrophysics Data System (ADS)

Skarlatos, D.; Miliaresis, G.; Georgiou, A.

2013-08-01

Land Surface Temperature (LST) is an extremely important parameter that controls the exchange of long wave radiation between surface and atmosphere. It is a good indicator of the energy balance at the Earth's surface and it is one of the key parameters in the physics of land-surface processes on regional as well as global scale. This paper utilizes monthly night and day averaged LST MODIS imagery over Cyprus for a 9 year period. Fourier analysis and Least squares estimation fitting are implemented to analyze mean daily data over Cyprus in an attempt to investigate possible temperature tenancy over these years and possible differences among areas with different land cover and land use, such as Troodos Mountain and Nicosia, the main city in the center of the island. The analysis of data over a long time period, allows questions such as whether there is a tenancy to temperature increase, to be answered in a statistically better way, provided that `noise' is removed correctly. Dealing with a lot of data, always provides a more accurate estimation, but on the other hand, more noise in implemented on the data, especially when dealing with temperature which is subject to daily and annual cycles. A brief description over semi-automated data acquisition and standardization using object-oriented programming and GIS-based techniques, will be presented. The paper fully describes the time series analysis implemented, the Fourier method and how it was used to analyze and filter mean daily data with high frequency. Comparison of mean monthly daily LST against day and night LSTs is also performed over the 9 year period in order to investigate whether use of the extended data series provide significant advantage over short.

Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice, Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel

2013-01-01

This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.

Land surface temperature downscaling using random forest regression: primary result and sensitivity analysis

NASA Astrophysics Data System (ADS)

Pan, Xin; Cao, Chen; Yang, Yingbao; Li, Xiaolong; Shan, Liangliang; Zhu, Xi

2018-04-01

The land surface temperature (LST) derived from thermal infrared satellite images is a meaningful variable in many remote sensing applications. However, at present, the spatial resolution of the satellite thermal infrared remote sensing sensor is coarser, which cannot meet the needs. In this study, LST image was downscaled by a random forest model between LST and multiple predictors in an arid region with an oasis-desert ecotone. The proposed downscaling approach was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The primary result of LST downscaling has been shown that the distribution of downscaled LST matched with that of the ecosystem of oasis and desert. By the way of sensitivity analysis, the most sensitive factors to LST downscaling were modified normalized difference water index (MNDWI)/normalized multi-band drought index (NMDI), soil adjusted vegetation index (SAVI)/ shortwave infrared reflectance (SWIR)/normalized difference vegetation index (NDVI), normalized difference building index (NDBI)/SAVI and SWIR/NDBI/MNDWI/NDWI for the region of water, vegetation, building and desert, with LST variation (at most) of 0.20/-0.22 K, 0.92/0.62/0.46 K, 0.28/-0.29 K and 3.87/-1.53/-0.64/-0.25 K in the situation of +/-0.02 predictor perturbances, respectively.

Innovative approach to retrieve land surface emissivity and land surface temperature in areas of highly dynamic emissivity changes by using thermal infrared data

NASA Astrophysics Data System (ADS)

Heinemann, S.

2015-12-01

The land surface temperature (LST) is an extremely significant parameter in order to understand the processes of energetic interactions between Earth's surface and atmosphere. This knowledge is significant for various environmental research questions, particularly with regard to the recent climate change. This study shows an innovative approach to retrieve land surface emissivity (LSE) and LST by using thermal infrared (TIR) data from satellite sensors, such as SEVIRI and AATSR. So far there are no methods to derive LSE/LST particularly in areas of highly dynamic emissivity changes. Therefore especially for regions with large surface temperature amplitude in the diurnal cycle such as bare and uneven soil surfaces but also for regions with seasonal changes in vegetation cover including various surface areas such as grassland, mixed forests or agricultural land different methods were investigated to identify the most appropriate one. The LSE is retrieved by using the day/night Temperature-Independent Spectral Indices (TISI) method, and the Generalised Split-Window (GSW) method is used to retrieve the LST. Nevertheless different GSW algorithms show that equal LSEs lead to large LST differences. Additionally LSE is also measured using a NDVI-based threshold method (NDVITHM) to distinguish between soil, dense vegetation cover and pixel composed of soil and vegetation. The data used for this analysis were derived from MODIS TIR. The analysis is implemented with IDL and an intercomparison is performed to determine the most effective methods. To compensate temperature differences between derived and ground truth data appropriate correction terms by comparing derived LSE/LST data with ground-based measurements are developed. One way to calibrate LST retrievals is by comparing the canopy leaf temperature of conifers derived from TIR data with the surrounding air temperature (e.g. from synoptic stations). Prospectively, the derived LSE/LST data become validated with near infrared data obtained from an UVA with a TIR camera (TIRC) onboard, and also compared with ground-based measurements. This study aims to generate an appropriate method by integrating developed correction terms to eventually obtain a high correlation between all, LSE/LST, TIRC and ground truth data.

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development

NASA Astrophysics Data System (ADS)

Perez Diaz, C. L.; Lakhankar, T.; Romanov, P.; Khanbilvardi, R.; Munoz Barreto, J.; Yu, Y.

2017-12-01

CREST-SAFE: Snow LST validation, wetness profiler creation, and depth/SWE product development The Field Snow Research Station (also referred to as Snow Analysis and Field Experiment, SAFE) is operated by the NOAA Center for Earth System Sciences and Remote Sensing Technologies (CREST) in the City University of New York (CUNY). The field station is located within the premises of the Caribou Municipal Airport (46°52'59'' N, 68°01'07'' W) and in close proximity to the National Weather Service (NWS) Regional Forecast Office. The station was established in 2010 to support studies in snow physics and snow remote sensing. The Visible Infrared Imager Radiometer Suite (VIIRS) Land Surface Temperature (LST) Environmental Data Record (EDR) and Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (provided by the Terra and Aqua Earth Observing System satellites) were validated using in situ LST (T-skin) and near-surface air temperature (T-air) observations recorded at CREST-SAFE for the winters of 2013 and 2014. Results indicate that T-air correlates better than T-skin with VIIRS LST data and that the accuracy of nighttime LST retrievals is considerably better than that of daytime. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that, although all the data sets showed high correlation with ground measurements, day values yielded slightly higher accuracy ( 1°C). Additionally, we created a liquid water content (LWC)-profiling instrument using time-domain reflectometry (TDR) at CREST-SAFE and tested it during the snow melt period (February-April) immediately after installation in 2014. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Lastly, to improve on global snow cover mapping, a snow product capable of estimating snow depth and snow water equivalent (SWE) using microwave remote sensing and the CREST Snow Depth Regression Tree Model (SDRTM) was developed. Data from AMSR2 onboard the JAXA GCOM-W1 satellite is used to produce daily global snow depth and SWE maps in automated fashion at a 10-km resolution.

Downscaling of Remotely Sensed Land Surface Temperature with multi-sensor based products

NASA Astrophysics Data System (ADS)

Jeong, J.; Baik, J.; Choi, M.

2016-12-01

Remotely sensed satellite data provides a bird's eye view, which allows us to understand spatiotemporal behavior of hydrologic variables at global scale. Especially, geostationary satellite continuously observing specific regions is useful to monitor the fluctuations of hydrologic variables as well as meteorological factors. However, there are still problems regarding spatial resolution whether the fine scale land cover can be represented with the spatial resolution of the satellite sensor, especially in the area of complex topography. To solve these problems, many researchers have been trying to establish the relationship among various hydrological factors and combine images from multi-sensor to downscale land surface products. One of geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS), has Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI). MI performing the meteorological mission produce Rainfall Intensity (RI), Land Surface Temperature (LST), and many others every 15 minutes. Even though it has high temporal resolution, low spatial resolution of MI data is treated as major research problem in many studies. This study suggests a methodology to downscale 4 km LST datasets derived from MI in finer resolution (500m) by using GOCI datasets in Northeast Asia. Normalized Difference Vegetation Index (NDVI) recognized as variable which has significant relationship with LST are chosen to estimate LST in finer resolution. Each pixels of NDVI and LST are separated according to land cover provided from MODerate resolution Imaging Spectroradiometer (MODIS) to achieve more accurate relationship. Downscaled LST are compared with LST observed from Automated Synoptic Observing System (ASOS) for assessing its accuracy. The downscaled LST results of this study, coupled with advantage of geostationary satellite, can be applied to observe hydrologic process efficiently.

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments

NASA Astrophysics Data System (ADS)

Zakšek, Klemen; Schroedter-Homscheidt, Marion

Some applications, e.g. from traffic or energy management, require air temperature data in high spatial and temporal resolution at two metres height above the ground ( T2m), sometimes in near-real-time. Thus, a parameterization based on boundary layer physical principles was developed that determines the air temperature from remote sensing data (SEVIRI data aboard the MSG and MODIS data aboard Terra and Aqua satellites). The method consists of two parts. First, a downscaling procedure from the SEVIRI pixel resolution of several kilometres to a one kilometre spatial resolution is performed using a regression analysis between the land surface temperature ( LST) and the normalized differential vegetation index ( NDVI) acquired by the MODIS instrument. Second, the lapse rate between the LST and T2m is removed using an empirical parameterization that requires albedo, down-welling surface short-wave flux, relief characteristics and NDVI data. The method was successfully tested for Slovenia, the French region Franche-Comté and southern Germany for the period from May to December 2005, indicating that the parameterization is valid for Central Europe. This parameterization results in a root mean square deviation RMSD of 2.0 K during the daytime with a bias of -0.01 K and a correlation coefficient of 0.95. This is promising, especially considering the high temporal (30 min) and spatial resolution (1000 m) of the results.

Comparison of S-NPP VIIRS land surface temperature with SEVIRI

NASA Astrophysics Data System (ADS)

Ermida, Sofia L.; Trigo, Isabel F.; Liu, Yuling; Yu, Yunyue

2017-04-01

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes. LST can be globally measured from space by infrared radiometers, with a wide range of spatial and temporal resolutions depending on the sensor design and orbit. The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument is the primary sensor onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite, which was launched in recent years. VIIRS was designed to improve upon the capabilities of the operational AVHRR and provide observation continuity with MODIS. A Split Window approach has been applied to the VIIRS moderate resolution channels M15 and M16 centered at 10.76 µm and 12.01 µm, respectively. VIIRS has a swath of 3000 km and a spatial resolution of 745m (nadir) up to about 1600 m (limb view), leading to relatively high re-visiting frequency. LST is retrieved for a wide range of viewing angles along the VIIRS path, allowing the study of the variability of LST with viewing geometry for various land cover types. Here we present a comparison of VIRS LST data with data provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG). SEVIRI-based LST is available every 15-minute, but at coarser spatial resolution (3-km at nadir) when compared to VIIRS LST. The analysis is performed over 6 areas over the SEVIRI disk characterized by different surface conditions. VIIRS has generally slightly warmer night-time LST compared with SEVIRI, with differences smaller than 2K. Larger differences are found during daytime, with VIIRS presenting overall lower LST values up to 5K. These differences are also analysed taking into account the surface type, view zenith angle (VZA) and topography. As seen in previous comparison studies, high VZA and elevation values are associated to higher discrepancies of the LST products.

Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates

NASA Astrophysics Data System (ADS)

Sánchez-Ruiz, Sergio; Piles, María; Sánchez, Nilda; Martínez-Fernández, José; Vall-llossera, Mercè; Camps, Adriano

2014-08-01

Sensors in the range of visible and near-shortwave-thermal infrared regions can be used in combination with passive microwave observations to provide soil moisture maps at much higher spatial resolution than the original resolution of current radiometers. To do so, a new downscaling algorithm ultimately based on the land surface temperature (LST) - Normalized Difference Vegetation Index (NDVI) - Brightness Temperature (TB) relationship is used, in which shortwave infrared indices are used as vegetation descriptors, instead of the more common near infrared ones. The theoretical basis of those indices, calculated as the normalized ratio of the 1240, 1640 and 2130 nm shortwave infrared (SWIR) bands and the 858 nm near infrared (NIR) band indicate that they are able to provide estimates of the vegetation water content. These so-called water indices extracted from MODIS products, have been used together with MODIS LST, and SMOS TB to improve the spatial resolution of ∼40 km SMOS soil moisture estimates. The aim was to retrieve soil moisture maps with the same accuracy as SMOS, but at the same resolution of the MODIS dataset, i.e., 500 m, which were then compared against in situ measurements from the REMEDHUS network in Spain. Results using two years of SMOS and MODIS data showed a similar performance for the four indices, with slightly better results when using the index derived from the first SWIR band. For the areal-average, a coefficient of correlation (R) of ∼0.61 and ∼0.72 for the morning and afternoon orbits, respectively, and a centered root mean square difference (cRMSD) of ∼0.04 m3 m-3 for both orbits was obtained. A twofold improvement of the current versions of this downscaling approach has been achieved by using more frequent and higher spatial resolution water indexes as vegetation descriptors: (1) the spatial resolution of the resulting soil moisture maps can be enhanced from ∼40 km up to 500 m, and (2) more accurate soil moisture maps (in terms of R and cRMSD) can be obtained, especially in periods of high vegetation activity. The results of this study support the use of high resolution LST and SWIR-based vegetation indices to disaggregate SMOS observations down to 500 m soil moisture maps, meeting the needs of fine-scale hydrological applications.

Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data

PubMed Central

Tang, Bohui; Bi, Yuyun; Li, Zhao-Liang; Xia, Jun

2008-01-01

On the basis of the radiative transfer theory, this paper addressed the estimate of Land Surface Temperature (LST) from the Chinese first operational geostationary meteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1, 10.3-11.3 μm and IR2, 11.5-12.5 μm), using the Generalized Split-Window (GSW) algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithm corresponding to a series of overlapping ranging of the mean emissivity, the atmospheric Water Vapor Content (WVC), and the LST were derived using a statistical regression method from the numerical values simulated with an accurate atmospheric radiative transfer model MODTRAN 4 over a wide range of atmospheric and surface conditions. The simulation analysis showed that the LST could be estimated by the GSW algorithm with the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with the Viewing Zenith Angle (VZA) less than 30° or for the sub-rangs with VZA less than 60° and the atmospheric WVC less than 3.5 g/cm2 provided that the Land Surface Emissivities (LSEs) are known. In order to determine the range for the optimum coefficients of the GSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according to the land surface classification or using the method proposed by Jiang et al. (2006); and the WVC could be obtained from MODIS total precipitable water product MOD05, or be retrieved using Li et al.' method (2003). The sensitivity and error analyses in term of the uncertainty of the LSE and WVC as well as the instrumental noise were performed. In addition, in order to compare the different formulations of the split-window algorithms, several recently proposed split-window algorithms were used to estimate the LST with the same simulated FY-2C data. The result of the intercomparsion showed that most of the algorithms give comparable results. PMID:27879744

Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C) Data.

PubMed

Tang, Bohui; Bi, Yuyun; Li, Zhao-Liang; Xia, Jun

2008-02-14

On the basis of the radiative transfer theory, this paper addressed the estimate ofLand Surface Temperature (LST) from the Chinese first operational geostationarymeteorological satellite-FengYun-2C (FY-2C) data in two thermal infrared channels (IR1,10.3-11.3 μ m and IR2, 11.5-12.5 μ m ), using the Generalized Split-Window (GSW)algorithm proposed by Wan and Dozier (1996). The coefficients in the GSW algorithmcorresponding to a series of overlapping ranging of the mean emissivity, the atmosphericWater Vapor Content (WVC), and the LST were derived using a statistical regressionmethod from the numerical values simulated with an accurate atmospheric radiativetransfer model MODTRAN 4 over a wide range of atmospheric and surface conditions.The simulation analysis showed that the LST could be estimated by the GSW algorithmwith the Root Mean Square Error (RMSE) less than 1 K for the sub-ranges with theViewing Zenith Angle (VZA) less than 30° or for the sub-rangs with VZA less than 60°and the atmospheric WVC less than 3.5 g/cm² provided that the Land Surface Emissivities(LSEs) are known. In order to determine the range for the optimum coefficients of theGSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according tothe land surface classification or using the method proposed by Jiang et al. (2006); and theWVC could be obtained from MODIS total precipitable water product MOD05, or beretrieved using Li et al.' method (2003). The sensitivity and error analyses in term of theuncertainty of the LSE and WVC as well as the instrumental noise were performed. Inaddition, in order to compare the different formulations of the split-window algorithms,several recently proposed split-window algorithms were used to estimate the LST with thesame simulated FY-2C data. The result of the intercomparsion showed that most of thealgorithms give comparable results.

Satellite-derived temperature data for monitoring water status in a floodplain forest of the Upper Sabine River, Texas

USGS Publications Warehouse

Lemon, Mary Grace T.; Allen, Scott T.; Edwards, Brandon L.; King, Sammy L.; Keim, Richard F.

2016-01-01

Decreased water availability due to hydrologic modifications, groundwater withdrawal, and climate change threaten bottomland hardwood (BLH) forest communities. We used satellite-derived (MODIS) land-surface temperature (LST) data to investigate spatial heterogeneity of canopy temperature (an indicator of plant-water status) in a floodplain forest of the upper Sabine River for 2008–2014. High LST pixels were generally further from the river and at higher topographic locations, indicating lower water-availability. Increasing rainfall-derived soil moisture corresponded with decreased heterogeneity of LST between pixels but there was weaker association between Sabine River stage and heterogeneity. Stronger dependence of LST convergence on rainfall rather than river flow suggests that some regions are less hydrologically connected to the river, and vegetation may rely on local precipitation and other contributions to the riparian aquifer to replenish soil moisture. Observed LST variations associated with hydrology encourage further investigation of the utility of this approach for monitoring forest stress, especially with considerations of climate change and continued river management.

Modelling the angular effects on satellite retrieved LST at global scale using a land surface classification

NASA Astrophysics Data System (ADS)

Ermida, Sofia; DaCamara, Carlos C.; Trigo, Isabel F.; Pires, Ana C.; Ghent, Darren

2017-04-01

Land Surface Temperature (LST) is a key climatological variable and a diagnostic parameter of land surface conditions. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Although LST estimation from remote sensing instruments operating in the Infrared (IR) is widely used and has been performed for nearly 3 decades, there is still a list of open issues. One of these is the LST dependence on viewing and illumination geometry. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a kernel model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. The model is calibrated using LST data as provided by a wide range of sensors to optimize spatial coverage, namely: 1) a LEO sensor - the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's TERRA and AQUA; and 2) 3 GEO sensors - the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG), the Japanese Meteorological Imager (JAMI) on-board the Japanese Meteorological Association (JMA) Multifunction Transport SATellite (MTSAT-2), and NASA's Geostationary Operational Environmental Satellites (GOES). As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the obtained model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is stratified by means of a cluster analysis using information on land cover type, fraction of vegetation cover and topography. The kernel model is then adjusted to LST data corresponding to each cluster. It is shown that the quality of the cluster based kernel model is very close to the pixel based one. Furthermore, the reduced number of parameters (limited to the number of identified clusters, instead of a pixel-by-pixel model calibration) allows improving the kernel model trough the incorporation of a seasonal component. The application of the here discussed procedure towards the harmonization of LST products from multi-sensors is on the framework of the ESA DUE GlobTemperature project.

Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands

NASA Astrophysics Data System (ADS)

Quan, Jinling; Zhan, Wenfeng; Chen, Yunhao; Wang, Mengjie; Wang, Jinfei

2016-03-01

Previous time series methods have difficulties in simultaneous characterization of seasonal, gradual, and abrupt changes of remotely sensed land surface temperature (LST). This study proposed a model to decompose LST time series into trend, seasonal, and noise components. The trend component indicates long-term climate change and land development and is described as a piecewise linear function with iterative breakpoint detection. The seasonal component illustrates annual insolation variations and is modeled as a sinusoidal function on the detrended data. This model is able to separate the seasonal variation in LST from the long-term (including gradual and abrupt) change. Model application to nighttime Moderate Resolution Imaging Spectroradiometer (MODIS)/LST time series during 2000-2012 over Beijing yielded an overall root-mean-square error of 1.62 K between the combination of the decomposed trend and seasonal components and the actual MODIS/LSTs. LST decreased (~ -0.086 K/yr, p < 0.1) in 53% of the study area, whereas it increased with breakpoints in 2009 (~0.084 K/yr before and ~0.245 K/yr after 2009) between the fifth and sixth ring roads. The decreasing trend was stronger over croplands than over urban lands (p < 0.05), resulting in an increasing trend in surface urban heat island intensity (SUHII, 0.022 ± 0.006 K/yr). This was mainly attributed to the trends in urban-rural differences in rainfall and albedo. The SUHII demonstrated a concave seasonal variation primarily due to the seasonal variations of urban-rural differences in temperature cooling rate (related to canyon structure, vegetation, and soil moisture) and surface heat dissipation (affected by humidity and wind).

MODIS-based spatiotemporal patterns of soil moisture and evapotranspiration interactions in Tampa Bay urban watershed

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Xuan, Zhemin; Wimberly, Brent

2011-09-01

Soil moisture and evapotranspiration (ET) is affected by both water and energy balances in the soilvegetation- atmosphere system, it involves many complex processes in the nexus of water and thermal cycles at the surface of the Earth. These impacts may affect the recharge of the upper Floridian aquifer. The advent of urban hydrology and remote sensing technologies opens new and innovative means to undertake eventbased assessment of ecohydrological effects in urban regions. For assessing these landfalls, the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing images can be used for the estimation of such soil moisture change in connection with two other MODIS products - Enhanced Vegetation Index (EVI), Land Surface Temperature (LST). Supervised classification for soil moisture retrieval was performed for Tampa Bay area on the 2 kmx2km grid with MODIS images. Machine learning with genetic programming model for soil moisture estimation shows advances in image processing, feature extraction, and change detection of soil moisture. ET data that were derived by Geostationary Operational Environmental Satellite (GOES) data and hydrologic models can be retrieved from the USGS web site directly. Overall, the derived soil moisture in comparison with ET time series changes on a seasonal basis shows that spatial and temporal variations of soil moisture and ET that are confined within a defined region for each type of surfaces, showing clustered patterns and featuring space scatter plot in association with the land use and cover map. These concomitant soil moisture patterns and ET fluctuations vary among patches, plant species, and, especially, location on the urban gradient. Time series plots of LST in association with ET, soil moisture and EVI reveals unique ecohydrological trends. Such ecohydrological assessment can be applied for supporting the urban landscape management in hurricane-stricken regions.

Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

NASA Astrophysics Data System (ADS)

Chatterjee, R. S.; Singh, Narendra; Thapa, Shailaja; Sharma, Dravneeta; Kumar, Dheeraj

2017-06-01

The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVITHM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images.

Utilization of satellite remote sensing data on land surface characteristics in water and heat balance component modeling for vegetation covered territories

NASA Astrophysics Data System (ADS)

Muzylev, Eugene; Uspensky, Alexander; Startseva, Zoya; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

2010-05-01

The model of vertical water and heat transfer in the "soil-vegetation-atmosphere" system (SVAT) for vegetation covered territory has been developed, allowing assimilating satellite remote sensing data on land surface condition as well as accounting for heterogeneities of vegetation and meteorological characteristics. The model provides the calculation of water and heat balance components (such as evapotranspiration Ev, soil water content W, sensible and latent heat fluxes and others ) as well as vertical soil moisture and temperature distributions, temperatures of soil surface and foliage, land surface brightness temperature for any time interval within vegetation season. To describe the landscape diversity soil constants and leaf area index LAI, vegetation cover fraction B, and other vegetation characteristics are used. All these values are considered to be the model parameters. Territory of Kursk region with square about 15 thousands km2 situated in the Black Earth zone of Central Russia was chosen for investigation. Satellite-derived estimates of land surface characteristics have been constructed under cloud-free condition basing AVHRR/NOAA, MODIS/EOS Terra and EOS Aqua, SEVIRI/Meteosat-8, -9 data. The developed technologies of AVHRR data thematic processing have been refined providing the retrieval of surface skin brightness temperature Tsg, air foliage temperature Ta, efficient surface temperature Ts.eff and emissivity E, as well as derivation of vegetation index NDVI, B, and LAI. The linear regression estimators for Tsg, Ta and LAI have been built using representative training samples for 2003-2009 vegetation seasons. The updated software package has been applied for AVHRR data thematic processing to generate named remote sensing products for various dates of the above vegetation seasons. The error statistics of Ta, Ts.eff and Тsg derivation has been investigated for various samples using comparison with in-situ measurements that has given RMS errors in the range 2.0-2.6, 2.5-3.7, and 3.5-4.9°C respectively. The dataset of remote sensing products has been compiled on the base of special technology using Internet resources, that includes MODIS-based estimates of land surface temperature (LST) Tsg, E, NDVI, LAI for the region of interest and the same vegetation seasons. Two types of MODIS-based Тsg and E estimates have been extracted from LP DAAC web-site (for separate dates of 2003-2009 time period): LST/E Daily L3 product (MOD11В1) with spatial resolution ~ 4.8 km and LST/E 5-Min L2 product (MOD11_L2) with spatial resolution ~ 1 km. The verification of Tsg estimates has been performed via comparison with analogous and collocated AVHRR-based ones. Along with this the sample of SEVIRI-based Tsg and E estimates has been accumulated for the Kursk area and surrounding territories for the time interval of several days during 2009 vegetation season. To retrieve Тsg and Е from SEVIRI/Meteosat-8, -9 data the new method has been developed. Being designed as the combination of well-known Split Window Technique and Two Temperature Method algorithms it provides the derivation of Тsg from SEVIRI/Meteosat-9 measurements carried out at three successive times (classified as 100% cloud-free) and covering the region under consideration without accurate a priory knowledge of E. Comparison of the SEVIRI-based Tsg retrievals with the independent collocated Tsg estimates gives the values of RMS deviation in the range of 0.9-1.4°C and it proves (indirectly) the efficiency of proposed approach. To assimilate satellite-derived estimates of vegetation characteristics and LST in the SVAT model some procedures have been developed. These procedures have included: 1) the replacement of LAI and B ground and point-wise estimates by their AVHRR- or MODIS-based analogues. The efficiency of such approach has been proved through comparison between satellite-derived and ground-based seasonal time behaviors of LAI and B, between satellite-derived, modeled, and in-situ measured temperatures as well as through comparison the modeled and actual values of evapotranspiration Ev and soil water content W for one meter soil layer. The discrepancies between mentioned temperatures do not exceed the RMS errors of satellite-derived estimates Ta, Ts.eff and Tsg while the modeled and measured values of Ev and W have been found close to each other within their standard estimation error; 2) the treating AVHRR- or MODIS-based LST as the input model variable within the SVAT model instead their standard ground-based estimates if the satisfactory time-matching of satellite and ground-based observations takes place. The SEVIRI-derived Tsg can be also used for these aims. Permissibility of such replacement has been verified while comparing remote sensed, modeled and ground-based temperatures as well as calculated and measured values of W and Ev. The SEVIRI-based Tsg estimates were found to be very informative and useful due to their high temporal resolution. Moreover the approach has been developed to account for space variability of vegetation cover parameters and meteorological characteristics. This approach includes the development of algorithms and programs for entering AVHRR- and MODIS-derived LAI and B, all named satellite-based LSTs as well as ground-based precipitation, air temperature and humidity data prepared by Inverse Distance Weighted Average Method into the model in each calculation grid unit. The calculations of vertical water and heat fluxes, soil water and heat contents and other water and heat balance components for Kursk region have been carried out with the help of the SVAT model using fields of AVHRR/3- and MODIS-derived LAI and B and AVHRR/3-, MODIS, and SEVIRI-derived LST for various vegetation seasons of 2003-2009. The acceptable accuracy levels of above values assessment have been achieved under all scenarios of parameter and input model variable specification. Thus, the results of this study confirm the opportunity of using area distributed satellite-derived estimates of land surface characteristics for the model calculations of water and heat balance components for large territories especially under the lack of ground observation data. The present study was carried out with support of the Russian Foundation of Basic Researches - grant N 10-05-00807.

Analyzing land surface temperature variations during Fogo Island (Cape Verde) 2014-2015 eruption with Landsat 8 images

NASA Astrophysics Data System (ADS)

Vieira, D.; Teodoro, A.; Gomes, A.

2016-10-01

Land Surface Temperature (LST) is an important parameter related to land surface processes that changes continuously through time. Assessing its dynamics during a volcanic eruption has both environmental and socio-economical interest. Lava flows and other volcanic materials produced and deposited throughout an eruption transform the landscape, contributing to its heterogeneity and altering LST measurements. This paper aims to assess variations of satellite-derived LST and to detect patterns during the latest Fogo Island (Cape Verde) eruption, extending from November 2014 through February 2015. LST data was obtained through four processed Landsat 8 images, focused on the caldera where Pico do Fogo volcano sits. QGIS' plugin Semi-Automatic Classification was used in order to apply atmospheric corrections and radiometric calibrations. The algorithm used to retrieve LST values is a single-channel method, in which emissivity values are known. The absence of in situ measurements is compensated by the use of MODIS sensor-derived LST data, used to compare with Landsat retrieved measurements. LST data analysis shows as expected that the highest LST values are located inside the caldera. High temperature values were also founded on the south-facing flank of the caldera. Although spatial patterns observed on the retrieved data remained roughly the same during the time period considered, temperature values changed throughout the area and over time, as it was also expected. LST values followed the eruption dynamic experiencing a growth followed by a decline. Moreover, it seems possible to recognize areas affected by lava flows of previous eruptions, due to well-defined LST spatial patterns.

Improved Remote Sensing Retrieval of Land Surface Temperature in the Thermal Infrared (TIR) Using Visible/Short Wave Infrared (VSWIR) Imaging Spectrometer Estimated Water Vapor

NASA Astrophysics Data System (ADS)

Grigsby, S.; Hulley, G. C.; Roberts, D. A.; Scheele, C. J.; Ustin, S.; Alsina, M. M.

2014-12-01

Land surface temperature (LST) is an important parameter in many ecological studies, where processes such as evapotranspiration have impacts at temperature gradients less than 1 K. Current errors in standard MODIS and ASTER LST products are greater than 1 K, and for ASTER can be greater than 2 K in humid conditions due to incomplete atmospheric correction of atmospheric water vapor. Estimates of water vapor, either derived from visible-to-shortwave-infrared (VSWIR) remote sensing data or taken from weather simulation data such as NCEP, can be combined with coincident Thermal-Infrared (TIR) remote sensing data to yield improved accuracy in LST measurements. This study compares LST retrieval accuracies derived using the standard JPL MASTER Temperature Emissivity Separation (TES) algorithm, and the Water Vapor Scaling (WVS) atmospheric correction method proposed for the Hyperspectral Infrared Imager, or HyspIRI, mission with ground observations. The 2011 ER-2 Delano/Lost Hills flights acquired TIR data from the MODIS/ASTER Simulator (MASTER) and VSWIR data from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) instruments flown concurrently. The TES and WVS retrieval methods are run with and without high spatial resolution AVIRIS-derived water vapor maps to assess the improvement using VSWIR water vapor estimates. We find improvement using VSWIR derived water vapor maps in both cases, with the WVS method being most accurate overall. For closed canopy agricultural vegetation we observed canopy temperature retrieval RMSEs of 0.49 K and 0.70 K using the WVS method on MASTER data with and without AVIRIS derived water vapor, respectively.

Annual and seasonal distribution of day and night Land Surface Temperature trend over Greece.

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Gemitzi, A.; Eleftheriou, D.; Kalea, A.; Kalmintzis, G.; Kiachidis, K.; Koumadoraki, P.; Mpantasis, C.; Spathara, M. E.; Tsolaki, A.; Tzampazidou, M. I.

2017-12-01

Climate change is one of the most challenging research topics during the last few decades, as temperature rise has already posed a significant impact on earth's functions affecting thus all life of the planet. The present study investigates the distribution of day and night Land Surface Temperature (LST) trends over Greece, a country in Mediterranean area which is identified as one of the main ``hot-spots" of climate change projections. Remotely sensed LST data were obtained from MODIS sensor in the form of 8-day composites of day and night values at a resolution of 1km for a 17-year period, i.e. from 2000 to 2017. Spatial aggregates of 10km x 10km were computed and the annual and seasonal temporal trends were determined for each one of those sub-areas. Results showed that annual trends of daily LST in the majority of areas demonstrated decrease ranging from -1*10-2 oC to -1.3*10-3 oC, with some sporadic parts showing a slight increase. A totally different outcome is observed in the fate of night LST, with all areas over Greece demonstrating increasing annual trends ranging from 4.6 * 10-5 oC to 3.1 * 10-3 oC, with highest values in the South-East parts of the country. Seasonal trends in day and night LST showed the same pattern, i.e., a general decrease in the day LST and a definite increase in night. An interesting finding is the increase in winter LST trends observed both for day and night LST, indicating that the absolute minimum annual LST observed during winter in Greece increases. Our results also indicate that the difference between the day and night LST is decreasing.

Comparison of surface energy budgets and feedbacks to microclimate among different land use types in an agro-pastoral ecotone of northern China.

PubMed

Zhao, Wei; Hu, Zhongmin; Li, Shenggong; Guo, Qun; Liu, Zhengjia; Zhang, Leiming

2017-12-01

The biophysical effect of land use conversion plays a significant role in regulating climate change. Owing to albedo and evapotranspiration (ET) change, the effect of energy budget difference on land surface temperature (LST) is important but unclear among contrasting land use types, especially in temperate semi-arid regions. Based on moderate-resolution imaging spectroradiometer (MODIS) data, we compared the differences in albedo, ET, and LST between cropland and grassland (CR-GR), and between planted forest and grassland (PF-GR) in the Horqin Sandy Land of Inner Mongolia, an agro-pastoral ecotone of northern China. Our main objective was to explore the magnitude and direction of albedo and ET change during the growing season and, subsequently, to estimate the biophysical effects on LST as a result of land use and land cover change. Our results indicate no significant difference in mean monthly albedo for CR-GR and PF-GR. Cropland lost more water through ET and significantly decreased daytime LST compared with grassland from July to September, but no significant differences in ET and LST were observed for PF-GR in any month. The biophysical climate effects were more pronounced for CR-GR compared with PF-GR. The response of LST to the changes in energy budget confirmed that ET was the critical driving factor relative to albedo. Compared with grassland, cropland and planted forest tended to cool the land surface by 5.15°C and 1.51°C during the growing season, respectively, because of the biophysical effects. Our findings suggest the significance of local-scale biophysical effect on climate variation after land use conversion in semi-arid regions. Copyright © 2017 Elsevier B.V. All rights reserved.

Geometric-Optical Modeling of Directional Thermal Radiance for Improvement of Land Surface Temperature Retrievals from MODIS, ASTER, and Landsat-7 Instruments

NASA Technical Reports Server (NTRS)

Li, Xiaowen; Friedl, Mark; Strahler, Alan

2002-01-01

The general objectives of this project were to improve understanding of the directional emittance properties of land surfaces in the thermal infrared (TIR) region of the electro-magnetic spectrum. To accomplish these objectives our research emphasized a combination of theoretical model development and empirical studies designed to improve land surface temperature (LST) retrievals from space-borne remote sensing instruments. Following the proposal, the main tasks for this project were to: (1) Participate in field campaigns; (2) Acquire and process field, aircraft, and ancillary data; (3) Develop and refine models of LST emission; (4) Develop algorithms for LST retrieval; and (5) Explore LST retrieval methods for use in energy balance models. In general all of these objectives were addressed, and for the most part achieved. The main results from this project are described in the publications arising from this effort. We summarize our efforts related to each of the objectives.

Utilizing Higher Resolution Land Surface Remote Sensing Data for Assessing Recent Trends over Asia Monsoon Region

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory

2010-01-01

The slide presentation discusses the integration of 1-kilometer spatial resolution land temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS), with 8-day temporal resolution, into the NASA Monsoon-Asia Integrated Regional Study (MAIRS) Data Center. The data will be available for analysis and visualization in the Giovanni data system. It discusses the NASA MAIRS Data Center, presents an introduction to the data access tools, and an introduction of Products available from the service, discusses the higher resolution Land Surface Temperature (LST) and presents preliminary results of LST Trends over China.

Estimation of Land Surface Temperature from GCOM-W1 AMSR2 Data over the Chinese Landmass

NASA Astrophysics Data System (ADS)

Zhou, Ji; Dai, Fengnan; Zhang, Xiaodong

2016-04-01

As one of the most important parameter at the interface between the earth's surface and atmosphere, land surface temperature (LST) plays a crucial role in many fields, such as climate change monitoring and hydrological modeling. Satellite remote sensing provides the unique possibility to observe LST of large regions at diverse spatial and temporal scales. Compared with thermal infrared (TIR) remote sensing, passive microwave (PW) remote sensing has a better ability in overcoming the influences of clouds; thus, it can be used to improve the temporal resolution of current satellite TIR LST. However, most of current methods for estimation LST from PW remote sensing are empirical and have unsatisfied generalization. In this study, a semi-empirical method is proposed to estimate LST from the observation of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission 1st-WATER "SHIZUKU" satellite (GCOM-W1). The method is based on the PW radiation transfer equation, which is simplified based on (1) the linear relationship between the emissivities of horizontal and vertical polarization channels at the same frequency and (2) the significant relationship between atmospheric parameters and the atmospheric water vapor content. An iteration approach is used to best fit the pixel-based coefficients in the simplified radiation transfer equation of the horizontal and vertical polarization channels at each frequency. Then an integration approach is proposed to generate the ensemble estimation from estimations of multiple frequencies for different land cover types. This method is trained with the AMSR2 brightness temperature and MODIS LST in 2013 over the entire Chinese landmass and then it is tested with the data in 2014. Validation based on in situ LSTs measured in northwestern China demonstrates that the proposed method has a better accuracy than the polarization radiation method, with a root-mean squared error of 3 K. Although the proposal method is applied to AMSR2 data, it has good ability to extend to other satellite PW sensors, such as the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) on board the Aqua satellite and the Special Sensor Microwave/Imager (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellite. It would be beneficial in providing LST to applications at continental and global scales.

Thermal anomaly before earthquake and damage assessment using remote sensing data for 2014 Yutian earthquake

NASA Astrophysics Data System (ADS)

Zhang, Yanmei; Huang, Haiying; Jiang, Zaisen; Fang, Ying; Cheng, Xiao

2014-12-01

Thermal anomaly appears to be a significant precursor of some strong earthquakes. In this study, time series of MODIS Land Surface Temperature (LST) products from 2001 to 2014 are processed and analyzed to locate possible anomalies prior to the Yutian earthquake (12 February 2014, Xinjiang, CHINA). In order to reduce the seasonal or annual effects from the LST variations, also to avoid the rainy and cloudy weather in this area, a background mean of ten-day nighttime LST are derived using averaged MOD11A2 products from 2001 to 2012. Then the ten-day LST data from Jan 2014 to FebJanuary 2014 were differenced using the above background. Abnormal LST increase before the earthquake is quite obvious from the differential images, indicating that this method is useful in such area with high mountains and wide-area deserts. Also, in order to assess the damage to infrastructure, China's latest civilian high-resolution remote sensing satellite - GF-1 remote sensed data are applied to the affected counties in this area. The damaged infrastructures and ground surface could be easily interpreted in the fused pan-chromatic and multi-spectral images integrating both texture and spectral information.

Using a thermal-based two source energy balance model with time-differencing to estimate surface energy fluxes with day-night MODIS observations

NASA Astrophysics Data System (ADS)

Guzinski, R.; Anderson, M. C.; Kustas, W. P.; Nieto, H.; Sandholt, I.

2013-07-01

The Dual Temperature Difference (DTD) model, introduced by Norman et al. (2000), uses a two source energy balance modelling scheme driven by remotely sensed observations of diurnal changes in land surface temperature (LST) to estimate surface energy fluxes. By using a time-differential temperature measurement as input, the approach reduces model sensitivity to errors in absolute temperature retrieval. The original formulation of the DTD required an early morning LST observation (approximately 1 h after sunrise) when surface fluxes are minimal, limiting application to data provided by geostationary satellites at sub-hourly temporal resolution. The DTD model has been applied primarily during the active growth phase of agricultural crops and rangeland vegetation grasses, and has not been rigorously evaluated during senescence or in forested ecosystems. In this paper we present modifications to the DTD model that enable applications using thermal observations from polar orbiting satellites, such as Terra and Aqua, with day and night overpass times over the area of interest. This allows the application of the DTD model in high latitude regions where large viewing angles preclude the use of geostationary satellites, and also exploits the higher spatial resolution provided by polar orbiting satellites. A method for estimating nocturnal surface fluxes and a scheme for estimating the fraction of green vegetation are developed and evaluated. Modification for green vegetation fraction leads to significantly improved estimation of the heat fluxes from the vegetation canopy during senescence and in forests. When the modified DTD model is run with LST measurements acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites, generally satisfactory agreement with field measurements is obtained for a number of ecosystems in Denmark and the United States. Finally, regional maps of energy fluxes are produced for the Danish Hydrological ObsErvatory (HOBE) in western Denmark, indicating realistic patterns based on land use.

Using local climate zone classifications to assess the influence of urban morphology on the urban heat island effect

NASA Astrophysics Data System (ADS)

Satcher, P. S.; Brunsell, N. A.

2017-12-01

Alterations to land cover resulting from urbanization interact with the atmospheric boundary layer inducing elevated surface and air temperatures, changes to the surface energy balance (SEB), and modifications to regional circulations and climates. These changes pose risks to public health, ecological systems, and have the potential to affect economic interests. We used Google Earth Engine's Landsat archive to classify local climate zones (LCZ) that consist of ten urban and seven non-urban classes to examine the influence of urban morphology on the urban heat island (UHI) effect. We used geostatistical methods to determine the significance of the spatial distributions of LCZs to land surface temperatures (LST) and normalized difference vegetation index (NDVI) Moderate Resolution Imaging Spectroradiometer (MODIS) products. We used the triangle method to assess the variability of SEB partitioning in relation to high, medium, and low density LCZ classes. Fractional vegetation cover (Fr) was calculated using NDVI data. Linear regressions of observations in Fr-LST space for select LCZ classes were compared for selected eight-day periods to determine changes in energy partitioning and relative soil moisture availability. The magnitude of each flux is not needed to determine changes to the SEB. The regressions will examine near surface soil moisture, which is indicative of how much radiation is partitioned into evaporation. To compare changes occurring over one decade, we used MODIS NDVI and LST data from 2005 and 2015. Results indicated that variations in the SEB can be detected using the LCZ classification method. The results from analysis in Fr-LST space of the annual cycles over several years can be used to detect changes in the SEB as urbanization increases.

Reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Cui, Y.; Long, D.; Hong, Y.; Zeng, C.; Han, Z.

2016-12-01

Reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau Yaokui Cui, Di Long, Yang Hong, Chao Zeng, and Zhongying Han State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China Abstract: Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the world's third pole. Large-scale consistent and continuous soil moisture datasets are of importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is one of relatively new passive microwave products. The FY-3B/MWRI soil moisture product is reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo using different gap-filling methods. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and the NDVI, LST, and albedo, but also the relationship between the soil moisture and the four-dimensional variation using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R2 larger than 0.63, and RMSE less than 0.1 cm3 cm-3 and bias less than 0.07 cm3 cm-3 for both frozen and unfrozen periods, compared with in-situ measurements in the central TP. The reconstruction method is subsequently applied to generate spatially consistent and temporally continuous surface soil moisture over the TP. The reconstructed FY-3B/MWRI soil moisture product could be valuable in studying meteorology, hydrology, and agriculture over the TP. Keywords: FY-3B/MWRI; Soil moisture; Reconstruction; Tibetan Plateau

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data*

PubMed Central

Zhang, Li-wen; Huang, Jing-feng; Guo, Rui-fang; Li, Xin-xing; Sun, Wen-bo; Wang, Xiu-zhen

2013-01-01

The accumulation of thermal time usually represents the local heat resources to drive crop growth. Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity. To solve the critical problems of estimating air temperature (T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days (GDDs) calculation from remotely sensed data, a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed. This is a preliminary study to calculate heat accumulation, expressed in accumulative growing degree days (AGDDs) above 10 °C, from reconstructed T a based on MODIS land surface temperature (LST) data. The verification results of maximum T a, minimum T a, GDD, and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels. Overall, MODIS-derived AGDD was slightly underestimated with almost 10% relative error. However, the feasibility of employing AGDD anomaly maps to characterize the 2001–2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper. Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring, agricultural climatic regionalization, and agro-meteorological disaster detection at the regional scale. PMID:23365013

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data.

PubMed

Zhang, Li-wen; Huang, Jing-feng; Guo, Rui-fang; Li, Xin-xing; Sun, Wen-bo; Wang, Xiu-zhen

2013-02-01

The accumulation of thermal time usually represents the local heat resources to drive crop growth. Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity. To solve the critical problems of estimating air temperature (T(a)) and filling in missing pixels due to cloudy and low-quality images in growing degree days (GDDs) calculation from remotely sensed data, a novel spatio-temporal algorithm for T(a) estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed. This is a preliminary study to calculate heat accumulation, expressed in accumulative growing degree days (AGDDs) above 10 °C, from reconstructed T(a) based on MODIS land surface temperature (LST) data. The verification results of maximum T(a), minimum T(a), GDD, and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels. Overall, MODIS-derived AGDD was slightly underestimated with almost 10% relative error. However, the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper. Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring, agricultural climatic regionalization, and agro-meteorological disaster detection at the regional scale.

NASA MEaSUREs Combined ASTER and MODIS Emissivity over Land (CAMEL)

NASA Astrophysics Data System (ADS)

Borbas, E. E.; Hulley, G. C.; Feltz, M.; Knuteson, R. O.; Hook, S. J.

2016-12-01

A land surface emissivity product of the NASA MEASUREs project called Combined ASTER and MODIS Emissivity over Land (CAMEL) is being made available as part of the Unified and Coherent Land Surface Temperature and Emissivity (LST&E) Earth System Data Record (ESDR). The CAMEL database has been created by merging the UW MODIS-based baseline-fit emissivity database (UWIREMIS) developed at the University of Wisconsin-Madison, and the ASTER Global Emissivity Database (ASTER GED V4) produced at JPL. This poster will introduce the beta version of the database, which is available globally for the period 2003 through 2015 at 5km in mean monthly time-steps and for 13 bands from 3.6-14.3 micron. An algorithm to create a high spectral emissivity on 417 wavenumbers is also provided for high spectral IR applications. On the poster the CAMEL database has been evaluated with the IASI Emissivity Atlas (Zhou et al, 2010) and laboratory measurements, and also through simulation of IASI BTs in the RTTOV Forward model.

Using a thermal-based two source energy balance model with time-differencing to estimate surface energy fluxes with day-night MODIS observations

NASA Astrophysics Data System (ADS)

Guzinski, R.; Anderson, M. C.; Kustas, W. P.; Nieto, H.; Sandholt, I.

2013-02-01

The Dual Temperature Difference (DTD) model, introduced by Norman et al. (2000), uses a two source energy balance modelling scheme driven by remotely sensed observations of diurnal changes in land surface temperature (LST) to estimate surface energy fluxes. By using a time differential temperature measurement as input, the approach reduces model sensitivity to errors in absolute temperature retrieval. The original formulation of the DTD required an early morning LST observation (approximately 1 h after sunrise) when surface fluxes are minimal, limiting application to data provided by geostationary satellites at sub-hourly temporal resolution. The DTD model has been applied primarily during the active growth phase of agricultural crops and rangeland vegetation grasses, and has not been rigorously evaluated during senescence or in forested ecosystems. In this paper we present modifications to the DTD model that enable applications using thermal observation from polar orbiting satellites, such as Terra and Aqua, with day and night overpass times over the area of interest. This allows the application of the DTD model in high latitude regions where large viewing angles preclude the use of geostationary satellites, and also exploits the higher spatial resolution provided by polar orbiting satellites. A method for estimating nocturnal surface fluxes and a scheme for estimating the fraction of green vegetation are developed and evaluated. Modification for green vegetation fraction leads to significantly improved estimation of the heat fluxes from the vegetation canopy during senescence and in forests. Land-cover based modifications to the Priestley-Taylor scheme, used to estimate transpiration fluxes, are explored based on prior findings for conifer forests. When the modified DTD model is run with LST measurements acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites, generally satisfactory agreement with field measurements is obtained for a number of ecosystems in Denmark and the United States. Finally, regional maps of energy fluxes are produced for the Danish Hydrological ObsErvatory (HOBE) in western Denmark, indicating realistic patterns based on land use.

Impact of Vegetation Cover Fraction Parameterization schemes on Land Surface Temperature Simulation in the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Lv, M.; Li, C.; Lu, H.; Yang, K.; Chen, Y.

2017-12-01

The parameterization of vegetation cover fraction (VCF) is an important component of land surface models. This paper investigates the impacts of three VCF parameterization schemes on land surface temperature (LST) simulation by the Common Land Model (CoLM) in the Tibetan Plateau (TP). The first scheme is a simple land cover (LC) based method; the second one is based on remote sensing observation (hereafter named as RNVCF) , in which multi-year climatology VCFs is derived from Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI (Normalized Difference Vegetation Index); the third VCF parameterization scheme derives VCF from the LAI simulated by LSM and clump index at every model time step (hereafter named as SMVCF). Simulated land surface temperature(LST) and soil temperature by CoLM with three VCF parameterization schemes were evaluated by using satellite LST observation and in situ soil temperature observation, respectively, during the period of 2010 to 2013. The comparison against MODIS Aqua LST indicates that (1) CTL produces large biases for both four seasons in early afternoon (about 13:30, local solar time), while the mean bias in spring reach to 12.14K; (2) RNVCF and SMVCF reduce the mean bias significantly, especially in spring as such reduce is about 6.5K. Surface soil temperature observed at 5 cm depth from three soil moisture and temperature monitoring networks is also employed to assess the skill of three VCF schemes. The three networks, crossing TP from West to East, have different climate and vegetation conditions. In the Ngari network, located in the Western TP with an arid climate, there are not obvious differences among three schemes. In Naqu network, located in central TP with a semi-arid climate condition, CTL shows a severe overestimates (12.1 K), but such overestimations can be reduced by 79% by RNVCF and 87% by SMVCF. In the third humid network (Maqu in eastern TP), CoLM performs similar to Naqu. However, at both Naqu and Maqu networks, RNVCF shows significant overestimation in summer, perhaps due to RNVCF ignores the growing characteristics of vegetation (mainly grass) in these two regions. Our results demonstrate that VCF schemes have significant influence on LSM performance, and indicate that it is important to consider vegetation growing characteristics in VCF schemes for different LCs.

Urban biophysical composition and its impact on thermal changes and ecosystem production

NASA Astrophysics Data System (ADS)

Sannigrahi, Srikanta; Rahmat, Shahid; Bhatt, Sandeep

2017-04-01

Human driving forces, especially, urbanization, population pressure, and socioeconomic development are significantly changing the efficiency of ecosystem service provision in an urban ecosystem. Greater Hyderabad Municipal Corporation (GHMC) is the sixth largest urban metropolitan region in India had faced an alarming pace of urban expansion from 1973 to 2015. MODerate Resolution Imaging Spectroradiometer (MODIS) thermal products MOD11A2 and surface reflectance products MOD09A1 were employed in this work to simulate areal and temporal dynamics of Urban Heat Island (UHI) and Diurnal Temperature Range (DTR) of the GHMC region from 2002 to 2015. A Light Use Efficiency (LUE) based Vegetation Photosynthesis Model (VPM) was adopted in this work to quantify Net Primary Production (NPP) and to assess the spatiotemporal changes of NPP during 2002 to 2015. MODIS yearly NPP products MOD17A3 were applied here for the purpose of model validation. Linear Spectral Mixture Analysis (LSMA) technique was employed in this research to generate impervious surface fraction image of GHMC. Spatially explicit gas regulation service included as a regulatory ecosystem service to assess the trade-off between economic viability and ecosystem conservation. Acute urban expansion (over 200%) is mainly accounted to changes the Land Surface Temperature (LST) over 3°C to 4°C in the inner city region during 1991 to 2015. Surface vegetation and moisture dynamics have been evaluated by incorporating Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI) and Land Surface Water Index (LSWI) for the year of 2002, 2011 and 2015, respectively. The four distinct UHI cluster, i.e. H-H, H-L, L-H & L-L were retrieved from the segmentation of estimated LST using Local Indicators Spatial Autocorrelation (LISA) technique. Further, the Getis-Ord-Gi hotspot analysis method has been employed to identify the local proximity of spatial hot and cold UHI clusters. The areal coverage of built-up urban class was changed from 208.11sq.km in 1973 to 419.55 sq.km in 2015 with 5.03 sq.km/year expansion rate. The selected biophysical indices are found highly sensitive to the changes in land use and land cover (LULC). LST hotspot (H-H) in 2002 was observed in the central and the southeast portion of the region due to the presence of higher thermal anomalies and high concentration of LST (°C). The Island (H-L) part of the city was mostly covered by the built-up urban area in 2002 exhibiting the highest concentration of LST, whereas the mean LST (°C) of the neighboring region is below than the average. GiZScore with low standard deviation value proven the existence of active hotspot of LST and UHI over the central urbanized area in GHMC.A strong negative correlation has found between the selected human driving forces: UHI, LST, population density, settlement density and impervious fraction with NPP ensembles the facts of human control in an urban ecosystem. This study demonstrated the necessity of proper quantification and valuation of urban ecosystem services to achieve effective and efficient decision for urban ecosystem management.

Land Surface Temperature in Łódź Obtained from Landsat 5TM

NASA Astrophysics Data System (ADS)

Jędruszkiewicz, Joanna; Zieliński, Mariusz

2012-01-01

The main aim of this paper is to present the spatial differentiation of Land Surface Temperature LST in Łódź based on Landsat 5 Thematic Mapper (L5TM) images. Analysis was performed for all L5TM images from 2011, with clear sky over Łódź. Land surface temperature (LST) play an important role in determination of weather conditions in boundary layer of atmosphere, especially connected with convection. Environmental satellites from Landsat series delivers the high resolution images of Earth's surface and according to the estimations made on the ground of it are precise. LST depends widely on surface emissivity. In this paper the emissivity was estimated from MODIS sensor as well as NDVI index, then both method were compared. The processed images allowed to determine the warmest and the coldest areas in the administrative boundaries of Łódź. The highest LST values has been found in industrial areas and the in the heart of the city. However, there are some places lying in city outskirts, where the LST values are as high, for instance Lodz Airport. On the contrary the lowest LST values occur mostly in terrains covered with vegetation i.e. forests or city parks. Głównym celem tego opracowania było oszacowanie temperatury powierzchni Ziemi w Łodzi, na podstawie obrazów satelitarnych pochodzących z satelity Landsat 5 Thematic Mapper (L5TM). Analizę wykonane dla obrazów wszystkich dostępnych obrazów z 2011 roku, na których zachmurzenie nie wystąpiło nad obszarem Łodzi. Temperatura powierzchni Ziemi odgrywa istotną rolę w kształtowaniu warunków pogodowych w warstwie granicznej, szczególnie związanych z konwekcją. Satelity środowiskowe z serii Landsat dostarczają obrazów w dużej rozdzielczości, dzięki czemu pozwalają na stosunkowo dokładne oszacowanie tego parametru. Wielkość temperatury w dużym stopniu zależy od emisyjności danej powierzchni. W niniejszym opracowaniu porównano temperaturę powierzchniową obliczoną dla emisyjności wyznaczonej z danych spektrometru MODIS, umieszczonego na satelicie Terra, jak również dla emisyjności oszacowanej przy wykorzystaniu wskaźnika NDVI obliczonego z danych L5TM. Opracowane obrazy satelitarne pozwoliły na wyznaczenie obszarów w Łodzi, cechujących się najwyższymi i najniższymi wartościami temperatury powierzchniowej. Najwyższe wartości LST na obszarze Łodzi występują w obszarach przemysłowych, jak również w najbardziej centralnej części miasta. Niekiedy jednakże obszary o podwyższonych wartościach LST spotykane są na przedmieściach, czego przykładem może łódzki port lotniczy. Z drugiej strony najniższe wartości LST występują w obszarach, na których występuje roślinność, przy czym dotyczy to głównie obszarów leśnych oraz parków śródmiejskich.

A comprehensive data set of lake surface water temperature over the Tibetan Plateau derived from MODIS LST products 2001–2015

PubMed Central

Wan, Wei; Li, Huan; Xie, Hongjie; Hong, Yang; Long, Di; Zhao, Limin; Han, Zhongying; Cui, Yaokui; Liu, Baojian; Wang, Cunguang; Yang, Wenting

2017-01-01

Lake surface water temperature (LSWT) is sensitive to long-term changes in thermal structure of lakes and regional air temperature. In the context of global climate change, recent studies showed a significant warming trend of LSWT based on investigating 291 lakes (71% are large lakes, ≥50 km2 each) globally. However, further efforts are needed to examine variation in LSWT at finer regional spatial and temporal scales. The Tibetan Plateau (TP), known as ‘the Roof of the World’ and ‘Asia’s water towers’, exerts large influences on and is sensitive to regional and even global climates. Aiming to examine detailed changing patterns and potential driven mechanisms for temperature variations of lakes across the TP region, this paper presents the first comprehensive data set of 15-year (2001–2015) nighttime and daytime LSWT for 374 lakes (≥10 km2 each), using MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products as well as four lake boundary shapefiles (i.e., 2002, 2005, 2009, and 2014) derived from Landsat/CBERS/GaoFen-1 satellite images. The data set itself reveals significant information on LSWT and its changes over the TP and is an indispensable variable for numerous applications related to climate change, water budget analysis (particularly lake evaporation), water storage changes, glacier melting and permafrost degradation, etc. PMID:28742066

A comprehensive data set of lake surface water temperature over the Tibetan Plateau derived from MODIS LST products 2001-2015.

PubMed

Wan, Wei; Li, Huan; Xie, Hongjie; Hong, Yang; Long, Di; Zhao, Limin; Han, Zhongying; Cui, Yaokui; Liu, Baojian; Wang, Cunguang; Yang, Wenting

2017-07-25

Lake surface water temperature (LSWT) is sensitive to long-term changes in thermal structure of lakes and regional air temperature. In the context of global climate change, recent studies showed a significant warming trend of LSWT based on investigating 291 lakes (71% are large lakes, ≥50 km 2 each) globally. However, further efforts are needed to examine variation in LSWT at finer regional spatial and temporal scales. The Tibetan Plateau (TP), known as 'the Roof of the World' and 'Asia's water towers', exerts large influences on and is sensitive to regional and even global climates. Aiming to examine detailed changing patterns and potential driven mechanisms for temperature variations of lakes across the TP region, this paper presents the first comprehensive data set of 15-year (2001-2015) nighttime and daytime LSWT for 374 lakes (≥10 km 2 each), using MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products as well as four lake boundary shapefiles (i.e., 2002, 2005, 2009, and 2014) derived from Landsat/CBERS/GaoFen-1 satellite images. The data set itself reveals significant information on LSWT and its changes over the TP and is an indispensable variable for numerous applications related to climate change, water budget analysis (particularly lake evaporation), water storage changes, glacier melting and permafrost degradation, etc.

Analysis of Summer 2002 Melt Extent on the Greenland Ice Sheet using MODIS and SSM/I Data

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Williams, Richard S., Jr.; Steffen, Konrad; Chien, Y. L.; Foster, James L.; Robinson, David A.; Riggs, George A.

2004-01-01

Previous work has shown that the summer of 2002 had the greatest area of snow melt extent on the Greenland ice sheet ever recorded using passive-microwave data. In this paper, we compare the 0 degree isotherm derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument, with Special Sensor Microwave/Imager (SSM/I)-derived melt, at the time of the maximum melt extent in 2002. To validate the MODIS-derived land-surface temperatures (LSTs), we compared the MODIS LSTs with air temperatures from nine stations (using 11 different data points) and found that they agreed to within 2.3 plus or minus 2.09 C, with station temperatures consistently lower than the MODIS LSTs. According to the MODIS LST, the maximum surface melt extended to approximately 2300 m in southern Greenland; while the SSM/I measurements showed that the maximum melt extended to nearly 2700 m in southeastern Greenland. The MODIS and SSM/I data are complementary in providing detailed information about the progression of surface and near-surface melt on the Greenland ice sheet.

Analysis of Summer 2002 Melt Extent on the Greenland Ice Sheet using MODIS and SSM/I Data

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Williams, Richard S.; Steffen, Konrad; Chien, Janet Y. L.

2004-01-01

Previous work has shown that the summer of 2002 had the greatest area of snow melt extent on the Greenland ice sheet ever recorded using passive-microwave data. In this paper, we compare the 0 deg. isotherm derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument, with Special Sensor Microwave/Imager (SSM/I)-derived melt, at the time of the maximum melt extent in 2002. To validate the MODIS derived land-surface temperatures (LSTs), we compared the MODIS LSTs with air temperatures from nine stations (using 11 different data points) and found that they agreed to within 2.3 +/- 2.09 C, with station temperatures consistently lower than the MODIS LSTs. According to the MODIS LST, the maximum surface melt extended to approx. 2300 m in southern Greenland; while the SSM/I measurements showed that the maximum melt extended to nearly 2700 m in southeastern Greenland. The MODIS and SSM/I data are complementary in providing detailed information about the progression of surface and near- surface melt on the Greenland ice sheet.

Analysis of summer 2002 melt extent on the Greenland ice sheet using MODIS and SSM/I data

USGS Publications Warehouse

Hall, D.K.; Williams, R.S.; Steffen, K.; Chien, Janet Y.L.

2004-01-01

Previous work has shown that the summer of 2002 had the greatest area of snow melt extent on the Greenland ice sheet ever recorded using passive-microwave data. In this paper, we compare the 0?? isotherm derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument, with Special Sensor Microwave/Imager (SSM/I)-derived melt, at the time of the maximum melt extent in 2002. To validate the MODIS-derived land-surface temperatures (LSTs), we compared the MODIS LSTs with air temperatures from nine stations (using 11 different data points) and found that they agreed to within 2.3??2.09??C, with station temperatures consistently lower than the MODIS LSTs. According to the MODIS LST, the maximum surface melt extended to ???2300 m in southern Greenland; while the SSM/I measurements showed that the maximum melt extended to nearly 2700 m in southeastern Greenland. The MODIS and SSM/I data are complementary in providing detailed information about the progression of surface and near-surface melt on the Greenland ice sheet.

Analysis of summer 2002 melt extent on the Greenland ice sheet using MODIS and SSM/I data

USGS Publications Warehouse

Hall, D. K.; Williams, R.S.; Steffen, K.; Chien, Janet Y.L.

2004-01-01

Previous work has shown that the summer of 2002 had the greatest area of snow melt extent on the Greenland ice sheet ever recorded using passive-microwave data. In this paper, we compare the 0deg isotherm derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument, with Special Sensor Microwave/Imager (SSM/I)-derived melt, at the time of the maximum melt extent in 2002. To validate the MODIS-derived land-surface temperatures (LSTs), we compared the MODIS LSTs with air temperatures from nine stations (using 11 different data points) and found that they agreed to within 2.3 plusmn 2.09 degC, with station temperatures consistently lower than the MODIS LSTs. According to the MODIS LST, the maximum surface melt extended to ~2300 m in southern Greenland; while the SSM/I measurements showed that the maximum melt extended to nearly 2700 m in southeastern Greenland. The MODIS and SSM/I data are complementary in providing detailed information about the progression of surface and near-surface melt on the Greenland ice sheet.

Black Sea impact on its west-coast land surface temperature

NASA Astrophysics Data System (ADS)

Cheval, Sorin; Constantin, Sorin

2018-03-01

This study investigates the Black Sea influence on the thermal characteristics of its western hinterland based on satellite imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). The marine impact on the land surface temperature (LST) values is detected at daily, seasonal and annual time scales, and a strong linkage with the land cover is demonstrated. The remote sensing products used within the study supply LST data with complete areal coverage during clear sky conditions at 1-km spatial resolution, which is appropriate for climate studies. The sea influence is significant up to 4-5 km, by daytime, while the nighttime influence is very strong in the first 1-2 km, and it gradually decreases westward. Excepting the winter, the daytime temperature increases towards the plateau with the distance from the sea, e.g. with a gradient of 0.9 °C/km in the first 5 km in spring or with 0.7 °C/km in summer. By nighttime, the sea water usually remains warmer than the contiguous land triggering higher LST values in the immediate proximity of the coastline in all seasons, e.g. mean summer LST is 19.0 °C for the 1-km buffer, 16.6 °C for the 5-km buffer and 16.0 °C for the 10-km buffer. The results confirm a strong relationship between the land cover and thermal regime in the western hinterland of the Black Sea coast. The satellite-derived LST and air temperature values recorded at the meteorological stations are highly correlated for similar locations, but the marine influence propagates differently, pledging for distinct analysis. Identified anomalies in the general observed trends are investigated in correlation with sea surface temperature dynamics in the coastal area.

Evaluating soil moisture constraints on surface fluxes in land surface models globally

NASA Astrophysics Data System (ADS)

Harris, Phil; Gallego-Elvira, Belen; Taylor, Christopher; Folwell, Sonja; Ghent, Darren; Veal, Karen; Hagemann, Stefan

2016-04-01

Soil moisture availability exerts a strong control over land evaporation in many regions. However, global climate models (GCMs) disagree on when and where evaporation is limited by soil moisture. Evaluation of the relevant modelled processes has suffered from a lack of reliable, global observations of land evaporation at the GCM grid box scale. Satellite observations of land surface temperature (LST) offer spatially extensive but indirect information about the surface energy partition and, under certain conditions, about soil moisture availability on evaporation. Specifically, as soil moisture decreases during rain-free dry spells, evaporation may become limited leading to increases in LST and sensible heat flux. We use MODIS Terra and Aqua observations of LST at 1 km from 2000 to 2012 to assess changes in the surface energy partition during dry spells lasting 10 days or longer. The clear-sky LST data are aggregated to a global 0.5° grid before being composited as a function dry spell day across many events in a particular region and season. These composites are then used to calculate a Relative Warming Rate (RWR) between the land surface and near-surface air. This RWR can diagnose the typical strength of short term changes in surface heat fluxes and, by extension, changes in soil moisture limitation on evaporation. Offline land surface model (LSM) simulations offer a relatively inexpensive way to evaluate the surface processes of GCMs. They have the benefits that multiple models, and versions of models, can be compared on a common grid and using unbiased forcing. Here, we use the RWR diagnostic to assess global, offline simulations of several LSMs (e.g., JULES and JSBACH) driven by the WATCH Forcing Data-ERA Interim. Both the observed RWR and the LSMs use the same 0.5° grid, which allows the observed clear-sky sampling inherent in the underlying MODIS LST to be applied to the model outputs directly. This approach avoids some of the difficulties in analysing free-running simulations in which land and atmosphere are coupled and, as such, it provides a flexible intermediate step in the assessment of surface processes in GCMs.

Impact of absorbing aerosol deposition on snow albedo reduction over the southern Tibetan plateau based on satellite observations

NASA Astrophysics Data System (ADS)

Lee, Wei-Liang; Liou, K. N.; He, Cenlin; Liang, Hsin-Chien; Wang, Tai-Chi; Li, Qinbin; Liu, Zhenxin; Yue, Qing

2017-08-01

We investigate the snow albedo variation in spring over the southern Tibetan Plateau induced by the deposition of light-absorbing aerosols using remote sensing data from moderate resolution imaging spectroradiometer (MODIS) aboard Terra satellite during 2001-2012. We have selected pixels with 100 % snow cover for the entire period in March and April to avoid albedo contamination by other types of land surfaces. A model simulation using GEOS-Chem shows that aerosol optical depth (AOD) is a good indicator for black carbon and dust deposition on snow over the southern Tibetan Plateau. The monthly means of satellite-retrieved land surface temperature (LST) and AOD over 100 % snow-covered pixels during the 12 years are used in multiple linear regression analysis to derive the empirical relationship between snow albedo and these variables. Along with the LST effect, AOD is shown to be an important factor contributing to snow albedo reduction. We illustrate through statistical analysis that a 1-K increase in LST and a 0.1 increase in AOD indicate decreases in snow albedo by 0.75 and 2.1 % in the southern Tibetan Plateau, corresponding to local shortwave radiative forcing of 1.5 and 4.2 W m-2, respectively.

Hydrological modelling of the Mabengnong catchment in the southeast Tibet with support of short term intensive precipitation observation

NASA Astrophysics Data System (ADS)

Wang, L.; Zhang, F.; Zhang, H.; Scott, C. A.; Zeng, C.; SHI, X.

2017-12-01

Precipitation is one of the crucial inputs for models used to better understand hydrological processes. In high mountain areas, it is a difficult task to obtain a reliable precipitation data set describing the spatial and temporal characteristic due to the limited meteorological observations and high variability of precipitation. This study carries out intensive observation of precipitation in a high mountain catchment in the southeast of the Tibet during July to August 2013. According to the rain gauges set up at different altitudes, it is found that precipitation is greatly influenced by altitude. The observed precipitation is used to depict the precipitation gradient (PG) and hourly distribution (HD), showing that the average duration is around 0.1, 0.8 and 6.0 hours and the average PG is 0.10, 0.28 and 0.26 mm/d/100m for trace, light and moderate rain, respectively. Based on the gridded precipitation derived from the PG and HD and the nearby Linzhi meteorological station at lower altitude, a distributed biosphere hydrological model based on water and energy budgets (WEB-DHM) is applied to simulate the hydrological processes. Beside the observed runoff, MODIS/Terra snow cover area (SCA) data, and MODIS/Terra land surface temperature (LST) data are also used for model calibration and validation. The resulting runoff, SCA and LST simulations are all reasonable. Sensitivity analyses indicate that runoff is greatly underestimated without considering PG, illustrating that short-term intensive precipitation observation contributes to improving hydrological modelling of poorly gauged high mountain catchments.

A closer look at temperature changes with remote sensing

NASA Astrophysics Data System (ADS)

Metz, Markus; Rocchini, Duccio; Neteler, Markus

2014-05-01

Temperature is a main driver for important ecological processes. Time series temperature data provide key environmental indicators for various applications and research fields. High spatial and temporal resolution is crucial in order to perform detailed analyses in various fields of research. While meteorological station data are commonly used, they often lack completeness or are not distributed in a representative way. Remotely sensed thermal images from polar orbiting satellites are considered to be a good alternative to the scarce meteorological data as they offer almost continuous coverage of the Earth with very high temporal resolution. A drawback of temperature data obtained by satellites is the occurrence of gaps (due to clouds, aerosols) that must be filled. We have reconstructed a seamless and gap-free time series for land surface temperature (LST) at continental scale for Europe from MODIS LST products (Moderate Resolution Imaging Sensor instruments onboard the Terra and Aqua satellites), keeping the temporal resolution of four records per day and enhancing the spatial resolution from 1 km to 250 m. Here we present a new procedure to reconstruct MODIS LST time series with unprecedented detail in space and time, at the same time providing continental coverage. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. We selected as auxiliary variables datasets which are globally available in order to propose a worldwide reproducible method. Compared to existing similar datasets, the substantial quantitative difference translates to a qualitative difference in applications and results. We consider both our dataset and the new procedure for its creation to be of utmost interest to a broad interdisciplinary audience. Moreover, we provide examples for its implications and applications, such as disease risk assessment, epidemiology, environmental monitoring, and temperature anomalies. In the near future, aggregated derivatives of our dataset (following the BIOCLIM variable scheme) will be freely made online available for direct usage in GIS based applications.

Combining remote sensing and eddy covariance data to monitor the gross primary production of an estuarine wetland ecosystem in East China.

PubMed

Wu, Mingquan; Muhammad, Shakir; Chen, Fang; Niu, Zheng; Wang, Changyao

2015-04-01

Wetland ecosystems are very important for ecological diversity and have a strong ability to sequester carbon. Through comparisons with field measured eddy covariance data, we evaluated the relationships between the light use efficiency (LUE) index and the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and land surface temperature (LST). Consequently, we have proposed a new model for the estimation of gross primary production (GPP) for wetland ecosystems using Moderate Resolution Imaging Spectroradiometer (MODIS) products, including these vegetation indices, LST and the fraction of photosynthetically active radiation (FAPAR) absorbed by the active vegetation. This model was developed and validated for a study site on Chongming Island, Shanghai, China. Our results show that photosynthetically active radiation (PAR) was highly correlated with the LST, with a coefficient of determination (R(2)) of 0.59 (p < 0.001). Vegetation indices, such as EVI, NDVI and LST, were highly correlated with LUE. We found that the product of vegetation indices (VIs) and a modified form of LST (Te) can be used to estimate LUE, with an R(2) of 0.82 (P < 0.0001) and an RMSE of 0.054 kg C per mol PAR. This new model can provide reliable estimates of GPP (R(2) of 0.87 and RMSE of 0.009 kg C m(-2) 8 d(-1) (P < 0.0001)).

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data

PubMed Central

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2016-01-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting. PMID:27667901

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data.

PubMed

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2015-08-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting.

Improving HJ-1B IRS land surface temperature product using ASTER global emissivity database

NASA Astrophysics Data System (ADS)

Li, H.; Hu, T.; Meng, X.; Yongming, D.; Cao, B.; Liu, Q.

2015-12-01

Land surface temperature (LST) is a key parameter for hydrological, meteorological, climatological and environmental studies. Currently many operational LST products have been generated using European and American satellite data, i.e., the Advanced Very High Resolution Radiometer (AVHRR), Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS). However, few LST product has been produced using Chinese satellite data. Thus, the objective of this study is to generate reliable LST product using Chinese HJ-1B satellite data. The HJ-1B satellite of China, were launched on September 6, 2008, which are used for disaster and environment monitoring. IRS (Infrared Scanner) is one of the key instruments onboard HJ-1B satellite, it can scan the earth every four days, has four spectral bands ranging from the near-infrared to thermal infrared bands (band 1 0.75 - 1.10μm, band 2 1.55-1.75μm, MIR band 3 3.50 - 3.90μm, band 4 10.5-12.5μm) with 720 km swath. It scans ±29° from nadir and the spatial resolution for band1-3 is 150m and 300m for band4. In this study, a single-channel parametric model (SC-PM) algorithm were used to produce 300m LST product from HJ-1B IRS data. The NCEP atmospheric profiles and a parametric model were used for atmospheric correction. In order to improve the accuracy of the land surface emissivity (LSE), the 1km ASTER Global Emissivity Database (GED) and self-developed 5-day 1km vegetation cover product were used for estimating the LSE based on the Vegetation Cover Method. Two years of HJ-1B IRS LST product in Heihe River basin (Gansu province, China) from June 2012 to June 2014 were generated. The LST products were evaluated against ground observations in an arid area of northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. Four barren surface sites and ten vegetated sites were chosen for the evaluation. The results show that the developed HJ-1B IRS LST products demonstrate a good accuracy, with an average bias of 0.11 K and an average root mean square error (RMSE) of 2.43 K for all the sites during daytime. In addition, the biases are within 1K for all the barren surface sites, this indicate that using ASTER GED can produce reliable LST products from HJ-1B IRS data, especially for the barren surfaces.

Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data

PubMed Central

Scharlemann, Jörn P. W.; Benz, David; Hay, Simon I.; Purse, Bethan V.; Tatem, Andrew J.; Wint, G. R. William; Rogers, David J.

2008-01-01

Background Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. Methodology/Principal Findings We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Conclusions/Significance Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling. PMID:18183289

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data.

PubMed

Scharlemann, Jörn P W; Benz, David; Hay, Simon I; Purse, Bethan V; Tatem, Andrew J; Wint, G R William; Rogers, David J

2008-01-09

Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.

A Framework for Mapping Global Evapotranspiration using 375-m VIIRS LST

NASA Astrophysics Data System (ADS)

Hain, C.; Anderson, M. C.; Schull, M. A.; Neale, C. M. U.

2017-12-01

As the world's water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use are becoming increasingly important, especially in areas of food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in morning land surface temperature to estimate the partitioning of sensible/latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring. Until recently, ALEXI has been limited to areas with high resolution temporal sampling of geostationary sensors. The use of geostationary sensors makes global mapping a complicated process, especially for real-time applications, as data from as many as five different sensors are required to be ingested and harmonized to create a global mosaic. However, our research team has developed a new and novel method of using twice-daily observations from polar-orbiting sensors such as MODIS and VIIRS to estimate the mid-morning rise in LST that is used to drive the energy balance estimations within ALEXI. This allows the method to be applied globally using a single sensor rather than a global compositing of all available geostationary data. Other advantages of this new method include the higher spatial resolution provided by MODIS and VIIRS and the increased sampling at high latitudes where oblique view angles limit the utility of geostationary sensors. Improvements to the spatial resolution of the thermal infrared wavelengths on the VIIRS instrument, as compared to MODIS (375-m VIIRS vs. 1-km MODIS), allows for a much higher resolution ALEXI product than has been previously available. Therefore, recent developments have been to generate 375-m ALEXI ET products over several pilot regions (e.g. western US and the MENA region). The monitoring of consumptive water use over regions where significant groundwater pumping for irrigation is employed is important to accurately quantify the efficiency of water use in the region.

Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data.

PubMed

Chakraborty, Surya Deb; Kant, Yogesh; Mitra, Debashis

2015-01-15

Surface energy processes has an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. The research was undertaken to analyze the potential of Landsat and MODIS data in retrieving biophysical parameters in estimating land surface temperature & heat fluxes diurnally in summer and winter seasons of years 2000 and 2010 and understanding its effect on anthropogenic heat disturbance over Delhi and surrounding region. Results show that during years 2000-2010, settlement and industrial area increased from 5.66 to 11.74% and 4.92 to 11.87% respectively which in turn has direct effect on land surface temperature (LST) and heat fluxes including anthropogenic heat flux. Based on the energy balance model for land surface, a method to estimate the increase in anthropogenic heat flux (Has) has been proposed. The settlement and industrial areas has higher amounts of energy consumed and has high values of Has in all seasons. The comparison of satellite derived LST with that of field measured values show that Landsat estimated values are in close agreement within error of ±2 °C than MODIS with an error of ±3 °C. It was observed that, during 2000 and 2010, the average change in surface temperature using Landsat over settlement & industrial areas of both seasons is 1.4 °C & for MODIS data is 3.7 °C. The seasonal average change in anthropogenic heat flux (Has) estimated using Landsat & MODIS is up by around 38 W/m(2) and 62 W/m(2) respectively while higher change is observed over settlement and concrete structures. The study reveals that the dynamic range of Has values has increased in the 10 year period due to the strong anthropogenic influence over the area. The study showed that anthropogenic heat flux is an indicator of the strength of urban heat island effect, and can be used to quantify the magnitude of the urban heat island effect. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evaluation of the relation between evapotranspiration and normalized difference vegetation index for downscaling the simplified surface energy balance model

USGS Publications Warehouse

Haynes, Jonathan V.; Senay, Gabriel B.

2012-01-01

The Simplified Surface Energy Balance (SSEB) model uses satellite imagery to estimate actual evapotranspiration (ETa) at 1-kilometer resolution. SSEB ETa is useful for estimating irrigation water use; however, resolution limitations restrict its use to regional scale applications. The U.S. Geological Survey investigated the downscaling potential of SSEB ETa from 1 kilometer to 250 meters by correlating ETa with the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer instrument (MODIS). Correlations were studied in three arid to semiarid irrigated landscapes of the Western United States (Escalante Valley near Enterprise, Utah; Palo Verde Valley near Blythe, California; and part of the Columbia Plateau near Quincy, Washington) during several periods from 2002 to 2008. Irrigation season ETa-NDVI correlations were lower than expected, ranging from R2 of 0.20 to 0.61 because of an eastward 2–3 kilometer shift in ETadata. The shift is due to a similar shift identified in the land-surface temperature (LST) data from the MODIS Terra satellite, which is used in the SSEB model. Further study is needed to delineate the Terra LST shift, its effect on SSEB ETa, and the relation between ETa and NDVI.

Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia

NASA Astrophysics Data System (ADS)

Sabajo, Clifton R.; le Maire, Guerric; June, Tania; Meijide, Ana; Roupsard, Olivier; Knohl, Alexander

2017-10-01

Indonesia is currently one of the regions with the highest transformation rate of land surface worldwide related to the expansion of oil palm plantations and other cash crops replacing forests on large scales. Land cover changes, which modify land surface properties, have a direct effect on the land surface temperature (LST), a key driver for many ecological functions. Despite the large historic land transformation in Indonesia toward oil palm and other cash crops and governmental plans for future expansion, this is the first study so far to quantify the impacts of land transformation on the LST in Indonesia. We analyze LST from the thermal band of a Landsat image and produce a high-resolution surface temperature map (30 m) for the lowlands of the Jambi province in Sumatra (Indonesia), a region which suffered large land transformation towards oil palm and other cash crops over the past decades. The comparison of LST, albedo, normalized differenced vegetation index (NDVI) and evapotranspiration (ET) between seven different land cover types (forest, urban areas, clear-cut land, young and mature oil palm plantations, acacia and rubber plantations) shows that forests have lower surface temperatures than the other land cover types, indicating a local warming effect after forest conversion. LST differences were up to 10.1 ± 2.6 °C (mean ± SD) between forest and clear-cut land. The differences in surface temperatures are explained by an evaporative cooling effect, which offsets the albedo warming effect. Our analysis of the LST trend of the past 16 years based on MODIS data shows that the average daytime surface temperature in the Jambi province increased by 1.05 °C, which followed the trend of observed land cover changes and exceeded the effects of climate warming. This study provides evidence that the expansion of oil palm plantations and other cash crops leads to changes in biophysical variables, warming the land surface and thus enhancing the increase of the air temperature because of climate change.

Land surface phenology and land surface temperature changes along an urban-rural gradient in Yangtze River Delta, china.

PubMed

Han, Guifeng; Xu, Jianhua

2013-07-01

Using SPOT/VGT NDVI time series images (2002-2009) and MODIS/LST images (2002-2009) smoothed by a Savitzky-Golay filter, the land surface phenology (LSP) and land surface temperature (LST), respectively, are extracted for six cities in the Yangtze River Delta, China, including Shanghai, Hangzhou, Nanjing, Changzhou, Wuxi, and Suzhou. The trends of the averaged LSP and LST are analyzed, and the relationship between these values is revealed along the urban-rural gradient. The results show that urbanization advances the start of the growing season, postpones the end of the growing season, prolongs the growing season length (GSL), and reduces the difference between maximal NDVI and minimal NDVI in a year (NDVIamp). More obvious changes occur in surface vegetation phenology as the urbanized area is approached. The LST drops monotonously and logarithmically along the urban-rural gradient. Urbanization generally affects the LSP of the surrounding vegetation within 6 km to the urban edge. Except for GSL, the difference in the LSP between urban and rural areas has a significant logarithmic relationship with the distance to the urban edge. In addition, there is a very strong linear relationship between the LSP and the LST along the urban-rural gradient, especially within 6 km to the urban edge. The correlations between LSP and gross domestic product and population density reveal that human activities have considerable influence on the land surface vegetation growth.

Land Surface Phenologies of the Northern Great Plains: Possible Futures Arising From Land and Climate Change

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Wimberly, M. C.; Senay, G.; Wang, A.; Chang, J.; Wright, C. R.; Hansen, M. C.

2008-12-01

Land cover change across the Northern Great Plains of North America over the past three decades has been driven by changes in agricultural management (conservation tillage; irrigation), government incentives (Conservation Reserve Program; subsidies to grain-based ethanol), crop varieties (cold-hardy soybean), and market dynamics (increasing world demand). Climate change across the Northern Great Plains over the past three decades has been evident in trends toward earlier warmth in the spring and a longer frost-free season. Together these land and climate changes induce shifts in local and regional land surface phenologies (LSPs). Any significant shift in LSP may correspond to a significant shift in evapotranspiration, with consequences for regional hydrometeorology. We explored possible future scenarios involving land use and climate change in six steps. First, we defined the nominal draw areas of current and future biorefineries in North Dakota, South Dakota, Nebraska, Minnesota, and Iowa and masked those land cover types within the draw areas that were unlikely to change to agricultural use (open water, settlements, forests, etc.). Second, we estimated the proportion of corn and soybean remaining within the masked draw areas using MODIS-derived crop maps. Third, in each draw area, we modified LSPs to simulate crop changes for a control and two treatment scenarios. In the control, we used LSP profiles identified from MODIS Collection 5 NBAR data. In one treatment, we increased the proportion of tallgrass LSPs in the draw areas to represent widespread cultivation of a perennial cellulosic crop, like switchgrass. In a second treatment, we increased the proportion of corn LSPs in the draw areas to represent increased corn cultivation. Fourth, we characterized the seasonal progression of the thermal regime associated with the LSP profiles using MODIS Land Surface Temperature (LST) products. Fifth, we modeled the LSP profile as a quadratic function of accumulated growing degree-days based on the LST time series. Sixth, we used representative IPCC AR4 mid-century projections to force the quadratic models and produce possible future LSPs. The resulting shifts in potential peak vegetation to earlier dates indicate potential seasonal shifts in evapotranspiration.

Geothermal Anomaly Mapping Using Landsat ETM+ Data in Ilan Plain, Northeastern Taiwan

NASA Astrophysics Data System (ADS)

Chan, Hai-Po; Chang, Chung-Pai; Dao, Phuong D.

2018-01-01

Geothermal energy is an increasingly important component of green energy in the globe. A prerequisite for geothermal energy development is to acquire the local and regional geothermal prospects. Existing geophysical methods of estimating the geothermal potential are usually limited to the scope of prospecting because of the operation cost and site reachability in the field. Thus, explorations in a large-scale area such as the surface temperature and the thermal anomaly primarily rely on satellite thermal infrared imagery. This study aims to apply and integrate thermal infrared (TIR) remote sensing technology with existing geophysical methods for the geothermal exploration in Taiwan. Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) imagery is used to retrieve the land surface temperature (LST) in Ilan plain. Accuracy assessment of satellite-derived LST is conducted by comparing with the air temperature data from 11 permanent meteorological stations. The correlation coefficient of linear regression between air temperature and LST retrieval is 0.76. The MODIS LST product is used for the cross validation of Landsat derived LSTs. Furthermore, Landsat ETM+ multi-temporal brightness temperature imagery for the verification of the LST anomaly results were performed. LST Results indicate that thermal anomaly areas appear correlating with the development of faulted structure. Selected geothermal anomaly areas are validated in detail by field investigation of hot springs and geothermal drillings. It implies that occurrences of hot springs and geothermal drillings are in good spatial agreement with anomaly areas. In addition, the significant low-resistivity zones observed in the resistivity sections are echoed with the LST profiles when compared with in the Chingshui geothermal field. Despite limited to detecting the surficial and the shallow buried geothermal resources, this work suggests that TIR remote sensing is a valuable tool by providing an effective way of mapping and quantifying surface features to facilitate the exploration and assessment of geothermal resources in Taiwan.

Intensive precipitation observation greatly improves hydrological modelling of the poorly gauged high mountain Mabengnong catchment in the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Wang, Li; Zhang, Fan; Zhang, Hongbo; Scott, Christopher A.; Zeng, Chen; Shi, Xiaonan

2018-01-01

Precipitation is one of the most critical inputs for models used to improve understanding of hydrological processes. In high mountain areas, it is challenging to generate a reliable precipitation data set capturing the spatial and temporal heterogeneity due to the harsh climate, extreme terrain and the lack of observations. This study conducts intensive observation of precipitation in the Mabengnong catchment in the southeast of the Tibetan Plateau during July to August 2013. Because precipitation is greatly influenced by altitude, the observed data are used to characterize the precipitation gradient (PG) and hourly distribution (HD), showing that the average PG is 0.10, 0.28 and 0.26 mm/d/100 m and the average duration is around 0.1, 0.8 and 5.2 h for trace, light and moderate rain, respectively. A distributed biosphere hydrological model based on water and energy budgets with improved physical process for snow (WEB-DHM-S) is applied to simulate the hydrological processes with gridded precipitation data derived from a lower altitude meteorological station and the PG and HD characterized for the study area. The observed runoff, MODIS/Terra snow cover area (SCA) data, and MODIS/Terra land surface temperature (LST) data are used for model calibration and validation. Runoff, SCA and LST simulations all show reasonable results. Sensitivity analyses illustrate that runoff is largely underestimated without considering PG, indicating that short-term intensive precipitation observation has the potential to greatly improve hydrological modelling of poorly gauged high mountain catchments.

Assessing the Urban Heat Island Effect Across Biomes in the Continental USA Using Landsat and MODIS

NASA Technical Reports Server (NTRS)

Imhoff, Marc L.; Bounoua, L.; Zhang, Ping; Wolfe, Robert

2011-01-01

Impervious surface area (ISA) from the Landsat TM and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined across urban gradients and used to stratify sampling of LST and NDVI. We find that ecological context significantly influences the amplitude of summer daytime UHI (urban - rural temperature difference) with the largest 8 C (average) for cities built in mixed forest biomes. For all cities ISA is the primary driver for increase in temperature explaining 70% of the total variance. Annually, urban areas are warmer than the non-urban fringe by 2.9 C, except in biomes with arid and semiarid climates. The average amplitude of the UHI is asymmetric with a 4.3 C difference in summer and 1.3 C in winter. In desert environments, UHI's point to a possible heat sink effect. Results show that the urban heat island amplitude increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.

Web-based interactive access, analysis and comparison of remotely sensed and in situ measured temperature data

NASA Astrophysics Data System (ADS)

Eberle, Jonas; Urban, Marcel; Hüttich, Christian; Schmullius, Christiane

2014-05-01

Numerous datasets providing temperature information from meteorological stations or remote sensing satellites are available. However, the challenging issue is to search in the archives and process the time series information for further analysis. These steps can be automated for each individual product, if the pre-conditions are complied, e.g. data access through web services (HTTP, FTP) or legal rights to redistribute the datasets. Therefore a python-based package was developed to provide data access and data processing tools for MODIS Land Surface Temperature (LST) data, which is provided by NASA Land Processed Distributed Active Archive Center (LPDAAC), as well as the Global Surface Summary of the Day (GSOD) and the Global Historical Climatology Network (GHCN) daily datasets provided by NOAA National Climatic Data Center (NCDC). The package to access and process the information is available as web services used by an interactive web portal for simple data access and analysis. Tools for time series analysis were linked to the system, e.g. time series plotting, decomposition, aggregation (monthly, seasonal, etc.), trend analyses, and breakpoint detection. Especially for temperature data a plot was integrated for the comparison of two temperature datasets based on the work by Urban et al. (2013). As a first result, a kernel density plot compares daily MODIS LST from satellites Aqua and Terra with daily means from GSOD and GHCN datasets. Without any data download and data processing, the users can analyze different time series datasets in an easy-to-use web portal. As a first use case, we built up this complimentary system with remotely sensed MODIS data and in situ measurements from meteorological stations for Siberia within the Siberian Earth System Science Cluster (www.sibessc.uni-jena.de). References: Urban, Marcel; Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane; Herold, Martin. 2013. "Comparison of Satellite-Derived Land Surface Temperature and Air Temperature from Meteorological Stations on the Pan-Arctic Scale." Remote Sens. 5, no. 5: 2348-2367. Further materials: Eberle, Jonas; Clausnitzer, Siegfried; Hüttich, Christian; Schmullius, Christiane. 2013. "Multi-Source Data Processing Middleware for Land Monitoring within a Web-Based Spatial Data Infrastructure for Siberia." ISPRS Int. J. Geo-Inf. 2, no. 3: 553-576.

The summer urban heat island of Bucharest (Romania) as retrieved from satellite imagery

NASA Astrophysics Data System (ADS)

Cheval, Sorin; Dumitrescu, Alexandru

2014-05-01

The summer Urban Heat Island (UHI) of the city of Bucharest (Romania) has been investigated in terms of its shape, intensity, extension, and links to land cover. The study integrates land surface temperature (LST) data retrieved by the MODIS sensors aboard the Terra and Aqua NASA satellites, and SEVIRI sensors on board of the geostationary platform MSG, along 2000-2012. Based on the Rodionov Regime Shift Index, the significant changing points in the land surface temperature values along transverse profiles crossing the city's centre were considered as UHI's limits. The study shows that the intensity calculated as the difference between the LST within the UHI limits and several surrounding buffers is an objective and flexible tool for describing the average thermal state of the urban-rural transition. The method secures the weight of comparing the UHI's intensity of different urban areas. There are little variations from one month to another, but UHI's shapes and intensities under clear-sky conditions are very specific to nighttime (more regular and 2-3°C less in the 7-km width buffer), and daytime (more twisted and more steep temperature decrease). For both cases, strong relationships with the land cover can be assumed. The nighttime UHI's geometry is more regular, and the intensity lower than the day situation, while the land cover exerts a strong influence on the Bucharest LST. After all, the study promotes an objective manner to delimitate and quantify the UHI based on satellite imagery. The study was performed within the STAR project 92/2013 (Urban Heat Island Monitoring under Present and Future Climate - UCLIMESA).

Terra and Aqua satellites track tiger mosquito invasion: modelling the potential distribution of Aedes albopictus in north-eastern Italy

PubMed Central

2011-01-01

Background The continuing spread of the Asian tiger mosquito Aedes albopictus in Europe is of increasing public health concern due to the potential risk of new outbreaks of exotic vector-borne diseases that this species can transmit as competent vector. We predicted the most favorable areas for a short term invasion of Ae. albopictus in north-eastern Italy using reconstructed daily satellite data time series (MODIS Land Surface Temperature maps, LST). We reconstructed more than 11,000 daily MODIS LST maps for the period 2001-09 (i.e. performed spatial and temporal gap-filling) in an Open Source GIS framework. We aggregated these LST maps over time and identified the potential distribution areas of Ae. albopictus by adapting published temperature threshold values using three variables as predictors (0°C for mean January temperatures, 11°C for annual mean temperatures and 1350 growing degree days filtered for areas with autumnal mean temperatures > 11°C). The resulting maps were integrated into the final potential distribution map and this was compared with the known current distribution of Ae. albopictus in north-eastern Italy. Results LST maps show the microclimatic characteristics peculiar to complex terrains, which would not be visible in maps commonly derived from interpolated meteorological station data. The patterns of the three indicator variables partially differ from each other, while winter temperature is the determining limiting factor for the distribution of Ae. albopictus. All three variables show a similar spatial pattern with some local differences, in particular in the northern part of the study area (upper Adige valley). Conclusions Reconstructed daily land surface temperature data from satellites can be used to predict areas of short term invasion of the tiger mosquito with sufficient accuracy (200 m pixel resolution size). Furthermore, they may be applied to other species of arthropod of medical interest for which temperature is a relevant limiting factor. The results indicate that, during the next few years, the tiger mosquito will probably spread toward northern latitudes and higher altitudes in north-eastern Italy, which will considerably expand the range of the current distribution of this species. PMID:21812983

Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds

PubMed Central

Fan, Xiwei; Tang, Bo-Hui; Wu, Hua; Yan, Guangjian; Li, Zhao-Liang

2015-01-01

Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11.0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0.55 μm of 0.4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0.55 μm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31–34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11.0 to 2.2 K. The sensitivity analysis indicated that the total errors from all the uncertainties of input parameters, extension algorithm accuracy, and GSW algorithm accuracy were less than 2.5 K in nadir view. Finally, the Great Lakes surface water temperatures measured by buoys showed that the retrieval accuracy of the GSW algorithm was improved by at least 1.5 K using the proposed extension algorithm for cirrus skies. PMID:25928059

The oasis effect and summer temperature rise in arid regions - case study in Tarim Basin

PubMed Central

Hao, Xingming; Li, Weihong; Deng, Haijun

2016-01-01

This study revealed the influence of the oasis effect on summer temperatures based on MODIS Land Surface Temperature (LST) and meteorological data. The results showed that the oasis effect occurs primarily in the summer. For a single oasis, the maximum oasis cold island intensity based on LST (OCILST) was 3.82 °C and the minimum value was 2.32 °C. In terms of the annual change in OCILST, the mean value of all oases ranged from 2.47 °C to 3.56 °C from 2001 to 2013. Net radiation (Rn) can be used as a key predictor of OCILST and OCItemperature (OCI based on air temperature). On this basis, we reconstructed a long time series (1961–2014) of OCItemperature and Tbase(air temperature without the disturbance of oasis effect). Our results indicated that the reason for the increase in the observed temperatures was the significant decrease in the OCItemperature over the past 50 years. In arid regions, the data recorded in weather stations not only underestimated the mean temperature of the entire study area but also overestimated the increasing trend of the temperature. These discrepancies are due to the limitations in the spatial distribution of weather stations and the disturbance caused by the oasis effect. PMID:27739500

Utilizing remote sensing data for modeling water and heat regimes of the Black Earth Region territory of the European Russia

NASA Astrophysics Data System (ADS)

Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena; Uspensky, Sergey

2014-05-01

At present physical-mathematical modeling processes of water and heat exchange between vegetation covered land surfaces and atmosphere is the most appropriate method to describe peculiarities of water and heat regime formation for large territories. The developed model of such processes (Land Surface Model, LSM) is intended for calculation evaporation, transpiration by vegetation, soil water content and other water and heat regime characteristics, as well as distributions of the soil temperature and humidity in depth utilizing remote sensing data from satellites on land surface and meteorological conditions. The model parameters and input variables are the soil and vegetation characteristics and the meteorological characteristics, correspondingly. Their values have been determined from ground-based observations or satellite-based measurements by radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua, SEVIRI/Meteosat-9, -10. The case study has been carried out for the part of the agricultural Central Black Earth region with coordinates 49.5 deg. - 54 deg. N, 31 deg. - 43 deg. E and a total area of 227,300 km2 located in the steppe-forest zone of the European Russia for years 2009-2012 vegetation seasons. From AVHRR data there have been derived the estimates of three types of land surface temperature (LST): land surface skin temperature Tsg, air-foliage temperature Ta and efficient radiation temperature Ts.eff, emissivity E, normalized vegetation index NDVI, vegetation cover fraction B, leaf area index LAI, cloudiness and precipitation. From MODIS data the estimates of LST Tls, E, NDVI and LAI have been obtained. The SEVIRI data have been used to build the estimates of Tls, Ta, E, LAI and precipitation. Previously developed method and technology of above AVHRR-derived estimates have been improved and adapted to the study area. To check the reliability of the Ts.eff and Ta estimations for named seasons the error statistics of their definitions has been analyzed through comparison with data of observations at agricultural meteorological stations of the study region. The mentioned MODIS-based remote sensing products for the same vegetation seasons have been built using data downloaded from the website LP DAAC (NASA). Reliability of the MODIS-derived Tls estimates have been confirmed by results of comparison with similar estimates from synchronous AVHRR, SEVIRI and ground-based data. To retrieve Tls and E from SEVIRI data at daylight and nighttime there have been developed the method and technology of thematic processing these data in IR channels NN 9, 10 (10.8 and 12.0 nm) at three successive times under cloud-free conditions without using exact values of E. This technology has been also adapted to the study area. Analysis of reliability of Tls estimation have been carried out through comparing with synchronous SEVIRI-derived Tls estimates obtained at Land Surface Analysis Satellite Applications Facility (LSA SAF, Lisbon, Portugal) and MODIS-derived Tls estimates. When the first comparison daily - or monthly-averaged values of RMS deviation have not been exceeded 2 deg. C for various dates and months during years 2009-2012 vegetation seasons. RMS deviation of Tls(SEVIRI) from Tls(MODIS) has been in the range of 1.0-3.0 deg. C. The method and technology have been also developed and tested to define Ta values from SEVIRI data at daylight and nighttime. This method is based on using satellite-derived estimates of Tls and regression relationship between Tls and ground-measured values of Ta. Comparison of satellite-based Ta estimates with data of synchronous standard term ground-based observations at the network of meteorological stations of the study area for summer periods of 2009-2012 has given RMS deviation values in the range of 1.8-3.0 deg. C. Formed archive of satellite products has been also supplemented with array of LAI estimates retrieved from SEVIRI data at LSA SAF for the study area and growing seasons 2011-2012. The possibility is shown to use the developed Multi Threshold Method (MTM) for generating the AVHRR- and SEVIRI-based estimates of daily and monthly precipitation amounts for the region of interest The MTM provides the cloud detection and identification of cloud types, estimation of the maximum liquid water content and cloud layer water content, allocation of precipitation zones and determination of instantaneous maximum of precipitation intensities in the pixel range around the clock throughout the year independently of the land surface type. In developing procedures of utilizing satellite estimates of precipitation during the vegetation season in the model there have been built up algorithms and programs of transition from estimating the rainfall intensity to assessment of their daily values. The comparison of the daily, monthly and seasonal AVHRR- and SEVIRI-derived precipitation sums with similar values retrieved from network ground-based observations using weighting interpolation procedure have been carried out. Agreement of all three evaluations is satisfactory. To assimilate remote sensing products into the model the special techniques have been developed including: 1) replacement of ground-measured model parameters LAI and B by their satellite-derived estimates. The possibility of such replacement has been confirmed through various comparisons of: a) LAI behavior for ground- and satellite-derived values; b) modeled values of Ts and Tf , satellite-based estimates of Ts.eff, Tls and Ta and ground-based measurements of LST; c) modeled and measured values of soil water content W and evapotranspiration Ev; 2) utilization of satellite-derived values of LSTs Ts.eff, Tls and Ta, and estimates of precipitation as the input model variables instead of the respective ground-measured temperatures and rainfall when assessing the accuracy of soil water content, evapotranspiration and soil temperature calculations; 3) accounting for the spatial variability of satellite-based LAI, B, LST and precipitation estimates by entering their area-distributed values into the model. For years 2009-2012 vegetation seasons there have been calculated the characteristics of the water and heat regimes of the region under investigation utilizing satellite estimates of vegetation characteristics, LST and precipitation in the model. The calculation results have shown that the discrepancies of evapotranspiration and soil water content values are within acceptable limits.

Examination of elevation dependency in observed and projected temperature change in the Upper Indus Basin and Western Himalaya

NASA Astrophysics Data System (ADS)

Fowler, H. J.; Forsythe, N. D.; Blenkinsop, S.; Archer, D.; Hardy, A.; Janes, T.; Jones, R. G.; Holderness, T.

2013-12-01

We present results of two distinct, complementary analyses to assess evidence of elevation dependency in temperature change in the UIB (Karakoram, Eastern Hindu Kush) and wider WH. The first analysis component examines historical remotely-sensed land surface temperature (LST) from the second and third generation of the Advanced Very High Resolution Radiometer (AVHRR/2, AVHRR/3) instrument flown on NOAA satellite platforms since the mid-1980s through present day. The high spatial resolution (<4km) from AVHRR instrument enables precise consideration of the relationship between estimated LST and surface topography. The LST data product was developed as part of initiative to produce continuous time-series for key remotely sensed spatial products (LST, snow covered area, cloud cover, NDVI) extending as far back into the historical record as feasible. Context for the AVHRR LST data product is provided by results of bias assessment and validation procedures against both available local observations, both manned and automatic weather stations. Local observations provide meaningful validation and bias assessment of the vertical gradients found in the AVHRR LST as the elevation range from the lowest manned meteorological station (at 1460m asl) to the highest automatic weather station (4733m asl) covers much of the key range yielding runoff from seasonal snowmelt. Furthermore the common available record period of these stations (1995 to 2007) enables assessment not only of the AVHRR LST but also performance comparisons with the more recent MODIS LST data product. A range of spatial aggregations (from minor tributary catchments to primary basin headwaters) is performed to assess regional homogeneity and identify potential latitudinal or longitudinal gradients in elevation dependency. The second analysis component investigates elevation dependency, including its uncertainty, in projected temperature change trajectories in the downscaling of a seventeen member Global Climate Model (GCM) perturbed physics ensemble (PPE) of transient (130-year) simulations using a moderate resolution (25km) regional climate model (RCM). The GCM ensemble is the17-member QUMP (Quantifying Uncertainty in Model Projections) ensemble and the downscaling is done using HadRM3P, part of the PRECIS regional climate modelling system. Both the RCM and GCMs are models developed the UK Met Office Hadley Centre and are based on the HadCM3 GCM. Use of the multi-member PPE enables quantification of uncertainty in projected temperature change while the spatial resolution of RCM improves insight into the role of elevation in projected rates of change. Furthermore comparison with the results of the remote sensing analysis component - considered to provide an 'observed climatology' - permits evaluation of individual ensemble members with regards to biases in spatial gradients in temperature as well timing and magnitude of annual cycles.

Assessment of SMOS Soil Moisture Retrieval Parameters Using Tau-Omega Algorithms for Soil Moisture Deficit Estimation

NASA Technical Reports Server (NTRS)

Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika

2014-01-01

Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.

Sensitivity of Satellite-Based Skin Temperature to Different Surface Emissivity and NWP Reanalysis Sources Demonstrated Using a Single-Channel, Viewing-Angle-Corrected Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Scarino, B. R.; Minnis, P.; Yost, C. R.; Chee, T.; Palikonda, R.

2015-12-01

Single-channel algorithms for satellite thermal-infrared- (TIR-) derived land and sea surface skin temperature (LST and SST) are advantageous in that they can be easily applied to a variety of satellite sensors. They can also accommodate decade-spanning instrument series, particularly for periods when split-window capabilities are not available. However, the benefit of one unified retrieval methodology for all sensors comes at the cost of critical sensitivity to surface emissivity (ɛs) and atmospheric transmittance estimation. It has been demonstrated that as little as 0.01 variance in ɛs can amount to more than a 0.5-K adjustment in retrieved LST values. Atmospheric transmittance requires calculations that employ vertical profiles of temperature and humidity from numerical weather prediction (NWP) models. Selection of a given NWP model can significantly affect LST and SST agreement relative to their respective validation sources. Thus, it is necessary to understand the accuracies of the retrievals for various NWP models to ensure the best LST/SST retrievals. The sensitivities of the single-channel retrievals to surface emittance and NWP profiles are investigated using NASA Langley historic land and ocean clear-sky skin temperature (Ts) values derived from high-resolution 11-μm TIR brightness temperature measured from geostationary satellites (GEOSat) and Advanced Very High Resolution Radiometers (AVHRR). It is shown that mean GEOSat-derived, anisotropy-corrected LST can vary by up to ±0.8 K depending on whether CERES or MODIS ɛs sources are used. Furthermore, the use of either NOAA Global Forecast System (GFS) or NASA Goddard Modern-Era Retrospective Analysis for Research and Applications (MERRA) for the radiative transfer model initial atmospheric state can account for more than 0.5-K variation in mean Ts. The results are compared to measurements from the Surface Radiation Budget Network (SURFRAD), an Atmospheric Radiation Measurement (ARM) Program ground station, and NOAA ESRL high-resolution Optimum Interpolation SST (OISST). Precise understanding of the influence these auxiliary inputs have on final satellite-based Ts retrievals may help guide refinement in ɛs characterization and NWP development, e.g., future Goddard Earth Observing System Data Assimilation System versions.

Retrieving Land Surface Temperature and Emissivity from Multispectral and Hyperspectral Thermal Infrared Instruments

NASA Astrophysics Data System (ADS)

Hook, Simon; Hulley, Glynn; Nicholson, Kerry

2017-04-01

Land Surface Temperature and Emissivity (LST&E) data are critical variables for studying a variety of Earth surface processes and surface-atmosphere interactions such as evapotranspiration, surface energy balance and water vapor retrievals. LST&E have been identified as an important Earth System Data Record (ESDR) by NASA and many other international organizations Accurate knowledge of the LST&E is a key requirement for many energy balance models to estimate important surface biophysical variables such as evapotranspiration and plant-available soil moisture. LST&E products are currently generated from sensors in low earth orbit (LEO) such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites as well as from sensors in geostationary Earth orbit (GEO) such as the Geostationary Operational Environmental Satellites (GOES) and airborne sensors such as the Hyperspectral Thermal Emission Spectrometer (HyTES). LST&E products are generated with varying accuracies depending on the input data, including ancillary data such as atmospheric water vapor, as well as algorithmic approaches. NASA has identified the need to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. We will discuss the different approaches that can be used to retrieve surface temperature and emissivity from multispectral and hyperspectral thermal infrared sensors using examples from a variety of different sensors such as those mentioned, and planned new sensors like the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Hyperspectral Infrared Imager (HyspIRI). We will also discuss a project underway at NASA to develop a single unified product from some the individual sensor products and assess the errors associated with the product.

Observed Thermal Impacts of Wind Farms Over Northern Illinois.

PubMed

Slawsky, Lauren M; Zhou, Liming; Baidya Roy, Somnath; Xia, Geng; Vuille, Mathias; Harris, Ronald A

2015-06-25

This paper assesses impacts of three wind farms in northern Illinois using land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Terra and Aqua satellites for the period 2003-2013. Changes in LST between two periods (before and after construction of the wind turbines) and between wind farm pixels and nearby non-wind-farm pixels are quantified. An areal mean increase in LST by 0.18-0.39 °C is observed at nighttime over the wind farms, with the geographic distribution of this warming effect generally spatially coupled with the layout of the wind turbines (referred to as the spatial coupling), while there is no apparent impact on daytime LST. The nighttime LST warming effect varies with seasons, with the strongest warming in winter months of December-February, and the tightest spatial coupling in summer months of June-August. Analysis of seasonal variations in wind speed and direction from weather balloon sounding data and Automated Surface Observing System hourly observations from nearby stations suggest stronger winds correspond to seasons with greater warming and larger downwind impacts. The early morning soundings in Illinois are representative of the nighttime boundary layer and exhibit strong temperature inversions across all seasons. The strong and relatively shallow inversion in summer leaves warm air readily available to be mixed down and spatially well coupled with the turbine. Although the warming effect is strongest in winter, the spatial coupling is more erratic and spread out than in summer. These results suggest that the observed warming signal at nighttime is likely due to the net downward transport of heat from warmer air aloft to the surface, caused by the turbulent mixing in the wakes of the spinning turbine rotor blades.

Observed Thermal Impacts of Wind Farms Over Northern Illinois

PubMed Central

Slawsky, Lauren M.; Zhou, Liming; Baidya Roy, Somnath; Xia, Geng; Vuille, Mathias; Harris, Ronald A.

2015-01-01

This paper assesses impacts of three wind farms in northern Illinois using land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Terra and Aqua satellites for the period 2003–2013. Changes in LST between two periods (before and after construction of the wind turbines) and between wind farm pixels and nearby non-wind-farm pixels are quantified. An areal mean increase in LST by 0.18–0.39 °C is observed at nighttime over the wind farms, with the geographic distribution of this warming effect generally spatially coupled with the layout of the wind turbines (referred to as the spatial coupling), while there is no apparent impact on daytime LST. The nighttime LST warming effect varies with seasons, with the strongest warming in winter months of December-February, and the tightest spatial coupling in summer months of June-August. Analysis of seasonal variations in wind speed and direction from weather balloon sounding data and Automated Surface Observing System hourly observations from nearby stations suggest stronger winds correspond to seasons with greater warming and larger downwind impacts. The early morning soundings in Illinois are representative of the nighttime boundary layer and exhibit strong temperature inversions across all seasons. The strong and relatively shallow inversion in summer leaves warm air readily available to be mixed down and spatially well coupled with the turbine. Although the warming effect is strongest in winter, the spatial coupling is more erratic and spread out than in summer. These results suggest that the observed warming signal at nighttime is likely due to the net downward transport of heat from warmer air aloft to the surface, caused by the turbulent mixing in the wakes of the spinning turbine rotor blades. PMID:26121613

InfoSequia: the first operational remote sensing-based Drought Monitoring System of Spain

NASA Astrophysics Data System (ADS)

Contreras, Sergio; Hunink, Johannes E.

2016-04-01

We present a satellite-based Drought Monitoring System that provides weekly updates of maps and bulletins with vegetation drought indices over the Iberian Peninsula. The web portal InfoSequía (http://infosequia.es) aims to complement the current Spanish Drought Monitoring System which relies on a hydrological drought index computed at the basin level using data on river flows and water stored in reservoirs. Drought indices computed by InfoSequia are derived from satellite data provided by MODIS sensors (TERRA and AQUA satellites), and report the relative anomaly observed in the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and in an additive combination of both. Similar to the U.S. Drought Monitoring System by NOAA, the indices include the Vegetation Condition Index (VCI, relative NDVI anomaly), the Temperature Condition Index (TCI, relative LST anomaly) and the Vegetation Health Index (VHI, relative NDVI-LST anomaly). Relative anomalies are codified into four warning levels, and all of them are provided for short periods of time (8-day windows), or longer periods (e.g. 1 year) in order to capture the cumulative effects of droughts in the state variables. Additionally, InfoSequia quantifies the seasonal trajectories of the cumulative deviation of the observed NDVI in relation with the averaged seasonal trajectory observed over a reference period. Through the weekly bulletins, the Drought Monitoring System InfoSequia aims to provide practical information to stakeholders on the sensitivity and resilience of native ecosystems and rainfed agrosystems during drought periods. Also, the remote sensed indices can be used as drought impact indicator to evaluate the skill of seasonal agricultural drought forecasting systems. InfoSequia is partly funded by the Spanish Ministry of Economy and Competiveness through a Torres-Quevedo grant.

A Unified and Coherent Land Surface Emissivity Earth System Data Record

NASA Astrophysics Data System (ADS)

Knuteson, R. O.; Borbas, E. E.; Hulley, G. C.; Hook, S. J.; Anderson, M. C.; Pinker, R. T.; Hain, C.; Guillevic, P. C.

2014-12-01

Land Surface Temperature and Emissivity (LST&E) data are essential for a wide variety of studies from calculating the evapo-transpiration of plant canopies to retrieving atmospheric water vapor. LST&E products are generated from data acquired by sensors in low Earth orbit (LEO) and by sensors in geostationary Earth orbit (GEO). Although these products represent the same measure, they are produced at different spatial, spectral and temporal resolutions using different algorithms. The different approaches used to retrieve the temperatures and emissivities result in discrepancies and inconsistencies between the different products. NASA has identified a major need to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. This poster will introduce the land surface emissivity product of the NASA MEASUREs project called A Unified and Coherent Land Surface Temperature and Emissivity (LST&E) Earth System Data Record (ESDR). To develop a unified high spectral resolution emissivity database, the MODIS baseline-fit emissivity database (MODBF) produced at the University of Wisconsin-Madison and the ASTER Global Emissivity Database (ASTER GED) produced at JPL will be merged. The unified Emissivity ESDR will be produced globally at 5km in mean monthly time-steps and for 12 bands from 3.6-14.3 micron and extended to 417 bands using a PC regression approach. The poster will introduce this data product. LST&E is a critical ESDR for a wide variety of studies in particular ecosystem and climate modeling.

Enhancing Remotely Sensed TIR Data for Public Health Applications: Is West Nile Virus Heat-Related?

NASA Astrophysics Data System (ADS)

Weng, Q.; Liu, H.; Jiang, Y.

2014-12-01

Public health studies often require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensors can deliver TIR data at both high temporal and spatial resolution. This technological limitation prevents the wide usage of remote sensing data in epidemiological studies. To solve this issue, we have developed a few image fusion techniques to generate high temporally-resolved image data. We downscaled GOES LST data to 15-minute 1-km resolution to assess community-based heat-related risk in Los Angeles County, California and simulated ASTER datasets by fusing ASTER and MODIS data to derive biophysical variables, including LST, NDVI, and normalized difference water index, to examine the effects of those environmental characteristics on WNV outbreak and dissemination. A spatio-temporal analysis of WNV outbreak and dissemination was conducted by synthesizing the remote sensing variables and mosquito surveillance data, and by focusing on WNV risk areas in July through September due to data sufficiency of mosquito pools. Moderate- and high-risk areas of WNV infections in mosquitoes were identified for five epidemiological weeks. These identified WNV-risk areas were then collocated in GIS with heat hazard, exposure, and vulnerability maps to answer the question of whether WNV is a heat related virus. The results show that elevation and built-up conditions were negatively associated with the WNV propagation, while LST positively correlated with the viral transmission. NDVI was not significantly associated with WNV transmission. San Fernando Valley was found to be the most vulnerable to mosquito infections of WNV. This research provides important insights into how high temporal resolution remote sensing imagery may be used to study time-dependant events in public health, especially in the operational surveillance and control of vector-borne, water-borne, or other epidemic diseases.

Estimation of land-atmosphere energy transfer over the Tibetan Plateau by a combination use of geostationary and polar-orbiting satellite data

NASA Astrophysics Data System (ADS)

Zhong, L.; Ma, Y.

2017-12-01

Land-atmosphere energy transfer is of great importance in land-atmosphere interactions and atmospheric boundary layer processes over the Tibetan Plateau (TP). The energy fluxes have high temporal variability, especially in their diurnal cycle, which cannot be acquired by polar-orbiting satellites alone because of their low temporal resolution. Therefore, it's of great practical significance to retrieve land surface heat fluxes by a combination use of geostationary and polar orbiting satellites. In this study, a time series of the hourly LST was estimated from thermal infrared data acquired by the Chinese geostationary satellite FengYun 2C (FY-2C) over the TP. The split window algorithm (SWA) was optimized using a regression method based on the observations from the Enhanced Observing Period (CEOP) of the Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet) and Tibetan observation and research platform (TORP), the land surface emissivity (LSE) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the water vapor content from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) project. The 10-day composite hourly LST data were generated via the maximum value composite (MVC) method to reduce the cloud effects. The derived LST was validated by the field observations of CAMP/Tibet and TORP. The results show that the retrieved LST and in situ data have a very good correlation (with root mean square error (RMSE), mean bias (MB), mean absolute error (MAE) and correlation coefficient (R) values of 1.99 K, 0.83 K, 1.71 K, and 0.991, respectively). Together with other characteristic parameters derived from polar-orbiting satellites and meteorological forcing data, the energy balance budgets have been retrieved finally. The validation results showed there was a good consistency between estimation results and in-situ measurements over the TP, which prove the robustness of the proposed estimation methodology.

Climatic Factors Driving Invasion of the Tiger Mosquito (Aedes albopictus) into New Areas of Trentino, Northern Italy

PubMed Central

Castellani, Cristina; Arnoldi, Daniele; Rizzoli, Annapaola

2011-01-01

Background The tiger mosquito (Aedes albopictus), vector of several emerging diseases, is expanding into more northerly latitudes as well as into higher altitudes in northern Italy. Changes in the pattern of distribution of the tiger mosquito may affect the potential spread of infectious diseases transmitted by this species in Europe. Therefore, predicting suitable areas of future establishment and spread is essential for planning early prevention and control strategies. Methodology/Principal Findings To identify the areas currently most suitable for the occurrence of the tiger mosquito in the Province of Trento, we combined field entomological observations with analyses of satellite temperature data (MODIS Land Surface Temperature: LST) and human population data. We determine threshold conditions for the survival of overwintering eggs and for adult survival using both January mean temperatures and annual mean temperatures. We show that the 0°C LST threshold for January mean temperatures and the 11°C threshold for annual mean temperatures provide the best predictors for identifying the areas that could potentially support populations of this mosquito. In fact, human population density and distance to human settlements appear to be less important variables affecting mosquito distribution in this area. Finally, we evaluated the future establishment and spread of this species in relation to predicted climate warming by considering the A2 scenario for 2050 statistically downscaled at regional level in which winter and annual temperatures increase by 1.5 and 1°C, respectively. Conclusions/Significance MODIS satellite LST data are useful for accurately predicting potential areas of tiger mosquito distribution and for revealing the range limits of this species in mountainous areas, predictions which could be extended to an European scale. We show that the observed trend of increasing temperatures due to climate change could facilitate further invasion of Ae. albopictus into new areas. PMID:21525991

Combined evaluation of optical and microwave satellite dataset for soil moisture deficit estimation

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Singh, Sudhir Kumar; Gupta, Manika; Gupta, Dileep Kumar; Kumar, Pradeep

2016-04-01

Soil moisture is a key variable responsible for water and energy exchanges from land surface to the atmosphere (Srivastava et al., 2014). On the other hand, Soil Moisture Deficit (or SMD) can help regulating the proper use of water at specified time to avoid any agricultural losses (Srivastava et al., 2013b) and could help in preventing natural disasters, e.g. flood and drought (Srivastava et al., 2013a). In this study, evaluation of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and soil moisture from Soil Moisture and Ocean Salinity (SMOS) satellites are attempted for prediction of Soil Moisture Deficit (SMD). Sophisticated algorithm like Adaptive Neuro Fuzzy Inference System (ANFIS) is used for prediction of SMD using the MODIS and SMOS dataset. The benchmark SMD estimated from Probability Distributed Model (PDM) over the Brue catchment, Southwest of England, U.K. is used for all the validation. The performances are assessed in terms of Nash Sutcliffe Efficiency, Root Mean Square Error and the percentage of bias between ANFIS simulated SMD and the benchmark. The performance statistics revealed a good agreement between benchmark and the ANFIS estimated SMD using the MODIS dataset. The assessment of the products with respect to this peculiar evidence is an important step for successful development of hydro-meteorological model and forecasting system. The analysis of the satellite products (viz. SMOS soil moisture and MODIS LST) towards SMD prediction is a crucial step for successful hydrological modelling, agriculture and water resource management, and can provide important assistance in policy and decision making. Keywords: Land Surface Temperature, MODIS, SMOS, Soil Moisture Deficit, Fuzzy Logic System References: Srivastava, P.K., Han, D., Ramirez, M.A., Islam, T., 2013a. Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. Journal of Hydrology 498, 292-304. Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Al-Shrafany, D., Islam, T., 2013b. Data fusion techniques for improving soil moisture deficit using SMOS satellite and WRF-NOAH land surface model. Water Resources Management 27, 5069-5087. Srivastava, P.K., Han, D., Rico-Ramirez, M.A., O'Neill, P., Islam, T., Gupta, M., 2014. Assessment of SMOS soil moisture retrieval parameters using tau-omega algorithms for soil moisture deficit estimation. Journal of Hydrology 519, 574-587.

Modeling water and heat balance components of large territory for vegetation season using information from polar-orbital and geostationary meteorological satellites

NASA Astrophysics Data System (ADS)

Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

2015-04-01

To date, physical-mathematical modeling processes of land surface-atmosphere interaction is considered to be the most appropriate tool for obtaining reliable estimates of water and heat balance components of large territories. The model of these processes (Land Surface Model, LSM) developed for vegetation period is destined for simulating soil water content W, evapotranspiration Ev, vertical latent LE and heat fluxes from land surface as well as vertically distributed soil temperature and moisture, soil surface Tg and foliage Tf temperatures, and land surface skin temperature (LST) Ts. The model is suitable for utilizing remote sensing data on land surface and meteorological conditions. In the study these data have been obtained from measurements by scanning radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua, SEVIRI/geostationary satellites Meteosat-9, -10 (MSG-2, -3). The heterogeneity of the land surface and meteorological conditions has been taken into account in the model by using soil and vegetation characteristics as parameters and meteorological characteristics as input variables. Values of these characteristics have been determined from ground observations and remote sensing information. So, AVHRR data have been used to build the estimates of effective land surface temperature (LST) Ts.eff and emissivity E, vegetation-air temperature (temperature at the vegetation level) Ta, normalized vegetation index NDVI, vegetation cover fraction B, the leaf area index LAI, and precipitation. From MODIS data the values of LST Tls, Å, NDVI, LAI have been derived. From SEVIRI data there have been retrieved Tls, E, Ta, NDVI, LAI and precipitation. All named retrievals covered the vast territory of the part of the agricultural Central Black Earth Region located in the steppe-forest zone of European Russia. This territory with coordinates 49°30'-54°N, 31°-43°E and a total area of 227,300 km2 has been chosen for investigation. It has been carried out for years 2009-2013 vegetation seasons. To provide the retrieval of Ts.eff, E, Ta, NDVI, B, and LAI the previously developed technologies of AVHRR data processing have been refined and adapted to the region of interest. The updated linear regression estimators for Ts.eff and Tà have been built using representative training samples compiled for above vegetation seasons. The updated software package has been applied for AVHRR data processing to generate estimates of named values. To verify the accuracy of these estimates the error statistics of Ts.eff and Ta derivation has been investigated for various days of named seasons using comparison with in-situ ground-based measurements. On the base of special technology and Internet resources the remote sensing products Tls, E, NDVI, LAI derived from MODIS data and covering the study area have been extracted from LP DAAC web-site for the same vegetation seasons. The reliability of the MODIS-derived Tls estimates has been confirmed via comparison with analogous and collocated ground-, AVHRR-, and SEVIRI-based ones. The prepared remote sensing dataset has also included the SEVIRI-derived estimates of Tls, E, NDVI, Ta at daylight and night-time and daily estimates of LAI. The Tls estimates has been built utilizing the method and technology developed for the retrieval of Tls and E from 15 minutes time interval SEVIRI data in IR channels 10.8 and 12.0 µm (classified as 100% cloud-free and covering the area of interest) at three successive times without accurate a priori knowledge of E. Comparison of the SEVIRI-based Tls retrievals with independent collocated Tls estimates generated at the Land Surface Analysis Satellite Applications Facility (LSA SAF, Lisbon, Portugal) has given daily- or monthly-averaged values of RMS deviation in the range of 2°C for various dates and months during the mentioned vegetation seasons which is quite acceptable result. The reliability of the SEVIRI-based Tls estimates for the study area has been also confirmed by comparing with AVHRR- and MODIS-derived LST estimates for the same seasons. The SEVIRI-derived values of Ta considered as the temperature of the vegetation cover has been obtained using Tls estimates and a previously found multiple linear regression relationship between Tls and Ta formulated accounting for solar zenith angle and land elevation. A comparison with ground-based collocated Ta observations has given RMS errors of 2.5°C and lower. It can be treated as a proof of the proposed technique's functionality. SEVIRI-derived LAI estimates have been retrieved at LSA SAF from measurements by this sensor in channels 0.6, 0.8, and 1.6 μm under cloud-free conditions at that when using data in the channel 1.6 μm the accuracy of these estimates has increased. In the study the AVHRR- and SEVIRI-derived estimates of daily and monthly precipitation sums for the territory under investigation for the years 2009 - 2013 vegetation seasons have been also used. These estimates have been obtained by the improved integrated Multi Threshold Method (MTM) providing detection and identification of cloud types around the clock throughout the year as well as identification of precipitation zones and determination of instantaneous precipitation maximum intensity within the pixel using the measurement data in different channels of named sensors as predictors. Validation of the MTM has been performed by comparing the daily and monthly precipitation sums with appropriate values resulted from ground-based observations at the meteorological stations of the region. The probability of detecting precipitation zones from satellite data corresponding to the actual ones has been amounted to 70-80%. AVHRR- and SEVIRI-derived daily and monthly precipitation sums have been in reasonable agreement with each other and with results of ground-based observations although they are smoother than the last values. Discrepancies have been noted only for local maxima for which satellite-based estimates of precipitation have been much less than ground-based ones. It may be due to the different spatial scales of areal satellite-derived and point ground-based estimates. To utilize satellite-derived vegetation and meteorological characteristics in the model the special procedures have been developed including: - replacement of ground-based LAI and B estimates used as model parameters by their satellite-derived estimates from AVHRR, MODIS and SEVIRI data. Correctness of such replacement has been confirmed by comparing the time behavior of LAI over the period of vegetation as well as modeled and measured values of evapotranspiration Ev and soil moisture content W; - entering AVHRR-, MODIS- and SEVIRI-derived estimates of Ts.eff Tls, and Ta into the model as input variables instead of ground-measured values with verification of adequacy of model operation under such a change through comparison of the calculated and measured values of W and Ev; - inputing satellite-derived estimates of precipitation during vegetation period retrieved from AVHRR and SEVIRI data using the MTM into the model as input variables. When developing given procedure algorithms and programs have been created to transit from assessment of the rainfall intensity to evaluation of its daily values. The implementation of such a transition requires controlling correctness of the estimates built at each time step. This control includes comparison of areal distributions of three-hour, daily and monthly precipitation amounts obtained from satellite data and calculated by interpolation of standard network observation data; - taking into account spatial heterogeneity of fields of satellite AVHRR-, MODIS- and SEVIRI-derived estimates of LAI, B, LST and precipitation. This has involved the development of algorithms and software for entering the values of all named characteristics into the model in each computational grid node. Values of evapotranspiration E, soil water content W, vertical latent and sensible heat fluxes and other water and heat balance components as well as land surface temperature and moisture area-distributed over the territory of interest have been resulted from the model calculations for the years 2009-2013 vegetation seasons. These calculations have been carried out utilizing satellite-derived estimates of the vegetation characteristics, LST and precipitation. E and W calculation errors have not exceeded the standard values.

Geostatistical interpolation of individual average monthly temperature supported by MODIS MOD11C3 product

NASA Astrophysics Data System (ADS)

Perčec Tadić, M.

2010-09-01

The increased availability of satellite products of high spatial and temporal resolution together with developing user support, encourages the climatologists to use this data in research and practice. Since climatologists are mainly interested in monthly or even annual averages or aggregates, this high temporal resolution and hence, large amount of data, can be challenging for the less experienced users. Even if the attempt is made to aggregate e. g. the 15' (temporal) MODIS LST (land surface temperature) to daily temperature average, the development of the algorithm is not straight forward and should be done by the experts. Recent development of many temporary aggregated products on daily, several days or even monthly scale substantially decrease the amount of satellite data that needs to be processed and rise the possibility for development of various climatological applications. Here the attempt is presented in incorporating the MODIS satellite MOD11C3 product (Wan, 2009), that is monthly CMG (climate modelling 0.05 degree latitude/longitude grids) LST, as predictor in geostatistical interpolation of climatological data in Croatia. While in previous applications, e. g. in Climate Atlas of Croatia (Zaninović et al. 2008), the static predictors as digital elevation model, distance to the sea, latitude and longitude were used for the interpolation of monthly, seasonal and annual 30-years averages (reference climatology), here the monthly MOD11C3 is used to support the interpolation of the individual monthly average in the regression kriging framework. We believe that this can be a valuable show case of incorporating the remote sensed data for climatological application, especially in the areas that are under-sampled by conventional observations. Zaninović K, Gajić-Čapka M, Perčec Tadić M et al (2008) Klimatski atlas Hrvatske / Climate atlas of Croatia 1961-1990, 1971-2000. Meteorological and Hydrological Service of Croatia, Zagreb, pp 200. Wan Z, 2009: Collection-5 MODIS Land Surface Temperature Products Users' Guide, ICESS, University of California, Santa Barbara, pp 30.

Linking Satellite Derived Land Surface Temperature with Cholera: A Case Study for South Sudan

NASA Astrophysics Data System (ADS)

Aldaach, H. S. V.; Jutla, A.; Akanda, A. S.; Colwell, R. R.

2014-12-01

A sudden onset of cholera in South Sudan, in April 2014 in Northern Bari in Juba town resulted in more than 400 cholera cases after four weeks of initial outbreak with a case of fatality rate of CFR 5.4%. The total number of reported cholera cases for the period of April to July, 2014 were 5,141 including 114 deaths. With the limited efficacy of cholera vaccines, it is necessary to develop mechanisms to predict cholera occurrence and thereafter devise intervention strategies for mitigating impacts of the disease. Hydroclimatic processes, primarily precipitation and air temperature are related to epidemic and episodic outbreak of cholera. However, due to coarse resolution of both datasets, it is not possible to precisely locate the geographical location of disease. Here, using Land Surface Temperature (LST) from MODIS sensors, we have developed an algorithm to identify regions susceptible for cholera. Conditions for occurrence of cholera were detectable at least one month in advance in South Sudan and were statistically sensitive to hydroclimatic anomalies of land surface and air temperature, and precipitation. Our results indicate significant spatial and temporal averaging required to infer usable information from LST over South Sudan. Preliminary results that geographically location of cholera outbreak was identifiable within 1km resolution of the LST data.

Testing two temporal upscaling schemes for the estimation of the time variability of the actual evapotranspiration

NASA Astrophysics Data System (ADS)

Maltese, A.; Capodici, F.; Ciraolo, G.; La Loggia, G.

2015-10-01

Temporal availability of grapes actual evapotranspiration is an emerging issue since vineyards farms are more and more converted from rainfed to irrigated agricultural systems. The manuscript aims to verify the accuracy of the actual evapotranspiration retrieval coupling a single source energy balance approach and two different temporal upscaling schemes. The first scheme tests the temporal upscaling of the main input variables, namely the NDVI, albedo and LST; the second scheme tests the temporal upscaling of the energy balance output, the actual evapotranspiration. The temporal upscaling schemes were implemented on: i) airborne remote sensing data acquired monthly during a whole irrigation season over a Sicilian vineyard; ii) low resolution MODIS products released daily or weekly; iii) meteorological data acquired by standard gauge stations. Daily MODIS LST products (MOD11A1) were disaggregated using the DisTrad model, 8-days black and white sky albedo products (MCD43A) allowed modeling the total albedo, and 8-days NDVI products (MOD13Q1) were modeled using the Fisher approach. Results were validated both in time and space. The temporal validation was carried out using the actual evapotranspiration measured in situ using data collected by a flux tower through the eddy covariance technique. The spatial validation involved airborne images acquired at different times from June to September 2008. Results aim to test whether the upscaling of the energy balance input or output data performed better.

Impacts of Wind Farms on Local Land Surface Temperature

NASA Astrophysics Data System (ADS)

Zhou, L.; Tian, Y.; Baidya Roy, S.; Thorncroft, C.; Bosart, L. F.; Hu, Y.

2012-12-01

The U.S. wind industry has experienced a remarkably rapid expansion of capacity in recent years and this rapid growth is expected to continue in the future. While converting wind's kinetic energy into electricity, wind turbines modify surface-atmosphere exchanges and transfer of energy, momentum, mass and moisture within the atmosphere. These changes, if spatially large enough, may have noticeable impacts on local to regional weather and climate. Here we present observational evidence for such impacts based on analyses of satellite derived land surface temperature (LST) data at ~1.1 km for the period of 2003-2011 over a region in West-Central Texas, where four of the world's largest wind farms are located. Our results show a warming effect of up to 0.7 degrees C at nighttime for the 9-year period during which data was collected, over wind farms relative to nearby non wind farm regions and this warming is gradually enhanced with time, while the effect at daytime is small. The spatial pattern and magnitude of this warming effect couple very well with the geographic distribution of wind turbines and such coupling is stronger at nighttime than daytime and in summer than winter. These results suggest that the warming effect is very likely attributable to the development of wind farms. This inference is consistent with the increasing number of operational wind turbines with time during the study period, the diurnal and seasonal variations in the frequency of wind speed and direction distribution, and the changes in near-surface atmospheric boundary layer conditions due to wind farm operations. Figure 1: Nighttime land surface temperature (LST, C) differences between 2010 and 2003 (2010 minus 2003) in summer (June-July-August). Pixels with plus symbol have at least one wind turbine. A regional mean value (0.592 C) was removed to emphasize the relative LST changes at pixel level and so the resulting warming or cooling rate represents a change relative to the regional mean value. The LST data were derived from MODIS (Moderate Imaging Spectroradiometer) instruments on NASA's Aqua and Terra satellites. Note that LST measures the radiometric temperature of the Earth's surface itself - It has a larger diurnal variation than surface air temperature used in daily weather reports.

Satellite Monitoring of Long Term Changes in Intertidal Thermal Conditions

NASA Astrophysics Data System (ADS)

Purvis, C. L.; Lakshmi, V.; Helmuth, B.

2006-12-01

Documented trends of global climate change have implications for species dynamics, range boundaries and mortality rates. A generalized assumption about global warming is that species will shift poleward in response to the increased temperatures, thereby displacing pre-existing species at higher latitudes. However, studies such as those conducted along the rocky shorelines of the U.S. have shown that such a simplified ecosystem response is unrealistic. Habitat alterations due to climate change are greatly influenced by local conditions, resulting in a patchwork of varying responses to temperature changes all along the intertidal. In order to capture these spatially- and temporally-dependent dynamics, satellite observations of land and sea surface temperatures (LST and SST) have been assimilated for the Pacific coast from Vancouver Island to southern California. Images from three satellite sensors were included in the study: MODIS/Terra, MODIS/Aqua and ASTER/Terra. MODIS has a spatial resolution of 1km (LST) and 4km (SST), daily coverage and overpass times of 10:30am and 1:30pm. ASTER has a spatial resolution of 90m (LST), sporadic temporal coverage due to an on-demand status and a 10:30am crossing time. The remotely sensed data were statistically compared to nearly 10 years of in situ measurements of body temperature of the California mussel along the Pacific coast. This species is prevalent among the rocky intertidal areas, physiologically well studied in terms of heat response and situated in a thermally harsh environment which demonstrates strong responses to climate change. A regression was performed to account for noise such as tidal signals, changes in latitude among sites as well as seasonal fluctuations in body temperature. Comparisons show that while the satellite data are unable to capture many of the daily maximum body temperatures (due to overpass times), they do offer a fairly accurate method of capturing high temporal resolution temperatures over large areas. In addition, satellite measurements were utilized to investigate the spatial distribution of intertidal mussels in Humboldt Bay, CA. In situ measurements are not prevalent enough to explain the potentially heat-driven range of mussels in this critical habitat, and therefore remotely sensed data will be used to gather new insight into thermally-regulated range boundaries of this species. By incorporating satellite measurements into in-depth habitat studies, long term thermal variations due to climate change can be monitored over large regions and aid in capturing larger-scale impacts which cannot be accomplished by tedious, site-specific in situ studies.

Satellite-based characterization of climatic conditions before large-scale general flowering events in Peninsular Malaysia

PubMed Central

Azmy, Muna Maryam; Hashim, Mazlan; Numata, Shinya; Hosaka, Tetsuro; Noor, Nur Supardi Md.; Fletcher, Christine

2016-01-01

General flowering (GF) is a unique phenomenon wherein, at irregular intervals, taxonomically diverse trees in Southeast Asian dipterocarp forests synchronize their reproduction at the community level. Triggers of GF, including drought and low minimum temperatures a few months previously has been limitedly observed across large regional scales due to lack of meteorological stations. Here, we aim to identify the climatic conditions that trigger large-scale GF in Peninsular Malaysia using satellite sensors, Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), to evaluate the climatic conditions of focal forests. We observed antecedent drought, low temperature and high photosynthetic radiation conditions before large-scale GF events, suggesting that large-scale GF events could be triggered by these factors. In contrast, we found higher-magnitude GF in forests where lower precipitation preceded large-scale GF events. GF magnitude was also negatively influenced by land surface temperature (LST) for a large-scale GF event. Therefore, we suggest that spatial extent of drought may be related to that of GF forests, and that the spatial pattern of LST may be related to that of GF occurrence. With significant new findings and other results that were consistent with previous research we clarified complicated environmental correlates with the GF phenomenon. PMID:27561887

Greenland ice sheet surface temperature, melt and mass loss: 2000-06

USGS Publications Warehouse

Hall, D.K.; Williams, R.S.; Luthcke, S.B.; DiGirolamo, N.E.

2008-01-01

A daily time series of 'clear-sky' surface temperature has been compiled of the Greenland ice sheet (GIS) using 1 km resolution moderate-resolution imaging spectroradiometer (MODIS) land-surface temperature (LST) maps from 2000 to 2006. We also used mass-concentration data from the Gravity Recovery and Climate Experiment (GRACE) to study mass change in relationship to surface melt from 2003 to 2006. The mean LST of the GIS increased during the study period by ???0.27??Ca-1. The increase was especially notable in the northern half of the ice sheet during the winter months. Melt-season length and timing were also studied in each of the six major drainage basins. Rapid (<15 days) and sustained mass loss below 2000 m elevation was triggered in 2004 and 2005 as recorded by GRACE when surface melt begins. Initiation of large-scale surface melt was followed rapidly by mass loss. This indicates that surface meltwater is flowing rapidly to the base of the ice sheet, causing acceleration of outlet glaciers, thus highlighting the metastability of parts of the GIS and the vulnerability of the ice sheet to air-temperature increases. If air temperatures continue to rise over Greenland, increased surface melt will play a large role in ice-sheet mass loss.

Satellite-based characterization of climatic conditions before large-scale general flowering events in Peninsular Malaysia.

PubMed

Azmy, Muna Maryam; Hashim, Mazlan; Numata, Shinya; Hosaka, Tetsuro; Noor, Nur Supardi Md; Fletcher, Christine

2016-08-26

General flowering (GF) is a unique phenomenon wherein, at irregular intervals, taxonomically diverse trees in Southeast Asian dipterocarp forests synchronize their reproduction at the community level. Triggers of GF, including drought and low minimum temperatures a few months previously has been limitedly observed across large regional scales due to lack of meteorological stations. Here, we aim to identify the climatic conditions that trigger large-scale GF in Peninsular Malaysia using satellite sensors, Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), to evaluate the climatic conditions of focal forests. We observed antecedent drought, low temperature and high photosynthetic radiation conditions before large-scale GF events, suggesting that large-scale GF events could be triggered by these factors. In contrast, we found higher-magnitude GF in forests where lower precipitation preceded large-scale GF events. GF magnitude was also negatively influenced by land surface temperature (LST) for a large-scale GF event. Therefore, we suggest that spatial extent of drought may be related to that of GF forests, and that the spatial pattern of LST may be related to that of GF occurrence. With significant new findings and other results that were consistent with previous research we clarified complicated environmental correlates with the GF phenomenon.

A simple estimate of ecosystem respiration across biomes based on MODIS products

NASA Astrophysics Data System (ADS)

Jaegermeyr, J.; Hostert, P.; Lucht, W.

2010-12-01

Beside carbon sequestration by terrestrial photosynthesis, in particular the subsequent carbon release by ecosystem respiration (Reco) is a crucial flux for estimating carbon budgets. Heterotrophic soil decomposition rates (Rh) and autotrophic respiration rates (Ra), which add up to Reco, are highly sensitive to environmental conditions and in some cases they determine net ecosystem productivity. Prior respiration modeling approaches revealed that a precise process-based and bottom-up modeling is important for realistic estimates. On a short timescale, as in the case of satellite environmental monitoring, simplified empirical models are not necessarily less accurate, though. For most major biomes, ecosystem carbon efflux is predominantly driven by air temperature. It can further be limited by water stress, plant activity and substrate quality. Developing simple, empirical and wall-to-wall respiration models from continuous Moderate Resolution Imaging Spectroradiometer (MODIS) land products on a continental scale can enhance our understanding of spatially explicit respiration patterns. We therefore accept model uncertainties by simplifying decay and respiratory processes in that we account for a single static carbon pool and do not include any feedback mechanisms. Preliminary results suggest that the 8-day MODIS 1km land surface temperature product (LST) and the vegetation-water index (NDWI) derived from the 8-day MODIS 500m surface reflectance product are sufficient to largely explain the variability of Reco. Spatial flux variations can be attributed to plant activity variation. We therefore introduce a site-specific, maximum leaf area index (LAI) from the MODIS 1km LAI product as a proxy. A biome-specific model parameterization and validation is performed, based on 8-day composite FLUXNET tower data representing major global biomes. We found that the frequently used temperature model by Loyd and Taylor (1994) does not show superior performance on 8-day ecosystem respiration data. The model by Del Grosso et al. (2005) is more flexible to account for lower Q10 values at high temperatures and thus it is used to describe the temperature dependency here. Although we cannot explain flux variations arising from overall carbon pool variations, results suggest that our approach may contribute to simplified Reco estimates.

One-dimensional soil temperature assimilation experiment based on unscented particle filter and Common Land Model

NASA Astrophysics Data System (ADS)

Fu, Xiao Lei; Jin, Bao Ming; Jiang, Xiao Lei; Chen, Cheng

2018-06-01

Data assimilation is an efficient way to improve the simulation/prediction accuracy in many fields of geosciences especially in meteorological and hydrological applications. This study takes unscented particle filter (UPF) as an example to test its performance at different two probability distribution, Gaussian and Uniform distributions with two different assimilation frequencies experiments (1) assimilating hourly in situ soil surface temperature, (2) assimilating the original Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) once per day. The numerical experiment results show that the filter performs better when increasing the assimilation frequency. In addition, UPF is efficient for improving the soil variables (e.g., soil temperature) simulation/prediction accuracy, though it is not sensitive to the probability distribution for observation error in soil temperature assimilation.

Agricultural drought assessment using remotely sensed data in Central America

NASA Astrophysics Data System (ADS)

Nguyen, S. T.; Chen, C. F.; Chen, C. R.

2017-12-01

Central America is one of the world's regions most vulnerable to negative effects of agricultural drought due to impacts of climate change. Famers in the region have been confronting risks of crop damages and production losses due to intense droughts throughout the growing seasons. Drought information is thus deemed vital for policymakers to assess their crop management strategies in tackling issues of food insecurity in the region. This study aimed to delineate drought-prone areas associated with cropped areas from eight-day MODIS data in 2016 using the commonly used temperature dryness vegetation index (TVDI), calculated based on the land surface temperature (LST) and enhanced vegetation index (EVI) data. The advantages of MODIS data for agricultural drought monitoring at a national/regional scale are that it has the spatial resolution (500 m-1 km) and relatively high temporal resolution of eight days, but the data are often contaminated by clouds. Detecting and reconstructing the data under cloud-affected areas are generally a challenging task without any robust methods up to date. In this study, we reconstructed the eight-day MODIS EVI and LST data for agricultural drought assessment using machine-learning approaches. The reconstructed data were then used for drought assessment. The TVDI results verified with the soil moisture active passive (SMAP) data showed that the correlation coefficient values (r) obtained for the apante season (December-March) were between -0.4 to -0.8, while the values for the primera season (April-August) and postrera season (September-November) were in ranges of 0 to -0.6 and -0.2 to -0.7, respectively. The larger area of very dry soil moisture was generally observed during the dry season (December-April) and declined in the rainy season (May-November). The cropping areas affected by severe and moderate droughts observed for the primera season were respectively 11,846 km2 and 60,557 km2, while the values for the postera season were 14,174 km2 and 56,809 km2, and those for the postera season were 16,532 km2 and 40,018 km2, respectively. This study could provide quantitative information on distributions of drought at an eight-day interval, which is important to assist officials to mitigate economic costs for vulnerable populations in drought-prone areas.

Multi precursors analysis associated with the powerful Ecuador (MW = 7.8) earthquake of 16 April 2016 using Swarm satellites data in conjunction with other multi-platform satellite and ground data

NASA Astrophysics Data System (ADS)

Akhoondzadeh, Mehdi; De Santis, Angelo; Marchetti, Dedalo; Piscini, Alessandro; Cianchini, Gianfranco

2018-01-01

After DEMETER satellite mission (2004-2010), the launch of the Swarm satellites (Alpha (A), Bravo (B) and Charlie (C)) has created a new opportunity in the study of earthquake ionospheric precursors. Nowadays, there is no doubt that multi precursors analysis is a necessary phase to better understand the LAIC (Lithosphere Atmosphere Ionosphere Coupling) mechanism before large earthquakes. In this study, using absolute scalar magnetometer, vector field magnetometer and electric field instrument on board Swarm satellites, GPS (Global Positioning System) measurements, MODIS-Aqua satellite and ECMWF (European Centre for Medium-Range Weather Forecasts) data, the variations of the electron density and temperature, magnetic field, TEC (Total Electron Content), LST (Land Surface Temperature), AOD (Aerosol Optical Depth) and SKT (SKin Temperature) have been surveyed to find the potential seismic anomalies around the strong Ecuador (Mw = 7.8) earthquake of 16 April 2016. The four solar and geomagnetic indices: F10.7, Dst, Kp and ap were investigated to distinguish whether the preliminary detected anomalies might be associated with the solar-geomagnetic activities instead of the seismo-ionospheric anomalies. The Swarm satellites (A, B and C) data analysis indicate the anomalies in time series of electron density variations on 7, 11 and 12 days before the event; the unusual variations in time series of electron temperature on 8 days preceding the earthquake; the analysis of the magnetic field scalar and vectors data show the considerable anomalies 52, 48, 23, 16, 11, 9 and 7 days before the main shock. A striking anomaly is detected in TEC variations on 1 day before earthquake at 9:00 UTC. The analysis of MODIS-Aqua night-time images shows that LST increase unusually on 11 days prior to main shock. In addition, the AOD variations obtained from MODIS measurements reach the maximum value on 10 days before the earthquake. The SKT around epicentral region presents anomalous higher value about 40 days before the earthquake. It should be noted that the different lead times of the observed anomalies could be acknowledged based on a reasonable LAIC earthquake mechanism. Our results emphasize that the Swarm satellites measurements play an undeniable role in progress the studies of the ionospheric precursors.

Phenology Analysis of Forest Vegetation to Environmental Variables during - and Post-Monsoon Seasons in Western Himalayan Region of India

NASA Astrophysics Data System (ADS)

Khare, S.; Latifi, H.; Ghosh, K.

2016-06-01

To assess the phenological changes in Moist Deciduous Forest (MDF) of western Himalayan region of India, we carried out NDVI time series analysis from 2013 to 2015 using Landsat 8 OLI data. We used the vegetation index differencing method to calculate the change in NDVI (NDVIchange) during pre and post monsoon seasons and these changes were used to assess the phenological behaviour of MDF by taking the effect of a set of environmental variables into account. To understand the effect of environmental variables on change in phenology, we designed a linear regression analysis with sample-based NDVIchange values as the response variable and elevation aspect, and Land Surface Temperature (LST) as explanatory variables. The Landsat-8 derived phenology transition stages were validated by calculating the phenology variation from Nov 2008 to April 2009 using Landsat-7 which has the same spatial resolution as Landsat-8. The Landsat-7 derived NDVI trajectories were plotted in accordance with MODIS derived phenology stages (from Nov 2008 to April 2009) of MDF. Results indicate that the Landsat -8 derived NDVI trajectories describing the phenology variation of MDF during spring, monsoon autumn and winter seasons agreed closely with Landsat-7 and MODIS derived phenology transition from Nov 2008 to April 2009. Furthermore, statistical analysis showed statistically significant correlations (p < 0.05) amongst the environmental variables and the NDVIchange between full greenness and maximum frequency stage of Onset of Greenness (OG) activity.. The major change in NDVI was observed in medium (600 to 650 m) and maximum (650 to 750 m) elevation areas. The change in LST showed also to be highly influential. The results of this study can be used for large scale monitoring of difficult-to-reach mountainous forests, with additional implications in biodiversity assessment. By means of a sufficient amount of available cloud-free imagery, detailed phenological trends across mountainous forests could be explained.

Thermal IR satellite data application for earthquake research in Pakistan

NASA Astrophysics Data System (ADS)

Barkat, Adnan; Ali, Aamir; Rehman, Khaista; Awais, Muhammad; Riaz, Muhammad Shahid; Iqbal, Talat

2018-05-01

The scientific progress in space research indicates earthquake-related processes of surface temperature growth, gas/aerosol exhalation and electromagnetic disturbances in the ionosphere prior to seismic activity. Among them surface temperature growth calculated using the satellite thermal infrared images carries valuable earthquake precursory information for near/distant earthquakes. Previous studies have concluded that such information can appear few days before the occurrence of an earthquake. The objective of this study is to use MODIS thermal imagery data for precursory analysis of Kashmir (Oct 8, 2005; Mw 7.6; 26 km), Ziarat (Oct 28, 2008; Mw 6.4; 13 km) and Dalbandin (Jan 18, 2011; Mw 7.2; 69 km) earthquakes. Our results suggest that there exists an evident correlation of Land Surface Temperature (thermal; LST) anomalies with seismic activity. In particular, a rise of 3-10 °C in LST is observed 6, 4 and 14 days prior to Kashmir, Ziarat and Dalbandin earthquakes. In order to further elaborate our findings, we have presented a comparative and percentile analysis of daily and five years averaged LST for a selected time window with respect to the month of earthquake occurrence. Our comparative analyses of daily and five years averaged LST show a significant change of 6.5-7.9 °C for Kashmir, 8.0-8.1 °C for Ziarat and 2.7-5.4 °C for Dalbandin earthquakes. This significant change has high percentile values for the selected events i.e. 70-100% for Kashmir, 87-100% for Ziarat and 84-100% for Dalbandin earthquakes. We expect that such consistent results may help in devising an optimal earthquake forecasting strategy and to mitigate the effect of associated seismic hazards.

Land surface energy budget during dry spells: global CMIP5 AMIP simulations vs. satellite observations

NASA Astrophysics Data System (ADS)

Gallego-Elvira, Belen; Taylor, Christopher M.; Harris, Phil P.; Ghent, Darren; Folwell, Sonja S.

2015-04-01

During extended periods without rain (dry spells), the soil can dry out due to vegetation transpiration and soil evaporation. At some point in this drying cycle, land surface conditions change from energy-limited to water-limited evapotranspiration, and this is accompanied by an increase of the ground and overlying air temperatures. Regionally, the characteristics of this transition determine the influence of soil moisture on air temperature and rainfall. Global Climate Models (GCMs) disagree on where and how strongly the surface energy budget is limited by soil moisture. Flux tower observations are improving our understanding of these dry down processes, but typical heterogeneous landscapes are too sparsely sampled to ascertain a representative regional response. Alternatively, satellite observations of land surface temperature (LST) provide indirect information about the surface energy partition at 1km resolution globally. In our study, we analyse how well the dry spell dynamics of LST are represented by GCMs across the globe. We use a spatially and temporally aggregated diagnostic to describe the composite response of LST during surface dry down in rain-free periods in distinct climatic regions. The diagnostic is derived from daytime MODIS-Terra LST observations and bias-corrected meteorological re-analyses, and compared against the outputs of historical climate simulations of seven models running the CMIP5 AMIP experiment. Dry spell events are stratified by antecedent precipitation, land cover type and geographic regions to assess the sensitivity of surface warming rates to soil moisture levels at the onset of a dry spell for different surface and climatic zones. In a number of drought-prone hot spot regions, we find important differences in simulated dry spell behaviour, both between models, and compared to observations. These model biases are likely to compromise seasonal forecasts and future climate projections.

An Algorithm for Retrieving Land Surface Temperatures Using VIIRS Data in Combination with Multi-Sensors

PubMed Central

Xia, Lang; Mao, Kebiao; Ma, Ying; Zhao, Fen; Jiang, Lipeng; Shen, Xinyi; Qin, Zhihao

2014-01-01

A practical algorithm was proposed to retrieve land surface temperature (LST) from Visible Infrared Imager Radiometer Suite (VIIRS) data in mid-latitude regions. The key parameter transmittance is generally computed from water vapor content, while water vapor channel is absent in VIIRS data. In order to overcome this shortcoming, the water vapor content was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The analyses on the estimation errors of vapor content and emissivity indicate that when the water vapor errors are within the range of ±0.5 g/cm2, the mean retrieval error of the present algorithm is 0.634 K; while the land surface emissivity errors range from −0.005 to +0.005, the mean retrieval error is less than 1.0 K. Validation with the standard atmospheric simulation shows the average LST retrieval error for the twenty-three land types is 0.734 K, with a standard deviation value of 0.575 K. The comparison between the ground station LST data indicates the retrieval mean accuracy is −0.395 K, and the standard deviation value is 1.490 K in the regions with vegetation and water cover. Besides, the retrieval results of the test data have also been compared with the results measured by the National Oceanic and Atmospheric Administration (NOAA) VIIRS LST products, and the results indicate that 82.63% of the difference values are within the range of −1 to 1 K, and 17.37% of the difference values are within the range of ±2 to ±1 K. In a conclusion, with the advantages of multi-sensors taken fully exploited, more accurate results can be achieved in the retrieval of land surface temperature. PMID:25397919

[Spatial epidemiological study on malaria epidemics in Hainan province].

PubMed

Wen, Liang; Shi, Run-He; Fang, Li-Qun; Xu, De-Zhong; Li, Cheng-Yi; Wang, Yong; Yuan, Zheng-Quan; Zhang, Hui

2008-06-01

To better understand the characteristics of spatial distribution of malaria epidemics in Hainan province and to explore the relationship between malaria epidemics and environmental factors, as well to develop prediction model on malaria epidemics. Data on Malaria and meteorological factors were collected in all 19 counties in Hainan province from May to Oct., 2000, and the proportion of land use types of these counties in this period were extracted from digital map of land use in Hainan province. Land surface temperatures (LST) were extracted from MODIS images and elevations of these counties were extracted from DEM of Hainan province. The coefficients of correlation of malaria incidences and these environmental factors were then calculated with SPSS 13.0, and negative binomial regression analysis were done using SAS 9.0. The incidence of malaria showed (1) positive correlations to elevation, proportion of forest land area and grassland area; (2) negative correlations to the proportion of cultivated area, urban and rural residents and to industrial enterprise area, LST; (3) no correlations to meteorological factors, proportion of water area, and unemployed land area. The prediction model of malaria which came from negative binomial regression analysis was: I (monthly, unit: 1/1,000,000) = exp (-1.672-0.399xLST). Spatial distribution of malaria epidemics was associated with some environmental factors, and prediction model of malaria epidemic could be developed with indexes which extracted from satellite remote sensing images.

City landscape changes effects on land surface temperature in Bucharest metropolitan area

NASA Astrophysics Data System (ADS)

Savastru, Dan M.; Zoran, Maria A.; Savastru, Roxana S.; Dida, Adrian I.

2017-10-01

This study investigated the influences of city land cover changes and extreme climate events on land surface temperature in relationship with several biophysical variables in Bucharest metropolitan area of Romania through satellite and in-situ monitoring data. Remote sensing data from IKONOS, Landsat TM/ETM+ and time series MODIS Terra/Aqua and NOAA AVHRR sensors have been used to assess urban land cover- temperature interactions over 2000 - 2016 period. Time series Thermal InfraRed (TIR) satellite remote sensing data in synergy with meteorological data (air temperatureAT, precipitations, wind, solar radiation, etc.) were applied mainly for analyzing land surface temperature (LST) pattern and its relationship with surface landscape characteristics, assessing urban heat island (UHI), and relating urban land cover temperatures (LST). The land surface temperature, a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) at city level. Results show that in the metropolitan area ratio of impervious surface in Bucharest increased significantly during investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, LST and AT possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at metropolitan scale respectively. The NDVI was significantly correlated with precipitation. The spatial average air temperatures in urban test areas rise with the expansion of the urban size.

Feedbacks between land cover and climate changes in the Brazilian Amazon and Cerrado biomes

NASA Astrophysics Data System (ADS)

Coe, M. T.; Silverio, D. V.; Bustamante, M.; Macedo, M.; Shimbo, J.; Brando, P. M.

2016-12-01

An estimated 20% of Amazon forests and 45% of Cerrado savannas have been cleared to make way for the expansion of croplands and pasturelands in Brazil. Although deforestation rates have decreased or remained steady over the last decade, the cumulative area deforested continues to grow in both biomes. These land-use transitions are expected to influence regional climate by reducing evapotranspiration (ET), increasing land surface temperatures (LST), and ultimately reducing regional precipitation. Here we present results from spatial analyses to quantify the impact of land-use transitions on the regional climate of the Amazon-Cerrado agricultural frontier. The analyses combine satellite observations and model outputs from the MODIS dataset. Results from the southeastern Amazon indicate that transitions from forest to pasture or cropland decreased mean annual ET (by 24% and 32%, respectively) and increased LST (by 4.2°C and 6.4°C). Preliminary results from the Cerrado indicate that transitions from woody savannas to pasture or cropland also result in substantial reductions in mean annual ET (23% and 20%, respectively) and increases in LST (by 1.6°C in both cases). These results reinforce the need to better understand how land-use change at regional scales may alter climate by changing ecosystem properties (beyond carbon stocks and fluxes). It is important to evaluate these responses across different biomes, particularly in tropical regions under increasing deforestation pressure.

Soil-vegetation-atmosphere energy fluxes: Land Surface Temperature evaluation by Terra/MODIS satellite images

NASA Astrophysics Data System (ADS)

Telesca, V.; Copertino, V. A.; Scavone, G.; Pastore, V.; Dal Sasso, S.

2009-04-01

Most of the hydrological models are by now founded on field and satellite data integration. In fact, the use of remote sensing techniques supplies the frequent lack of field-measured variables and parameters required to apply evaluation models of the hydrological cycle components at a regional scale. These components are very sensitive to the climatic and surface features and conditions. Remote sensing represent a complementary contribution to in situ investigation methodologies, furnishing repeated and real time observations. Naturally, the interest of these techniques is tied up to the existence of a solid correlation among the greatness to evaluate and the remote sensing information obtainable from the images. In this context, satellite remote sensing has become a basic tool since it allows the regular monitoring of extensive areas. Different surface variables and parameters can be extracted from the combination of the multi-spectral information contained in a satellite image. Land Surface Temperature (LST) is a fundamental parameter to estimate most of the components of the hydrological cycle and the soil-atmosphere energy balance, such as the net radiation, the sensible heat flux and the actual evapotranspiration. Besides, LST maps can be used in models for the fire monitoring and prevention. The aim of this work is to realize, exploiting the contribution of the remote sensing, some Land Surface Temperature maps, applying different "Split Windows" algorithms and to compare them with the "Day/Night" LST/MODIS, to select the best algorithm to apply in a Two-Source Energy Balance model (STSEB). Integrated into a rainfall/runoff model, it can contribute to cope with problems of land management for the protection from natural hazards. In particular, the energy balance procedure will be included into a model for the ‘in continuous' simulation and the forecast of floods. Another important application of our model is tied up to the forecast of scenarios connected to drought problems. In this context, they can contribute to the planning and the realization of mitigation interventions for the desertification risk.

Predicting the Invasion Potential of a Puerto Rican Frog in Hawaii using MODIS Satellite Imagery

NASA Astrophysics Data System (ADS)

Bisrat, S. A.; White, M. A.

2008-12-01

The Puerto Rican coqui frog (Eleutherodactylus coqui, hereafter coqui), which was introduced into Hawaii accidentally via commercial nurseries, is an aggressive invasive species in Hawaii. The coqui threatens Hawaii's unique ecological communities because it predates upon endemic invertebrates, which comprise the large majority of Hawaii's endemic fauna. Coqui frogs also affect real estate valuations because of their loud mating calls. Despite this widespread problem, the potential coqui range in Hawaii is currently unknown, making control and management efforts difficult. We fitted linear discriminant analysis (LDA), logistic regression (LR) via generalized linear models (GLMs), generalized additive models (GAMs), classification trees (CTs), random forests (RF), and support vector machine (SVM) to model the species distribution and map their invasion potential. We used five MODIS satellite imagery-derived biophysical variables as explanatory variables: leaf area index (LAI), fraction of photosynthetically active radiation absorbed by vegetation (FPAR), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and land surface temperature (LST) from three MODIS products: MOD11 (LST), MOD13 (LAI and FPAR), and MOD15 (Vegetation Index) (collection 4). We used 2000-2005 MODIS data from Aqua and Terra satellites to generate monthly climatologies for each biophysical variable. We collected presence/absence data from Puerto Rico and Hawaii using a 1 km grid overlaid over the entire islands of Puerto Rico and the Island of Hawaii by sampling every other pixel of the grid intersecting with the road network. We then used the dataset from Puerto Rico to train the six models while the Hawaii dataset was used as a test set. All six models predicted the invasion potential of coqui frogs in Hawaii with a moderate success with mean Kappa value of 0.31, mean area under the curve of receiver operating characteristics (AUC) of 0.75 and mean classification accuracy (CA) of 0.69. RF and SVM outperformed the other classifiers with Kappa value of 0.4, AUC value of 0.79 and CA of 0.71 for RF and Kappa value of 0.4, AUC value of 0.71 and CA value of 0.72 for SVM. These results suggest climate matching between the native and the introduced habitats of coqui frogs is not strong. Further, the results suggest coqui frogs in their introduced habitat are not showing strong niche conservation.

Characterization of 2014 summer drought over Henan province using remotely sensed data

NASA Astrophysics Data System (ADS)

Lu, Jing; Jia, Li; Zhou, Jie

2015-12-01

An exceptional drought struck Henan province during the summer of 2014. It caused directly the financial loss reaching to hundreds of billion Yuan (RMB), and brought the adverse influence for people's life, agricultural production as well as the ecosystem. The study in this paper characterized the Henan 2014 summer drought event through analyzing the spatial distribution of drought severity using precipitation data from Tropical Rainfall Measuring Mission (TRMM) sensor and Normalized difference vegetation index (NDVI) and land surface temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The trend analysis of the annual precipitation from 2003 to 2014 showed that the region over Henan province is becoming dry. Especially in the east of Henan province, the decrease of precipitation is more obvious with the maximum change rate of ~48 mm/year. The rainfall in summer (from June to August) of 2014 was the largest negtive anomaly in contrast with the same period of historical years, which was 43% lower than the average of the past ten years. Drought severity derived from Standardized Precipitation Index (SPI) indicated that all areas of Henan province experienced drought in summer of 2014 with different severity levels. The extreme drought, accounting for about 22.7 % of Henan total area, mainly occurred in Luohe, Xuchang, and Pingdingshan regions, and partly in Nanyang, Zhengzhou, and Jiaozuo. This is consistent with the statistics from local municipalities. The Normalized Drought Index Anomaly (NDAI), calculated from MODIS NDVI and LST products, can capture the evolution of the Henan 2014 summer drought effectively. Drought severity classified by NDAI also agreed well with the result from the SPI.

A Useful Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data

NASA Astrophysics Data System (ADS)

Rivalland, Vincent; Tardy, Benjamin; Huc, Mireille; Hagolle, Olivier; Marcq, Sébastien; Boulet, Gilles

2016-04-01

Land Surface temperature (LST) is a critical variable for studying the energy and water budgets at the Earth surface, and is a key component of many aspects of climate research and services. The Landsat program jointly carried out by NASA and USGS has been providing thermal infrared data for 40 years, but no associated LST product has been yet routinely proposed to community. To derive LST values, radiances measured at sensor-level need to be corrected for the atmospheric absorption, the atmospheric emission and the surface emissivity effect. Until now, existing LST products have been generated with multi channel methods such as the Temperature/Emissivity Separation (TES) adapted to ASTER data or the generalized split-window algorithm adapted to MODIS multispectral data. Those approaches are ill-adapted to the Landsat mono-window data specificity. The atmospheric correction methodology usually used for Landsat data requires detailed information about the state of the atmosphere. This information may be obtained from radio-sounding or model atmospheric reanalysis and is supplied to a radiative transfer model in order to estimate atmospheric parameters for a given coordinate. In this work, we present a new automatic tool dedicated to Landsat thermal data correction which improves the common atmospheric correction methodology by introducing the spatial dimension in the process. The python tool developed during this study, named LANDARTs for LANDsat Automatic Retrieval of surface Temperature, is fully automatic and provides atmospheric corrections for a whole Landsat tile. Vertical atmospheric conditions are downloaded from the ERA Interim dataset from ECMWF meteorological organization which provides them at 0.125 degrees resolution, at a global scale and with a 6-hour-time step. The atmospheric correction parameters are estimated on the atmospheric grid using the commercial software MODTRAN, then interpolated to 30m resolution. We detail the processing steps implemented in LANDARTs and propose a local and spatial validation of the LST products from Landsat dataset archive over two climatically contrasted zones: south-west France and centre of Tunisia. In both sites, long term datasets of in-situ surface temperature measurements have been compared to LST obtained for Landsat data processed by LANDARTs and filtered from clouds. This temporal comparison presents RMSE between 1.84K and 2.55K. Then, Landsat LST products are compared to ASTER kinetic surface temperature products on two synchronous dates from both zones. This comparison presents satisfactory RMSE about 2.55K with a good correlation coefficient of 0.9. Finally, a sensibility analysis to the spatial variation of parameters presents a variability reaching 2K at the Landsat image scale and confirms the improved accuracy in Landsat LST estimation linked to our spatial approach.

MODIS-informed greenness responsesto daytime land surface temperaturefluctuations and wildfire disturbancesin the Alaskan Yukon River Basin

USGS Publications Warehouse

Tan, Zhengxi; Liu, Shu-Guang; Jenkerson, Calli B.; Oeding, Jennifer; Wylie, Bruce K.; Rover, Jennifer R.; Young, Claudia J.

2012-01-01

Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Moderate Resolution Imaging Spectroradiometer (MODIS) data) acquired between 2000 and 2009. Our analyses indicate measureable effects on NDVI values from vegetation type, burn severity, post-fire time, and climatic variables. The NDVI observations from both fire scars and unburned areas across the Alaskan YRB showed a tendency of an earlier start to the growing season (GS); the annual variations in NDVI were significantly correlated to daytime land surface temperature (LST) fluctuations; and the rate of post-fire green-up depended mainly on burn severity and the time of post-fire succession. The higher average NDVI values for the study period in the fire scars than in the unburned areas between 1950 and 2000 suggest that wildfires enhance post-fire greenness due to an increase in post-fire evergreen and deciduous species components

Energy fluxes retrieval on an Alaskan Arctic and Sub-Arctic vegetation by means MODIS imagery and the DTD method

NASA Astrophysics Data System (ADS)

Cristobal, J.; Prakash, A.; Starkenburg, D. P.; Fochesatto, G. J.; Anderson, M. C.; Gens, R.; Kane, D. L.; Kustas, W.; Alfieri, J. G.

2012-12-01

Evapotranspiration (ET) plays a significant role in the hydrologic cycle of Arctic and Sub-Arctic basins. Surface-atmosphere exchanges due to ET are estimated from water balance computations to be about 74% of summer precipitation or 50% of annual precipitation. Even though ET is a significant component of the hydrologic cycle in this region, the bulk estimates don't accurately account for spatial and temporal variability due to vegetation type, topography, etc. (Kane and Yang, 2004). Nowadays, remote sensing is the only technology capable of providing the necessary radiometric measurements for the calculation of the ET at global scales and in a feasible economic way, especially in Arctic and Sub-Arctic Alaskan basins with a very sparse network of both meteorological and flux towers. In this work we present the implementation and validation of the Dual-Time-Difference model (Kustas et al., 2001) to retrieve energy fluxes (ET, sensible heat flux, net radiation and soil heat flux) in tundra vegetation in Arctic conditions and in a black spruce (Picea mariana) forest in Sub-Arctic conditions. In order to validate the model in tundra vegetation we used a flux tower from the Imnavait Creek sites of the Arctic Observatory Network (Euskirchen et al. 2012). In the case of the black spruce forest, on September 2011 we installed a flux tower in the University of Alaska Fairbanks north campus that includes an eddy-covariance system as well a net radiometer, air temperature probes, soil heat flux plates, soil moisture sensors and thermistors to fully estimate energy fluxes in the field (see http://www.et.alaska.edu/ for further details). Additionally, in order to upscale energy fluxes into MODIS spatial resolution, a scintillometer was also installed covering 1.2 km across the flux tower. DTD model mainly requires meteorological inputs as well as land surface temperature (LST) and leaf area index (LAI) data, both coming from satellite imagery, at two different times: after local sunrise and from mid morning to mid afternoon. As remote sensing data we used 11 TERRA/AQUA MODIS dates from July to September 2008. For these dates we selected the LST that better fits this two times using the LST MODIS product (MOD11/MYD11) and as LAI input we used the LAI daily product (MOD15/MYD15). In the case of tundra validation, preliminary results show an acceptable agreement between DTD model and flux tower data. RMSE obtained in the case of at satellite pass evapotranspiration, sensible heat flux and soil heat flux were 50, 80 and 33 W m-2, respectively, and R2 of 0.92, 0.76 and 0.69, respectively. Results from the black spruce forest will be discussed in later work. Further efforts will be focused on the daily energy flux integration by means of the implementation of the ALEXi/DisALEXI model (Anderson et al., 2007), the energy fluxes upscaling validated by means of scintillometer data as well as the energy balance computation in snow conditions.

Cloud Tolerance of Remote-Sensing Technologies to Measure Land Surface Temperature

NASA Technical Reports Server (NTRS)

Holmes, Thomas R. H.; Hain, Christopher R.; Anderson, Martha C.; Crow, Wade T.

2016-01-01

Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared(TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave(MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product. This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR based LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images

PubMed Central

Zhou, Yuting; Xiao, Xiangming; Qin, Yuanwei; Dong, Jinwei; Zhang, Geli; Kou, Weili; Jin, Cui; Wang, Jie; Li, Xiangping

2016-01-01

Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between RiceLandsat and RiceNLCD are in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region. PMID:27688742

Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images.

PubMed

Zhou, Yuting; Xiao, Xiangming; Qin, Yuanwei; Dong, Jinwei; Zhang, Geli; Kou, Weili; Jin, Cui; Wang, Jie; Li, Xiangping

2016-04-01

Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between Rice Landsat and Rice NLCD are in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region.

Permanents Stations for Calibration/Validation of Thermal Sensors over Spain: Ready for the Advent of Sentinel-3

NASA Astrophysics Data System (ADS)

Sobrino, J. A.; Skokovic, D.; Jimenez-Munoz, J. C.; Soria, G.; Julien, Y.

2016-08-01

The Global Change Unit (GCU) at the University of Valencia has been involved in several calibration/validation (cal/val) activities carried out in dedicated field campaigns organized by ESA and other organisms. However, permanent stations are required in order to ensure a long-term and continuous calibration of on-orbit sensors. In the framework of the CEOS-Spain project, the GCU has managed the setting-up and launch of experimental sites in Spain for the calibration of thermal infrared sensors and the validation of Land Surface Temperature (LST) products derived from those data. Currently, three sites have been identified and equipped: the agricultural area of Barrax (39.05N, 2.1W), the marshland area in the National Park of Doñana (36.99N, 6.44W), and the semi-arid area of the National Park of Cabo de Gata (36.83N, 2.25W). The activities of the CEOS-Spain project also included the implementation of an operational processing chain in order to provide in near-real time different remote sensing products, including LST. This work presents the performance of the permanent stations installed over the different test areas, as well as the cal/val results obtained for a number of Earth Observation sensors: SEVIRI, MODIS and Landsat series. We also show the results obtained in the validation of LST products derived from AATSR, with discussion on the implications for the forthcoming Sentinel-3/SLSTR.

Satellite-derived NDVI, LST, and climatic factors driving the distribution and abundance of Anopheles mosquitoes in a former malarious area in northwest Argentina.

PubMed

Dantur Juri, María Julia; Estallo, Elizabet; Almirón, Walter; Santana, Mirta; Sartor, Paolo; Lamfri, Mario; Zaidenberg, Mario

2015-06-01

Distribution and abundance of disease vectors are directly related to climatic conditions and environmental changes. Remote sensing data have been used for monitoring environmental conditions influencing spatial patterns of vector-borne diseases. The aim of this study was to analyze the effect of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic factors (temperature, humidity, wind velocity, and accumulated rainfall) on the distribution and abundance of Anopheles species in northwestern Argentina using Poisson regression analyses. Samples were collected from December, 2001 to December, 2005 at three localities, Aguas Blancas, El Oculto and San Ramón de la Nueva Orán. We collected 11,206 adult Anopheles species, with the major abundance observed at El Oculto (59.11%), followed by Aguas Blancas (22.10%) and San Ramón de la Nueva Orán (18.79%). Anopheles pseudopunctipennis was the most abundant species at El Oculto, Anopheles argyritarsis predominated in Aguas Blancas, and Anopheles strodei in San Ramón de la Nueva Orán. Samples were collected throughout the sampling period, with the highest peaks during the spring seasons. LST and mean temperature appear to be the most important variables determining the distribution patterns and major abundance of An. pseudopunctipennis and An. argyritarsis within malarious areas. © 2015 The Society for Vector Ecology.

Analysis of Giga-size Earth Observation Data in Open Source GRASS GIS 7 - from Desktop to On-line Solutions.

NASA Astrophysics Data System (ADS)

Stepinski, T. F.; Mitasova, H.; Jasiewicz, J.; Neteler, M.; Gebbert, S.

2014-12-01

GRASS GIS is a leading open source GIS for geospatial analysis and modeling. In addition to being utilized as a desktop GIS it also serves as a processing engine for high performance geospatial computing for applications in diverse disciplines. The newly released GRASS GIS 7 supports big data analysis including temporal framework, image segmentation, watershed analysis, synchronized 2D/3D animations and many others. This presentation will focus on new GRASS GIS 7-powered tools for geoprocessing giga-size earth observation (EO) data using spatial pattern analysis. Pattern-based analysis connects to human visual perception of space as well as makes geoprocessing of giga-size EO data possible in an efficient and robust manner. GeoPAT is a collection of GRASS GIS 7 modules that fully integrates procedures for pattern representation of EO data and patterns similarity calculations with standard GIS tasks of mapping, maps overlay, segmentation, classification(Fig 1a), change detections etc. GeoPAT works very well on a desktop but it also underpins several GeoWeb applications (http://sil.uc.edu/ ) which allow users to do analysis on selected EO datasets without the need to download them. The GRASS GIS 7 temporal framework and high resolution visualizations will be illustrated using time series of giga-size, lidar-based digital elevation models representing the dynamics of North Carolina barrier islands over the past 15 years. The temporal framework supports efficient raster and vector data series analysis and simplifies data input for visual analysis of dynamic landscapes (Fig. 1b) allowing users to rapidly identify vulnerable locations, changes in built environment and eroding coastlines. Numerous improvements in GRASS GIS 7 were implemented to support terabyte size data processing for reconstruction of MODIS land surface temperature (LST) at 250m resolution using multiple regressions and PCA (Fig. 1c) . The new MODIS LST series (http://gis.cri.fmach.it/eurolst/) includes 4 maps per day since year 2000, provide improved data for the epidemiological predictions, viticulture, assessment of urban heat islands and numerous other applications. The presentation will conclude with outline of future development for big data interfaces to further enhance the web-based GRASS GIS data analysis.

Microwave implementation of two-source energy balance approach for estimating evapotranspiration

NASA Astrophysics Data System (ADS)

Holmes, Thomas R. H.; Hain, Christopher R.; Crow, Wade T.; Anderson, Martha C.; Kustas, William P.

2018-02-01

A newly developed microwave (MW) land surface temperature (LST) product is used to substitute thermal infrared (TIR)-based LST in the Atmosphere-Land Exchange Inverse (ALEXI) modeling framework for estimating evapotranspiration (ET) from space. ALEXI implements a two-source energy balance (TSEB) land surface scheme in a time-differential approach, designed to minimize sensitivity to absolute biases in input records of LST through the analysis of the rate of temperature change in the morning. Thermal infrared retrievals of the diurnal LST curve, traditionally from geostationary platforms, are hindered by cloud cover, reducing model coverage on any given day. This study tests the utility of diurnal temperature information retrieved from a constellation of satellites with microwave radiometers that together provide six to eight observations of Ka-band brightness temperature per location per day. This represents the first ever attempt at a global implementation of ALEXI with MW-based LST and is intended as the first step towards providing all-weather capability to the ALEXI framework. The analysis is based on 9-year-long, global records of ALEXI ET generated using both MW- and TIR-based diurnal LST information as input. In this study, the MW-LST (MW-based LST) sampling is restricted to the same clear-sky days as in the IR-based implementation to be able to analyze the impact of changing the LST dataset separately from the impact of sampling all-sky conditions. The results show that long-term bulk ET estimates from both LST sources agree well, with a spatial correlation of 92 % for total ET in the Europe-Africa domain and agreement in seasonal (3-month) totals of 83-97 % depending on the time of year. Most importantly, the ALEXI-MW (MW-based ALEXI) also matches ALEXI-IR (IR-based ALEXI) very closely in terms of 3-month inter-annual anomalies, demonstrating its ability to capture the development and extent of drought conditions. Weekly ET output from the two parallel ALEXI implementations is further compared to a common ground measured reference provided by the Fluxnet consortium. Overall, the two model implementations generate similar performance metrics (correlation and RMSE) for all but the most challenging sites in terms of spatial heterogeneity and level of aridity. It is concluded that a constellation of MW satellites can effectively be used to provide LST for estimating ET through ALEXI, which is an important step towards all-sky satellite-based retrieval of ET using an energy balance framework.

A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud

NASA Astrophysics Data System (ADS)

Zeng, Chao; Long, Di; Shen, Huanfeng; Wu, Penghai; Cui, Yaokui; Hong, Yang

2018-07-01

Land surface temperature (LST) is one of the most important parameters in land surface processes. Although satellite-derived LST can provide valuable information, the value is often limited by cloud contamination. In this paper, a two-step satellite-derived LST reconstruction framework is proposed. First, a multi-temporal reconstruction algorithm is introduced to recover invalid LST values using multiple LST images with reference to corresponding remotely sensed vegetation index. Then, all cloud-contaminated areas are temporally filled with hypothetical clear-sky LST values. Second, a surface energy balance equation-based procedure is used to correct for the filled values. With shortwave irradiation data, the clear-sky LST is corrected to the real LST under cloudy conditions. A series of experiments have been performed to demonstrate the effectiveness of the developed approach. Quantitative evaluation results indicate that the proposed method can recover LST in different surface types with mean average errors in 3-6 K. The experiments also indicate that the time interval between the multi-temporal LST images has a greater impact on the results than the size of the contaminated area.

Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data

NASA Astrophysics Data System (ADS)

Yoo, Cheolhee; Im, Jungho; Park, Seonyoung; Quackenbush, Lindi J.

2018-03-01

Urban air temperature is considered a significant variable for a variety of urban issues, and analyzing the spatial patterns of air temperature is important for urban planning and management. However, insufficient weather stations limit accurate spatial representation of temperature within a heterogeneous city. This study used a random forest machine learning approach to estimate daily maximum and minimum air temperatures (Tmax and Tmin) for two megacities with different climate characteristics: Los Angeles, USA, and Seoul, South Korea. This study used eight time-series land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), with seven auxiliary variables: elevation, solar radiation, normalized difference vegetation index, latitude, longitude, aspect, and the percentage of impervious area. We found different relationships between the eight time-series LSTs with Tmax/Tmin for the two cities, and designed eight schemes with different input LST variables. The schemes were evaluated using the coefficient of determination (R2) and Root Mean Square Error (RMSE) from 10-fold cross-validation. The best schemes produced R2 of 0.850 and 0.777 and RMSE of 1.7 °C and 1.2 °C for Tmax and Tmin in Los Angeles, and R2 of 0.728 and 0.767 and RMSE of 1.1 °C and 1.2 °C for Tmax and Tmin in Seoul, respectively. LSTs obtained the day before were crucial for estimating daily urban air temperature. Estimated air temperature patterns showed that Tmax was highly dependent on the geographic factors (e.g., sea breeze, mountains) of the two cities, while Tmin showed marginally distinct temperature differences between built-up and vegetated areas in the two cities.

Spatiotemporal variability of Canadian High Arctic glacier surface albedo from MODIS data, 2001-2016

NASA Astrophysics Data System (ADS)

Mortimer, Colleen A.; Sharp, Martin

2018-02-01

Inter-annual variations and longer-term trends in the annual mass balance of glaciers in Canada's Queen Elizabeth Islands (QEI) are largely attributable to changes in summer melt. The largest source of melt energy in the QEI in summer is net shortwave radiation, which is modulated by changes in glacier surface albedo. We used measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors to investigate large-scale spatial patterns, temporal trends, and variability in the summer surface albedo of QEI glaciers from 2001 to 2016. Mean summer black-sky shortwave broadband albedo (BSA) decreased at a rate of 0.029±0.025 decade-1 over that period. Larger reductions in BSA occurred in July (-0.050±0.031 decade-1). No change in BSA was observed in either June or August. Most of the decrease in BSA, which was greatest at lower elevations around the margins of the ice masses, occurred between 2007 and 2012, when mean summer BSA was anomalously low. The first principal component of the 16-year record of mean summer BSA was well correlated with the mean summer North Atlantic Oscillation index, except in 2006, 2010, and 2016, when the mean summer BSA appears to have been dominated by the August BSA. During the period 2001-2016, the mean summer land surface temperature (LST) over the QEI glaciers and ice caps increased by 0.049±0.038 °C yr-1, and the BSA record was negatively correlated (r: -0.86) with the LST record, indicative of a positive ice-albedo feedback that would increase rates of mass loss from the QEI glaciers.

Mapping Daily Evapotranspiration based on Spatiotemporal Fusion of ASTER and MODIS Images over Irrigated Agricultural Areas in the Heihe River Basin, Northwest China

NASA Astrophysics Data System (ADS)

Huang, C.; LI, Y.

2017-12-01

Continuous monitoring of daily evapotranspiration (ET) is crucial for allocating and managing water resources in irrigated agricultural areas in arid regions. In this study, continuous daily ET at a 90-m spatial resolution was estimated using the Surface Energy Balance System (SEBS) by fusing Moderate Resolution Imaging Spectroradiometer (MODIS) images with high temporal resolution and Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER) images with high spatial resolution. The spatiotemporal characteristics of these sensors were obtained using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The performance of this approach was validated over a heterogeneous oasis-desert region covered by cropland, residential, woodland, water, Gobi desert, sandy desert, desert steppe, and wetland areas using in situ observations from automatic meteorological systems (AMS) and eddy covariance (EC) systems in the middle reaches of the Heihe River Basin in Northwest China. The error introduced during the data fusion process based on STARFM is within an acceptable range for predicted LST at a 90-m spatial resolution. The surface energy fluxes estimated using SEBS based on predicted remotely sensed data that combined the spatiotemporal characteristics of MODIS and ASTER agree well with the surface energy fluxes observed using EC systems for all land cover types, especially for vegetated area with MAP values range from 9% to 15%, which are less than the uncertainty (18%) of the observed in this study area. Time series of daily ET modelled from SEBS were compared to that modelled from PT-JPL (one of Satellite-based Priestley-Taylor ET model) and observations from EC systems. SEBS performed generally better than PT-JPL for vegetated area, especially irrigated cropland with bias, RMSE, and MAP values of 0.29 mm/d, 0.75 mm/d, 13% at maize site, -0.33 mm/d, 0.81 mm/d, and 14% at vegetable sites.

Evaluation of eco-physiological indicators in Northeast Asia dryland regions based on MODIS products and ecological models

NASA Astrophysics Data System (ADS)

Kang, W.

2017-12-01

Ecosystem carbon-energy-water circles have significant effect on function and structure and vice verse. Based on these circles mechanism, some eco-physiological indicators, like Transpiration (T), gross primary productivity (GPP), light use efficiency (LUE) and water use efficiency (WUE), are commonly applied to assess terrestrial ecosystem function and structure dynamics. The ecosystem weakened function and simple structure in Northeast dryland regions resulted from land degradation or desertification, which could be demonstrated by above-mentioned indicators. In this study, based on MODIS atmosphere (MYD07, MYD04, MYD06 data) and land products (MYD13A2 NDVI, MYD11A1 LST, MYD15A2 LAI and land cover data), we first retrieved transpiration and LUE via Penman-Monteith Model and modified Vegetation Photosynthesis Model (VPM), respectively; and then evaluated dynamics of these eco-physiological indicators (Tair, VPD, T, LUE, GPP and WUE) and some hotspots were found for next land degradation assessment. The results showed: (1) LUE and WUE are lower in barren or sparsely vegetated area and grasslands than in forest and croplands. (2) Whereas, all indicators presented higher variability in grassland area, particularly in east Mongolia. (3) GPP and transpiration have larger variability than other indicators due to fraction of absorbed Photosynthetically active radiation (FPAR). These eco-physiological indicators are expected to continue to change under future climate change and to help to assess land degradation from ecosystem energy-water-carbon perspectives.

Implications of atmospheric conditions for analysis of surface temperature variability derived from landscape-scale thermography.

PubMed

Hammerle, Albin; Meier, Fred; Heinl, Michael; Egger, Angelika; Leitinger, Georg

2017-04-01

Thermal infrared (TIR) cameras perfectly bridge the gap between (i) on-site measurements of land surface temperature (LST) providing high temporal resolution at the cost of low spatial coverage and (ii) remotely sensed data from satellites that provide high spatial coverage at relatively low spatio-temporal resolution. While LST data from satellite (LST sat ) and airborne platforms are routinely corrected for atmospheric effects, such corrections are barely applied for LST from ground-based TIR imagery (using TIR cameras; LST cam ). We show the consequences of neglecting atmospheric effects on LST cam of different vegetated surfaces at landscape scale. We compare LST measured from different platforms, focusing on the comparison of LST data from on-site radiometry (LST osr ) and LST cam using a commercially available TIR camera in the region of Bozen/Bolzano (Italy). Given a digital elevation model and measured vertical air temperature profiles, we developed a multiple linear regression model to correct LST cam data for atmospheric influences. We could show the distinct effect of atmospheric conditions and related radiative processes along the measurement path on LST cam , proving the necessity to correct LST cam data on landscape scale, despite their relatively low measurement distances compared to remotely sensed data. Corrected LST cam data revealed the dampening effect of the atmosphere, especially at high temperature differences between the atmosphere and the vegetated surface. Not correcting for these effects leads to erroneous LST estimates, in particular to an underestimation of the heterogeneity in LST, both in time and space. In the most pronounced case, we found a temperature range extension of almost 10 K.

Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

NASA Technical Reports Server (NTRS)

Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

2014-01-01

Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

Near Real Time Change-Point detection in Optical and Thermal Infrared Time Series Using Bayesian Inference over the Dry Chaco Forest

NASA Astrophysics Data System (ADS)

Barraza Bernadas, V.; Grings, F.; Roitberg, E.; Perna, P.; Karszenbaum, H.

2017-12-01

The Dry Chaco region (DCF) has the highest absolute deforestation rates of all Argentinian forests. The most recent report indicates a current deforestation rate of 200,000 Ha year-1. In order to better monitor this process, DCF was chosen to implement an early warning program for illegal deforestation. Although the area is intensively studied using medium resolution imagery (Landsat), the products obtained have a yearly pace and therefore unsuited for an early warning program. In this paper, we evaluated the performance of an online Bayesian change-point detection algorithm for MODIS Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) datasets. The goal was to to monitor the abrupt changes in vegetation dynamics associated with deforestation events. We tested this model by simulating 16-day EVI and 8-day LST time series with varying amounts of seasonality, noise, length of the time series and by adding abrupt changes with different magnitudes. This model was then tested on real satellite time series available through the Google Earth Engine, over a pilot area in DCF, where deforestation was common in the 2004-2016 period. A comparison with yearly benchmark products based on Landsat images is also presented (REDAF dataset). The results shows the advantages of using an automatic model to detect a changepoint in the time series than using only visual inspection techniques. Simulating time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes, revealed that this model is robust against noise, and is not influenced by changes in amplitude of the seasonal component. Furthermore, the results compared favorably with REDAF dataset (near 65% of agreement). These results show the potential to combine LST and EVI to identify deforestation events. This work is being developed within the frame of the national Forest Law for the protection and sustainable development of Native Forest in Argentina in agreement with international legislation (REDD+).

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

A comparison of classical and intelligent methods to detect potential thermal anomalies before the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4)

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-04-01

In this paper, a number of classical and intelligent methods, including interquartile, autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM), have been proposed to quantify potential thermal anomalies around the time of the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4). The duration of the data set, which is comprised of Aqua-MODIS land surface temperature (LST) night-time snapshot images, is 62 days. In order to quantify variations of LST data obtained from satellite images, the air temperature (AT) data derived from the meteorological station close to the earthquake epicenter has been taken into account. For the models examined here, results indicate the following: (i) ARIMA models, which are the most widely used in the time series community for short-term forecasting, are quickly and easily implemented, and can efficiently act through linear solutions. (ii) A multilayer perceptron (MLP) feed-forward neural network can be a suitable non-parametric method to detect the anomalous changes of a non-linear time series such as variations of LST. (iii) Since SVMs are often used due to their many advantages for classification and regression tasks, it can be shown that, if the difference between the predicted value using the SVM method and the observed value exceeds the pre-defined threshold value, then the observed value could be regarded as an anomaly. (iv) ANN and SVM methods could be powerful tools in modeling complex phenomena such as earthquake precursor time series where we may not know what the underlying data generating process is. There is good agreement in the results obtained from the different methods for quantifying potential anomalies in a given LST time series. This paper indicates that the detection of the potential thermal anomalies derive credibility from the overall efficiencies and potentialities of the four integrated methods.

Effect of laser surface texturing (LST) on tribochemical films dynamics and friction and wear performance

DOE PAGES

Olofinjana, Bolutife; Lorenzo-Martin, Cinta; Ajayi, Oyelayo O.; ...

2015-06-06

Surface texturing or topographical design is one of the primary techniques to control friction and wear performance of surfaces in tribological contact. Laser surface texturing (LST), whereby a laser beam is used to produce regular arrays of dimples on a surface, has been demonstrated to reduce friction in conformal lubricated contacts. Friction and wear behavior under boundary lubrication is also known to be dependent on the formation and durability of the tribochemical film formed from lubricant additives. In this paper, the effects of LST on the formation and durability of tribochemical films and its consequent impacts on friction and wearmore » behavior in various lubrication regimes were evaluated. Friction and wear tests that cycled through different lubrication regimes were conducted with both polished and LST treated surfaces using a synthetic lubricant with and without model additives of ZDDP and MoDTC mixture. In the base oil without additives, LST produced noticeable reduction in friction in all lubrication regimes. However, with low-friction model additives, friction was higher in tests with LST due to significant differences in the tribochemical film formation in the polished and LST surfaces, as well as the sliding counterface. Continuous tribo-films were formed on ball conterface rubbed against polished surfaces while the films were streaky and discontinuous in ball rubbed against LST surfaces. LST produced more wear on the ball counterface in both base and additized oils. Lastly, no measurable wear was observed in both the polished and LST flat specimens.« less

Simulation of urban land surface temperature based on sub-pixel land cover in a coastal city

NASA Astrophysics Data System (ADS)

Zhao, Xiaofeng; Deng, Lei; Feng, Huihui; Zhao, Yanchuang

2014-11-01

The sub-pixel urban land cover has been proved to have obvious correlations with land surface temperature (LST). Yet these relationships have seldom been used to simulate LST. In this study we provided a new approach of urban LST simulation based on sub-pixel land cover modeling. Landsat TM/ETM+ images of Xiamen city, China on both the January of 2002 and 2007 were used to acquire land cover and then extract the transformation rule using logistic regression. The transformation possibility was taken as its percent in the same pixel after normalization. And cellular automata were used to acquire simulated sub-pixel land cover on 2007 and 2017. On the other hand, the correlations between retrieved LST and sub-pixel land cover achieved by spectral mixture analysis in 2002 were examined and a regression model was built. Then the regression model was used on simulated 2007 land cover to model the LST of 2007. Finally the LST of 2017 was simulated for urban planning and management. The results showed that our method is useful in LST simulation. Although the simulation accuracy is not quite satisfactory, it provides an important idea and a good start in the modeling of urban LST.

Validation of the MODIS "Clear-Sky" Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Koenig, L. S.; DiGirolamo, N. E.; Comiso, J.; Shuman, C. A.

2011-01-01

Surface temperatures on the Greenland Ice Sheet have been studied on the ground, using automatic weather station (AWS) data from the Greenland-Climate Network (GC-Net), and from analysis of satellite sensor data. Using Advanced Very High Frequency Radiometer (AVHRR) weekly surface temperature maps, warming of the surface of the Greenland Ice Sheet has been documented from 1981 to present. We extend and refine this record using higher-resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) data from March 2000 to the present. To permit changes to be observed over time, we are developing a well-characterized monthly climate-data record (CDR) of the "clear-sky" surface temperature of the Greenland Ice Sheet using data from both the Terra and Aqua satellites. We use the MODIS ice-surface temperature (IST) algorithm. Validation of the CDR consists of several facets: 1) comparisons between the Terra and Aqua IST maps; 2) comparisons between ISTs and in-situ measurements; 3) comparisons between ISTs and AWS data; and 4) comparisons of ISTs with surface temperatures derived from other satellite instruments such as the Thermal Emission and Reflection Radiometer. In this work, we focus on 1) and 2) above. Surface temperatures on the Greenland Ice Sheet have been studied on the ground, using automatic weather station (AWS) data from the Greenland-Climate Network (GC-Net), and from analysis of satellite sensor data. Using Advanced Very High Frequency Radiometer (AVHRR) weekly surface temperature maps, warming of the surface of the Greenland Ice Sheet has been documented from 1981 to present. We extend and refine this record using higher-resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) data from March 2000 to the present. To permit changes to be observed over time, we are developing a well-characterized monthly climate-data record (CDR) of the "clear-sky" surface temperature of the Greenland Ice Sheet using data from both the Terra and Aqua satellites. We use the MODIS ice-surface temperature (IST) algorithm. Validation of the CDR consists of several facets: 1) comparisons between the Terra and Aqua IST maps; 2) comparisons between ISTs and in-situ measurements; 3) comparisons between ISTs and AWS data; and 4) comparisons of ISTs with surface temperatures derived from other satellite instruments such as the Thermal Emission and Reflection Radiometer. In this work, we focus on 1) and 2) above. First we provide comparisons between Terra and Aqua swath-based ISTs at approximately 14:00 Local Solar Time, reprojected to 12.5 km polar stereographic cells. Results show good correspondence when Terra and Aqua data were acquired within 2 hrs of each other. For example, for a cell centered over Summit Camp (72.58 N, 38.5 W), the average agreement between Terra and Aqua ISTs is 0.74 K (February 2003), 0.47 K (April 2003), 0.7 K (August 2003) and 0.96 K (October 2003) with the Terra ISTs being generally lower than the Aqua ISTs. More precise comparisons will be calculated using pixel data at the swath level, and correspondence between Terra and Aqua IST is expected to be closer. (Because of cloud cover and other considerations, only a few common cloud-free swaths are typically available for each month for comparison.) Additionally, previous work comparing land-surface temperatures (LSTs) from the standard MODIS LST product and in-situ surface-temperature data at Summit Camp on the Greenland Ice Sheet show that Terra MODIS LSTs are about 3 K lower than in-situ temperatures at Summit Camp, during the winter of 2008-09. This work will be repeated using both Terra and Aqua IST pixel data (in place of LST data). In conclusion, we demonstrate that the uncertainties in the CDR will be well characterized as we work through the various facets of its validation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olofinjana, Bolutife; Lorenzo-Martin, Cinta; Ajayi, Oyelayo O.

Surface texturing or topographical design is one of the primary techniques to control friction and wear performance of surfaces in tribological contact. Laser surface texturing (LST), whereby a laser beam is used to produce regular arrays of dimples on a surface, has been demonstrated to reduce friction in conformal lubricated contacts. Friction and wear behavior under boundary lubrication is also known to be dependent on the formation and durability of the tribochemical film formed from lubricant additives. In this paper, the effects of LST on the formation and durability of tribochemical films and its consequent impacts on friction and wearmore » behavior in various lubrication regimes were evaluated. Friction and wear tests that cycled through different lubrication regimes were conducted with both polished and LST treated surfaces using a synthetic lubricant with and without model additives of ZDDP and MoDTC mixture. In the base oil without additives, LST produced noticeable reduction in friction in all lubrication regimes. However, with low-friction model additives, friction was higher in tests with LST due to significant differences in the tribochemical film formation in the polished and LST surfaces, as well as the sliding counterface. Continuous tribo-films were formed on ball conterface rubbed against polished surfaces while the films were streaky and discontinuous in ball rubbed against LST surfaces. LST produced more wear on the ball counterface in both base and additized oils. Lastly, no measurable wear was observed in both the polished and LST flat specimens.« less

Observation of angular effects on thermal infrared emissivity derived with the MODTES algorithm and MODIS data

NASA Astrophysics Data System (ADS)

García-Santos, Vicente; Niclòs, Raquel; Coll, César; Valor, Enric; Caselles, Vicente

2015-04-01

The MOD21 Land Surface Temperature and Emissivity (LST&E) product will be included in forthcoming MODIS Collection 6. Surface temperature and emissivities for thermal infrared (TIR) bands 29 (8.55 μm), 31 (11 μm) and 32 (12 μm) will be retrieved using the ASTER TES method adapted to MODIS at-sensor spectral radiances, previously corrected with the Water Vapor Scaling method (MODTES algorithm). LSE of most natural surfaces changes with soil moisture content, type of surface cover, surface roughness or sensor viewing geometry. The present study addresses the observation of anisotropy effects on LSE of bare soils using MODIS data and a processor simulator of the MOD21 product, since it is not available yet. Two highly homogeneous and quasi-invariant desert sites were selected to carry out the present study. The first one is the White Sands National Monument, located in Tularosa Valley (South-central New Mexico, USA), which is a dune system desert at 1216 m above sea level, with an area of 704 km2 and a maximum dune height of 10 m. The grain size is considered fine sand and the major mineralogy component is gypsum. The second site selected was the Great Sands National Park, located in the San Luis Valley (Colorado, USA). Great Sands is also a sand dune system desert, created from quartz and volcanic fragments derived from Santa Fe and Alamosa formations. The major mineral is quartz, with minor traces of potassium and feldspar. The grain size of the sand is medium to coarse according to the X-Ray Diffraction measurements. Great Sands covers an area of 104 km2 at 2560 m above sea level and the maximum dune height is 230 m. The obtained LSEs and their dependence on azimuth and zenith viewing angles were analyzed, based on series of MODIS scenes from 2010 to 2013. MODTES nadir and off-nadir LSEs showed a good agreement with laboratory emissivity measurements. Results show that band 29 LSE decreases with the zenithal angle up to 0.041 from its nadir value, while LSEs for bands 31 and 32 do not show significant changes with zenith angle.

Spatial and Temporal Variation of Land Surface Temperature in Fujian Province from 2001 TO 2015

NASA Astrophysics Data System (ADS)

Li, Y.; Wang, X.; Ding, Z.

2018-04-01

Land surface temperature (LST) is an essential parameter in the physics of land surface processes. The spatiotemporal variations of LST on the Fujian province were studied using AQUA Moderate Resolution Imaging Spectroradiometer LST data. Considering the data gaps in remotely sensed LST products caused by cloud contamination, the Savitzky-Golay (S-G) filter method was used to eliminate the influence of cloud cover and to describe the periodical signals of LST. Observed air temperature data from 27 weather stations were employed to evaluate the fitting performance of the S-G filter method. Results indicate that S-G can effectively fit the LST time series and remove the influence of cloud cover. Based on the S-G-derived result, Spatial and temporal Variations of LST in Fujian province from 2001 to 2015 are analysed through slope analysis. The results show that: 1) the spatial distribution of annual mean LST generally exhibits consistency with altitude in the study area and the average of LST was much higher in the east than in the west. 2) The annual mean temperature of LST declines slightly among 15 years in Fujian. 3) Slope analysis reflects the spatial distribution characteristics of LST changing trend in Fujian.Improvement areas of LST are mainly concentrated in the urban areas of Fujian, especially in the eastern urban areas. Apparent descent areas are mainly distributed in the area of Zhangzhou and eastern mountain area.

Interannual variations in surface urban heat island intensity and associated drivers in China.

PubMed

Yao, Rui; Wang, Lunche; Huang, Xin; Zhang, Wenwen; Li, Junli; Niu, Zigeng

2018-09-15

The spatial, diurnal and seasonal variations of surface urban heat islands (SUHIs) have been investigated in many places, but we still have limited understanding of the interannual variations of SUHIs and associated drivers. In this study, the interannual variations in SUHI intensity (SUHII, derived from MODIS land surface temperature (LST) data (8-day composites of twice-daily observations), urban LST minus rural) and their relationships with climate variability and urbanization were analyzed in 31 cities in China for the period 2001-2015. Significant increasing trends of SUHII were observed in 71.0%, 58.1%, 25.8% and 54.8% the cities in summer days (SDs), summer nights (SNs), winter days (WDs) and winter nights (WNs), respectively. Pearson's correlation analyses were first performed from a temporal perspective, which were different from a spatial perspective as previous studies. The results showed that the SUHII in SDs and WDs was negatively correlated with the background LST and mean air temperature in most of the cities. The nighttime SUHII in most cities was negatively and positively correlated with total precipitation and total sunshine duration, respectively. Average wind speed has little effect on SUHII. Decreasing vegetation and increased population were the main factors that contributed to the increased SUHII in SDs and SNs, while albedo only influenced the SUHII in WDs. In addition, Pearson's correlation analyses across cities showed that cities with higher decreasing rates of vegetation exhibited higher increasing rates of the SUHII in SDs and WDs. Cities with larger population growth rates do not necessarily have higher increasing rates of SUHII. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cloud tolerance of remote sensing technologies to measure land surface temperature

USDA-ARS?s Scientific Manuscript database

Conventional means to estimate land surface temperature (LST) from space relies on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave (MW) obse...

Characteristics of Surface Urban Heat Island (SUHI) over the Gangetic Plain of Bihar, India

NASA Astrophysics Data System (ADS)

Barat, Archisman; Kumar, Sunny; Kumar, Praveen; Parth Sarthi, P.

2018-05-01

The rapid urbanisation impacts on environment, climate, agriculture, water resources trigger several problems to human beings. The present study is carried out to estimate intensity and trend of Urban Heat Island (UHI) as Surface UHI (SUHI) over towns/cities of the Gangetic plain of the state of Bihar, India, in which urban areas show relatively greater Land Surface Temperature (LST) than its rural surroundings especially during night times. The LST data (2001-14) of Moderate Resolution Imaging Spectroradiometer (MODIS) is used for five major towns/cities of Bihar namely, Bhagalpur, Gaya, Patna, Purnea and Muzzaffarpur. Each city is classified into Urban, Suburban and Rural zones as per land cover of the area. During winter months (January, February, November and December), UHI is more intense over all towns/cities. Mann-Kendall Test is applied on Surface Urban Heat Island Intensity (SUHII) in which MK-Test Statistic (S) shows a significant increasing trend. This trend would alarm a risk to increase in air pollution, heat related biohazards, energy demand in the region. This study shows the need of urban greening and proper town planning over the considered areas to mitigate the changes.

Impacts of urban and industrial development on Arctic land surface temperature in Lower Yenisei River Region.

NASA Astrophysics Data System (ADS)

Li, Z.; Shiklomanov, N. I.

2015-12-01

Urbanization and industrial development have significant impacts on arctic climate that in turn controls settlement patterns and socio-economic processes. In this study we have analyzed the anthropogenic influences on regional land surface temperature of Lower Yenisei River Region of the Russia Arctic. The study area covers two consecutive Landsat scenes and includes three major cities: Norilsk, Igarka and Dudingka. Norilsk industrial region is the largest producer of nickel and palladium in the world, and Igarka and Dudingka are important ports for shipping. We constructed a spatio-temporal interpolated temperature model by including 1km MODIS LST, field-measured climate, Modern Era Retrospective-analysis for Research and Applications (MERRA), DEM, Landsat NDVI and Landsat Land Cover. Those fore-mentioned spatial data have various resolution and coverage in both time and space. We analyzed their relationships and created a monthly spatio-temporal interpolated surface temperature model at 1km resolution from 1980 to 2010. The temperature model then was used to examine the characteristic seasonal LST signatures, related to several representative assemblages of Arctic urban and industrial infrastructure in order to quantify anthropogenic influence on regional surface temperature.

Enhanced spectrophotometric determination of Losartan potassium based on its physicochemical interaction with cationic surfactant

NASA Astrophysics Data System (ADS)

Abdel-Fattah, Laila; Abdel-Aziz, Lobna; Gaied, Mariam

2015-02-01

In this study, a simple and sensitive spectrophotometric method was developed for determination of Losartan potassium (LST K), an angiotensin-II receptor (type AT1) antagonist, in presence of cationic surfactant cetyltrimethylammonium bromide (CTAB). The physicochemical interaction of LST K with CTAB was investigated. The effect of cationic micelles on the spectroscopic and acid-base properties of LST K was studied at pH 7.4. The binding constant (Kb) and the partition coefficient (Kx) of LST K-CTAB were 1.62 × 105 M-1 and 1.38 × 105; respectively. The binding of LST K to CTAB micelles implied a shift in drug acidity constant (ΔpKa = 0.422). The developed method is linear over the range 0.5-28 μg mL-1. The accuracy was evaluated and was found to be 99.79 ± 0.509% and the relative standard deviation for intraday and interday precision was 0.821 and 0.963; respectively. The method was successfully applied to determine LST K in pharmaceutical formulations.

Enhanced spectrophotometric determination of Losartan potassium based on its physicochemical interaction with cationic surfactant.

PubMed

Abdel-Fattah, Laila; Abdel-Aziz, Lobna; Gaied, Mariam

2015-02-05

In this study, a simple and sensitive spectrophotometric method was developed for determination of Losartan potassium (LST K), an angiotensin-II receptor (type AT1) antagonist, in presence of cationic surfactant cetyltrimethylammonium bromide (CTAB). The physicochemical interaction of LST K with CTAB was investigated. The effect of cationic micelles on the spectroscopic and acid-base properties of LST K was studied at pH 7.4. The binding constant (Kb) and the partition coefficient (Kx) of LST K-CTAB were 1.62×10(5) M(-1) and 1.38×10(5); respectively. The binding of LST K to CTAB micelles implied a shift in drug acidity constant (ΔpKa=0.422). The developed method is linear over the range 0.5-28 μg mL(-1). The accuracy was evaluated and was found to be 99.79±0.509% and the relative standard deviation for intraday and interday precision was 0.821 and 0.963; respectively. The method was successfully applied to determine LST K in pharmaceutical formulations. Copyright © 2014 Elsevier B.V. All rights reserved.

Least Square Approach for Estimating of Land Surface Temperature from LANDSAT-8 Satellite Data Using Radiative Transfer Equation

NASA Astrophysics Data System (ADS)

Jouybari-Moghaddam, Y.; Saradjian, M. R.; Forati, A. M.

2017-09-01

Land Surface Temperature (LST) is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE). However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11) and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI) and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE) is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data.

An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake of 11 August 2012

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-09-01

Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.

A comparison of all-weather land surface temperature products

NASA Astrophysics Data System (ADS)

Martins, Joao; Trigo, Isabel F.; Ghilain, Nicolas; Goettche, Frank-M.; Ermida, Sofia; Olesen, Folke-S.; Gellens-Meulenberghs, Françoise; Arboleda, Alirio

2017-04-01

The Satellite Application Facility on Land Surface Analysis (LSA-SAF, http://landsaf.ipma.pt) has been providing land surface temperature (LST) estimates using SEVIRI/MSG on an operational basis since 2006. The LSA-SAF service has since been extended to provide a wide range of satellite-based quantities over land surfaces, such as emissivity, albedo, radiative fluxes, vegetation state, evapotranspiration, and fire-related variables. Being based on infra-red measurements, the SEVIRI/MSG LST product is limited to clear-sky pixels only. Several all-weather LST products have been proposed by the scientific community either based on microwave observations or using Soil-Vegetation-Atmosphere Transfer models to fill the gaps caused by clouds. The goal of this work is to provide a nearly gap-free operational all-weather LST product and compare these approaches. In order to estimate evapotranspiration and turbulent energy fluxes, the LSA-SAF solves the surface energy budget for each SEVIRI pixel, taking into account the physical and physiological processes occurring in vegetation canopies. This task is accomplished with an adapted SVAT model, which adopts some formulations and parameters of the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) model operated at the European Center for Medium-range Weather Forecasts (ECMWF), and using: 1) radiative inputs also derived by LSA-SAF, which includes surface albedo, down-welling fluxes and fire radiative power; 2) a land-surface characterization obtained by combining the ECOCLIMAP database with both LSA-SAF vegetation products and the H(ydrology)-SAF snow mask; 3) meteorological fields from ECMWF forecasts interpolated to SEVIRI pixels, and 4) soil moisture derived by the H-SAF and LST from LSA-SAF. A byproduct of the SVAT model is surface skin temperature, which is needed to close the surface energy balance. The model skin temperature corresponds to the radiative temperature of the interface between soil and atmosphere, which is assumed to have no heat storage. The modelled skin temperatures are in fair agreement with LST directly estimated from SEVIRI observations. However, in contrast to LST retrievals from SEVIRI/MSG (or other infrared sensors) the SVAT model solves the energy budget equation under all-sky conditions. The SVAT surface skin temperature is then used to fill gaps in LST fields caused by clouds. Since under cloudy conditions the direct incoming solar radiation is greatly reduced, thermal balance at the surface is more easily achieved and directional effects are also less important. Therefore, a better performance of the model skin temperature may be expected. In contrast, under clear skies the satellite LST showed to be more reliable, since the SVAT model shows biases in the daily amplitude of the skin temperature. In the context of the GlobTemperature project (http://www.globtemperature.info/), all-weather LST datasets using AMSR-E microwave radiances were produced, which are compared here to the SVAT-based LST. Both products were validated against in situ data - particularly from Gobabeb & Farm Heimat (Namibia), and Évora (Portugal) - to show that under cloudy conditions the agreement between in-situ LST and modelled skin temperature is acceptable. Compared to the SVAT-based LST, AMSR-E LST is closer to satellite observations (level 2 product); the complementarity of the two approaches is assessed.

Spatiotemporal variability in wildfire patterns and analysis of the main drivers in Honduras using GIS and MODIS data

NASA Astrophysics Data System (ADS)

Valdez Vasquez, M. C.; Chen, C. F.

2017-12-01

Wildfires are unrestrained fires in an area of flammable vegetation and they are one of the most frequent disasters in Honduras during the dry season. During this period, anthropogenic activity combined with the harsh climatic conditions, dry vegetation and topographical variables, cause a large amount of wildfires. For this reason, there is a need to identify the drivers of wildfires and their susceptibility variations during the wildfire season. In this study, we combined the wildfire points during the 2010-2016 period every 8 days with a series of variables using the random forest (RF) algorithm. In addition to the wildfire points, we randomly generated a similar amount of background points that we use as pseudo-absence data. To represent the human imprint, we included proximity to different types of roads, trails, settlements and agriculture sites. Other variables included are the Moderate Resolution Imaging Spectra-radiometer (MODIS)-derived 8-day composites of land surface temperature (LST) and the normalized multi-band drought index (NMDI), derived from the MODIS surface reflectance data. We also included monthly average precipitation, solar radiation, and topographical variables. The exploratory analysis of the variables reveals that low precipitation combined with the low NMDI and accessibility to non-paved roads were the major drivers of wildfires during the early months of the dry season. During April, which is the peak of the dry season, the explanatory variables of relevance also included elevation and LST in addition to the proximity to paved and non-paved roads. During May, proximity to crops becomes relevant, in addition to the aforesaid variables. The average estimated area with high and very high wildfire susceptibility was 22% of the whole territory located mainly in the central and eastern regions, drifting towards the northeast areas during May. We validated the results using the area under the receiver operating characteristic (ROC) curve (AUC) for each 8-day period, and the average AUC acquired was acceptable using an independent test data. We suggest that the 8-day frequency spatiotemporal mapping of wildfire patterns and the identification of the most relevant drivers can lead to localized prevention and control actions in specific time-frames in areas of high wildfire susceptibility.

Monitoring Lake Victoria Water Quality from Space: Opportunities for Strengthening Trans-boundary Information Sharing for Effective Resource Management

NASA Astrophysics Data System (ADS)

Mugo, R. M.; Korme, T.; Farah, H.; Nyaga, J. W.; Irwin, D.; Flores, A.; Limaye, A. S.; Artis, G.

2014-12-01

Lake Victoria (LV) is an important freshwater resource in East Africa, covering 68,800 km2, and a catchment that spans 193,000km2. It is an important source of food, energy, drinking and irrigation water, transport and a repository for agricultural, human and industrial wastes generated from its catchment. For such a lake, and a catchment transcending 5 international boundaries, collecting data to guide informed decision making is a hard task. Remote sensing is currently the only tool capable of providing information on environmental changes at high spatio-temporal scales. To address the problem of information availability for LV, we tackled two objectives; (1) we analyzed water quality parameters retrieved from MODIS data, and (2) assessed land cover changes in the catchment area using Landsat data. We used L1A MODIS-Aqua data to retrieve lake surface temperature (LST), total suspended matter (TSM), chlorophyll-a (CHLa) and diffuse attenuation coefficient (KD490) in four temporal periods i.e. daily, weekly, monthly and seasonal scales. An Empirical Orthogonal Function (EOF) analysis was done on monthly data. An analysis of land cover change was done using Landsat data for 3 epochs in order to assess if land degradation contributes to water quality changes. Our results indicate that MODIS-Aqua data provides synoptic views of water quality changes in LV at different temporal scales. The Winam Gulf in Kenya, the shores of Jinja town in Uganda, as well as the Mwanza region in Tanzania represent water quality hotspots due to their relatively high TSM and CHLa concentrations. High levels of KD490 in these areas would also indicate high turbidity and thus low light penetration due to the presence of suspended matter, algal blooms, and/or submerged vegetation. The EOF analysis underscores the areas where LST and water color variability are more significant. The changes can be associated with corresponding land use changes in the catchment, where for instance wetlands are converted to croplands. On-going dissemination of our findings together with capacity building efforts with the three main fishery and research institutions working in the lake, will enable informed decision making for the water management of LV. Enhanced capacity in trans-boundary water resources research is critical for successful decision making.

Validating Satellite-Derived Land Surface Temperature with in Situ Measurements: A Public Health Perspective

PubMed Central

Brines, Shannon J.; Brown, Daniel G.; Dvonch, J. Timothy; Gronlund, Carina J.; Zhang, Kai; Oswald, Evan M.; O’Neill, Marie S.

2013-01-01

Background: Land surface temperature (LST) and percent surface imperviousness (SI), both derived from satellite imagery, have been used to characterize the urban heat island effect, a phenomenon in which urban areas are warmer than non-urban areas. Objectives: We aimed to assess the correlations between LSTs and SI images with actual temperature readings from a ground-based network of outdoor monitors. Methods: We evaluated the relationships among a) LST calculated from a 2009 summertime satellite image of the Detroit metropolitan region, Michigan; b) SI from the 2006 National Land Cover Data Set; and c) ground-based temperature measurements monitored during the same time period at 19 residences throughout the Detroit metropolitan region. Associations between these ground-based temperatures and the average LSTs and SI at different radii around the point of the ground-based temperature measurement were evaluated at different time intervals. Spearman correlation coefficients and corresponding p-values were calculated. Results: Satellite-derived LST and SI values were significantly correlated with 24-hr average and August monthly average ground temperatures at all but two of the radii examined (100 m for LST and 0 m for SI). Correlations were also significant for temperatures measured between 0400 and 0500 hours for SI, except at 0 m, but not LST. Statistically significant correlations ranging from 0.49 to 0.91 were observed between LST and SI. Conclusions: Both SI and LST could be used to better understand spatial variation in heat exposures over longer time frames but are less useful for estimating shorter-term, actual temperature exposures, which can be useful for public health preparedness during extreme heat events. PMID:23777856

Temperature trends in desert cities: how vegetation and urbanization affect the urban heat island dynamics in hyper-arid climates

NASA Astrophysics Data System (ADS)

Marpu, P. R.; Lazzarini, M.; Molini, A.; Ghedira, H.

2013-12-01

Urban areas represent a unique micro-climatic system, mainly characterized by scarcity of vegetation and ground moisture, an albedo strictly dependent on building materials and urban forms, high heat capacity, elevated pollutants emissions, anthropogenic heat production, and a characteristic boundary layer dynamics. For obvious historical reasons, the first to be addressed in the literature were the effects of urbanization on the local microclimate of temperate regions, where most of the urban development took place in the last centuries. Here micro-climatic characteristics all contribute to the warming of urban areas, also known as 'urban heat island' effect, and are expected to crucially impact future energy and water consumption, air quality, and human health. However, rapidly increasing urbanization rates in arid and hyper-arid developing countries could soon require more attention towards studying the effects of urban development on arid climates, which remained mainly unexplored till now. In this talk we investigate the climatology of urban heat islands in seven highly urbanized desert cities based on day and night temporal trends of land surface temperature (LST) and normalized difference vegetation index (NDVI) acquired using MODIS satellite during 2000-2012. Urban and rural areas are distinguished by analyzing the high-resolution temporal variability and averaged monthly values of LST, NDVI and Surface Urban Heat Island (SUHI) for all the seven cities and adjacent sub-urban areas. Different thermal behaviors were observed at the selected sites, also including temperature mitigation and inverse urban heat island, and are here discussed together with detailed analysis of the corresponding trends.

Synergistically combining Optical and Thermal radiative transfer modelswithin the EO-LDAS data assimilation framework to estimate land surfaceand component temperatures from MODIS and Sentinel-3

NASA Astrophysics Data System (ADS)

Timmermans, J.; Gomez-Dans, J. L.; Verhoef, W.; Tol, C. V. D.; Lewis, P.

2017-12-01

Evapotranspiration (ET) cannot be directly measured from space. Instead it relies on modelling approaches that use several land surface parameters (LSP), LAI and LST, in conjunction with meteorological parameters. Such a modelling approach presents two caveats: the validity of the model, and the consistency between the different input parameters. Often this second step is not considered, ignoring that without good inputs no decent output can provided. When LSP- dynamics contradict each other, the output of the model cannot be representative of reality. At present however, the LSPs used in large scale ET estimations originate from different single-sensor retrieval-approaches and even from different satellite sensors. In response, the Earth Observation Land Data Assimilation System (EOLDAS) was developed. EOLDAS uses a multi-sensor approach to couple different satellite observations/types to radiative transfer models (RTM), consistently. It is therefore capable of synergistically estimating a variety of LSPs. Considering that ET is most sensitive to the temperatures of the land surface (components), the goal of this research is to expand EOLDAS to the thermal domain. This research not only focuses on estimating LST, but also on retrieving (soil/vegetation, Sunlit/shaded) component temperatures, to facilitate dual/quad-source ET models. To achieve this, The Soil Canopy Observations of Photosynthesis and Energy (SCOPE) model was integrated into EOLDAS. SCOPE couples key-parameters to key-processes, such as photosynthesis, ET and optical/thermal RT. In this research SCOPE was also coupled to MODTRAN RTM, in order to estimate BOA component temperatures directly from TOA observations. This paper presents the main modelling steps of integrating these complex models into an operational platform. In addition it highlights the actual retrieval using different satellite observations, such as MODIS and Sentinel-3, and meteorological variables from the ERA-Interim.

Use of Land Surface Temperature Observations in a Two-Source Energy Balance Model Towards Improved Monitoring of Evapotranspiration and Drought

NASA Astrophysics Data System (ADS)

Hain, C.; Anderson, M. C.; Otkin, J.; Semmens, K. A.; Zhan, X.; Fang, L.; Li, Z.

2014-12-01

As the world's water resources come under increasing tension due to the dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. However, direct validation of ET models is challenging due to lack of available observations that are sufficiently representative at the model grid scale (10-100 km). Prognostic land-surface models require accurate information about observed precipitation, soil moisture storage, groundwater, and artificial controls on water supply (e.g., irrigation, dams, etc.) to reliably link rainfall to evaporative fluxes. In contrast, diagnostic estimates of ET can be generated, with no prior knowledge of the surface moisture state, by energy balance models using thermal-infrared remote sensing of land-surface temperature (LST) as a boundary condition. One such method, the Atmosphere Land Exchange Inverse (ALEXI) model provides estimates of surface energy fluxes through the use of mid-morning change in LST and radiation inputs. The LST inputs carry valuable proxy information regarding soil moisture and its effect on soil evaporation and canopy transpiration. Additionally, the Evaporative Stress Index (ESI) representing anomalies in the ratio of actual-to-potential ET has shown to be a reliable indicator of drought. ESI maps over the continental US show good correspondence with standard drought metrics and with patterns of precipitation, but can be generated at significantly higher spatial resolution due to a limited reliance on ground observations. Furthermore, ESI is a measure of actual stress rather than potential for stress, and has physical relevance to projected crop development. Because precipitation is not used in construction of the ESI, it provides an independent assessment of drought conditions and has particular utility for real-time monitoring in regions with sparse rainfall data or significant delays in meteorological reporting. An initial analysis of a new prototype global ALEXI system using twice-daily observations of MODIS LST will be presented. The newly generated global ET and ESI datasets will be compared to other globally available ET and drought products during a multi-year evaluation period (2000-2013).

Drought Risk Assessment based on Natural and Social Factors

NASA Astrophysics Data System (ADS)

Huang, Jing; Wang, Huimin; Han, Dawei

2015-04-01

In many parts of the world, drought hazard is becoming more frequent and severe due to climate change and human activities. It is crucial to monitor and assess drought conditions, especially for decision making support in agriculture sector. The vegetation index (VI) decreases, and the land surface temperature (LST) increases when the vegetation is under drought stress. Therefore both of these remotely sensed indices are widely used in drought monitoring and assessment. Temperature-Vegetation Dryness Index (TVDI) is obtained by establishing the feature space of the normalized difference vegetation index (NDVI) and LST, which reflects agriculture dry situation by inverting soil moisture. However, these indices only concern the natural hazard-causing factors. Our society is a complex large-scale system with various natural and social elements. The drought risk is the joint consequence of hazard-causing factors and hazard-affected bodies. For example, as the population increases, the exposure of the hazard-affected bodies also tends to increase. The high GDP enhances the response ability of government, and the irrigation and water conservancy reduces the vulnerability. Such characteristics of hazard-affected bodies should be coupled with natural factors. In this study, the 16-day moderate-resolution imaging spectroradiometer (MODIS) NDVI and LST data are combined to establish NDVI-Ts space according to different land use types in Yunnan Province, China. And then, TVDIs are calculated through dry and wet edges modeled as a linear fit to data for each land cover type. Next, the efforts are turned to establish an integrated drought assessment index of social factors and TVDI through ascertaining attribute weight based on rough sets theory. Thus, the new CDI (comprehensive drought index) recorded during spring of 2010 and the spatial variations in drought are analyzed and compared with TVDI dataset. Moreover, actual drought risk situation in the study area is given to verify the effectiveness of the CDI. In addition, GIS is applied to provide geographically referenced information, i.e. information involving location, elevation, land use, water resources distance and so on, which are essential inputs for spatial analysis in drought risk assessment. On the whole, this study has proposed a new idea on drought risk assessment integrating natural factors with social factors, as well as providing a real-time drought monitoring method in a social context.

Remote Sensing of the Urban Heat Island Effect Across Biomes in the Continental USA

NASA Technical Reports Server (NTRS)

Imhoff, Marc L.; Zhang, Ping; Wolfe, Robert E.; Bounoua, Lahouari

2010-01-01

Impervious surface area (ISA) from the Landsat TM-based NLCD 2001 dataset and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined for each urban area emanating outward from the urban core to the nonurban rural areas nearby and used to stratify sampling for land surface temperatures and NDVI. Sampling is further constrained by biome and elevation to insure objective intercomparisons between zones and between cities in different biomes permitting the definition of hierarchically ordered zones that are consistent across urban areas in different ecological setting and across scales. We find that ecological context significantly influences the amplitude of summer daytime UHI (urban-rural temperature difference) the largest (8 C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9 C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.3 C temperature difference in summer and only 1.3 C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic "U-shaped" horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.

Pre-seismic anomalies in remotely sensed land surface temperature measurements: The case study of 2003 Boumerdes earthquake

NASA Astrophysics Data System (ADS)

Bellaoui, Mebrouk; Hassini, Abdelatif; Bouchouicha, Kada

2017-05-01

Detection of thermal anomaly prior to earthquake events has been widely confirmed by researchers over the past decade. One of the popular approaches for anomaly detection is the Robust Satellite Approach (RST). In this paper, we use this method on a collection of six years of MODIS satellite data, representing land surface temperature (LST) images to predict 21st May 2003 Boumerdes Algeria earthquake. The thermal anomalies results were compared with the ambient temperature variation measured in three meteorological stations of Algerian National Office of Meteorology (ONM) (DELLYS-AFIR, TIZI-OUZOU, and DAR-EL-BEIDA). The results confirm the importance of RST as an approach highly effective for monitoring the earthquakes.

Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

NASA Astrophysics Data System (ADS)

Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

2016-06-01

The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

Spatiotemporal variations in the difference between satellite-observed daily maximum land surface temperature and station-based daily maximum near-surface air temperature

NASA Astrophysics Data System (ADS)

Lian, Xu; Zeng, Zhenzhong; Yao, Yitong; Peng, Shushi; Wang, Kaicun; Piao, Shilong

2017-02-01

There is an increasing demand to integrate land surface temperature (LST) into climate research due to its global coverage, which requires a comprehensive knowledge of its distinctive characteristics compared to near-surface air temperature (Tair). Using satellite observations and in situ station-based data sets, we conducted a global-scale assessment of the spatial and seasonal variations in the difference between daily maximum LST and daily maximum Tair (δT, LST - Tair) during 2003-2014. Spatially, LST is generally higher than Tair over arid and sparsely vegetated regions in the middle-low latitudes, but LST is lower than Tair in tropical rainforests due to strong evaporative cooling, and in the high-latitude regions due to snow-induced radiative cooling. Seasonally, δT is negative in tropical regions throughout the year, while it displays a pronounced seasonality in both the midlatitudes and boreal regions. The seasonality in the midlatitudes is a result of the asynchronous responses of LST and Tair to the seasonal cycle of radiation and vegetation abundance, whereas in the boreal regions, seasonality is mainly caused by the change in snow cover. Our study identified substantial spatial heterogeneity and seasonality in δT, as well as its determinant environmental drivers, and thus provides a useful reference for monitoring near-surface air temperature changes using remote sensing, particularly in remote regions.

Validation of Land Surface Temperature from Sentinel-3

NASA Astrophysics Data System (ADS)

Ghent, D.

2017-12-01

One of the main objectives of the Sentinel-3 mission is to measure sea- and land-surface temperature with high-end accuracy and reliability in support of environmental and climate monitoring in an operational context. Calibration and validation are thus key criteria for operationalization within the framework of the Sentinel-3 Mission Performance Centre (S3MPC). Land surface temperature (LST) has a long heritage of satellite observations which have facilitated our understanding of land surface and climate change processes, such as desertification, urbanization, deforestation and land/atmosphere coupling. These observations have been acquired from a variety of satellite instruments on platforms in both low-earth orbit and in geostationary orbit. Retrieval accuracy can be a challenge though; surface emissivities can be highly variable owing to the heterogeneity of the land, and atmospheric effects caused by the presence of aerosols and by water vapour absorption can give a bias to the underlying LST. As such, a rigorous validation is critical in order to assess the quality of the data and the associated uncertainties. Validation of the level-2 SL_2_LST product, which became freely available on an operational basis from 5th July 2017 builds on an established validation protocol for satellite-based LST. This set of guidelines provides a standardized framework for structuring LST validation activities. The protocol introduces a four-pronged approach which can be summarised thus: i) in situ validation where ground-based observations are available; ii) radiance-based validation over sites that are homogeneous in emissivity; iii) intercomparison with retrievals from other satellite sensors; iv) time-series analysis to identify artefacts on an interannual time-scale. This multi-dimensional approach is a necessary requirement for assessing the performance of the LST algorithm for the Sea and Land Surface Temperature Radiometer (SLSTR) which is designed around biome-based coefficients, thus emphasizing the importance of non-traditional forms of validation such as radiance-based techniques. Here we present examples of the ongoing routine application of the protocol to operational Sentinel-3 LST data.

Satellite-observed snow cover variations over the Tibetan Plateau for the period 2001-2014

NASA Astrophysics Data System (ADS)

Long, D.; Chen, X.

2016-12-01

Snow is an integral component of the global climate system. Owing to its high albedo and thermal and water storage properties, snow has important linkages and feedbacks through its influence on surface energy and moisture fluxes, clouds, precipitation, hydrology, and atmospheric circulation. As the "Roof of the World" and the "Third Pole" with the highest mountains in middle latitudes, the Tibetan Plateau (TP) is one of the most hot spots in climate change and hydrological studies, in which seasonal snow cover is a critical aspect. Unlike large-scale snow cover and regional-scale glaciers over other cryospheric regions, changes in snow cover over the TP has been largely unknown due mostly to the quality of observations. Based on improved MODIS daily snow cover products, this study aims to quantify the distribution and changes in snow cover over the TP for the period 2001 to 2014. Results show that the spatial distribution of changes in snow cover fraction (SCF) over the 14-year study period exhibited a general negative trend over the TP driven primarily by increasing land surface temperature (LST), except some areas of the upper Golden-Sanded River and upper Brahmaputra River basins. However, decreased LST and increased precipitation in the accumulation season (September to the following February) resulted in increased SCF in the accumulation season, coinciding with large-scale cold snaps and heavy snowfall events at middle latitudes. Detailed analyses of the intra-annual variability of SCF in the TP regions show an increase in SCF in the accumulation season but a decrease in SCF in the melting season (March to August), indicating that the intra-annual amplitude of SCF increased during the study period and more snow cover was released as snowmelt in the spring season.

Chemical compatibility and properties of suspension plasma-sprayed SrTiO3-based anodes for intermediate-temperature solid oxide fuel cells

NASA Astrophysics Data System (ADS)

Zhang, Shan-Lin; Li, Cheng-Xin; Li, Chang-Jiu

2014-10-01

La-doped strontium titanate (LST) is a promising, redox-stable perovskite material for direct hydrocarbon oxidation anodes in intermediate-temperature solid oxide fuel cells (IT-SOFCs). In this study, nano-sized LST and Sm-doped ceria (SDC) powders are produced by the sol-gel and glycine-nitrate processes, respectively. The chemical compatibility between LST and electrolyte materials is studied. A LST-SDC composite anode is prepared by suspension plasma spraying (SPS). The effects of annealing conditions on the phase structure, microstructure, and chemical stability of the LST-SDC composite anode are investigated. The results indicate that the suspension plasma-sprayed LST-SDC anode has the same phase structure as the original powders. LST exhibits a good chemical compatibility with SDC and Mg/Sr-doped lanthanum gallate (LSGM). The anode has a porosity of ∼40% with a finely porous structure that provides high gas permeability and a long three-phase boundary for the anode reaction. Single cells assembled with the LST-SDC anode, La0.8Sr0.2Ga0.8Mg0.2O3 electrolyte, and La0.8Sr0.2CoO3-SDC cathode show a good performance at 650-800 °C. The annealing reduces the impedances due to the enhancement in the bonding between the particles in the anode and interface of anode and LSGM electrolyte, thus improving the output performance of the cell.

Exploring the Influence of Impervious Surface Density and Shape on Urban Heat Islands in the Northeast USA Using MODIS and Landsat

NASA Technical Reports Server (NTRS)

Zhang, Ping; Imhoff, Marc L.; Bounoua, Lahouri; Wolfe, Robert E.

2011-01-01

Impervious surface area (ISA) from the National Land Cover Database (NLCD) 2001 and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) signature and its relationship to settlement size and shape, development intensity distribution, and land cover composition for 42 urban settlements embedded in forest biomes in the Northeastern United States. Development intensity zones, based on percent ISA, are defined for each urban area emanating outward from the urban core to nearby rural areas and are used to stratify land surface temperature. The stratification is further constrained by biome type and elevation to insure objective intercomparisons between urban zones within an urban settlement and between settlements. Stratification based on ISA allows the definition of hierarchically ordered urban zones that are consistent across urban settlements and scales. In addition to the surrounding ecological context, we find that the settlement size and shape as well as the development intensity distribution significantly influence the amplitude of summer daytime UHI. Within the Northeastern US temperate broadleaf mixed forest, UHI magnitude is positively related to the logarithm of the urban area size. Our study indicates that for similar urban area sizes, the development intensity distribution is one of the major drivers of UHI. In addition to urban area size and development intensity distribution, this analysis shows that both the shape of the urban area and the land cover composition in the surrounding rural area play an important role in modulating the UHI magnitude in different urban settlements. Our results indicate that remotely sensed urban area size and shape as well as the development intensity distribution influence UHI amplitude across regional scales.

Reevaluating leishmanin skin test as a marker for immunity against cutaneous leishmaniasis.

PubMed

Momeni Boroujeni, Amir; Aminjavaheri, Malih; Moshtaghian, Bahador; Momeni, Arash; Momeni, Ali Z

2013-07-01

The leishmanin skin test (LST) has been used for clinical diagnosis of leishmaniasis and epidemiological studies of the disease. Thus far, evidence has suggested that LST conversion indicates a degree of protection against leishmaniasis. In this study, we have put this assumption to test. A total of 273 participants with positive LST living in a hyperendemic area for leishmaniasis were followed for three years for any occurrence of cutaneous leishmaniasis. Twenty-two of the 273 participants contracted leishmaniasis during the 3-year follow-up. These new cases included participants who had a previous history of active disease, those who had a history of leishmanization, or those who were suspected of having a history of subclinical infection. In this study, the incidence of leishmaniasis in individuals with positive LST was close to the general incidence of the disease in the same hyperendemic area. These results suggest that although LST conversion may be a marker for partial immunity towards leishmaniasis, it may not, however, indicate complete protection against the disease, and consequently there is a need for revision of current vaccine development approaches which are based on rendering vaccinated individuals LST positive. © 2013 The International Society of Dermatology.

Spatiotemporal Variations in the Difference between Satellite-observed Land Surface Temperature and Station-based Near-surface Air Temperature

NASA Astrophysics Data System (ADS)

Lian, X.

2016-12-01

There is an increasing demand to integrate land surface temperature (LST) into climate research due to its global coverage, which requires a comprehensive knowledge of its distinctive characteristics compared to near-surface air temperature ( ). Using satellite observations and in-situ station-based datasets, we conducted a global-scale assessment of the spatial, seasonal, and interannual variations in the difference between daytime maximum LST and daytime maximum ( , LST - ) during 2003-2014. Spatially, LST is generally higher than over arid and sparsely vegetated regions in the mid-low latitudes, but LST is lower than in the tropical rainforests due to strong evaporative cooling, and in the high-latitude regions due to snow-induced radiative cooling. Seasonally, is negative in tropical regions throughout the year, while it displays a pronounced seasonality in both the mid-latitudes and boreal regions. The seasonality in the mid-latitudes is a result of the asynchronous responses of LST and to the seasonal cycle of radiation and vegetation abundance, whereas in the boreal regions, seasonality is mainly caused by the change in snow cover. At an interannual scale, only a small proportion of the land surface displays a statistically significant trend (P <0.05) due to the short time span of current measurements. Our study identified substantial spatial heterogeneity and seasonality in , as well as its determinant environmental drivers, and thus provides a useful reference for monitoring near-surface temperature changes using remote sensing, particularly in remote regions.

Synegies Between Visible/Near-Infrared Imaging Spectrometry and the Thermal Infrared in an Urban Environment: An Evaluation of the Hyperspectral Infrared Imager (HYSPIRI) Mission

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Quattrochi, Dale A.; Hulley, Glynn C.; Hook, Simon J.; Green, Robert O.

2012-01-01

A majority of the human population lives in urban areas and as such, the quality of urban environments is becoming increasingly important to the human population. Furthermore, these areas are major sources of environmental contaminants and sinks of energy and materials. Remote sensing provides an improved understanding of urban areas and their impacts by mapping urban extent, urban composition (vegetation and impervious cover fractions), and urban radiation balance through measures of albedo, emissivity and land surface temperature (LST). Recently, the National Research Council (NRC) completed an assessment of remote sensing needs for the next decade (NRC, 2007), proposing several missions suitable for urban studies, including a visible, near-infrared and shortwave infrared (VSWIR) imaging spectrometer and a multispectral thermal infrared (TIR) instrument called the Hyperspectral Infrared Imagery (HyspIRI). In this talk, we introduce the HyspIRI mission, focusing on potential synergies between VSWIR and TIR data in an urban area. We evaluate potential synergies using an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS-ASTER (MASTER) image pair acquired over Santa Barbara, United States. AVIRIS data were analyzed at their native spatial resolutions (7.5m VSWIR and 15m TIR), and aggregated 60 m spatial resolution similar to HyspIRI. Surface reflectance was calculated using ACORN and a ground reflectance target to remove atmospheric and sensor artifacts. MASTER data were processed to generate estimates of spectral emissivity and LST using Modtran radiative transfer code and the ASTER Temperature Emissivity Separation algorithm. A spectral library of common urban materials, including urban vegetation, roofs and roads was assembled from combined AVIRIS and field-measured reflectance spectra. LST and emissivity were also retrieved from MASTER and reflectance/emissivity spectra for a subset of urban materials were retrieved from co-located MASTER and AVIRIS pixels. Fractions of Impervious, Soil, Green Vegetation (GV) and Non-photosynthetic Vegetation (NPV), were estimated using Multiple Endmember Spectral Mixture Analysis (MESMA) applied to AVIRIS data at 7.5, 15 and 60 m spatial scales. Surface energy parameters, including albedo, vegetation cover fraction, broadband emissivity and LST were also determined for urban and natural land-cover classes in the region. Fractions were validated using 1m digital photography.

Detecting Changes of Thermal Environment over the Bohai Coastal Region by Spectral Change Vector Analysis

NASA Astrophysics Data System (ADS)

Hu, Y.; Jia, G.

2009-12-01

Change vector analysis (CVA) is an effective approach for detecting and characterizing land-cover change by comparing pairs of multi-spectral and multi-temporal datasets over certain area derived from various satellite platforms. NDVI is considered as an effective detector for biophysical changes due to its sensitivity to red and near infrared signals, while land surface temperature (LST) is considered as a valuable indicator for changes of ground thermal conditions. Here we try to apply CVA over satellite derived LST datasets to detect changes of land surface thermal properties parallel to climate change and anthropogenic influence in a city cluster since 2001. In this study, monthly land surface temperature datasets from 2001-2008 derived from MODIS collection 5 were used to examine change pattern of thermal environment over the Bohai coastal region by using spectral change vector analysis. The results from principle component analysis (PCA) for LST show that the PC 1-3 contain over 80% information on monthly variations and these PCA components represent the main processes of land thermal environment change over the study area. Time series of CVA magnitude combined with land cover information show that greatest change occurred in urban and heavily populated area, featured with expansion of urban heat island, while moderate change appeared in grassland area in the north. However few changes were observed over large plain area and forest area. Strong signals also are related to economy level and especially the events of surface cover change, such as emergence of railway and port. Two main processes were also noticed about the changes of thermal environment. First, weak signal was detected in mostly natural area influenced by interannual climate change in temperate broadleaf forest area. Second, land surface temperature changes were controlled by human activities as 1) moderate change of LST happened in grassland influenced by grazing and 2) urban heat island was intensifier in major cities, such as Beijing and Tianjin. Further, the continual drier climate combined with human actions over past fifties years have intensified land thermal pattern change and the continuation will be an important aspects to understand land surface processes and local climate change. Land surface temperature trends from 2000-2008 over the Bohai coastal region

A novel recursive Fourier transform for nonuniform sampled signals: application to heart rate variability spectrum estimation.

PubMed

Holland, Alexander; Aboy, Mateo

2009-07-01

We present a novel method to iteratively calculate discrete Fourier transforms for discrete time signals with sample time intervals that may be widely nonuniform. The proposed recursive Fourier transform (RFT) does not require interpolation of the samples to uniform time intervals, and each iterative transform update of N frequencies has computational order N. Because of the inherent non-uniformity in the time between successive heart beats, an application particularly well suited for this transform is power spectral density (PSD) estimation for heart rate variability. We compare RFT based spectrum estimation with Lomb-Scargle Transform (LST) based estimation. PSD estimation based on the LST also does not require uniform time samples, but the LST has a computational order greater than Nlog(N). We conducted an assessment study involving the analysis of quasi-stationary signals with various levels of randomly missing heart beats. Our results indicate that the RFT leads to comparable estimation performance to the LST with significantly less computational overhead and complexity for applications requiring iterative spectrum estimations.

Indonesian drought monitoring from space. A report of SAFE activity: Assessment of drought impact on rice production in Indonesia by satellite remote sensing and dissemination with web-GIS

NASA Astrophysics Data System (ADS)

Shofiyati, Rizatus; Takeuchi, Wataru; Sofan, Parwati; Darmawan, Soni; Awaluddin; Supriatna, Wahyu

2014-06-01

Long droughts experienced in Indonesia in the past are identified as one of the main factors in the failure of rice production. In this regard, special attention to monitor the condition is encouraged to reduce the damage. Currently, various satellite data and approaches can withdraw valuable information for monitoring and anticipating drought hazards. Two types of drought, Meteorology and Agriculture, have been assessed. During the last 10 years, daily and monthly rainfall data derived from TRMM and GSMaP. MTSAT and AMSR-E data have been analyzed to identify meteorological drought. Agricultural drought has been studied by observing the character of some indices (EVI, VCI, VHI, LST, and NDVI) of sixteen-day and monthly MODIS data at a period of 5 years (2009 - 2013). Network for data transfer has been built between LAPAN (data provider), ICALRD (implementer), IAARD Cloud Computing, and University of Tokyo (technical supporter). A Web-GIS based Drought Monitoring Information System has been developed to disseminate the information to end users. This paper describes the implementation of remote sensing drought monitoring model and development of Web-GIS and satellite based information system.

Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities.

PubMed

Morabito, Marco; Crisci, Alfonso; Gioli, Beniamino; Gualtieri, Giovanni; Toscano, Piero; Di Stefano, Valentina; Orlandini, Simone; Gensini, Gian Franco

2015-01-01

Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks. Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥ 65). A long time-series (2001-2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using "Crichton's Risk Triangle" hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI). The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities. This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat-related emergencies.

Urban-Hazard Risk Analysis: Mapping of Heat-Related Risks in the Elderly in Major Italian Cities

PubMed Central

Morabito, Marco; Crisci, Alfonso; Gioli, Beniamino; Gualtieri, Giovanni; Toscano, Piero; Di Stefano, Valentina; Orlandini, Simone; Gensini, Gian Franco

2015-01-01

Background Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks. Objectives Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥65). Methods A long time-series (2001–2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using “Crichton’s Risk Triangle” hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI). Results The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities. Conclusions This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat-related emergencies. PMID:25985204

Unmanned airborne thermal and mutilspectral imagery for estimating evapotranspiration in irrigated vineyards

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature (LST) provides valuable information for quantifying rootzone water availability, evapotranspiration (ET) and crop condition. This paper describes the most recent modifications applied to the robust but relatively simple LST-based energy bal...

A Physically-Based Drought Product Using Thermal Remote Sensing of Evapotranspiration

USDA-ARS?s Scientific Manuscript database

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonst...

Estimation of Mangrove Net Primary Production and Carbon Sequestration service using Light Use Efficiency model in the Sunderban Biosphere region, India

NASA Astrophysics Data System (ADS)

Sannigrahi, Srikanta; Sen, Somnath; Paul, Saikat

2016-04-01

Net Primary Production (NPP) of mangrove ecosystem and its capacity to sequester carbon from the atmosphere may be used to quantify the regulatory ecosystem services. Three major group of parameters has been set up as BioClimatic Parameters (BCP): (Photosynthetically Active Radiation (PAR), Absorbed PAR (APAR), Fraction of PAR (FPAR), Photochemical Reflectance Index (PRI), Light Use Efficiency (LUE)), BioPhysical Parameters (BPP) :(Normalize Difference Vegetation Index (NDVI), scaled NDVI, Enhanced Vegetation Index (EVI), scaled EVI, Optimised and Modified Soil Adjusted Vegetation Index (OSAVI, MSAVI), Leaf Area Index (LAI)), and Environmental Limiting Parameters (ELP) (Temperature Stress (TS), Land Surface Water Index (LSWI), Normalize Soil Water Index (NSWI), Water Stress Scalar (WS), Inversed WS (iWS) Land Surface Temperature (LST), scaled LST, Vapor Pressure Deficit (VPD), scaled VPD, and Soil Water Deficit Index (SWDI)). Several LUE models namely Carnegie Ames Stanford Approach (CASA), Eddy Covariance - LUE (EC-LUE), Global Production Efficiency Model (GloPEM), Vegetation Photosynthesis Model (VPM), MOD NPP model, Temperature and Greenness Model (TG), Greenness and Radiation model (GR) and MOD17 was adopted in this study to assess the spatiotemporal nature of carbon fluxes. Above and Below Ground Biomass (AGB & BGB) was calculated using field based estimation of OSAVI and NDVI. Microclimatic zonation has been set up to assess the impact of coastal climate on environmental limiting factors. MODerate Resolution Imaging Spectroradiometer (MODIS) based yearly Gross Primary Production (GPP) and NPP product MOD17 was also tested with LUE based results with standard model validation statistics: Root Mean Square of Error (RMSE), Mean Absolute Error (MEA), Bias, Coefficient of Variation (CV) and Coefficient of Determination (R2). The performance of CASA NPP was tested with the ground based NPP with R2 = 0.89 RMSE = 3.28 P = 0.01. Among the all adopted models, EC-LUE and VPM models has explained the maximum variances (>80%) in comparison to the other model. Study result has also showed that the BPP has explained the maximum model variances (>93%) followed by BCP (>65%) and ELP (>50%). Scaled WS, iWS, LST, VPD, NDVI was performed better in a minimum ELP condition whereas surface moisture and wetness was highly correlated with the AGB and NPP (R2 = 0.86 RMSE = 1.83). During this study period (2000-2013), it was found that there was a significantly declining trend (R2 = 0.32 P = 0.05) of annual NPP and the maximum decrease was found in the eastern part where built-up area was mainly accounted for reduction of NPP. BCP are explained higher variances (>80%) in the optimum climatic condition exist along the coastal stretches in comparison to the landward extent (>45%).

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

NASA Astrophysics Data System (ADS)

Huang, Ni; Gu, Lianhong; Black, T. Andrew; Wang, Li; Niu, Zheng

2015-11-01

Soil respiration (Rs), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual Rs at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual Rs estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zone soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites. In addition, a multicollinearity problem among LST-night, root-zone soil moisture, and plant photosynthesis factor was effectively avoided by selecting the LST-night-driven model. Cross validation showed that temporal variation in Rs was captured by the LST-night-driven model with a mean absolute error below 1 µmol CO2 m-2 s-1 at both forest sites. An obvious overestimation that occurred in 2005 and 2007 at the Missouri Ozark site reduced the evaluation accuracy of cross validation because of summer drought. However, no significant difference was found between the Arrhenius-type function driven by LST-night and the function considering LST-night and root-zone soil moisture. This finding indicated that the contribution of soil moisture to Rs was relatively small at our multiyear data set. To predict intersite Rs, maximum leaf area index (LAImax) was used as an upscaling factor to calibrate the site-specific reference respiration rates. Independent validation demonstrated that the model incorporating LST-night and LAImax efficiently predicted the spatial and temporal variabilities of Rs. Based on the Arrhenius-type function using LST-night as an input parameter, the rates of annual C release from Rs were 894-1027 g C m-2 yr-1 at the BC-Campbell River 1949 Douglas-fir site and 818-943 g C m-2 yr-1 at the Missouri Ozark site. The ratio between annual Rs estimates based on remotely sensed data and the total annual ecosystem respiration from eddy covariance measurements fell within the range reported in previous studies. Our results demonstrated that estimating annual Rs based on remote sensing data products was possible at deciduous and evergreen forest sites.

Controlled Donation After Circulatory Determination of Death.

PubMed

Dalle Ave, Anne L; Shaw, David M

2017-03-01

Controlled donation after circulatory determination of death (cDCDD) concerns donation after withdrawal of life-sustaining therapy (W-LST). We examine the ethical issues raised by W-LST in the cDCDD context in the light of a review of cDCDD protocols and the ethical literature. Our analysis confirms that W-LST procedures vary considerably among cDCDD centers and that despite existing recommendations, the conflict of interest in the W-LST decision and process might be difficult to avoid, the process of W-LST might interfere with usual end-of-life care, and there is a risk of hastening death. In order to ensure that the practice of W-LST meets already well-established ethical recommendations, we suggest that W-LST should be managed in the ICU by an ICU physician who has been part of the W-LST decision. Recommending extubation for W-LST, when this is not necessarily the preferred procedure, is inconsistent with the recommendation to follow usual W-LST protocol. As the risk of conflicts of interest in the decision of W-LST and in the process of W-LST exists, this should be acknowledged and disclosed. Finally, when cDCDD programs interfere with W-LST and end-of-life care, this should be transparently disclosed to the family, and specific informed consent is necessary.

An effective approach for gap-filling continental scale remotely sensed time-series

PubMed Central

Weiss, Daniel J.; Atkinson, Peter M.; Bhatt, Samir; Mappin, Bonnie; Hay, Simon I.; Gething, Peter W.

2014-01-01

The archives of imagery and modeled data products derived from remote sensing programs with high temporal resolution provide powerful resources for characterizing inter- and intra-annual environmental dynamics. The impressive depth of available time-series from such missions (e.g., MODIS and AVHRR) affords new opportunities for improving data usability by leveraging spatial and temporal information inherent to longitudinal geospatial datasets. In this research we develop an approach for filling gaps in imagery time-series that result primarily from cloud cover, which is particularly problematic in forested equatorial regions. Our approach consists of two, complementary gap-filling algorithms and a variety of run-time options that allow users to balance competing demands of model accuracy and processing time. We applied the gap-filling methodology to MODIS Enhanced Vegetation Index (EVI) and daytime and nighttime Land Surface Temperature (LST) datasets for the African continent for 2000–2012, with a 1 km spatial resolution, and an 8-day temporal resolution. We validated the method by introducing and filling artificial gaps, and then comparing the original data with model predictions. Our approach achieved R2 values above 0.87 even for pixels within 500 km wide introduced gaps. Furthermore, the structure of our approach allows estimation of the error associated with each gap-filled pixel based on the distance to the non-gap pixels used to model its fill value, thus providing a mechanism for including uncertainty associated with the gap-filling process in downstream applications of the resulting datasets. PMID:25642100

The urban heat island in Rio de Janeiro, Brazil, in the last 30 years using remote sensing data

NASA Astrophysics Data System (ADS)

Peres, Leonardo de Faria; Lucena, Andrews José de; Rotunno Filho, Otto Corrêa; França, José Ricardo de Almeida

2018-02-01

The aim of this work is to study urban heat island (UHI) in Metropolitan Area of Rio de Janeiro (MARJ) based on the analysis of land-surface temperature (LST) and land-use patterns retrieved from Landsat-5/Thematic Mapper (TM), Landsat-7/Enhanced Thematic Mapper Plus (ETM+) and Landsat-8/Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) data covering a 32-year period between 1984 and 2015. LST temporal evolution is assessed by comparing the average LST composites for 1984-1999 and 2000-2015 where the parametric Student t-test was conducted at 5% significance level to map the pixels where LST for the more recent period is statistically significantly greater than the previous one. The non-parametric Mann-Whitney-Wilcoxon rank sum test has also confirmed at the same 5% significance level that the more recent period (2000-2015) has higher LST values. UHI intensity between ;urban; and ;rural/urban low density; (;vegetation;) areas for 1984-1999 and 2000-2015 was established and confirmed by both parametric and non-parametric tests at 1% significance level as 3.3 °C (5.1 °C) and 4.4 °C (7.1 °C), respectively. LST has statistically significantly (p-value < 0.01) increased over time in two of three land cover classes (;urban; and ;urban low density;), respectively by 1.9 °C and 0.9 °C, except in ;vegetation; class. A spatial analysis was also performed to identify the urban pixels within MARJ where UHI is more intense by subtracting the LST of these pixels from the LST mean value of ;vegetation; land-use class.

Preparation, characterization and efficacy of lysostaphin-chitosan gel against Staphylococcus aureus.

PubMed

Nithya, Sai; Nimal, T R; Baranwal, Gaurav; Suresh, Maneesha K; C P, Anju; Anil Kumar, V; Gopi Mohan, C; Jayakumar, R; Biswas, Raja

2018-04-15

Lysostaphin (LST) is a bacteriocin that cleaves within the pentaglycine cross bridge of Staphylococcus aureus peptidoglycan. Previous studies have reported the high efficiency of LST even against multi drug resistant S. aureus including methicillin resistant S. aureus (MRSA). In this study, we have developed a new chitosan based hydrogel formulation of LST to exploit its anti-staphylococcal activity. The atomic interactions of LST with chitosan were studied by molecular docking studies. The rheology and the antibacterial properties of the developed LSTC gel were evaluated. The developed LST containing chitosan hydrogel (LSTC gel) was flexible, flows smoothly and remains stable at physiological temperature. The in vitro studies by agar well diffusion and ex vivo studies in porcine skin model exhibited a reduction in S. aureus survival by ∼3 Log 10 CFU/mL in the presence of LSTC gel. The cytocompatibility of the gel was tested in vitro using macrophage RAW 264.7 cell line and in vivo in Drosophila melanogaster. A gradual disruption of S. aureus biofilms with the increase of LST concentrations in the LSTC gel was observed which was confirmed by SEM analysis. We conclude that LSTC gel could be highly effectual and advantageous over antibiotics in treating staphylococcal-topical and biofilm infections. Copyright © 2018 Elsevier B.V. All rights reserved.

Microwave implementation of two-source energy balance approach for estimating evapotranspiration

USDA-ARS?s Scientific Manuscript database

A newly developed microwave (MW) land surface temperature (LST) product is used to effectively substitute thermal infrared (TIR) based LST in the two-source energy balance approach (TSEB) for estimating ET from space. This TSEB land surface scheme, used in the Atmosphere Land Exchange Inverse (ALEXI...

Utilization of remote sensing data on meteorological and vegetation characteristics for modeling water and heat regimes of large agricultural region

NASA Astrophysics Data System (ADS)

Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena

2016-04-01

Presently, physical-mathematical models such as SVAT (Soil-Vegetation-Atmosphere-Transfer) developed with varying degrees of detail are one of the most effective tools to evaluate the characteristics of the water and heat regimes of vegetation covered territories. The produced SVAT model is designed to calculate the soil water content, evapotranspiration (evaporation from bare soil and transpiration), infiltration of water into the soil, vertical latent and sensible heat fluxes and other water and heat regime characteristics as well as vegetation and soil surface temperatures and the temperature and soil moisture distributions in depth. The model is adapted to satellite-derived estimates of precipitation, land surface temperatures and vegetation cover characteristics. The case study has been carried out for the located in the forest-steppe zone territory of part of the agricultural Central Black Earth Region of Russia with coordinates 49° 30'-54° N and 31° -43° E and area of 227 300 km2 for years 2011-2014 vegetation seasons. The soil and vegetation characteristics are used as the model parameters and the meteorological characteristics are considered to be input variables. These values have been obtained from ground-based observations and satellite-based measurements by radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua, SEVIRI/MSG-2,-3 (Meteosat-9, -10). To provide the retrieval of meteorological and vegetation cover characteristics the new and pre-existing methods and technologies of above radiometer thematic processing data have been developed or refined. From AVHRR data there have been derived estimates of precipitation P, efficient land surface temperature (LST) Ts.eff and emissivity E, surface-air temperature at a level of vegetation cover Ta, normalized difference vegetation index NDVI, leaf area index LAI and vegetation cover fraction B. The remote sensing products LST Tls, E, NDVI, LAI derived from MODIS data and covering the study area have been downloaded from LP DAAC web-site for the same vegetation seasons. The SEVIRI data have been used to retrieve P (every three hours and daily), Tls, E, Ta (at daylight and nighttime), LAI, and B (daily). All named technologies have been adapted to the territory of interest. To verify exactness of assessing AVHRR- and MODIS-based LST (Ts.eff, Ta and Tls) the error statistics of their derivation has been investigated for various samples using comparison with in-situ measurements during the all considered vegetation seasons. When developing the method to derive LST from the SEVIRI data its validation has been carried out through comparison of given Tls retrievals with independent collocated Tls estimates generated at LSA SAF (Lisbon, Portugal).The later check of SEVIRI-derived Tls and Ta estimates has been performed by their comparing with ground-based observation data. Correctness of LAI and B estimates has been confirmed when comparing time behavior of satellite- and ground-based LAI and B during each vegetation season. The all-important part of the study is to improve the developed Multi Threshold Method (MTM) intended for assessing daily and monthly rainfall from AVHRR and SEVIRI data, to check the correctness of carried out calculations for the considered territory and to develop procedures of utilizing obtained satellite-derived estimates of precipitation in the SVAT model. The MTM allows automatic pixel-by-pixel classifying AVHRR- and SEVIRI-measured data for the cloud detection, identification of its types, allocation of precipitation zones, and determination of instantaneous maximum intensities of precipitation in the pixel range around the clock throughout the year independently of land surface type. Measurement data from 5 AVHRR and 11 SEVIRI channels as well as their differences are used in the MTM as predictors. Calibration and verification of the MTM have been carried out using observation data on daily precipitation at agricultural meteorological stations of the region. In the frame of this approach the transition from the rainfall intensity estimation to the calculation of their daily sums has been fulfilled at that two variants of this calculation have been realized which focusing on climate researches and operational monitoring. Such transition has required verifying the accuracy of the estimates obtained in both variants at each time step. This verification has included comparison of area distributions of satellite-derived precipitation estimates and analogous estimates obtained by the interpolation of ground-based observation data. The probability of correct precipitation zone detection from satellite data when comparing with ground-based meteorological observations has amounted 75-85 %. In both variants of calculating precipitation for the region of interest in addition to the fields of daily rainfall the fields of their monthly and annual sums have been built. All three sums are consistent with each other and with a ground-based observation data although the satellite-derived estimates are more "smooth" in comparison with ground-based ones. Their discrepancies are in the range of the rainfall estimation errors using the MTM and they are peculiar to the local maxima for which satellite-derived rainfall is less than ground-measured values. This may be due to different scales of space-averaged satellite and point-wise ground-based estimates. To utilize satellite-derived estimates of meteorological and vegetation characteristics in the SVAT model the procedures of replacing the ground-based values of precipitation, LST, LAI and B by corresponding satellite-derived values have been developed taking into account spatial heterogeneity of their fields. The correctness of such replacement has been confirmed by the results of comparing the values of soil water content W and evapotranspiration Ev modeled and measured at agricultural meteorological stations. In particular, when the difference of precipitation sums for the vegetation season resulted from the model calculation in both above variants having been 20% the discrepancy between corresponding modeled values of W for the same period has not exceeded 8% and the discrepancy between values of E has been within 15%. Such discrepancies are within the limits of the standard W and Ev estimation errors. The final results of the SVAT model calculation utilizing satellite data are the fields of soil water content W, evapotranspiration Ev, vertical water and heat fluxes, land surface temperatures and other water and heat regime characteristics area-distributed over the territory of interest in their dynamics for the year 2011-2014 vegetation seasons. Discrepancies between Ev and W calculation results and observation data (~ 20-25 and 10-15%) have not exceeded the standard error of their estimation which corresponds to the adopted accuracy criteria of such estimates.

Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Cui, Yaokui; Long, Di; Hong, Yang; Zeng, Chao; Zhou, Jie; Han, Zhongying; Liu, Ronghua; Wan, Wei

2016-12-01

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the Earth's 'third pole'. Large-scale spatially consistent and temporally continuous soil moisture datasets are of great importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is a relatively new passive microwave product, with the satellite being launched on November 5, 2010. This study validates and reconstructs FY-3B/MWRI soil moisture across the TP. First, the validation is performed using in situ measurements within two in situ soil moisture measurement networks (1° × 1° and 0.25° × 0.25°), and also compared with the Essential Climate Variable (ECV) soil moisture product from multiple active and passive satellite soil moisture products using new merging procedures. Results show that the ascending FY-3B/MWRI product outperforms the descending product. The ascending FY-3B/MWRI product has almost the same correlation as the ECV product with the in situ measurements. The ascending FY-3B/MWRI product has better performance than the ECV product in the frozen season and under the lower NDVI condition. When the NDVI is higher in the unfrozen season, uncertainty in the ascending FY-3B/MWRI product increases with increasing NDVI, but it could still capture the variability in soil moisture. Second, the FY-3B/MWRI soil moisture product is subsequently reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and NDVI, LST, and albedo, but also the relationship between the soil moisture and four-dimensional variations using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R2 higher than 0.56, RMSE less than 0.1 cm3 cm-3, and Bias less than 0.07 cm3 cm-3 for both frozen and unfrozen seasons, compared with the in situ measurements at the two networks. Third, the reconstruction method is applied to generate surface soil moisture over the TP. Both original and reconstructed FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, and ecosystems over the TP.

LST phase A design update study

NASA Technical Reports Server (NTRS)

1973-01-01

An update is presented of the Phase A study of the Large Space Telescope (LST), based on changes in guidelines and new data developed subsequent to the Phase A study. The study defines an LST concept based on the broad mission guidelines provided by the Office of Space Science (OSS), the scientific requirements developed by OSS with the scientific community, and an understanding of long range NASA planning current at the time the study was performed. A low cost design approach was followed. This resulted in the use of standard spacecraft hardware, the provision for maintenance at the black box level, growth potential in systems designs, and sharing of shuttle maintenance flights with other payloads (See N73-18449 through N73-18453)

Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia.

PubMed

Estoque, Ronald C; Murayama, Yuji; Myint, Soe W

2017-01-15

Due to its adverse impacts on urban ecological environment and the overall livability of cities, the urban heat island (UHI) phenomenon has become a major research focus in various interrelated fields, including urban climatology, urban ecology, urban planning, and urban geography. This study sought to examine the relationship between land surface temperature (LST) and the abundance and spatial pattern of impervious surface and green space in the metropolitan areas of Bangkok (Thailand), Jakarta (Indonesia), and Manila (Philippines). Landsat-8 OLI/TIRS data and various geospatial approaches, including urban-rural gradient, multiresolution grid-based, and spatial metrics-based techniques, were used to facilitate the analysis. We found a significant strong correlation between mean LST and the density of impervious surface (positive) and green space (negative) along the urban-rural gradients of the three cities, depicting a typical UHI profile. The correlation of impervious surface density with mean LST tends to increase in larger grids, whereas the correlation of green space density with mean LST tends to increase in smaller grids, indicating a stronger influence of impervious surface and green space on the variability of LST in larger and smaller areas, respectively. The size, shape complexity, and aggregation of the patches of impervious surface and green space also had significant relationships with mean LST, though aggregation had the most consistent strong correlation. On average, the mean LST of impervious surface is about 3°C higher than that of green space, highlighting the important role of green spaces in mitigating UHI effects, an important urban ecosystem service. We recommend that the density and spatial pattern of urban impervious surfaces and green spaces be considered in landscape and urban planning so that urban areas and cities can have healthier and more comfortable living urban environments. Copyright © 2016 Elsevier B.V. All rights reserved.

Monitoring Drought at Continental Scales Using Thermal Remote Sensing of Evapotranspiration (Invited)

NASA Astrophysics Data System (ADS)

Anderson, M. C.; Hain, C.; Mecikalski, J. R.; Kustas, W. P.

2009-12-01

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status: soil surface temperature increases with decreasing water content, while moisture depletion in the plant root zone leads to stomatal closure, reduced transpiration, and elevated canopy temperatures that can be effectively detected from space. Empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring drought conditions over large areas, but may provide ambiguous results when vegetation growth is limited by energy (radiation, air temperature) rather than moisture. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. In this approach, moisture stress can be quantified in terms of the reduction of evapotranspiration (ET) from the potential rate (PET) expected under non-moisture limiting conditions. The Atmosphere-Land Exchange Inverse (ALEXI) model couples a two-source (soil+canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map fluxes across the U.S. continent at 5-10km resolution using thermal band imagery from the Geostationary Operational Environmental Satellites (GOES). Finer resolution flux maps can be generated through spatial disaggregation using TIR data from polar orbiting instruments such as Landsat (60-120m) and MODIS (1km). A derived Evaporative Stress Index (ESI), given by 1-ET/PET, shows good correspondence with standard drought metrics and with patterns of antecedent precipitation, but can be produced at significantly higher spatial resolution due to limited reliance on ground observations. Because the ESI does not use precipitation data as input, it provides an independent means for assessing standard meteorologically-based drought indicators, and may be more robust in regions with limited monitoring networks. In this study, monthly maps of ESI anomalies for 2000-2008 are compared to standard drought indices and to drought classifications in the U.S. Drought Monitor. The ESI shows better skill in ranking drought severity than do precipitation-based indices composited over comparable time intervals. The thermal remote sensing inputs to ALEXI detect drought conditions even under the dense forest cover along the East Coast of the United States, where microwave soil moisture retrievals typically lose sensitivity. On the other hand, microwave observations are not constrained by cloud cover and provide better temporal continuity, but typically at significantly lower spatial resolution. A merged TIR-microwave moisture anomaly product may have potential for optimizing both spatial and temporal coverage in continental-scale drought monitoring.

A protocol for validating Land Surface Temperature from Sentinel-3

NASA Astrophysics Data System (ADS)

Ghent, D.

2015-12-01

One of the main objectives of the Sentinel-3 mission is to measure sea- and land-surface temperature with high-end accuracy and reliability in support of environmental and climate monitoring in an operational context. Calibration and validation are thus key criteria for operationalization within the framework of the Sentinel-3 Mission Performance Centre (S3MPC).Land surface temperature (LST) has a long heritage of satellite observations which have facilitated our understanding of land surface and climate change processes, such as desertification, urbanization, deforestation and land/atmosphere coupling. These observations have been acquired from a variety of satellite instruments on platforms in both low-earth orbit and in geostationary orbit. Retrieval accuracy can be a challenge though; surface emissivities can be highly variable owing to the heterogeneity of the land, and atmospheric effects caused by the presence of aerosols and by water vapour absorption can give a bias to the underlying LST. As such, a rigorous validation is critical in order to assess the quality of the data and the associated uncertainties. The Sentinel-3 Cal-Val Plan for evaluating the level-2 SL_2_LST product builds on an established validation protocol for satellite-based LST. This set of guidelines provides a standardized framework for structuring LST validation activities, and is rapidly gaining international recognition. The protocol introduces a four-pronged approach which can be summarised thus: i) in situ validation where ground-based observations are available; ii) radiance-based validation over sites that are homogeneous in emissivity; iii) intercomparison with retrievals from other satellite sensors; iv) time-series analysis to identify artefacts on an interannual time-scale. This multi-dimensional approach is a necessary requirement for assessing the performance of the LST algorithm for SLSTR which is designed around biome-based coefficients, thus emphasizing the importance of non-traditional forms of validation such as radiance-based techniques. Here we present examples of the application of the protocol to data produced within the ESA DUE GlobTemperature Project. The lessons learnt here are helping to fine-tune the methodology in preparation for Sentinel-3 commissioning.

Life Skills Training: Preventing Substance Misuse by Enhancing Individual and Social Competence

ERIC Educational Resources Information Center

Botvin, Gilbert J.; Griffin, Kenneth W.

2014-01-01

Research concerning the etiology and prevention of substance misuse has led to the development of preventive interventions that are theory-based and effective. One such approach, Life Skills Training (LST), targets key etiologic factors using a conceptual framework derived from social learning theory and problem behavior theory. LST has been…

Quantifying the influences of various ecological factors on land surface temperature of urban forests.

PubMed

Ren, Yin; Deng, Lu-Ying; Zuo, Shu-Di; Song, Xiao-Dong; Liao, Yi-Lan; Xu, Cheng-Dong; Chen, Qi; Hua, Li-Zhong; Li, Zheng-Wei

2016-09-01

Identifying factors that influence the land surface temperature (LST) of urban forests can help improve simulations and predictions of spatial patterns of urban cool islands. This requires a quantitative analytical method that combines spatial statistical analysis with multi-source observational data. The purpose of this study was to reveal how human activities and ecological factors jointly influence LST in clustering regions (hot or cool spots) of urban forests. Using Xiamen City, China from 1996 to 2006 as a case study, we explored the interactions between human activities and ecological factors, as well as their influences on urban forest LST. Population density was selected as a proxy for human activity. We integrated multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) to develop a database on a unified urban scale. The driving mechanism of urban forest LST was revealed through a combination of multi-source spatial data and spatial statistical analysis of clustering regions. The results showed that the main factors contributing to urban forest LST were dominant tree species and elevation. The interactions between human activity and specific ecological factors linearly or nonlinearly increased LST in urban forests. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. In conclusion, quantitative studies based on spatial statistics and GeogDetector models should be conducted in urban areas to reveal interactions between human activities, ecological factors, and LST. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Kumar, Sujay V.; Mahanama, P. P.; Koster, Randal D.; Liu, Q.

2010-01-01

Land surface (or "skin") temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. Here we assimilate LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) into the Noah and Catchment (CLSM) land surface models using an ensemble-based, off-line land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LST typically exhibit different mean values and variability. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation ("open loop") are comparable to each other and superior to that of ISCCP retrievals. For LST, RMSE values are 4.9 K (CLSM), 5.6 K (Noah), and 7.6 K (ISCCP), and anomaly correlation coefficients (R) are 0.62 (CLSM), 0.61 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over open loop) of up to 0.7 K in RMSE and 0.05 in anomaly R. The skill of surface turbulent flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.

Antibacterial and antibiofilm surfaces through polydopamine-assisted immobilization of lysostaphin as an antibacterial enzyme.

PubMed

Yeroslavsky, Gil; Girshevitz, Olga; Foster-Frey, Juli; Donovan, David M; Rahimipour, Shai

2015-01-27

Antibiotic resistance and the colonization of bacteria on surfaces, often as biofilms, prolong hospitalization periods, increase mortality, and are thus major concerns for health care providers. There is an urgent need for antimicrobial and antibiofilm surface treatments that are permanent, can eradicate both biofilms and planktonic pathogens over long periods of time, and do not select for resistant strains. In this study, we have demonstrated a simple, robust, and biocompatible method that utilizes the adhesive property of polydopamine (PDA) to covalently attach the antimicrobial enzyme lysostaphin (Lst) to a variety of surfaces to generate antibacterial and antibiofilm interfaces. The immobilization of the recombinant Lst onto PDA-coated surfaces was carried out under physiological conditions, most probably through the C-terminal His6-tag fragment of the enzyme, minimizing the losses of bioagent activity. The modified surfaces were extensively characterized by X-ray photoelectron spectroscopy and peak force quantitative nanomechanical mapping (PeakForce QNM) AFM-based method, and the presence of Lst on the surfaces was further confirmed immunochemically using anti-Lst antibody. We also found that, in contrast to the physically adsorbed Lst, the covalently attached Lst does not leach from the surfaces and maintains its endopeptidase activity to degrade the staphylococcal cell wall, avoiding most intracellular bacterial resistance mechanisms. Moreover, the Lst-coated surfaces kill hospital strains of Staphylococcus aureus in less than 15 min and prevent biofilm formation. This immobilization method should be applicable also to other proteins and enzymes that are recombinantly expressed to include the His6-tag fragment.

Ecosystem Disturbance Effects on Land Surface Temperature, Forest Carbon Stocks, and Primary Productivity in the Western United States

NASA Astrophysics Data System (ADS)

Cooper, L. A.; Ballantyne, A.; Holden, Z. A.; Landguth, E.

2015-12-01

Disturbance plays an important role in the structure, composition, and nutrient cycling of forest ecosystems. Climate change is resulting in an increase in disturbance frequency and intensity, making it critical that we quantify the physical and chemical impacts of disturbances on forests. The impacts of disturbance are thought to vary widely depending on disturbance type, location, and climate. More specifically, fires, insect infestations, and other types of disturbances differ in their timing, extent, and intensity making it difficult to assess the true impact of disturbances on local energy budgets and carbon cycling. Here, we provide a regional analysis of the impacts of fire, insect attack, and other disturbances on land surface temperature (LST), carbon stocks, and gross primary productivity (GPP). Using disturbances detected with MODIS Enhanced Vegetation Index (EVI) time series between 2002 and 2012, we find that the impacts of disturbance on LST, carbon stocks, and GPP vary widely according to local climate, vegetation, and disturbance type and intensity. Fires resulted in the most distinct impacts on all response variables. Forest responses to insect epidemics were more varied in their magnitude and timing. The results of this study provide an important estimation of the variability of climate and ecosystem responses to disturbance across a large and heterogeneous landscape. With disturbance projected to increase in both frequency and intensity around the globe in the coming years, this information is vitally important to effectively manage forests into the future.

Forecasting and Monitoring Agricultural Drought in the Philippines

NASA Astrophysics Data System (ADS)

Perez, G. J.; Macapagal, M.; Olivares, R.; Macapagal, E. M.; Comiso, J. C.

2016-06-01

A monitoring and forecasting sytem is developed to assess the extent and severity of agricultural droughts in the Philippines at various spacial scales and across different time periods. Using Earth observation satellite data, drought index, hazard and vulnerability maps are created. The drought index, called Standardized Vegetation-Temperature Ratio (SVTR), is derived using the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). SVTR is evaluated by correlating its values with existing agricultural drought index, particulary Evaporative Stress Index (ESI). Moreover, the performance of SVTR in detecting drought occurrences was assessed for the 2015-2016 drought event. This period is a strong El Niño year and a large portion of the country was affected by drought at varying degrees, making it a good case study for evaluating drought indices. Satellitederived SVTR was validated through several field visits and surveys across different major agricultural areas in the country, and was found to be 73% accurate. The drought hazard and vulnerability maps are produced by utilizing the evapotranspration product of MODIS, rainfall climatology from the Tropical Rainfall Microwave Mission (TRMM) and ancillary data, including irrigation, water holding capacity and land use. Finally, we used statistical techniques to determine trends in NDVI and LST and generate a sixmonth forecast of drought index. Outputs of this study are being assessed by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) and the Department of Agriculture Bureau of Soils and Water Management (DABSWM) for future integration in their operations.

A framework for global diurnally-resolved observations of Land Surface Temperature

NASA Astrophysics Data System (ADS)

Ghent, D.; Remedios, J.; Pinnock, S.

2013-12-01

Land surface temperature (LST) is the radiative skin temperature of the land, and is one of the key parameters in the physics of land-surface processes on regional and global scales. Being a key boundary condition in land surface models, which determine the surface to atmosphere fluxes of heat, water and carbon; thus influencing cloud cover, precipitation and atmospheric chemistry predictions within Global models, the requirement for global diurnal observations of LST is well founded. Earth Observation satellites offer an opportunity to obtain global coverage of LST, with the appropriate exploitation of data from multiple instruments providing a capacity to resolve the diurnal cycle on a global scale. Here we present a framework for the production of global, diurnally resolved, data sets for LST which is a key request from users of LST data. We will show how the sampling of both geostationary and low earth orbit data sets could conceptually be employed to build combined, multi-sensor, pole-to-pole data sets. Although global averages already exist for individual instruments and merging of geostationary based LST is already being addressed operationally (Freitas, et al., 2013), there are still a number of important challenges to overcome. In this presentation, we will consider three of the issues still open in LST remote sensing: 1) the consistency amongst retrievals; 2) the clear-sky bias and its quantification; and 3) merging methods and the propagation of uncertainties. For example, the combined use of both geostationary earth orbit (GEO) and low earth orbit (LEO) data, and both infra-red and microwave data are relatively unexplored but are necessary to make the most progress. Hence this study will suggest what is state-of-the-art and how considerable advances can be made, accounting also for recent improvements in techniques and data quality. The GlobTemperature initiative under the Data User Element of ESA's 4th Earth Observation Envelope Programme (2013-2017), which aims to support the wider uptake of global-scale satellite LST by the research and operational user communities, will be a particularly important element in the development and subsequent provision of global diurnal LST. This new project, with its emphasis on promoting the coherence and openness of interactions within the LST and user communities, will be well placed to deliver appropriate data, engage a wide audience and hence be a key promoter of LST research and development for the LST community. References Freitas, S.C., Trigo, I.F., Macedo, J., Barroso, C., Silva, R., & Perdigao, R., 2013, Land surface temperature from multiple geostationary satellites, International Journal of Remote Sensing, 34, 3051-3068.

Enhancement of the Oral Bioavailability of Fexofenadine Hydrochloride via Cremophor® El-Based Liquisolid Tablets

PubMed Central

Yehia, Soad Ali; El-Ridi, Mohamed Shafik; Tadros, Mina Ibrahim; El-Sherif, Nolwa Gamal

2015-01-01

Purpose: The current work aimed to develop promising Fexofenadine hydrochloride (FXD) liquisolid tablets able to increase its oral bioavailability and shorten time to reach maximum plasma concentrations (Tmax). Methods: Eighteen liquisolid powders were developed based on 3 variables; (i) vehicle type [Propylene glycol (PG) or Cremophor® EL (CR)], (ii) carrier [Avicel® PH102] to coat [Aerosil® 200] ratio (15, 20, 25) and (iii) FXD concentration in vehicle (30, 35, 40 %, w/w). Pre-compression studies involved identification of physicochemical interactions and FXD crystallinity (FT-IR, DSC, XRD), topographic visualization (SEM) and estimation of flow properties (angle of repose, Carr’s index, Hausner’s ratio). CR-based liquisolid powders were compressed as liquisolid tablets (LST 9 – 18) and evaluated for weight-variation, drug-content, friability-percentage, disintegration-time and drug-release. The pharmacokinetics of LST-18 was evaluated in healthy volunteers relative to Allegra® tablets. Results: Pre-compression studies confirmed FXD dispersion in vehicles, conversion to amorphous form and formation of liquisolid powders. CR-based liquisolid powders showed acceptable-to-good flow properties suitable for compaction. CR-based LSTs had appropriate physicochemical properties and short disintegration times. Release profile of LST-18 showed a complete drug release within 5 min. Conclusion: LST-18 succeeded in increasing oral FXD bioavailability by 62% and reducing Tmax to 2.16 h. PMID:26819931

Short-term outcome of treatment limitation discussions for newborn infants, a multicentre prospective observational cohort study.

PubMed

Aladangady, Narendra; Shaw, Chloe; Gallagher, Katie; Stokoe, Elizabeth; Marlow, Neil

2017-03-01

To determine the short-term outcomes of babies for whom clinicians or parents discussed the limitation of life-sustaining treatment (LST). Prospective multicentre observational study. Two level 3, six level 2 and one level 1 neonatal units in the North-East London Neonatal Network. A total of 87 babies including 68 for whom limiting LST was discussed with parents and 19 babies died without discussion of limiting LST in the labour ward or neonatal unit. Final decision reached after discussions about limiting LST and neonatal unit outcomes (death or survived to discharge) for babies. Withdrawing LST, withholding LST and do not resuscitate (DNR) order was discussed with 48, 16 and 4 parents, respectively. In 49/68 (72%) cases decisions occurred in level 3 and 19 cases in level 2 units. Following the initial discussions, 34/68 parents made the decision to continue LST. In 33/68 cases, a second opinion was obtained. The parents of 14/48 and 2/16 babies did not agree to withdraw and withhold LST, respectively. Forty-seven out of 87 babies (54%) died following limitation of LST, 28/87 (32%) died receiving full intensive care support, 5/87 (6%) survived following a decision to limit LST and 7/87 (8%) babies survived following decision to continue LST. A significant proportion of parents chose to continue treatment following discussions regarding limiting LST for their babies, and a proportion of these babies survived to neonatal unit discharge. The long-term outcomes of babies who survive following limiting LST discussion need to be investigated. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Maximising the benefits of satellite LST within the user community: ESA DUE GlobTemperature

NASA Astrophysics Data System (ADS)

Ghent, D.

2014-12-01

Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean balance of solar heating and land-atmosphere cooling fluxes. It is a basic determinant of the terrestrial thermal behaviour, as it controls the effective radiating temperature of the Earth's surface. The sensitivity of LST to soil moisture and vegetation cover means it is an important component in numerous applications. With the demand for LST data from Earth Observation currently experiencing considerable growth it is important that the users of this data are appropriately engaged by the LST data providers. The GlobTemperature project under the Data User Element of ESA's 4th Earth Observation Envelope Programme (2013-2017) aims to promote the wider uptake of global-scale satellite LST by the research and operational user communities; the key to success depending on the coherence and openness of the interactions between the LST and user communities. By incorporating detailed user input into the specifications, their subsequent testing of the LST data sets, and sustained access to data in a user-friendly manner through common data formats GlobTemperature is enhancing the portfolio of LST products from Earth Observation, while concurrently breaking down the barriers to successful application of such data through its programme of dialogue between the data providers and data users. Here we present the outcomes from the first phase of the project, which is achieving some innovative developments: a globally representative and consistent matchup database enabling validation and intercomparison of multi-sensor LST data sets; a prototype combined geostationary earth orbit (GEO) and low earth orbit (LEO) global data set for LST to resolve the diurnal cycle which is a key request from users of LST data; the delivery of the first LST data sets via a dedicated Data Portal in harmonised data format; and the establishment, in collaboration with international colleagues of a first working group (ILSTE-WG) dedicated to LST and Emissivity, whereby user evaluation of products by climate services aims to provide a thrust to sustained operational support of this group meeting a critical need amongst users of LST data.

Effects of salvianolic acid B and tanshinone IIA on the pharmacokinetics of losartan in rats by regulating the activities and expression of CYP3A4 and CYP2C9.

PubMed

Wang, Rong; Zhang, Hai; Wang, Yujie; Yu, Xiaoyan; Yuan, Yongfang

2016-03-02

Losartan (LST) is a common chemical drug used to treat high blood pressure and reduce the risk of stroke in certain people with heart disease. Danshen, prepared from the dried root and rhizome of Salvia miltiorrhiza Bunge, has been widely used for prevention and treatment of various cardiovascular and cerebrovascular diseases. There are more than 35 formulations containing Danshen indexed in the 2010 Chinese Pharmacopoeia, which are often combined with LST to treat cardiovascular and cerebrovascular diseases in the clinic. The effects of the two major components of Danshen, salvianolic acid B (SA-B) and tanshinone IIA (Tan IIA), on the pharmacokinetics of losartan and its metabolite, EXP3174, in rats were investigated by liquid chromatography coupled with mass spectrometry (LC-MS). Male Sprague-Dawley rats were randomly assigned to 3 groups: LST, LST+SA-B and LST+Tan IIA, and the main pharmacokinetic parameters were estimated after oral administration of LST, LST+SA-B and LST+Tan IIA. It was found that there are significant differences in the pharmacokinetic parameters among the three groups: Cmax, t1/2, AUC, AUMC in the LST+SA-B group was smaller than those in group LST, while larger in group LST+Tan IIA. Further, the effects of SA-B and Tan IIA on the metabolism of losartan was also investigated using rat liver microsomes in vitro. The results indicated that SA-B can induce the metabolism of LST, while Tan IIA can inhibit the metabolism of LST in rat liver microsomes in vitro by regulating activities of CYP450 enzymes. In addition, the effect of SA-B and Tan IIA on CYP3A4 and CYP2C9 expression was studied in Chang liver cells by western-blotting and Real-time PCR. It was concluded that the two components of Danshen, SA-B and Tan IIA have different influences on the metabolism of LST: SA-B can obviously speed up the metabolism of LST by inducing CYP3A4/CYP2C9 activities and expression, however, Tan IIA can slow down the metabolism of LST by inhibiting CYP3A4/CYP2C9 activities. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Utilization of Satellite Data to Identify and Monitor Changes in Frequency of Meteorological Events

NASA Astrophysics Data System (ADS)

Mast, J. C.; Dessler, A. E.

2017-12-01

Increases in temperature and climate variability due to human-induced climate change is increasing the frequency and magnitude of extreme heat events (i.e., heatwaves). This will have a detrimental impact on the health of human populations and habitability of certain land locations. Here we seek to utilize satellite data records to identify and monitor extreme heat events. We analyze satellite data sets (MODIS and AIRS land surface temperatures (LST) and water vapor profiles (WV)) due to their global coverage and stable calibration. Heat waves are identified based on the frequency of maximum daily temperatures above a threshold, determined as follows. Land surface temperatures are gridded into uniform latitude/longitude bins. Maximum daily temperatures per bin are determined and probability density functions (PDF) of these maxima are constructed monthly and seasonally. For each bin, a threshold is calculated at the 95th percentile of the PDF of maximum temperatures. Per each bin, an extreme heat event is defined based on the frequency of monthly and seasonal days exceeding the threshold. To account for the decreased ability of the human body to thermoregulate with increasing moisture, and to assess lethality of the heat events, we determine the wet-bulb temperature at the locations of extreme heat events. Preliminary results will be presented.

Camera Systems Rapidly Scan Large Structures

NASA Technical Reports Server (NTRS)

2013-01-01

Needing a method to quickly scan large structures like an aircraft wing, Langley Research Center developed the line scanning thermography (LST) system. LST works in tandem with a moving infrared camera to capture how a material responds to changes in temperature. Princeton Junction, New Jersey-based MISTRAS Group Inc. now licenses the technology and uses it in power stations and industrial plants.

Upscaling and Downscaling of Land Surface Fluxes with Surface Temperature

NASA Astrophysics Data System (ADS)

Kustas, W. P.; Anderson, M. C.; Hain, C.; Albertson, J. D.; Gao, F.; Yang, Y.

2015-12-01

Land surface temperature (LST) is a key surface boundary condition that is significantly correlated to surface flux partitioning between latent and sensible heat. The spatial and temporal variation in LST is driven by radiation, wind, vegetation cover and roughness as well as soil moisture status in the surface and root zone. Data from airborne and satellite-based platforms provide LST from ~10 km to sub meter resolutions. A land surface scheme called the Two-Source Energy Balance (TSEB) model has been incorporated into a multi-scale regional modeling system ALEXI (Atmosphere Land Exchange Inverse) and a disaggregation scheme (DisALEXI) using higher resolution LST. Results with this modeling system indicates that it can be applied over heterogeneous land surfaces and estimate reliable surface fluxes with minimal in situ information. Consequently, this modeling system allows for scaling energy fluxes from subfield to regional scales in regions with little ground data. In addition, the TSEB scheme has been incorporated into a large Eddy Simulation (LES) model for investigating dynamic interactions between variations in the land surface state reflected in the spatial pattern in LST and the lower atmospheric air properties affecting energy exchange. An overview of research results on scaling of fluxes and interactions with the lower atmosphere from the subfield level to regional scales using the TSEB, ALEX/DisALEX and the LES-TSEB approaches will be presented. Some unresolved issues in the use of LST at different spatial resolutions for estimating surface energy balance and upscaling fluxes, particularly evapotranspiration, will be discussed.

Radiation Products based on a constellation of Geostationary Satellites

NASA Astrophysics Data System (ADS)

Trigo, I. F.; Freitas, S. C.; Barroso, C.; Macedo, J.; Perdigão, R.; Silva, R.; Viterbo, P.

2012-04-01

The various components of the surface radiation budget present high variability in time and space, particularly over land surfaces where spatial heterogeneity of the upward fluxes is high. Geostationary satellites are well-suited to describe the daily cycle of downward and upward radiation fluxes and present spatial resolutions of the order of 3-to-5 km at sub-satellite point, acceptable for many applications. The work presented here is being carried out within the framework of Geoland-2 project, and aims the use of data from geostationary platforms to generate, archive and distribute in near real time four component of the surface radiation budget: land surface albedo, land surface temperature (LST) and downward short- and long-wave fluxes at the surface. All four components are retrieved from the following satellites - GOES-W covering North and South America, Meteosat Second Generation (MSG) covering essentially Europe and Africa, and MTSAT covering part of Asia and Australia. The variables are retrieved independently from each satellite and then merged into a single field, with a 5 km spatial resolution. Data are generated hourly in the case of the downward fluxes and LST, and 10-daily in the case of albedo. In regions covered by both GOES and MSG disks, the interpolated field makes use of both retrievals, giving more weight to those with lower uncertainty. The four components of the surface radiation budget described above are assessed through comparisons with similar parameters retrieved from other sensors (e.g., MODIS, CERES) or from models (e.g., ECMWF forecasts), as well as with in situ observations when available. The presentation will be focused on a brief description of algorithms and auxiliary data used in product estimation. The results of inter-comparisons with other data sources, along with the identification of the retrieval conditions that allow optimal / sub-optimal estimation of these surface radiation parameters will also be analysed. The radiation products generated within the Geoland-2 project are freely available to the user community.

Actual evapotranspiration estimation in a Mediterranean mountain region by means of Landsat-5 TM and TERRA/AQUA MODIS imagery and Sap Flow measurements in Pinus sylvestris forest stands.

NASA Astrophysics Data System (ADS)

Cristóbal, J.; Poyatos, R.; Ninyerola, M.; Pons, X.; Llorens, P.

2009-04-01

Evapotranspiration monitoring has important implications on global and regional climate modelling, as well as in the knowledge of the hydrological cycle and in the assessment of environmental stress that affects forest and agricultural ecosystems. An increase of evapotranspiration while precipitation remains constant, or is reduced, could decrease water availability for natural and agricultural systems and human needs. Consequently, water balance methods, as the evapotranspiration modelling, have been widely used to estimate crop and forest water needs, as well as the global change effects. Nowadays, radiometric measurements provided by Remote Sensing and GIS analysis are the technologies used to compute evapotranspiration at regional scales in a feasible way. Currently, the 38% of Catalonia (NE of the Iberian Peninsula) is covered by forests, and one of the most important forest species is Scots Pine (Pinus sylvestris) which represents the 18.4% of the area occupied by forests. The aim of this work is to model actual evapotranspiration in Pinus sylvestris forest stands, in a Mediterranean mountain region, using remote sensing data, and compare it with stand-scale sap flow measurements measured in the Vallcebre research area (42° 12' N, 1° 49' E), in the Eastern Pyrenees. To perform this study a set of 30 cloud-free TERRA-MODIS images and 10 Landsat-5 TM images of path 198 and rows 31 and 32 from June 2003 to January 2005 have been selected to perform evapotranspiration modelling in Pinus sylvestris forest stands. TERRA/AQUA MODIS images have been downloaded by means of the EOS Gateway. We have selected two different types of products which contain the remote sensing data we have used to model daily evapotranspiration, daily LST product and daily calibrated reflectances product. Landsat-5 TM images have been corrected by means of conventional techniques based on first order polynomials taking into account the effect of land surface relief using a Digital Elevation Model, obtaining an RMS less than 30 m. Radiometric correction of Landsat non-thermal bands has been done following the methodology proposed by Pons and Solé (1994) which allows to reduce the number of undesired artifacts that are due to the effects of the atmosphere or to the differential illumination which is, in turn, due to the time of the day, the location in the Earth and the relief (zones being more illuminated than others, shadows, etc). Atmospheric correction of Landsat thermal band has been carried out by means of a single-channel algorithm improvement developed by Cristóbal et al. (2009). To compute actual evapotranspiration (AET) we have used the B-Method proposed by Jakson et al. (1977) and modified by Carlson et al. (1995) and Caselles et al. (1998), based on the energy budget, that needs as an input variables net radiation (Rn) and the difference between land surface temperature (LST) and air temperature (Ta). Air temperature has been modelled by means of multiple regression analysis and GIS interpolation using ground meteorological stations. Net radiation have been computed following two approaches based on the energy balance equation using albedo, land surface temperature, air temperature and solar radiation. Both air temperature and net radiation have been modelled at a regional scale. We have compared remote sensing daily actual evapotranspiration estimates with measured canopy transpiration. Sap flux density was measured by means of Heat dissipation sensors in 12 trees per stand, sampled according to diametric distribution, corrected to account for radial patter of sap flow using the Heat Field Deformation method and then scaled-up to stand level transpiration using tree sapwood areas. Sap flow measurements are comparable with AETd as in the Scots pine stand understorey evaporation is not significant. Measurements with sap flow technique show a mean, minimum and maximum values of AETd = 2.2, 0.6 and 3.6 mm day -1, respectively (Poyatos et al. 2005). Results show, in the case of MODIS AETd modelling, a RMSE of 1.6 mm compared with sap flow measurements. These results show that computing AETd by means of MODIS data in a heterogeneous area do not offer good results due to its spatial resolution (1 km). In the case of Landsat-5 TM AETd modelling, we have obtained better results with a RMSE of 0.6 mm which are in agreement with other studies that present an estimated error of about ± 30%. Moreover, we have to take into account that Landsat-like spatial resolution seems to be the best option to estimate AETd in this kind of areas. Keywords: Actual evapotranspiration modelling, Sap Flow, Remote Sensing, Pinus sylvestris, Mediterranian region.

Satellite observations of surface temperature during the March 2015 total solar eclipse.

PubMed

Good, Elizabeth

2016-09-28

The behaviour of remotely sensed land surface temperatures (LSTs) from the spinning-enhanced visible and infrared imager (SEVIRI) during the total solar eclipse of 20 March 2015 is analysed over Europe. LST is found to drop by up to several degrees Celcius during the eclipse, with the minimum LST occurring just after the eclipse mid-point (median=+1.5 min). The drop in LST is typically larger than the drop in near-surface air temperatures reported elsewhere, and correlates with solar obscuration (r=-0.47; larger obscuration = larger LST drop), eclipse duration (r=-0.62; longer duration = larger LST drop) and time (r=+0.37; earlier eclipse = larger LST drop). Locally, the LST drop is also correlated with vegetation (up to r=+0.6), with smaller LST drops occurring over more vegetated surfaces. The LSTs at locations near the coast and at higher elevation are also less affected by the eclipse. This study covers the largest area and uses the most observations of eclipse-induced surface temperature drops to date, and is the first full characterization of satellite LST during an eclipse (known to the author). The methods described could be applied to Geostationary Operational Environmental Satellite (GOES) LST data over North America during the August 2017 total solar eclipse.This article is part of the themed issue 'Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse'. © 2016 The Author(s).

Satellite observations of surface temperature during the March 2015 total solar eclipse

PubMed Central

2016-01-01

The behaviour of remotely sensed land surface temperatures (LSTs) from the spinning-enhanced visible and infrared imager (SEVIRI) during the total solar eclipse of 20 March 2015 is analysed over Europe. LST is found to drop by up to several degrees Celcius during the eclipse, with the minimum LST occurring just after the eclipse mid-point (median=+1.5 min). The drop in LST is typically larger than the drop in near-surface air temperatures reported elsewhere, and correlates with solar obscuration (r=−0.47; larger obscuration = larger LST drop), eclipse duration (r=−0.62; longer duration = larger LST drop) and time (r=+0.37; earlier eclipse = larger LST drop). Locally, the LST drop is also correlated with vegetation (up to r=+0.6), with smaller LST drops occurring over more vegetated surfaces. The LSTs at locations near the coast and at higher elevation are also less affected by the eclipse. This study covers the largest area and uses the most observations of eclipse-induced surface temperature drops to date, and is the first full characterization of satellite LST during an eclipse (known to the author). The methods described could be applied to Geostationary Operational Environmental Satellite (GOES) LST data over North America during the August 2017 total solar eclipse. This article is part of the themed issue ‘Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse’. PMID:27550764

Retrieving Land Surface Temperature from Hyperspectral Thermal Infrared Data Using a Multi-Channel Method

PubMed Central

Zhong, Xinke; Huo, Xing; Ren, Chao; Labed, Jelila; Li, Zhao-Liang

2016-01-01

Land Surface Temperature (LST) is a key parameter in climate systems. The methods for retrieving LST from hyperspectral thermal infrared data either require accurate atmospheric profile data or require thousands of continuous channels. We aim to retrieve LST for natural land surfaces from hyperspectral thermal infrared data using an adapted multi-channel method taking Land Surface Emissivity (LSE) properly into consideration. In the adapted method, LST can be retrieved by a linear function of 36 brightness temperatures at Top of Atmosphere (TOA) using channels where LSE has high values. We evaluated the adapted method using simulation data at nadir and satellite data near nadir. The Root Mean Square Error (RMSE) of the LST retrieved from the simulation data is 0.90 K. Compared with an LST product from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat, the error in the LST retrieved from the Infared Atmospheric Sounding Interferometer (IASI) is approximately 1.6 K. The adapted method can be used for the near-real-time production of an LST product and to provide the physical method to simultaneously retrieve atmospheric profiles, LST, and LSE with a first-guess LST value. The limitations of the adapted method are that it requires the minimum LSE in the spectral interval of 800–950 cm−1 larger than 0.95 and it has not been extended for off-nadir measurements. PMID:27187408

A Spatio-Temporal Analysis of the Relationship Between Near-Surface Air Temperature and Satellite Land Surface Temperatures Using 17 Years of Data from the ATSR Series

NASA Astrophysics Data System (ADS)

Ghent, D.; Good, E.; Bulgin, C.; Remedios, J. J.

2017-12-01

Surface temperatures (ST) over land have traditionally been measured at weather stations. There are many parts of the globe with very few stations, e.g. across much of Africa, leading to gaps in ST datasets, affecting our understanding of how ST is changing, and the impacts of extreme events. Satellites can provide global ST data but these observations represent how hot the land ST (LST; including the uppermost parts of e.g. trees, buildings) is to touch, whereas stations measure the air temperature just above the surface (T2m). Satellite LST data may only be available in cloud-free conditions and data records are frequently <10-15 years in length. Consequently, satellite LST data have not yet featured widely in climate studies. In this study, the relationship between clear-sky satellite LST and all-sky T2m is characterised in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer (ATSR) series, which has been produced within the European Space Agency GlobTemperature project. The results demonstrate the dependency of the global LST-T2m differences on location, land cover, vegetation and elevation. LSTnight ( 10 pm local solar time) is found to be closely coupled with minimum T2m (Tmin) and the two temperatures generally consistent to within ±5 °C (global median LSTnight- Tmin= 1.8 °C, interquartile range = 3.8 °C). The LSTday ( 10 am local time)-maximum T2m (Tmax) variability is higher because LST is strongly influenced by insolation and surface regime (global median LSTday-Tmax= -0.1 °C, interquartile range = 8.1 °C). Correlations for both temperature pairs are typically >0.9 outside of the tropics. A crucial aspect of this study is a comparison between the monthly global anomaly time series of LST and CRUTEM4 T2m. The time series agree remarkably well, with a correlation of 0.9 and 90% of the CDR anomalies falling within the T2m 95% confidence limits (see figure). This analysis provides independent verification of the 1995-2012 T2m anomaly time series, suggesting that LST can provide a complementary perspective on global ST change. The results presented give justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at weather stations.

Large space telescope, phase A. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

1972-01-01

The Phase A study of the Large Space Telescope (LST) is reported. The study defines an LST concept based on the broad mission guidelines provided by the Office of Space Science (OSS), the scientific requirements developed by OSS with the scientific community, and an understanding of long range NASA planning current at the time the study was performed. The LST is an unmanned astronomical observatory facility, consisting of an optical telescope assembly (OTA), scientific instrument package (SIP), and a support systems module (SSM). The report consists of five volumes. The report describes the constraints and trade off analyses that were performed to arrive at a reference design for each system and for the overall LST configuration. A low cost design approach was followed in the Phase A study. This resulted in the use of standard spacecraft hardware, the provision for maintenance at the black box level, growth potential in systems designs, and the sharing of shuttle maintenance flights with other payloads.

Global observation-based diagnosis of soil moisture control on land surface flux partition

NASA Astrophysics Data System (ADS)

Gallego-Elvira, Belen; Taylor, Christopher M.; Harris, Phil P.; Ghent, Darren; Veal, Karen L.; Folwell, Sonja S.

2016-04-01

Soil moisture plays a central role in the partition of available energy at the land surface between sensible and latent heat flux to the atmosphere. As soils dry out, evapotranspiration becomes water-limited ("stressed"), and both land surface temperature (LST) and sensible heat flux rise as a result. This change in surface behaviour during dry spells directly affects critical processes in both the land and the atmosphere. Soil water deficits are often a precursor in heat waves, and they control where feedbacks on precipitation become significant. State-of-the-art global climate model (GCM) simulations for the Coupled Model Intercomparison Project Phase 5 (CMIP5) disagree on where and how strongly the surface energy budget is limited by soil moisture. Evaluation of GCM simulations at global scale is still a major challenge owing to the scarcity and uncertainty of observational datasets of land surface fluxes and soil moisture at the appropriate scale. Earth observation offers the potential to test how well GCM land schemes simulate hydrological controls on surface fluxes. In particular, satellite observations of LST provide indirect information about the surface energy partition at 1km resolution globally. Here, we present a potentially powerful methodology to evaluate soil moisture stress on surface fluxes within GCMs. Our diagnostic, Relative Warming Rate (RWR), is a measure of how rapidly the land warms relative to the overlying atmosphere during dry spells lasting at least 10 days. Under clear skies, this is a proxy for the change in sensible heat flux as soil dries out. We derived RWR from MODIS Terra and Aqua LST observations, meteorological re-analyses and satellite rainfall datasets. Globally we found that on average, the land warmed up during dry spells for 97% of the observed surface between 60S and 60N. For 73% of the area, the land warmed faster than the atmosphere (positive RWR), indicating water stressed conditions and increases in sensible heat flux. Higher RWRs were observed for shorter vegetation and bare soil compared to tall, deep-rooted vegetation due to differences in both aerodynamic and hydrological properties. The variation of RWR with antecedent rainfall provides information on which evaporation regime a particular region lies in climatologically. Different drying stages for a given antecedent rainfall can thus be observed depending on land cover type. For instance, our results suggest that forests in a continental climate remain unstressed during a 10 day dry spell provided the previous month saw at least 95 mm of rain. Conversely, RWR values indicate that under similar conditions regions of grass/crop cover are water-stressed.

A framework for global diurnally-resolved observations of Land Surface Temperature

NASA Astrophysics Data System (ADS)

Ghent, Darren; Remedios, John

2014-05-01

Land surface temperature (LST) is the radiative skin temperature of the land, and is one of the key parameters in the physics of land-surface processes on regional and global scales. Being a key boundary condition in land surface models, which determine the surface to atmosphere fluxes of heat, water and carbon; thus influencing cloud cover, precipitation and atmospheric chemistry predictions within Global models, the requirement for global diurnal observations of LST is well founded. Earth Observation satellites offer an opportunity to obtain global coverage of LST, with the appropriate exploitation of data from multiple instruments providing a capacity to resolve the diurnal cycle on a global scale. Here we present a framework for the production of global, diurnally resolved, data sets for LST which is a key request from users of LST data. We will show how the sampling of both geostationary and low earth orbit data sets could conceptually be employed to build combined, multi-sensor, pole-to-pole data sets. Although global averages already exist for individual instruments and merging of geostationary based LST is already being addressed operationally (Freitas, et al., 2013), there are still a number of important challenges to overcome. In this presentation, we will consider three of the issues still open in LST remote sensing: 1) the consistency amongst retrievals; 2) the clear-sky bias and its quantification; and 3) merging methods and the propagation of uncertainties. For example, the combined use of both geostationary earth orbit (GEO) and low earth orbit (LEO) data, and both infra-red and microwave data are relatively unexplored but are necessary to make the most progress. Hence this study will suggest what is state-of-the-art and how considerable advances can be made, accounting also for recent improvements in techniques and data quality. The GlobTemperature initiative under the Data User Element of ESA's 4th Earth Observation Envelope Programme (2013-2017), which aims to support the wider uptake of global-scale satellite LST by the research and operational user communities, will be a particularly important element in the development and subsequent provision of global diurnal LST. References Freitas, S.C., Trigo, I.F., Macedo, J., Barroso, C., Silva, R., & Perdigao, R., 2013, Land surface temperature from multiple geostationary satellites, International Journal of Remote Sensing, 34, 3051-3068.

Quantifying the Terrestrial Surface Energy Fluxes Using Remotely-Sensed Satellite Data

NASA Astrophysics Data System (ADS)

Siemann, Amanda Lynn

The dynamics of the energy fluxes between the land surface and the atmosphere drive local and regional climate and are paramount to understand the past, present, and future changes in climate. Although global reanalysis datasets, land surface models (LSMs), and climate models estimate these fluxes by simulating the physical processes involved, they merely simulate our current understanding of these processes. Global estimates of the terrestrial, surface energy fluxes based on observations allow us to capture the dynamics of the full climate system. Remotely-sensed satellite data is the source of observations of the land surface which provide the widest spatial coverage. Although net radiation and latent heat flux global, terrestrial, surface estimates based on remotely-sensed satellite data have progressed, comparable sensible heat data products and ground heat flux products have not progressed at this scale. Our primary objective is quantifying and understanding the terrestrial energy fluxes at the Earth's surface using remotely-sensed satellite data with consistent development among all energy budget components [through the land surface temperature (LST) and input meteorology], including validation of these products against in-situ data, uncertainty assessments, and long-term trend analysis. The turbulent fluxes are constrained by the available energy using the Bowen ratio of the un-constrained products to ensure energy budget closure. All final products are within uncertainty ranges of literature values, globally. When validated against the in-situ estimates, the sensible heat flux estimates using the CFSR air temperature and constrained with the products using the MODIS albedo produce estimates closest to the FLUXNET in-situ observations. Poor performance over South America is consistent with the largest uncertainties in the energy budget. From 1984-2007, the longwave upward flux increase due to the LST increase drives the net radiation decrease, and the decrease in the available energy balances the decrease in the sensible heat flux. These datasets are useful for benchmarking climate models and LSM output at the global annual scale and the regional scale subject to the regional uncertainties and performance. Future work should improve the input data, particularly the temperature gradient and Zilitinkevich empirical constant, to reduce uncertainties.

Numerical simulation of diurnally varying thermal environment in a street canyon under haze-fog conditions

NASA Astrophysics Data System (ADS)

Tan, Zijing; Dong, Jingliang; Xiao, Yimin; Tu, Jiyuan

2015-10-01

The impact of haze-fog on surface temperature, flow pattern, pollutant dispersion and pedestrian thermal comfort are investigated using computational fluid dynamics (CFD) approach based on a three-dimensional street canyon model under different haze-fog conditions. In this study, light extinction coefficient (Kex) is adopted to represent haze-fog pollution level. Numerical simulations are performed for different Kex values at four representative time events (1000 LST, 1300 LST, 1600 LST and 2000 LST). The numerical results suggest that the surface temperature is strongly affected by the haze-fog condition. Surface heating induced by the solar radiation is enhanced by haze-fog, as higher surface temperature is observed under thicker haze-fog condition. Moreover, the temperature difference between sunlit and shadow surfaces is reduced, while that for the two shadow surfaces is slightly increased. Therefore, the surface temperature among street canyon facets becomes more evenly distributed under heavy haze-fog conditions. In addition, flow patterns are considerably altered by different haze-fog conditions, especially for the afternoon (1600 LST) case, in which thermal-driven flow has opposite direction as that of the wind-driven flow direction. Consequently, pollutants such as vehicular emissions will accumulate at pedestrian level, and pedestrian thermal comfort may lower under thicker haze-fog condition.

Vegetation classification and soil moisture calculation using land surface temperature (LST) and vegetation index (VI)

NASA Astrophysics Data System (ADS)

Liu, Liangyun; Zhang, Bing; Xu, Genxing; Zheng, Lanfen; Tong, Qingxi

2002-03-01

In this paper, the temperature-missivity separating (TES) method and normalized difference vegetation index (NDVI) are introduced, and the hyperspectral image data are analyzed using land surface temperature (LST) and NDVI channels which are acquired by Operative Module Imaging Spectral (OMIS) in Beijing Precision Agriculture Demonstration Base in Xiaotangshan town, Beijing in 26 Apr, 2001. Firstly, the 6 kinds of ground targets, which are winter wheat in booting stage and jointing stage, bare soil, water in ponds, sullage in dry ponds, aquatic grass, are well classified using LST and NDVI channels. Secondly, the triangle-like scatter-plot is built and analyzed using LST and NDVI channels, which is convenient to extract the information of vegetation growth and soil's moisture. Compared with the scatter-plot built by red and near-infrared bands, the spectral distance between different classes are larger, and the samples in the same class are more convergent. Finally, we design a logarithm VIT model to extract the surface soil water content (SWC) using LST and NDVI channel, which works well, and the coefficient of determination, R2, between the measured surface SWC and the estimated is 0.634. The mapping of surface SWC in the wheat area are calculated and illustrated, which is important for scientific irrigation and precise agriculture.

A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

NASA Astrophysics Data System (ADS)

Good, Elizabeth J.; Ghent, Darren J.; Bulgin, Claire E.; Remedios, John J.

2017-09-01

The relationship between satellite land surface temperature (LST) and ground-based observations of 2 m air temperature (T2m) is characterized in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/). Global LST-T2m differences are analyzed with respect to location, land cover, vegetation fraction, and elevation, all of which are found to be important influencing factors. LSTnight ( 10 P.M. local solar time, clear-sky only) is found to be closely coupled with minimum T2m (Tmin, all-sky) and the two temperatures generally consistent to within ±5°C (global median LSTnight-Tmin = 1.8°C, interquartile range = 3.8°C). The LSTday ( 10 A.M. local solar time, clear-sky only)-maximum T2m (Tmax, all-sky) variability is higher (global median LSTday-Tmax = -0.1°C, interquartile range = 8.1°C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T2m—which are completely independent data sets—compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at meteorological stations.

Analysis of the Landing Ship Tank (LST) and its Influence on Amphibious Warfare During World War Two

DTIC Science & Technology

2013-12-13

tour of LST-325 and sharing his in-depth knowledge about the LST. In memory of Captain Lawrence Jack Goddard, USNR (Ret), Commanding Officer, LST...utilized during amphibious combat operation for the Normandy invasion. The numerous memoirs, biographical and autobiographical works that have been

Trends in LST over the peninsular Spain as derived from the AVHRR imagery data

NASA Astrophysics Data System (ADS)

Khorchani, Makki; Vicente-Serrano, Sergio M.; Azorin-Molina, Cesar; Garcia, Monica; Martin-Hernandez, Natalia; Peña-Gallardo, Marina; El Kenawy, Ahmed; Domínguez-Castro, Fernando

2018-07-01

This study analyzes the spatio-temporal variability and trends of land surface temperature (LST) over peninsular Spain, considering all the available historical satellite imagery data from the NOAA-AVHRR product from July 1981 to June 2015 and explores whether changes in LST are related to the observed changes in air temperature, solar radiation and land cover. We found that LST showed a significant increase between 1982 and 2014, with an average increase on the order of 0.71 °C decade-1, being stronger during summertime (1.53 °C decade-1). The results also indicate a strong spatial coherence between LST and NDVI changes. The areas that experienced an increase in the LST were spatially consistent with those areas with no changes or even a dominant decrease in vegetation coverage. In addition, the strong increase of LST is coherent with observations of the recent radiative forcing affecting Spain, particularly during summertime. The confidence of the obtained LST trends during summer is also reinforced by the spatial differences recorded in trends, in addition to the differences found between land cover types. Specifically, the magnitude of this increase was much higher in the dryland non-permanent agricultural areas, which are usually harvested during summer, when soil is dominantly nude. In contrast, in well-developed forests, the magnitude of LST was much lower. Our results on the observed LST trends and their spatial patterns can contribute to better understanding of the recent eco-hydrological processes in peninsular Spain.

Novel hydrophilic nanostructured microtexture on direct metal laser sintered Ti-6Al-4V surfaces enhances osteoblast response in vitro and osseointegration in a rabbit model.

PubMed

Hyzy, Sharon L; Cheng, Alice; Cohen, David J; Yatzkaier, Gustavo; Whitehead, Alexander J; Clohessy, Ryan M; Gittens, Rolando A; Boyan, Barbara D; Schwartz, Zvi

2016-08-01

The purpose of this study was to compare the biological effects in vivo of hierarchical surface roughness on laser sintered titanium-aluminum-vanadium (Ti-6Al-4V) implants to those of conventionally machined implants on osteoblast response in vitro and osseointegration. Laser sintered disks were fabricated to have micro-/nano-roughness and wettability. Control disks were computer numerical control (CNC) milled and then polished to be smooth (CNC-M). Laser sintered disks were polished smooth (LST-M), grit blasted (LST-B), or blasted and acid etched (LST-BE). LST-BE implants or implants manufactured by CNC milling and grit blasted (CNC-B) were implanted in the femurs of male New Zealand white rabbits. Most osteoblast differentiation markers and local factors were enhanced on rough LST-B and LST-BE surfaces in comparison to smooth CNC-M or LST-M surfaces for MG63 and normal human osteoblast cells. To determine if LST-BE implants were osteogenic in vivo, we compared them to implant surfaces used clinically. LST-BE implants had a unique surface with combined micro-/nano-roughness and higher wettability than conventional CNC-B implants. Histomorphometric analysis demonstrated a significant improvement in cortical bone-implant contact of LST-BE implants compared to CNC-B implants after 3 and 6 weeks. However, mechanical testing revealed no differences between implant pullout forces at those time points. LST surfaces enhanced osteoblast differentiation and production of local factors in vitro and improved the osseointegration process in vivo. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 104A: 2086-2098, 2016. © 2016 Wiley Periodicals, Inc.

GlobTemperature

NASA Astrophysics Data System (ADS)

Ghent, Darren; Remedios, John; Bruniquel, Jerome; Sardou, Olivier; Trigo, Isabel; Merchant, Chris; Bulgin, Claire; Goettsche, Frank; Olesen, Folke; Prigent, Catherine; Pinnock, Simon

2014-05-01

Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean balance of solar heating and land-atmosphere cooling fluxes. It is a basic determinant of the terrestrial thermal behaviour, as it controls the effective radiating temperature of the Earth's surface. The sensitivity of LST to soil moisture and vegetation cover means it is an important component in numerous applications. For instance, LST is a key boundary condition in land surface models, which determine the surface to atmosphere fluxes of heat, water and carbon; thus influencing cloud cover, precipitation and atmospheric chemistry predictions within General Circulation Models. Changes in land-surface cover can affect global climate, and also can be identified by changes in their surface temperatures. With the demand of LST data from Earth Observation currently experiencing considerable growth it is important that the users of this data are appropriately engaged by the LST community. The GlobTemperature project under the Data User Element of ESA's 4th Earth Observation Envelope Programme (2013-2017) aims to promote the wider uptake of global-scale satellite LST by the research and operational user communities. As such, the programme of work is focussed on achieving some innovative milestones for LST data which include: detailed global merged geostationary (GEO) and low earth orbit (LEO) data sets with estimates of both clear-sky and under-cloud LST; a first Climate Data Record for LST for the ATSR series of instruments; and the provision of a globally representative and consistent in-situ validation and intercomparison matchup database. Furthermore, the strength of such a venture lies in the coherence and openness of the interactions with the LST and user communities. For instance: detailed user input into the specifications and subsequent testing of the LST data sets; sustained access to data in a user-friendly manner through common data formats; and the establishment of an LST working group (LST-WG) involving strong guidance of key international experts. GlobTemperature is thus a timely initiative to both enhance the portfolio of LST products from Earth Observation, while concurrently breaking down the barriers to successful application of such data through a programme of dialogue between the data providers and data users. This will require activities at a range of national facilities. For example, GlobTemperature is supported by the National Centre for Earth Observation (NCEO) in the UK with significant data processing and archiving to be performed on the Climate and Environmental Monitoring from Space (CEMS) facility. The project will have a very beneficial impact on global measurements of LST and will meet a critical need amongst users of LST data. Here we present the key challenges of such a programme of work and the methods to be employed in order to overcome them.

Linking the Local Climate Zones and Land Surface Temperature to Investigate the Surface Urban Heat Island, a Case Study of San Antonio, Texas, U.S.

NASA Astrophysics Data System (ADS)

Zhao, Chunhong

2018-04-01

The Local Climate Zones (LCZs) concept was initiated in 2012 to improve the documentation of Urban Heat Island (UHI) observations. Despite the indispensable role and initial aim of LCZs concept in metadata reporting for atmospheric UHI research, its role in surface UHI investigation also needs to be emphasized. This study incorporated LCZs concept to study surface UHI effect for San Antonio, Texas. LCZ map was developed by a GIS-based LCZs classification scheme with the aid of airborne Lidar dataset and other freely available GIS data. Then, the summer LST was calculated based Landsat imagery, which was used to analyse the relations between LST and LCZs and the statistical significance of the differences of LST among the typical LCZs, in order to test if LCZs are able to efficiently facilitate SUHI investigation. The linkage of LCZs and land surface temperature (LST) indicated that the LCZs mapping can be used to compare and investigate the SUHI. Most of the pairs of LCZs illustrated significant differences in average LSTs with considerable significance. The intra-urban temperature comparison among different urban classes contributes to investigate the influence of heterogeneous urban morphology on local climate formation.

Spatial-temporal change of land surface temperature across 285 cities in China: An urban-rural contrast perspective.

PubMed

Peng, Jian; Ma, Jing; Liu, Qianyuan; Liu, Yanxu; Hu, Yi'na; Li, Yingru; Yue, Yuemin

2018-09-01

As an important theme in global climate change and urban sustainable development, the changes of land surface temperature (LST) and surface urban heat island (SUHI) have been more and more focused by urban ecologists. This study used land-use data to identify the urban-rural areas in 285 cities in China and comparatively analyzed LST in urban-rural areas with the perspective of spatial-temporal dynamics heterogeneity. The results showed that, 98.9% of the cities exhibited SUHI effect in summer nighttime and the effect was stronger in northern cities than that in southern cities. In 2010, the mean SUHI intensity was the largest in summer daytime, with 4.6% of the cities having extreme SUHI of over 4°C. From 2001 to 2010, the nighttime LST of most cities increased more quickly in urban areas compared with rural areas, with an increasing tendency of the urban-rural LST difference. The difference in the urban- rural LST change rate was concentrated in the range of 0-0.1°C/year for 68.0% of cities in winter and 70.8% of cities in summer. For the higher LST increasing in urban areas compared with rural areas, there were more cities in summer than winter, indicating that the summer nighttime was the key temporal period for SUHI management. Based on the change slope of urban-rural LST, cities were clustered into four types and the vital and major zones for urban thermal environment management were identified in China. The vital zone included cities in Hunan, Hubei and other central rising provinces as well as the Beibu Gulf of Guangxi Province. The major zone included most of the cities in Central Plain Urban Agglomeration, Yangtze River Delta and Pearl River Delta. These results can provide scientific basis for SUHI adaptation in China. Copyright © 2018 Elsevier B.V. All rights reserved.

Mutations in the Arabidopsis Homolog of LST8/GβL, a Partner of the Target of Rapamycin Kinase, Impair Plant Growth, Flowering, and Metabolic Adaptation to Long Days[C][W

PubMed Central

Moreau, Manon; Azzopardi, Marianne; Clément, Gilles; Dobrenel, Thomas; Marchive, Chloé; Renne, Charlotte; Martin-Magniette, Marie-Laure; Taconnat, Ludivine; Renou, Jean-Pierre; Robaglia, Christophe; Meyer, Christian

2012-01-01

The conserved Target of Rapamycin (TOR) kinase forms high molecular mass complexes and is a major regulator of cellular adaptations to environmental cues. The Lethal with Sec Thirteen 8/G protein β subunit-like (LST8/GβL) protein is a member of the TOR complexes, and two putative LST8 genes are present in Arabidopsis thaliana, of which only one (LST8-1) is significantly expressed. The Arabidopsis LST8-1 protein is able to complement yeast lst8 mutations and interacts with the TOR kinase. Mutations in the LST8-1 gene resulted in reduced vegetative growth and apical dominance with abnormal development of flowers. Mutant plants were also highly sensitive to long days and accumulated, like TOR RNA interference lines, higher amounts of starch and amino acids, including proline and glutamine, while showing reduced concentrations of inositol and raffinose. Accordingly, transcriptomic and enzymatic analyses revealed a higher expression of genes involved in nitrate assimilation when lst8-1 mutants were shifted to long days. The transcriptome of lst8-1 mutants in long days was found to share similarities with that of a myo-inositol 1 phosphate synthase mutant that is also sensitive to the extension of the light period. It thus appears that the LST8-1 protein has an important role in regulating amino acid accumulation and the synthesis of myo-inositol and raffinose during plant adaptation to long days. PMID:22307851

Interannual variation of the surface temperature of tropical forests from satellite observations

DOE PAGES

Gao, Huilin; Zhang, Shuai; Fu, Rong; ...

2016-01-01

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability ofmore » cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Lastly, the differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data).« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Huilin; Zhang, Shuai; Fu, Rong

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability ofmore » cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Lastly, the differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data).« less

Dichotomy of protective cellular immune responses to human visceral leishmaniasis.

PubMed

Khalil, E A G; Ayed, N B; Musa, A M; Ibrahim, M E; Mukhtar, M M; Zijlstra, E E; Elhassan, I M; Smith, P G; Kieny, P M; Ghalib, H W; Zicker, F; Modabber, F; Elhassan, A M

2005-05-01

Healing/protective responses in human visceral leishmaniasis (VL) are associated with stimulation/production of Th1 cytokines, such as interferon IFN-gamma, and conversion in the leishmanin skin test (LST). Such responses were studied for 90 days in 44 adult healthy volunteers from VL non-endemic areas, with no past history of VL/cutaneous leishmaniasis (CL) and LST non-reactivity following injection with one of four doses of Alum-precipitated autoclaved Leishmania major (Alum/ALM) +/- bacille Calmette-Guerin (BCG), a VL candidate vaccine. The vaccine was well tolerated with minimal localized side-effects and without an increase in antileishmanial antibodies or interleukin (IL)-5. Five volunteers (5/44; 11.4%) had significant IFN-gamma production by peripheral blood mononuclear cells (PBMCs) in response to Leishmania antigens in their prevaccination samples (P = 0.001) but were LST non-reactive. On day 45, more than half the volunteers (26/44; 59.0%) had significantly high LST indurations (mean 9.2 +/- 2.7 mm) and high IFN-gamma levels (mean 1008 +/- 395; median 1247 pg/ml). Five volunteers had significant L. donovani antigen-induced IFN-gamma production (mean 873 +/- 290; median 902; P = 0.001), but were non-reactive in LST. An additional five volunteers (5/44; 11.4%) had low IFN-gamma levels (mean 110 +/- 124 pg/ml; median 80) and were non-reactive in LST (induration = 00 mm). The remaining eight volunteers had low IFN-gamma levels, but significant LST induration (mean 10 +/- 2.9 mm; median 11). By day 90 the majority of volunteers (27/44; 61.4%) had significant LST induration (mean 10.8 +/- 9.9 mm; P < 0.001), but low levels of L. donovani antigen-induced IFN-gamma (mean 66.0 +/- 62 pg/ml; P > 0.05). Eleven volunteers (11/44; 25%) had significantly high levels of IFN-gamma and LST induration, while five volunteers had low levels of IFN-gamma (<100 pg/ml) and no LST reactivity (00 mm). One volunteer was lost to follow-up. In conclusion, it is hypothesized that cellular immune responses to human VL are dichotomatous, and that IFN-gamma production and the LST response are not in a causal relationship. Following vaccination and probably cure of VL infection, the IFN-gamma response declines with time while the LST response persists. LST is a simple test that can be used to assess candidate vaccine efficacy.

Distinct Molecular Features of Different Macroscopic Subtypes of Colorectal Neoplasms

PubMed Central

Konda, Kenichi; Konishi, Kazuo; Yamochi, Toshiko; Ito, Yoichi M.; Nozawa, Hisako; Tojo, Masayuki; Shinmura, Kensuke; Kogo, Mari; Katagiri, Atsushi; Kubota, Yutaro; Muramoto, Takashi; Yano, Yuichiro; Kobayashi, Yoshiya; Kihara, Toshihiro; Tagawa, Teppei; Makino, Reiko; Takimoto, Masafumi; Imawari, Michio; Yoshida, Hitoshi

2014-01-01

Background Colorectal adenoma develops into cancer with the accumulation of genetic and epigenetic changes. We studied the underlying molecular and clinicopathological features to better understand the heterogeneity of colorectal neoplasms (CRNs). Methods We evaluated both genetic (mutations of KRAS, BRAF, TP53, and PIK3CA, and microsatellite instability [MSI]) and epigenetic (methylation status of nine genes or sequences, including the CpG island methylator phenotype [CIMP] markers) alterations in 158 CRNs including 56 polypoid neoplasms (PNs), 25 granular type laterally spreading tumors (LST-Gs), 48 non-granular type LSTs (LST-NGs), 19 depressed neoplasms (DNs) and 10 small flat-elevated neoplasms (S-FNs) on the basis of macroscopic appearance. Results S-FNs showed few molecular changes except SFRP1 methylation. Significant differences in the frequency of KRAS mutations were observed among subtypes (68% for LST-Gs, 36% for PNs, 16% for DNs and 6% for LST-NGs) (P<0.001). By contrast, the frequency of TP53 mutation was higher in DNs than PNs or LST-Gs (32% vs. 5% or 0%, respectively) (P<0.007). We also observed significant differences in the frequency of CIMP between LST-Gs and LST-NGs or PNs (32% vs. 6% or 5%, respectively) (P<0.005). Moreover, the methylation level of LINE-1 was significantly lower in DNs or LST-Gs than in PNs (58.3% or 60.5% vs. 63.2%, P<0.05). PIK3CA mutations were detected only in LSTs. Finally, multivariate analyses showed that macroscopic morphologies were significantly associated with an increased risk of molecular changes (PN or LST-G for KRAS mutation, odds ratio [OR] 9.11; LST-NG or DN for TP53 mutation, OR 5.30; LST-G for PIK3CA mutation, OR 26.53; LST-G or DN for LINE-1 hypomethylation, OR 3.41). Conclusion We demonstrated that CRNs could be classified into five macroscopic subtypes according to clinicopathological and molecular differences, suggesting that different mechanisms are involved in the pathogenesis of colorectal tumorigenesis. PMID:25093594

Lst1p and Sec24p Cooperate in Sorting of the Plasma Membrane Atpase into Copii Vesicles in Saccharomyces cerevisiae

PubMed Central

Shimoni, Yuval; Kurihara, Tatsuo; Ravazzola, Mariella; Amherdt, Mylène; Orci, Lelio; Schekman, Randy

2000-01-01

Formation of ER-derived protein transport vesicles requires three cytosolic components, a small GTPase, Sar1p, and two heterodimeric complexes, Sec23/24p and Sec13/31p, which comprise the COPII coat. We investigated the role of Lst1p, a Sec24p homologue, in cargo recruitment into COPII vesicles in Saccharomyces cerevisiae. A tagged version of Lst1p was purified and eluted as a heterodimer complexed with Sec23p comparable to the Sec23/24p heterodimer. We found that cytosol from an lst1-null strain supported the packaging of α-factor precursor into COPII vesicles but was deficient in the packaging of Pma1p, the essential plasma membrane ATPase. Supplementation of mutant cytosol with purified Sec23/Lst1p restored Pma1p packaging into the vesicles. When purified COPII components were used in the vesicle budding reaction, Pma1p packaging was optimal with a mixture of Sec23/24p and Sec23/Lst1p; Sec23/Lst1p did not replace Sec23/24p. Furthermore, Pma1p coimmunoprecipitated with Lst1p and Sec24p from vesicles. Vesicles formed with a mixture of Sec23/Lst1p and Sec23/24p were similar morphologically and in their buoyant density, but larger than normal COPII vesicles (87-nm vs. 75-nm diameter). Immunoelectronmicroscopic and biochemical studies revealed both Sec23/Lst1p and Sec23/24p on the membranes of the same vesicles. These results suggest that Lst1p and Sec24p cooperate in the packaging of Pma1p and support the view that biosynthetic precursors of plasma membrane proteins must be sorted into ER-derived transport vesicles. Sec24p homologues may comprise a more complex coat whose combinatorial subunit composition serves to expand the range of cargo to be packaged into COPII vesicles. By changing the geometry of COPII coat polymerization, Lst1p may allow the transport of bulky cargo molecules, polymers, or particles. PMID:11086000

Effects of Land Use/Cover Changes and Urban Forest Configuration on Urban Heat Islands in a Loess Hilly Region: Case Study Based on Yan'an City, China.

PubMed

Zhang, Xinping; Wang, Dexiang; Hao, Hongke; Zhang, Fangfang; Hu, Youning

2017-07-26

In this study Yan'an City, a typical hilly valley city, was considered as the study area in order to explain the relationships between the surface urban heat island (SUHI) and land use/land cover (LULC) types, the landscape pattern metrics of LULC types and land surface temperature (LST) and remote sensing indexes were retrieved from Landsat data during 1990-2015, and to find factors contributed to the green space cool island intensity (GSCI) through field measurements of 34 green spaces. The results showed that during 1990-2015, because of local anthropogenic activities, SUHI was mainly located in lower vegetation cover areas. There was a significant suburban-urban gradient in the average LST, as well as its heterogeneity and fluctuations. Six landscape metrics comprising the fractal dimension index, percentage of landscape, aggregation index, division index, Shannon's diversity index, and expansion intensity of the classified LST spatiotemporal changes were paralleled to LULC changes, especially for construction land, during the past 25 years. In the urban area, an index-based built-up index was the key positive factor for explaining LST increases, whereas the normalized difference vegetation index and modified normalized difference water index were crucial factors for explaining LST decreases during the study periods. In terms of the heat mitigation performance of green spaces, mixed forest was better than pure forest, and the urban forest configuration had positive effects on GSCI. The results of this study provide insights into the importance of species choice and the spatial design of green spaces for cooling the environment.

Effects of Land Use/Cover Changes and Urban Forest Configuration on Urban Heat Islands in a Loess Hilly Region: Case Study Based on Yan’an City, China

PubMed Central

Zhang, Xinping; Hao, Hongke; Zhang, Fangfang; Hu, Youning

2017-01-01

In this study Yan’an City, a typical hilly valley city, was considered as the study area in order to explain the relationships between the surface urban heat island (SUHI) and land use/land cover (LULC) types, the landscape pattern metrics of LULC types and land surface temperature (LST) and remote sensing indexes were retrieved from Landsat data during 1990–2015, and to find factors contributed to the green space cool island intensity (GSCI) through field measurements of 34 green spaces. The results showed that during 1990–2015, because of local anthropogenic activities, SUHI was mainly located in lower vegetation cover areas. There was a significant suburban-urban gradient in the average LST, as well as its heterogeneity and fluctuations. Six landscape metrics comprising the fractal dimension index, percentage of landscape, aggregation index, division index, Shannon’s diversity index, and expansion intensity of the classified LST spatiotemporal changes were paralleled to LULC changes, especially for construction land, during the past 25 years. In the urban area, an index-based built-up index was the key positive factor for explaining LST increases, whereas the normalized difference vegetation index and modified normalized difference water index were crucial factors for explaining LST decreases during the study periods. In terms of the heat mitigation performance of green spaces, mixed forest was better than pure forest, and the urban forest configuration had positive effects on GSCI. The results of this study provide insights into the importance of species choice and the spatial design of green spaces for cooling the environment. PMID:28933770

Temperature changes in Three Gorges Reservoir Area and linkage with Three Gorges Project

NASA Astrophysics Data System (ADS)

Song, Zhen; Liang, Shunlin; Feng, Lian; He, Tao; Song, Xiao-Peng; Zhang, Lei

2017-05-01

The Three Gorges Project (TGP) is one of the largest hydroelectric projects throughout the world. It has brought many benefits to the society but also led to endless debates about its environmental and climatic impacts. Monitoring the spatiotemporal variations of temperature in the Three Gorges Reservoir Area (TGRA) is important for understanding the climatic impacts of the TGP. In this study, we used remote sensing-based land surface temperature (LST) and ground-measured air temperature data to investigate temperature changes in the TGRA. Results showed that during the daytime in summer, LST exhibited significant cooling (1-5°C) in the downstream region of the reservoir, whereas LST during the nighttime in winter exhibited significant warming (1-5°C) across the entire reservoir. However, these cooling and warming effects were both locally constrained within 5 km buffer along the reservoir. The changes in air temperature were consistent with those in LST, with 0.67°C cooling in summer and 0.33°C warming in winter. The temperature changes along the reservoir not only resulted from the land-water conversion induced by the dam impounding but were also related to the increase of vegetation cover caused by the ecological restoration projects. Significant warming trends were also found in the upstream of TGRA, especially during the daytime in summer, with up to 5°C for LST and 0.52°C for air temperature. The warming was caused mainly by urban expansion, which was driven in part by the population resettlement of TGP. Based on satellite observations, we investigated the comprehensive climatic impacts of TGP caused by multiple factors.

Life histories of two deep-water Australian endemic elasmobranchs: Argus skate Dipturus polyommata and eastern spotted gummy shark Mustelus walkeri.

PubMed

Rigby, C L; White, W T; Smart, J J; Simpfendorfer, C A

2016-03-01

Two Australian endemic elasmobranchs, the Argus skate Dipturus polyommata and the eastern spotted gummy shark Mustelus walkeri, were collected from the by-catch of a prawn Melicertus plebejus trawl fishery off Queensland. Age and growth parameters were estimated from growth band counts in vertebral sections of 220 D. polyommata and 44 M. walkeri. Dipturus polyommata males and females had an observed maximum age of 10 years and reached maximum sizes of 369 and 371 mm total length (LT ), respectively. Mustelus walkeri lived longer, with the oldest female aged 16 years and measuring 1050 mm stretched total length (LST ), and oldest male aged 9 years and 805 mm LST . Dipturus polyommata grew relatively fast with a von Bertalanffy growth completion parameter of k = 0·208 year(-1) with males reaching maturity at 4·0 years (c. 278 mm LT ) and females at 5·1 years (c. 305 mm LT ). Mustelus walkeri grew more slowly with k = 0·033 year(-1) with males estimated to mature at 7-9 years (670-805 mm LST ) and females at 10-14 years (833-1012 mm LST ). Length at birth inferred from neonate D. polyommata was 89-111 mm LT while for M. walkeri it was estimated to be 273 LST based on the value of L0 from the von Bertalanffy growth model. Both species appeared to have continuous reproductive cycles and low fecundity with an average ovarian fecundity of eight follicles for D. polyommata and a litter size of five to seven pups for M. walkeri. Based on these life-history traits, D. polyommata is more resilient to fishing pressure than M. walkeri. © 2016 The Fisheries Society of the British Isles.

Estimation of Land Surface Temperature for the Quantitative Analysis of Land Cover of Lower Areas of Sindh to Assess the Impacts of Climate Variability

NASA Astrophysics Data System (ADS)

Qaisar, Maha

2016-07-01

Due to the present land use practices and climate variability, drastic shifts in regional climate and land covers are easily seen and their future reduction and gain are too well predicted. Therefore, there is an increasing need for data on land-cover changes at narrow and broad spatial scales. In this study, a remote sensing-based technique for land-cover-change analysis is applied to the lower Sindh areas for the last decade. Landsat satellite products were analyzed on an alternate yearly basis, from 1990 to 2016. Then Land-cover-change magnitudes were measured and mapped for alternate years. Land Surface Temperature (LST) is one of the critical elements in the natural phenomena of surface energy and water balance at local and global extent. However, LST was computed by using Landsat thermal bands via brightness temperature and a vegetation index. Normalized difference vegetation index (NDVI) was interpreted and maps were achieved. LST reflected NDVI patterns with complexity of vegetation patterns. Along with this, Object Based Image Analysis (OBIA) was done for classifying 5 major classes of water, vegetation, urban, marshy lands and barren lands with significant map layouts. Pakistan Meteorological Department provided the climate data in which rainfall, temperature and air temperature are included. Once the LST and OBIA are performed, overlay analysis was done to correlate the results of LST with OBIA and LST with meteorological data to ascertain the changes in land covers due to increasing centigrade of LST. However, satellite derived LST was also correlated with climate data for environmental analysis and to estimate Land Surface Temperature for assessing the inverse impacts of climate variability. This study's results demonstrate the land-cover changes in Lower Areas of Sindh including the Indus Delta mostly involve variations in land-cover conditions due to inter-annual climatic variability and temporary shifts in seasonality. However it is too concluded that transitory alteration of the biophysical characteristics of the surface driven by variations in rainfall is the prevailing progression. Moreover, future work will focus on finer-scale analysis and validations of patterns of changes due to rapid urbanization and population explosion in poverty stricken areas of Sindh which are posing an adverse impact on the land utilization and in turn increasing the land surface temperature and ultimately more stress on the low lying areas of Sindh i.e. Indus Delta will be losing its productivity and capacity to bear biodiversity whether the fauna or flora. Hence, this regional scale problem will become a global concern. Therefore, it is needed to stop the menace in its starting phase to mitigate the problem and to bring minds on this horrendous situation.

Relationship among land surface temperature and LUCC, NDVI in typical karst area.

PubMed

Deng, Yuanhong; Wang, Shijie; Bai, Xiaoyong; Tian, Yichao; Wu, Luhua; Xiao, Jianyong; Chen, Fei; Qian, Qinghuan

2018-01-12

Land surface temperature (LST) can reflect the land surface water-heat exchange process comprehensively, which is considerably significant to the study of environmental change. However, research about LST in karst mountain areas with complex topography is scarce. Therefore, we retrieved the LST in a karst mountain area from Landsat 8 data and explored its relationships with LUCC and NDVI. The results showed that LST of the study area was noticeably affected by altitude and underlying surface type. In summer, abnormal high-temperature zones were observed in the study area, perhaps due to karst rocky desertification. LSTs among different land use types significantly differed with the highest in construction land and the lowest in woodland. The spatial distributions of NDVI and LST exhibited opposite patterns. Under the spatial combination of different land use types, the LST-NDVI feature space showed an obtuse-angled triangle shape and showed a negative linear correlation after removing water body data. In summary, the LST can be retrieved well by the atmospheric correction model from Landsat 8 data. Moreover, the LST of the karst mountain area is controlled by altitude, underlying surface type and aspect. This study provides a reference for land use planning, ecological environment restoration in karst areas.

Ecological Model to Predict Potential Habitats of Oncomelania hupensis, the Intermediate Host of Schistosoma japonicum in the Mountainous Regions, China.

PubMed

Zhu, Hong-Ru; Liu, Lu; Zhou, Xiao-Nong; Yang, Guo-Jing

2015-01-01

Schistosomiasis japonica is a parasitic disease that remains endemic in seven provinces in the People's Republic of China (P.R. China). One of the most important measures in the process of schistosomiasis elimination in P.R. China is control of Oncomelania hupensis, the unique intermediate host snail of Schistosoma japonicum. Compared with plains/swamp and lake regions, the hilly/mountainous regions of schistosomiasis endemic areas are more complicated, which makes the snail survey difficult to conduct precisely and efficiently. There is a pressing call to identify the snail habitats of mountainous regions in an efficient and cost-effective manner. Twelve out of 56 administrative villages distributed with O. hupensis in Eryuan, Yunnan Province, were randomly selected to set up the ecological model. Thirty out of the rest of 78 villages (villages selected for building model were excluded from the villages for validation) in Eryuan and 30 out of 89 villages in Midu, Yunnan Province were selected via a chessboard method for model validation, respectively. Nine-year-average Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) as well as Digital Elevation Model (DEM) covering Eryuan and Midu were extracted from MODIS and ASTER satellite images, respectively. Slope, elevation and the distance from every village to its nearest stream were derived from DEM. Suitable survival environment conditions for snails were defined by comparing historical snail presence data and remote sensing derived images. According to the suitable conditions for snails, environment factors, i.e. NDVI, LST, elevation, slope and the distance from every village to its nearest stream, were integrated into an ecological niche model to predict O. hupensis potential habitats in Eryuan and Midu. The evaluation of the model was assessed by comparing the model prediction and field investigation. Then, the consistency rate of model validation was calculated in Eryuan and Midu Counties, respectively. The final ecological niche model for potential O. hupensis habitats prediction comprised the following environmental factors, namely: NDVI (≥ 0.446), LST (≥ 22.70°C), elevation (≤ 2,300 m), slope (≤ 11°) and the distance to nearest stream (≤ 1,000 m). The potential O. hupensis habitats in Eryuan distributed in the Lancang River basin and O. hupensis in Midu shows a trend of clustering in the north and spotty distribution in the south. The consistency rates of the ecological niche model in Eryuan and Midu were 76.67% and 83.33%, respectively. The ecological niche model integrated with NDVI, LST, elevation, slope and distance from every village to its nearest stream adequately predicted the snail habitats in the mountainous regions.

Implementing the LifeSkills Training drug prevention program: factors related to implementation fidelity.

PubMed

Mihalic, Sharon F; Fagan, Abigail A; Argamaso, Susanne

2008-01-18

Widespread replication of effective prevention programs is unlikely to affect the incidence of adolescent delinquency, violent crime, and substance use until the quality of implementation of these programs by community-based organizations can be assured. This paper presents the results of a process evaluation employing qualitative and quantitative methods to assess the extent to which 432 schools in 105 sites implemented the LifeSkills Training (LST) drug prevention program with fidelity. Regression analysis was used to examine factors influencing four dimensions of fidelity: adherence, dosage, quality of delivery, and student responsiveness. Although most sites faced common barriers, such as finding room in the school schedule for the program, gaining full support from key participants (i.e., site coordinators, principals, and LST teachers), ensuring teacher participation in training workshops, and classroom management difficulties, most schools involved in the project implemented LST with very high levels of fidelity. Across sites, 86% of program objectives and activities required in the three-year curriculum were delivered to students. Moreover, teachers were observed using all four recommended teaching practices, and 71% of instructors taught all the required LST lessons. Multivariate analyses found that highly rated LST program characteristics and better student behavior were significantly related to a greater proportion of material taught by teachers (adherence). Instructors who rated the LST program characteristics as ideal were more likely to teach all lessons (dosage). Student behavior and use of interactive teaching techniques (quality of delivery) were positively related. No variables were related to student participation (student responsiveness). Although difficult, high implementation fidelity by community-based organizations can be achieved. This study suggests some important factors that organizations should consider to ensure fidelity, such as selecting programs with features that minimize complexity while maximizing flexibility. Time constraints in the classroom should be considered when choosing a program. Student behavior also influences program delivery, so schools should train teachers in the use of classroom management skills. This project involved comprehensive program monitoring and technical assistance that likely facilitated the identification and resolution of problems and contributed to the overall high quality of implementation. Schools should recognize the importance of training and technical assistance to ensure quality program delivery.

Analyzing remotely sensed datasets for improved characterization of field-scale interventions for food security

NASA Astrophysics Data System (ADS)

Limaye, A. S.; Ellenburg, W. L., II; Coffee, K.; Ashmall, W.; Stanton, K.; Burks, J.; Irwin, D.

2017-12-01

Agriculture interventions such as irrigation, improved fertilization, and advanced cultivars have the potential to increase food security and ensure climate resilience. However, in order broaden the support of activities like these, environmental managers must be able to assess their impact. Often field data are difficult to obtain and decisions are made with limited information. Satellite products can provide relevant information at field and village wide scales that can assist in this process. SERVIR is taking an aim of helping connect the space-based products to help the efficacy of village scale interventions through a couple of web-based tools, called ClimateSERV and AgriSERV. ClimateSERV has been active since 2014, and has increased in the data holdings and access points. Currently, ClimateSERV enables users to create geographic regions of their choosing and to compute key statistics for those regions. Rainfall (GPM IMERG, CHIRPS), vegetation indices (eMODIS Normalized Difference Vegetation Index - NDVI; Evaporative Stress Index), and North American Multi-model Ensemble-based seasonal climate forecasts of rainfall and temperature. ClimateSERV can also query the Google Earth Engine holdings for datasets, currently, ClimateSERV provides access to the daytime MODIS Land Surface Temperature (LST). Our first such derived product is a monthly rainfall analysis feature which combines CHIRPS historic rainfall with seasonal forecast models AgriSERV is a derived web-based tool based on the ClimateSERV data holdings. It is designed to provide easy to interpret analysis, based NDVI and rainfall. This tool allows users to draw two areas of interest, one control with no intervention and another that has experienced intervention. An on-demand comparative analysis is performed and the user is presented with side-by-side charts and summary data that highlight the differences of the two areas in terms of vegetation health, derived growing season lengths and rainfall. The analysis is based on an area-weighted average of the gridded NDVI and rainfall data. The users can download the summary data table as well as the full dataset for the period specified. This presentation is intended to showcase the utility of the intervention programs and to provide an objective rationale for expansion of those intervention programs.

Dichotomy of protective cellular immune responses to human visceral leishmaniasis

PubMed Central

Khalil, E A G; Ayed, N B; Musa, A M; Ibrahim, M E; Mukhtar, M M; Zijlstra, E E; Elhassan, I M; Smith, P G; Kieny, P M; Ghalib, H W; Zicker, F; Modabber, F; Elhassan, A M

2005-01-01

Healing/protective responses in human visceral leishmaniasis (VL) are associated with stimulation/production of Th1 cytokines, such as interferon IFN-γ, and conversion in the leishmanin skin test (LST). Such responses were studied for 90 days in 44 adult healthy volunteers from VL non-endemic areas, with no past history of VL/cutaneous leishmaniasis (CL) and LST non-reactivity following injection with one of four doses of Alum-precipitated autoclaved Leishmania major (Alum/ALM) ± bacille Calmette–Guérin (BCG), a VL candidate vaccine. The vaccine was well tolerated with minimal localized side-effects and without an increase in antileishmanial antibodies or interleukin (IL)-5. Five volunteers (5/44; 11·4%) had significant IFN-γ production by peripheral blood mononuclear cells (PBMCs) in response to Leishmania antigens in their prevaccination samples (P = 0·001) but were LST non-reactive. On day 45, more than half the volunteers (26/44; 59·0%) had significantly high LST indurations (mean 9·2 ± 2·7 mm) and high IFN-γ levels (mean 1008 ± 395; median 1247 pg/ml). Five volunteers had significant L. donovani antigen-induced IFN-γ production (mean 873 ± 290; median 902; P = 0·001), but were non-reactive in LST. An additional five volunteers (5/44; 11·4%) had low IFN-γ levels (mean 110 ± 124 pg/ml; median 80) and were non-reactive in LST (induration = 00 mm). The remaining eight volunteers had low IFN-γ levels, but significant LST induration (mean 10 ± 2·9 mm; median 11). By day 90 the majority of volunteers (27/44; 61·4%) had significant LST induration (mean 10·8 ± 9·9 mm; P < 0·001), but low levels of L. donovani antigen-induced IFN-γ (mean 66·0 ± 62 pg/ml; P > 0.05). Eleven volunteers (11/44; 25%) had significantly high levels of IFN-γ and LST induration, while five volunteers had low levels of IFN-γ (<100 pg/ml) and no LST reactivity (00 mm). One volunteer was lost to follow-up. In conclusion, it is hypothesized that cellular immune responses to human VL are dichotomatous, and that IFN-γ production and the LST response are not in a causal relationship. Following vaccination and probably cure of VL infection, the IFN-γ response declines with time while the LST response persists. LST is a simple test that can be used to assess candidate vaccine efficacy. PMID:15807861

Evaluation of spatio-temporal variability in Land Surface Temperature: A case study of Zonguldak, Turkey.

PubMed

Sekertekin, Aliihsan; Kutoglu, Senol Hakan; Kaya, Sinasi

2016-01-01

The aim of this study is to analyze spatio-temporal variability in Land Surface Temperature (LST) in and around the city of Zonguldak as a result of the growing urbanization and industrialization during the last decade. Three Landsat 5 data and one Landsat 8 data acquired on different dates were exploited in acquiring LST maps utilizing mono-window algorithm. The outcomes obtained from this study indicate that there exists a significant temperature rise in the region for the time period between 1986 and 2015. Some cross sections were selected in order to examine the relationship between the land use and LST changes in more detail. The mean LST difference between 1986 and 2015 in ERDEMIR iron and steel plant (6.8 °C), forestland (3 °C), city and town centers (4.2 °C), municipal rubbish tip (-3.9 °C), coal dump site (12.2 °C), and power plants' region (7 °C) were presented. In addition, the results indicated that the mean LST difference between forestland and city centers was approximately 5 °C, and the difference between forestland and industrial enterprises was almost 8 °C for all years. Spatio-temporal variability in LST in Zonguldak was examined in that study and due to the increase in LST, policy makers and urban planners should consider LST and urban heat island parameters for sustainable development.

Association of CAD, a multifunctional protein involved in pyrimidine synthesis, with mLST8, a component of the mTOR complexes

PubMed Central

2013-01-01

Background mTOR is a genetically conserved serine/threonine protein kinase, which controls cell growth, proliferation, and survival. A multifunctional protein CAD, catalyzing the initial three steps in de novo pyrimidine synthesis, is regulated by the phosphorylation reaction with different protein kinases, but the relationship with mTOR protein kinase has not been known. Results CAD was recovered as a binding protein with mLST8, a component of the mTOR complexes, from HEK293 cells transfected with the FLAG-mLST8 vector. Association of these two proteins was confirmed by the co-immuoprecipitaiton followed by immunoblot analysis of transfected myc-CAD and FLAG-mLST8 as well as that of the endogenous proteins in the cells. Analysis using mutant constructs suggested that CAD has more than one region for the binding with mLST8, and that mLST8 recognizes CAD and mTOR in distinct ways. The CAD enzymatic activity decreased in the cells depleted of amino acids and serum, in which the mTOR activity is suppressed. Conclusion The results obtained indicate that mLST8 bridges between CAD and mTOR, and plays a role in the signaling mechanism where CAD is regulated in the mTOR pathway through the association with mLST8. PMID:23594158

Trends of urban surface temperature and heat island characteristics in the Mediterranean

NASA Astrophysics Data System (ADS)

Benas, Nikolaos; Chrysoulakis, Nektarios; Cartalis, Constantinos

2017-11-01

Urban air temperature studies usually focus on the urban canopy heat island phenomenon, whereby the city center experiences higher near surface air temperatures compared to its surrounding non-urban areas. The Land Surface Temperature (LST) is used instead of urban air temperature to identify the Surface Urban Heat Island (SUHI). In this study, the nighttime LST and SUHI characteristics and trends in the seventeen largest Mediterranean cities were investigated, by analyzing satellite observations for the period 2001-2012. SUHI averages and trends were based on an innovative approach of comparing urban pixels to randomly selected non-urban pixels, which carries the potential to better standardize satellite-derived SUHI estimations. A positive trend for both LST and SUHI for the majority of the examined cities was documented. Furthermore, a 0.1 °C decade-1 increase in urban LST corresponded to an increase in SUHI by about 0.04 °C decade-1. A longitudinal differentiation was found in the urban LST trends, with higher positive values appearing in the eastern Mediterranean. Examination of urban infrastructure and development factors during the same period revealed correlations with SUHI trends, which can be used to explain differences among cities. However, the majority of the cities examined show considerably increased trends in terms of the enhancement of SUHI. These findings are considered important so as to promote sustainable urbanization, as well as to support the development of heat island adaptation and mitigation plans in the Mediterranean.

Determining the impact of urban components on land surface temperature of Istanbul by using remote sensing indices.

PubMed

Bektaş Balçik, Filiz

2014-02-01

For the past 60 years, Istanbul has been experiencing an accelerated urban expansion. This urban expansion is leading to the replacement of natural surfaces by various artificial materials. This situation has a critical impact on the environment due to the alteration of heat energy balance. In this study, the effect upon the urban heat island (UHI) of Istanbul was analyzed using 2009 dated Landsat 5 Thematic Mapper (TM) data. An Index Based Built-up Index (IBI) was used to derive artificial surfaces in the study area. To produce the IBI index, Soil-Adjusted Vegetation Index, Normalized Difference Built-up Index, and Modified Normalized Difference Water Index were calculated. Land surface temperature (LST) distribution was derived from Landsat 5 TM images using a mono-window algorithm. In addition, 24 transects were selected, and different regression models were applied to explore the correlation between LST and IBI index. The results show that artificial surfaces have a positive exponential relationship with LST rather than a simple linear one. An ecological evaluation index of the region was calculated to explore the impact of both the vegetated land and the artificial surfaces on the UHI. Therefore, the quantitative relationship of urban components (artificial surfaces, vegetation, and water) and LST was examined using multivariate statistical analysis, and the correlation coefficient was obtained as 0.829. This suggested that the areas with a high rate of urbanization will accelerate the rise of LST and UHI in Istanbul.

Impacts of land use and land cover on surface and air temperature in urban landscapes

NASA Astrophysics Data System (ADS)

Crum, S.; Jenerette, D.

2015-12-01

Accelerating urbanization affects regional climate as the result of changing land cover and land use (LCLU). Urban land cover composition may provide valuable insight into relationships among urbanization, air, and land-surface temperature (Ta and LST, respectively). Climate may alter these relationships, where hotter climates experience larger LULC effects. To address these hypotheses we examined links between Ta, LST, LCLU, and vegetation across an urban coastal to desert climate gradient in southern California, USA. Using surface temperature radiometers, continuously measuring LST on standardized asphalt, concrete, and turf grass surfaces across the climate gradient, we found a 7.2°C and 4.6°C temperature decrease from asphalt to vegetated cover in the coast and desert, respectively. There is 131% more temporal variation in asphalt than turf grass surfaces, but 37% less temporal variation in concrete than turf grass. For concrete and turf grass surfaces, temporal variation in temperature increased from coast to desert. Using ground-based thermal imagery, measuring LST for 24 h sequences over citrus orchard and industrial use locations, we found a 14.5°C temperature decrease from industrial to orchard land use types (38.4°C and 23.9°C, respectively). Additionally, industrial land use types have 209% more spatial variation than orchard (CV=0.20 and 0.09, respectively). Using a network of 300 Ta (iButton) sensors mounted in city street trees throughout the region and hyperspectral imagery data we found urban vegetation greenness, measured using the normalized difference vegetation index (NDVI), was negatively correlated to Ta at night across the climate gradient. Contrasting previous findings, the closest coupling between NDVI and Ta is at the coast from 0000 h to 0800 h (highest r2 = 0.6, P < 0.05) while relationships at the desert are weaker (highest r2 = 0.38, P < 0.05). These findings indicate that vegetation cover in urbanized regions of southern California, USA decrease Ta and LST and spatial variation in LST, while built surfaces and land uses have the opposite effect. Furthermore these relationships are regulated by regional climate patterns, with decreases in Ta and LST being strongest in the coastal sub-region.

Estimation of daily minimum land surface air temperature using MODIS data in southern Iran

NASA Astrophysics Data System (ADS)

Didari, Shohreh; Norouzi, Hamidreza; Zand-Parsa, Shahrokh; Khanbilvardi, Reza

2017-11-01

Land surface air temperature (LSAT) is a key variable in agricultural, climatological, hydrological, and environmental studies. Many of their processes are affected by LSAT at about 5 cm from the ground surface (LSAT5cm). Most of the previous studies tried to find statistical models to estimate LSAT at 2 m height (LSAT2m) which is considered as a standardized height, and there is not enough study for LSAT5cm estimation models. Accurate measurements of LSAT5cm are generally acquired from meteorological stations, which are sparse in remote areas. Nonetheless, remote sensing data by providing rather extensive spatial coverage can complement the spatiotemporal shortcomings of meteorological stations. The main objective of this study was to find a statistical model from the previous day to accurately estimate spatial daily minimum LSAT5cm, which is very important in agricultural frost, in Fars province in southern Iran. Land surface temperature (LST) data were obtained using the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites at daytime and nighttime periods with normalized difference vegetation index (NDVI) data. These data along with geometric temperature and elevation information were used in a stepwise linear model to estimate minimum LSAT5cm during 2003-2011. The results revealed that utilization of MODIS Aqua nighttime data of previous day provides the most applicable and accurate model. According to the validation results, the accuracy of the proposed model was suitable during 2012 (root mean square difference ( RMSD) = 3.07 °C, {R}_{adj}^2 = 87 %). The model underestimated (overestimated) high (low) minimum LSAT5cm. The accuracy of estimation in the winter time was found to be lower than the other seasons ( RMSD = 3.55 °C), and in summer and winter, the errors were larger than in the remaining seasons.

Patient-related factors and circumstances surrounding decisions to forego life-sustaining treatment, including intensive care unit admission refusal.

PubMed

Reignier, Jean; Dumont, Romain; Katsahian, Sandrine; Martin-Lefevre, Laurent; Renard, Benoit; Fiancette, Maud; Lebert, Christine; Clementi, Eva; Bontemps, Frederic

2008-07-01

To assess decisions to forego life-sustaining treatment (LST) in patients too sick for intensive care unit (ICU) admission, comparatively to patients admitted to the ICU. Prospective observational cohort study. A medical-surgical ICU. Consecutive patients referred to the ICU during a one-yr period. None. Of 898 triaged patients, 147 were deemed too well to benefit from ICU admission. Decisions to forego LST were made in 148 of 666 (22.2%) admitted patients and in all 85 patients deemed too sick for ICU admission. Independent predictors of decisions to forego LST at ICU refusal rather than after ICU admission were: age; underlying disease; living in an institution; preexisting cognitive impairment; admission for medical reasons; and acute cardiac failure, acute central neurologic illness, or sepsis. Hospital mortality after decisions to forego LST was not significantly different in refused and admitted patients (77.5% vs. 86.5%; p = .1). Decisions to forego LST were made via telephone in 58.8% of refused patients and none of the admitted patients. Nurses caring for the patient had no direct contact with the ICU physicians for 62.3% of the decisions in refused patients, whereas meetings between nurses and physicians occurred in 70.3% of decisions to forego LST in the ICU. Patients or relatives were involved in 28.2% of decisions to forego LST at ICU refusal compared with 78.4% of decisions to forego LST in ICU patients (p < .001). All patients deemed too sick for ICU admission had decisions to forego LST. These decisions were made without direct patient examination in two-thirds of refused patients (vs. none of admitted patients) and were associated with less involvement of nurses and relatives compared with decisions in admitted patients. Further work is needed to improve decisions to forego LST made under the distinctive circumstances of triage.

Terminally Ill Taiwanese Cancer Patients' and Family Caregivers' Agreement on Patterns of Life-Sustaining Treatment Preferences Is Poor to Fair and Declines Over a Decade: Results From Two Independent Cross-Sectional Studies.

PubMed

Liu, Tsang-Wu; Wen, Fur-Hsing; Wang, Cheng-Hsu; Hong, Ruey-Long; Chow, Jyh-Ming; Chen, Jen-Shi; Chiu, Chang-Fang; Tang, Siew Tzuh

2017-07-01

Temporal changes have not been examined in patient-caregiver agreement on life-sustaining treatment (LST) preferences at end of life (EOL). We explored the extent of and changes in patient-caregiver agreement on LST-preference patterns for two independent cohorts of Taiwanese cancer patient-family caregiver dyads recruited a decade apart. We surveyed preferences for cardiopulmonary resuscitation, intensive care unit care, cardiac massage, intubation with mechanical ventilation, intravenous nutritional support, tube feeding, and dialysis among 1049 and 1901 dyads in 2003-2004 and 2011-2012, respectively. LST-preference patterns were examined by multi-group latent class analysis. Extent of patient-caregiver agreement on LST-preference patterns was determined by percentage agreement and kappa coefficients. For both patients and family caregivers, we identified seven distinct LST-preference classes. Patient-caregiver agreement on LST-preference patterns was poor to fair across both study cohorts, indicated by 24.4%-43.5% agreement and kappa values of 0.06 (95% CI: 0.04, 0.09) to 0.27 (0.23, 0.30), and declined significantly over time. Agreement on LST-preference patterns was most likely when both patients and caregivers uniformly rejected LSTs. When patients disagreed with caregivers on LST-preference patterns, discrepancies were most likely when patients totally rejected LSTs but caregivers uniformly preferred LSTs or preferred nutritional support but rejected other treatments. Patients and family caregivers had poor-to-fair agreement on LST-preference patterns, and agreement declined significantly over a decade. Encouraging an open dialogue between patients and their family caregivers about desired EOL care would facilitate patient-caregiver agreement on LST-preference patterns, thus honoring terminally ill cancer patients' wishes when they cannot make EOL-care decisions. Copyright © 2017 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape

NASA Astrophysics Data System (ADS)

Trlica, A.; Hutyra, L. R.; Schaaf, C. L.; Erb, A.; Wang, J. A.

2017-11-01

Land surface albedo is a key parameter controlling the local energy budget, and altering the albedo of built surfaces has been proposed as a tool to mitigate high near-surface temperatures in the urban heat island. However, most research on albedo in urban landscapes has used coarse-resolution data, and few studies have attempted to relate albedo to other urban land cover characteristics. This study provides an empirical description of urban summertime albedo using 30 m remote sensing measurements in the metropolitan area around Boston, Massachusetts, relating albedo to metrics of impervious cover fraction, tree canopy coverage, population density, and land surface temperature (LST). At 30 m spatial resolution, median albedo over the study area (excluding open water) was 0.152 (0.112-0.187). Trends of lower albedo with increasing urbanization metrics and temperature emerged only after aggregating data to 500 m or the boundaries of individual towns, at which scale a -0.01 change in albedo was associated with a 29 (25-35)% decrease in canopy cover, a 27 (24-30)% increase in impervious cover, and an increase in population from 11 to 386 km-2. The most intensively urbanized towns in the region showed albedo up to 0.035 lower than the least urbanized towns, and mean mid-morning LST 12.6°C higher. Trends in albedo derived from 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were comparable, but indicated a strong contribution of open water at this coarser resolution. These results reveal linkages between albedo and urban land cover character, and offer empirical context for climate resilient planning and future landscape functional changes with urbanization.

Distinguishing State Variability From Trait Change in Longitudinal Data: The Role of Measurement (Non)Invariance in Latent State-Trait Analyses

PubMed Central

Geiser, Christian; Keller, Brian T.; Lockhart, Ginger; Eid, Michael; Cole, David A.; Koch, Tobias

2014-01-01

Researchers analyzing longitudinal data often want to find out whether the process they study is characterized by (1) short-term state variability, (2) long-term trait change, or (3) a combination of state variability and trait change. Classical latent state-trait (LST) models are designed to measure reversible state variability around a fixed set-point or trait, whereas latent growth curve (LGC) models focus on long-lasting and often irreversible trait changes. In the present paper, we contrast LST and LGC models from the perspective of measurement invariance (MI) testing. We show that establishing a pure state-variability process requires (a) the inclusion of a mean structure and (b) establishing strong factorial invariance in LST analyses. Analytical derivations and simulations demonstrate that LST models with non-invariant parameters can mask the fact that a trait-change or hybrid process has generated the data. Furthermore, the inappropriate application of LST models to trait change or hybrid data can lead to bias in the estimates of consistency and occasion-specificity, which are typically of key interest in LST analyses. Four tips for the proper application of LST models are provided. PMID:24652650

Probability density function of a puff dispersing from the wall of a turbulent channel

NASA Astrophysics Data System (ADS)

Nguyen, Quoc; Papavassiliou, Dimitrios

2015-11-01

Study of dispersion of passive contaminants in turbulence has proved to be helpful in understanding fundamental heat and mass transfer phenomena. Many simulation and experimental works have been carried out to locate and track motions of scalar markers in a flow. One method is to combine Direct Numerical Simulation (DNS) and Lagrangian Scalar Tracking (LST) to record locations of markers. While this has proved to be useful, high computational cost remains a concern. In this study, we develop a model that could reproduce results obtained by DNS and LST for turbulent flow. Puffs of markers with different Schmidt numbers were released into a flow field at a frictional Reynolds number of 150. The point of release was at the channel wall, so that both diffusion and convection contribute to the puff dispersion pattern, defining different stages of dispersion. Based on outputs from DNS and LST, we seek the most suitable and feasible probability density function (PDF) that represents distribution of markers in the flow field. The PDF would play a significant role in predicting heat and mass transfer in wall turbulence, and would prove to be helpful where DNS and LST are not always available.

[Pathomorphosis of the mammary gland tissue during radical interventions using high-frequency electrosurgical welding].

PubMed

Bondar', G V; Sedakov, I E; Kobets, R A

2011-04-01

High-frequency electric welding of a live soft tissues (HFEW LST) is applied widely in all surgical specialties. Its application in surgery of mammary gland cancer constitutes a perspective trend. The impact of HFEW LST and monopolar electrocoagulation on tissues while performing radical operations in patients-women for mammary gland cancer was studied up. Basing on analysis of pathomorphological investigations data, the possibility and perspective of the welding technologies application, while performing radical operations on mammary glands, were established.

Cystatin C is not a good candidate biomarker for HNF1A-MODY.

PubMed

Nowak, Natalia; Szopa, Magdalena; Thanabalasingham, Gaya; McDonald, Tim J; Colclough, Kevin; Skupien, Jan; James, Timothy J; Kiec-Wilk, Beata; Kozek, Elzbieta; Mlynarski, Wojciech; Hattersley, Andrew T; Owen, Katharine R; Malecki, Maciej T

2013-10-01

Cystatin C is a marker of glomerular filtration rate (GFR). Its level is influenced, among the others, by CRP whose concentration is decreased in HNF1A-MODY. We hypothesized that cystatin C level might be altered in HNF1A-MODY. We aimed to evaluate cystatin C in HNF1A-MODY both as a diagnostic marker and as a method of assessing GFR. We initially examined 51 HNF1A-MODY patients, 56 subjects with type 1 diabetes (T1DM), 39 with type 2 diabetes (T2DM) and 43 non-diabetic individuals (ND) from Poland. Subjects from two UK centres were used as replication panels: including 215 HNF1A-MODY, 203 T2DM, 39 HNF4A-MODY, 170 GCK-MODY, 17 HNF1B-MODY and 58 T1DM patients. The data were analysed with additive models, adjusting for gender, age, BMI and estimated GFR (creatinine). In the Polish subjects, adjusted cystatin C level in HNF1A-MODY was lower compared with T1DM, T2DM and ND (p < 0.05). Additionally, cystatin C-based GFR was higher than that calculated from creatinine level (p < 0.0001) in HNF1A-MODY, while the two GFR estimates were similar or cystatin C-based lower in the other groups. In the UK subjects, there were no differences in cystatin C between HNF1A-MODY and the other diabetic subgroups, except HNF1B-MODY. In UK HNF1A-MODY, cystatin C-based GFR estimate was higher than the creatinine-based one (p < 0.0001). Concluding, we could not confirm our hypothesis (supported by the Polish results) that cystatin C level is altered by HNF1A mutations; thus, it cannot be used as a biomarker for HNF1A-MODY. In HNF1A-MODY, the cystatin C-based GFR estimate is higher than the creatinine-based one.

Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds

PubMed Central

Yang, Yingbao; Li, Xiaolong; Pan, Xin; Zhang, Yong; Cao, Chen

2017-01-01

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions. PMID:28368301

Regional Mapping of Coupled Fluxes of Carbon and Water Using Multi-Sensor Fusion Techniques

NASA Astrophysics Data System (ADS)

Schull, M. A.; Anderson, M. C.; Semmens, K. A.; Yang, Y.; Gao, F.; Hain, C.; Houborg, R.

2014-12-01

In an ever-changing climate there is an increasing need to measure the fluxes of water, energy and carbon for decision makers to implement policies that will help mitigate the effects of climate change. In an effort to improve drought monitoring, water resource management and agriculture assessment capabilities, a multi-scale and multi-sensor framework for routine mapping of land-surface fluxes of water and energy at field to regional scales has been established. The framework uses the ALEXI (Atmosphere Land Exchange Inverse)/DisALEXI (Disaggregated ALEXI) suite of land-surface models forced by remotely sensed data from Landsat, MODIS (MODerate resolution Imaging Spectroradiometer), and GOES (Geostationary Operational Environmental Satellite). Land-surface temperature (LST) can be an effective substitute for in-situ surface moisture observations and a valuable metric for constraining land-surface fluxes at sub-field scales. The adopted multi-scale thermal-based land surface modeling framework facilitates regional to local downscaling of water and energy fluxes by using a combination of shortwave reflective and thermal infrared (TIR) imagery from GOES (4-10 km; hourly), MODIS (1 km; daily), and Landsat (30-100 m; bi-weekly). In this research the ALEXI/DisALEXI modeling suite is modified to incorporate carbon fluxes using a stomatal resistance module, which replaces the Priestley-Taylor latent heat approximation. In the module, canopy level nominal light-use-efficiency (βn) is the parameter that modulates the flux of water and carbon in and out of the canopy. Leaf chlorophyll (Chl) is a key parameter for quantifying variability in photosynthetic efficiency to facilitate the spatial distribution of coupled carbon and water retrievals. Spatial distribution of Chl are retrieved from Landsat (30 m) using a surface reflectance dataset as input to the REGularized canopy reFLECtance (REGFLEC) tool. The modified ALEXI/DisALEXI suite is applied to regions of rain fed and irrigated soybean and maize agricultural landscapes within the continental U.S. and flux estimates are compared with flux tower observations.

Study on cavitation effect of mechanical seals with laser-textured porous surface

NASA Astrophysics Data System (ADS)

Liu, T.; Chen, H. l.; Liu, Y. H.; Wang, Q.; Liu, Z. B.; Hou, D. H.

2012-11-01

Study on the mechanisms underlying generation of hydrodynamic pressure effect associated with laser-textured porous surface on mechanical seal, is the key to seal and lubricant properties. The theory model of mechanical seals with laser-textured porous surface (LES-MS) based on cavitation model was established. The LST-MS was calculated and analyzed by using Fluent software with full cavitation model and non-cavitation model and film thickness was predicted by the dynamic mesh technique. The results indicate that the effect of hydrodynamic pressure and cavitation are the important reasons to generate liquid film opening force on LST-MS; Cavitation effect can enhance hydrodynamic pressure effect of LST-MS; The thickness of liquid film could be well predicted with the method of dynamic mesh technique on Fluent and it becomes larger as the increasing of shaft speed and the decreasing of pressure.

Global Assessment of Land Surface Temperature From Geostationary Satellites and Model Estimates

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Q.; Minnis, P.; daSilva, A. M., Jr.; Palikonda, R.; Yost, C. R.

2012-01-01

Land surface (or 'skin') temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research we compare two global and independent data sets: (i) LST retrievals from five geostationary satellites generated at the NASA Langley Research Center (LaRC) and (ii) LST estimates from the quasi-operational NASA GEOS-5 global modeling and assimilation system. The objective is to thoroughly understand both data sets and their systematic differences in preparation for the assimilation of the LaRC LST retrievals into GEOS-5. As expected, mean differences (MD) and root-mean-square differences (RMSD) between modeled and retrieved LST vary tremendously by region and time of day. Typical (absolute) MD values range from 1-3 K in Northern Hemisphere mid-latitude regions to near 10 K in regions where modeled clouds are unrealistic, for example in north-eastern Argentina, Uruguay, Paraguay, and southern Brazil. Typically, model estimates of LST are higher than satellite retrievals during the night and lower during the day. RMSD values range from 1-3 K during the night to 2-5 K during the day, but are larger over the 50-120 W longitude band where the LST retrievals are derived from the FY2E platform

Lubrication and thermal characteristics of mechanical seal with porous surface based on cavitation

NASA Astrophysics Data System (ADS)

Huilong, Chen; Muzi, Zuo; Tong, Liu; Yu, Wang; Cheng, Xu; Qiangbo, Wu

2014-04-01

The theory model of mechanical seals with laser-textured porous surface (LST-MS) was established. The liquid film of LST-MS was simulated by the Fluent software, using full cavitation model and non-cavitation model separately. Dynamic mesh technique and relationship between viscosity and temperature were applied to simulate the internal flow field and heat characteristics of LST-MS, based on the more accurate cavitation model. Influence of porous depth ratio porous diameter ɛ and porous density SP on lubrication performance and the variation of lubrication and thermal properties with shaft speed and sealing pressure were analyzed. The results indicate that the strongest hydrodynamic pressure effect and the biggest thickness of liquid film are obtained when ɛ and SP are respectively about 0.025 and 0.5 which were thought to be the optimum value. The frictional heat leads to the increase of liquid film temperature and the decrease of medium viscosity with the shaft speed increasing. The hydrodynamic pressure effect increases as shaft speed increasing, however it decreases as the impact of frictional heat.

Earth Observations for Early Detection of Agricultural Drought in Countries at Risk: Contributions of the Famine Early Warning Systems Network (FEWS NET) (Invited)

NASA Astrophysics Data System (ADS)

Verdin, J. P.; Rowland, J.; Senay, G. B.; Funk, C. C.; Budde, M. E.; Husak, G. J.; Jayanthi, H.

2013-12-01

The Group on Earth Observations' Global Agricultural Monitoring (GEOGLAM) implementation plan emphasizes the information needs of countries at risk of food insecurity emergencies. Countries in this category are often vulnerable to disruption of agricultural production due to drought, while at the same time they lack well developed networks of in-situ observations to support early drought detection. Consequently, it is vital that Earth observations by satellites supplement those available from surface stations. The USGS, in its role as a FEWS NET implementing partner, has recently developed a number of new applications of satellite observations for this purpose. (1) In partnership with the University of California, Santa Barbara, a 30+ year time series of gridded precipitation estimates (CHIRPS) has been developed by blending NOAA GridSat B1 geostationary thermal infrared imagery with station observations using robust geostatistical methods. The core data set consists of pentadal (5-daily) accumulations from 1981-2013 at 0.05 degree spatial resolution between +/- 50 degrees latitude. Validation has been recently completed, and applications for gridded crop water balance calculations and mapping the Standardized Precipitation Index are in development. (2) Actual evapotranspiration (ETa) estimates using MODIS Land Surface Temperature (LST) data at 1-km have been successfully demonstrated using the operational Simplified Surface Energy Balance model with 8-day composites from the LPDAAC. A new, next-day latency implementation using daily LST swath data from the NASA LANCE server is in development for all the crop growing regions of the world. This ETa processing chain follows in the footsteps of (3) the expedited production of MODIS 250-meter NDVI images every five days at USGS EROS, likewise using LANCE daily swath data as input since 2010. Coverage includes Africa, Central Asia, the Middle East, Central America, and the Caribbean. (4) A surface water point monitoring method for pastoralist areas has been successfully demonstrated. It involves mapping small surface water bodies with ASTER and Landsat imagery, delineating their catchment areas with SRTM elevation data, and maintaining a continuous water balance calculation with satellite rainfall and weather model evaporation estimates to track relative fullness of these ephemeral water bodies. Piloted with NASA funds in partnership with Texas A&M University, the technique is now being implemented across the Sahel. (5) To move beyond monitoring and early warning to disaster risk management, loss exceedence probability functions are being derived for crop production shortfalls in FEWS NET countries. Drought hazard indicators, based on both ETa and crop water balance modeling forced by CHIRPS, have been used to develop regional crop drought risk models. In the case of ETa, the drought risk model provides the basis for index insurance in experiments being conducted in Senegal. A program of training events with GEO partners ensures that the data sets and applications are made available to scientists in FEWS NET countries.

Genetic particle filter application to land surface temperature downscaling

NASA Astrophysics Data System (ADS)

Mechri, Rihab; Ottlé, Catherine; Pannekoucke, Olivier; Kallel, Abdelaziz

2014-03-01

Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through land surface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote-sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to downscale LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a land surface model which simulates prior subpixel temperatures. First, the GPF downscaling scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau-Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root-mean-square errors less than 2.4 K which is a very promising result for downscaling LST satellite products.

Diurnal Cycles of High Resolution Land Surface Temperatures (LSTs) Determined from UAV Platforms Across a Range of Surface Types

NASA Astrophysics Data System (ADS)

McCabe, M.; Rosas Aguilar, J.; Parkes, S. D.; Aragon, B.

2017-12-01

Observation of land surface temperature (LST) has many practical uses, from studying boundary layer dynamics and land-atmosphere coupling, to investigating surface properties such as soil moisture status, heat stress and surface heat fluxes. Typically, LST is observed via satellite based sensors such as LandSat or via point measurements using IR radiometers. These measurements provide either good spatial coverage and resolution or good temporal coverage. However, neither are able to provide the needed spatial and temporal resolution for many of the research applications described above. Technological developments in the use of Unmanned Aerial Vehicles (UAVs), together with small thermal frame cameras, has enabled a capacity to overcome this spatiotemporal constraint. Utilising UAV platforms to collect LST measurements across diurnal cycles provides an opportunity to study how meteorological and surface properties vary in both space and time. Here we describe the collection of LST data from a multi-rotor UAV across a study domain that is observed multiple times throughout the day. Flights over crops of Rhodes grass and alfalfa, along with a bare desert surface, were repeated with between 8 and 11 surveys covering the period from early morning to sunset. Analysis of the collected thermal imagery shows that the constructed LST maps illustrate a strong diurnal cycle consistent with expected trends, but with considerable spatial and temporal variability observed within and between the different domains. These results offer new insights into the dynamics of land surface behavior in both dry and wet soil conditions and at spatiotemporal scales that are unable to be replicated using traditional satellite platforms.

The impact of urban morphology and land cover on the sensible heat flux retrieved by satellite and in-situ observations

NASA Astrophysics Data System (ADS)

Gawuc, L.; Łobocki, L.; Kaminski, J. W.

2017-12-01

Land surface temperature (LST) is a key parameter in various applications for urban environments research. However, remotely-sensed radiative surface temperature is not equivalent to kinetic nor aerodynamic surface temperature (Becker and Li, 1995; Norman and Becker, 1995). Thermal satellite observations of urban areas are also prone to angular anisotropy which is directly connected with the urban structure and relative sun-satellite position (Hu et al., 2016). Sensible heat flux (Qh) is the main component of surface energy balance in urban areas. Retrieval of Qh, requires observations of, among others, a temperature gradient. The lower level of temperature measurement is commonly replaced by remotely-sensed radiative surface temperature (Chrysoulakis, 2003; Voogt and Grimmond, 2000; Xu et al., 2008). However, such replacement requires accounting for the differences between aerodynamic and radiative surface temperature (Chehbouni et al., 1996; Sun and Mahrt, 1995). Moreover, it is important to avoid micro-scale processes, which play a major role in the roughness sublayer. This is due to the fact that Monin-Obukhov similarity theory is valid only in dynamic sublayer. We will present results of the analyses of the impact of urban morphology and land cover on the seasonal changes of sensible heat flux (Qh). Qh will be retrieved by two approaches. First will be based on satellite observations of radiative surface temperature and second will be based on in-situ observations of kinetic road temperature. Both approaches will utilize wind velocity, and air temperature observed in-situ. We will utilize time series of MODIS LST observations for the period of 2005-2014 as well as simultaneous in-situ observations collected by road weather network (9 stations). Ground stations are located across the city of Warsaw, outside the city centre in low-rise urban structure. We will account for differences in urban morphology and land cover in the proximity of ground stations. We will utilize DEM and Urban Atlas LULC database and freely available visible aerial and satellite imagery. All the analyses will be conducted for single pixels, which will be closest to the locations of the ground stations (nearest neighbour approach). Appropriate figures showing the seasonal variability of Qh will be presented.

Effect of land cover and green space on land surface temperature of a fast growing economic region in Malaysia

NASA Astrophysics Data System (ADS)

Sheikhi, A.; Kanniah, K. D.; Ho, C. H.

2015-10-01

Green space must be increased in the development of new cities as green space can moderate temperature in the cities. In this study we estimated the land surface temperature (LST) and established relationships between LST and land cover and various vegetation and urban surface indices in the Iskandar Malaysia (IM) region. IM is one of the emerging economic gateways of Malaysia, and is envisaged to transform into a metropolis by 2025. This change may cause increased temperature in IM and therefore we conducted a study by using Landsat 5 image covering the study region (2,217 km2) to estimate LST, classify different land covers and calculate spectral indices. Results show that urban surface had highest LST (24.49 °C) and the lowest temperature was recorded in, forest, rubber and water bodies ( 20.69 to 21.02°C). Oil palm plantations showed intermediate mean LST values with 21.65 °C. We further investigated the relationship between vegetation and build up densities with temperature. We extracted 1000 collocated pure pixels of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), Urban Index (UI) and LST in the study area. Results show a strong and significant negative correlation with (R2= -0.74 and -0.79) respectively between NDVI, NDWI and LST . Meanwhile a strong positive correlation (R2=0.8 and 0.86) exists between NDBI, UI and LST. These results show the importance of increasing green cover in urban environment to combat any adverse effects of climate change.

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

NASA Astrophysics Data System (ADS)

Johnson, David M.

2016-10-01

An exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250 m spatial resolution while the other products were 1000 m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250 m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250 m vs. 1000 m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250 m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008-2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000 m resolution NDVI showed somewhat poorer performance than the 250 m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.

Estimation of Actual Crop ET of Paddy Using the Energy Balance Model SMARET and Validation with Field Water Balance Measurements and a Crop Growth Model (ORYZA)

NASA Astrophysics Data System (ADS)

Nallasamy, N. D.; Muraleedharan, B. V.; Kathirvel, K.; Narasimhan, B.

2014-12-01

Sustainable management of water resources requires reliable estimates of actual evapotranspiration (ET) at fine spatial and temporal resolution. This is significant in the case of rice based irrigation systems, one of the major consumers of surface water resources and where ET forms a major component of water consumption. However huge tradeoff in the spatial and temporal resolution of satellite images coupled with lack of adequate number of cloud free images within a growing season act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. The scale at which ET is determined is decided by the spatial and temporal scale of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which form inputs to energy balance models. In this context, the current study employed disaggregation algorithms (NL-DisTrad and DisNDVI) to generate time series of LST and NDVI images at fine resolution. The disaggregation algorithms aimed at generating LST and NDVI at finer scale by integrating temporal information from concurrent coarse resolution data and spatial information from a single fine resolution image. The temporal frequency of the disaggregated images is further improved by employing composite images of NDVI and LST in the spatio-temporal disaggregation method. The study further employed half-hourly incoming surface insolation and outgoing long wave radiation obtained from the Indian geostationary satellite (Kalpana-1) to convert the instantaneous ET into daily ET and subsequently to the seasonal ET, thereby improving the accuracy of ET estimates. The estimates of ET were validated with field based water balance measurements carried out in Gadana, a subbasin predominated by rice paddy fields, located in Tamil Nadu, India.

The influence of the interactions between anthropogenic activities and multiple ecological factors on land surface temperatures of urban forests

NASA Astrophysics Data System (ADS)

Ren, Y.

2017-12-01

Context Land surface temperatures (LSTs) spatio-temporal distribution pattern of urban forests are influenced by many ecological factors; the identification of interaction between these factors can improve simulations and predictions of spatial patterns of urban cold islands. This quantitative research requires an integrated method that combines multiple sources data with spatial statistical analysis. Objectives The purpose of this study was to clarify urban forest LST influence interaction between anthropogenic activities and multiple ecological factors using cluster analysis of hot and cold spots and Geogdetector model. We introduced the hypothesis that anthropogenic activity interacts with certain ecological factors, and their combination influences urban forests LST. We also assumed that spatio-temporal distributions of urban forest LST should be similar to those of ecological factors and can be represented quantitatively. Methods We used Jinjiang as a representative city in China as a case study. Population density was employed to represent anthropogenic activity. We built up a multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) on a unified urban scale to support urban forest LST influence interaction research. Through a combination of spatial statistical analysis results, multi-source spatial data, and Geogdetector model, the interaction mechanisms of urban forest LST were revealed. Results Although different ecological factors have different influences on forest LST, in two periods with different hot spots and cold spots, the patch area and dominant tree species were the main factors contributing to LST clustering in urban forests. The interaction between anthropogenic activity and multiple ecological factors increased LST in urban forest stands, linearly and nonlinearly. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. Conclusions In conclusion, a combination of spatial statistics and GeogDetector models should be effective for quantitatively evaluating interactive relationships among ecological factors, anthropogenic activity and LST.

Evaluating land cover changes in Eastern and Southern Africa from 2000 to 2010 using validated Landsat and MODIS data

NASA Astrophysics Data System (ADS)

Al-Hamdan, Mohammad Z.; Oduor, Phoebe; Flores, Africa I.; Kotikot, Susan M.; Mugo, Robinson; Ababu, Jaffer; Farah, Hussein

2017-10-01

In this study, we assessed land cover land use (LCLU) changes and their potential environmental drivers (i.e., precipitation, temperature) in five countries in Eastern & Southern (E&S) Africa (Rwanda, Botswana, Tanzania, Malawi and Namibia) between 2000 and 2010. Landsat-derived LCLU products developed by the Regional Centre for Mapping of Resources for Development (RCMRD) through the SERVIR (Spanish for ;to serve;) program, a joint initiative of NASA and USAID, and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to evaluate and quantify the LCLU changes in these five countries. Given that the original development of the MODIS land cover type standard products included limited training sites in Africa, we performed a two-level verification/validation of the MODIS land cover product in these five countries. Precipitation data from CHIRPS dataset were used to evaluate and quantify the precipitation changes in these countries and see if it was a significant driver behind some of these LCLU changes. MODIS Land Surface Temperature (LST) data were also used to see if temperature was a main driver too. Our validation analysis revealed that the overall accuracies of the regional MODIS LCLU product for this African region alone were lower than that of the global MODIS LCLU product overall accuracy (63-66% vs. 75%). However, for countries with uniform or homogenous land cover, the overall accuracy was much higher than the global accuracy and as high as 87% and 78% for Botswana and Namibia, respectively. In addition, the wetland and grassland classes had the highest user's accuracies in most of the countries (89%-99%), which are the ones with the highest number of MODIS land cover classification algorithm training sites. Our LCLU change analysis revealed that Botswana's most significant changes were the net reforestation, net grass loss and net wetland expansion. For Rwanda, although there have been significant forest, grass and crop expansions in some areas, there also have been significant forest, grass and crop loss in other areas that resulted in very minimal net changes. As for Tanzania, its most significant changes were the net deforestation and net crop expansion. Malawi's most significant changes were the net deforestation, net crop expansion, net grass expansion and net wetland loss. Finally, Namibia's most significant changes were the net deforestation and net grass expansion. The only noticeable environmental driver was in Malawi, which had a significant net wetland loss and could be due to the fact that it was the only country that had a reduction in total precipitation between the periods when the LCLU maps were developed. Not only that, but Malawi also happened to have a slight increase in temperature, which would cause more evaporation and net decrease in wetlands if the precipitation didn't increase as was the case in that country. In addition, within our studied countries, forestland expansion and loss as well as crop expansion and loss were happening in the same country almost equally in some cases. All of that implies that non-environmental factors, such as socioeconomics and governmental policies, could have been the main drivers of these LCLU changes in many of these countries in E&S Africa. It will be important to further study in the future the detailed effects of such drivers on these LCLU changes in this part of the world.

A low cost LST pointing control system

NASA Technical Reports Server (NTRS)

Glaese, J. R.; Kennel, H. F.; Nurre, G. S.; Seltzer, S. M.; Shelton, H. L.

1975-01-01

Vigorous efforts to reduce costs, coupled with changes in LST guidelines, took place in the Fall of 1974. These events made a new design of the LST and its Pointing and Attitude Control System possible. The major design changes are summarized as: an annular Support Systems Module; removal of image motion compensation; reaction wheels instead of CMG's; a magnetic torquer system to also perform the emergency and backup functions, eliminating the previously required mass expulsion system. Preliminary analysis indicates the Low Cost LST concept can meet the newly defined requirements and results in a significantly reduced development cost.

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad; Crosson, William; Economou, Sigrid; Estes, Maurice, Jr.; Estes, Sue; Hemmings, Sarah; Kent, Shia; Quattrochi, Dale; Wade, Gina; McClure, Leslie

2011-01-01

NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) National Center for Public Health Informatics to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this study are to develop high-quality spatial data sets of environmental variables, link these with public health data from a national cohort study, and deliver the linked data sets and associated analyses to local, state and federal end-user groups. Three daily environmental data sets will be developed for the conterminous U.S. on different spatial resolutions for the period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA's MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be disseminated to end-users for decision making through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system.

Withholding or withdrawing of life-sustaining therapy in older adults (≥ 80 years) admitted to the intensive care unit.

PubMed

Guidet, Bertrand; Flaatten, Hans; Boumendil, Ariane; Morandi, Alessandro; Andersen, Finn H; Artigas, Antonio; Bertolini, Guido; Cecconi, Maurizio; Christensen, Steffen; Faraldi, Loredana; Fjølner, Jesper; Jung, Christian; Marsh, Brian; Moreno, Rui; Oeyen, Sandra; Öhman, Christina Agwald; Pinto, Bernardo Bollen; Soliman, Ivo W; Szczeklik, Wojciech; Valentin, Andreas; Watson, Ximena; Zafeiridis, Tilemachos; De Lange, Dylan W

2018-05-17

To document and analyse the decision to withhold or withdraw life-sustaining treatment (LST) in a population of very old patients admitted to the ICU. This prospective study included intensive care patients aged ≥ 80 years in 309 ICUs from 21 European countries with 30-day mortality follow-up. LST limitation was identified in 1356/5021 (27.2%) of patients: 15% had a withholding decision and 12.2% a withdrawal decision (including those with a previous withholding decision). Patients with LST limitation were older, more frail, more severely ill and less frequently electively admitted. Patients with withdrawal of LST were more frequently male and had a longer ICU length of stay. The ICU and 30-day mortality were, respectively, 29.1 and 53.1% in the withholding group and 82.2% and 93.1% in the withdrawal group. LST was less frequently limited in eastern and southern European countries than in northern Europe. The patient-independent factors associated with LST limitation were: acute ICU admission (OR 5.77, 95% CI 4.32-7.7), Clinical Frailty Scale (CFS) score (OR 2.08, 95% CI 1.78-2.42), increased age (each 5 years of increase in age had a OR of 1.22 (95% CI 1.12-1.34) and SOFA score [OR of 1.07 (95% CI 1.05-1.09 per point)]. The frequency of LST limitation was higher in countries with high GDP and was lower in religious countries. The most important patient variables associated with the instigation of LST limitation were acute admission, frailty, age, admission SOFA score and country. ClinicalTrials.gov (ID: NTC03134807).

Spring Soil Temperature Anomalies over Northwest U.S. and later Spring-Summer Droughts/Floods over Southern Plains and Adjacent Areas

NASA Astrophysics Data System (ADS)

Xue, Y.; Diallo, I.; Li, W.; Neelin, J. D.; Chu, P. C.; Vasic, R.; Zhu, Y.; LI, Q.; Robinson, D. A.

2017-12-01

Recurrent droughts/floods are high-impact meteorological events. Many studies have attributed these episodes to variability and anomaly of global sea surface temperatures (SST). However, studies have consistently shown that SST along is unable to fully explain the extreme climate events. Remote effects of large-scale spring land surface temperature (LST) and subsurface temperature (SUBT) variability in Northwest U.S. over the Rocky Mountain area on later spring-summer droughts/floods over the Southern Plains and adjacent areas, however, have been largely ignored. In this study, evidence from climate observations and model simulations addresses these effects. The Maximum Covariance Analysis of observational data identifies that a pronounce spring LST anomaly pattern over Northwest U.S. is closely associated with summer precipitation anomalies in Southern Plains: negative/positive spring LST anomaly is associated with the summer drought/flood over the Southern Plains. The global and regional weather forecast models were used to demonstrate a causal relationship. The modeling study suggests that the observed LST and SUBT anomalies produced about 29% and 31% of observed May 2015 heavy precipitation and June 2011 precipitation deficit, respectively. The analyses discovered that the LST/SUBT's downstream effects are associated with a large-scale atmospheric stationary wave extending eastward from the LST/SUBT anomaly region. For comparison, the SST effect was also tested and produced about 31% and 45% of the May 2015 heavy precipitation and June 2011 drought conditions, respectively. This study suggests that consideration of both SST and LST/SUBT anomalies are able to explain a substantial amount of variance in precipitation at sub-seasonal scale and inclusion of the LST/SUBT effect is essential to make reliable sub-seasonal and seasonal North American drought/flood predictions.

Research study on stabilization and control: Modern sampled-data control theory. Continuous and discrete describing function analysis of the LST system. [with emphasis on the control moment gyroscope control loop

NASA Technical Reports Server (NTRS)

Kuo, B. C.; Singh, G.

1974-01-01

The dynamics of the Large Space Telescope (LST) control system were studied in order to arrive at a simplified model for computer simulation without loss of accuracy. The frictional nonlinearity of the Control Moment Gyroscope (CMG) Control Loop was analyzed in a model to obtain data for the following: (1) a continuous describing function for the gimbal friction nonlinearity; (2) a describing function of the CMG nonlinearity using an analytical torque equation; and (3) the discrete describing function and function plots for CMG functional linearity. Preliminary computer simulations are shown for the simplified LST system, first without, and then with analytical torque expressions. Transfer functions of the sampled-data LST system are also described. A final computer simulation is presented which uses elements of the simplified sampled-data LST system with analytical CMG frictional torque expressions.

A caution regarding the use of low-molecular weight heparin in pediatric otogenic lateral sinus thrombosis.

PubMed

Shah, Udayan K; Jubelirer, Tracey F; Fish, Jonathan D; Elden, Lisa M

2007-02-01

Lateral sinus thrombosis (LST), a rare complication of otitis media, is managed by antibiotics, surgery and anticoagulation. Traditionally, post-operative anticoagulation has been achieved by intravenous unfractionated heparin followed by oral warfarin. Fractionated, or low-molecular weight heparin derivatives (LMWH) have been introduced recently. There has been minimal literature to date regarding their use for the management of LST. We present use of the LMWH enoxaparin (Lovenox) for otogenic LST in two children, both of whom experienced hemorrhagic complications. On this basis and in the context of a literature review, we urge caution when using LMWH for pediatric otogenic LST.

Inter-annual variation of the surface temperature of tropical forests from SSM/I observations

NASA Astrophysics Data System (ADS)

Gao, H.; Fu, R.; Li, W.; Zhang, S.; Dickinson, R. E.

2014-12-01

Land surface temperatures (LST) within tropical rain forests contribute to climate variation, but observational data are very limited in these regions. In this study, all weather canopy sky temperatures were retrieved using the passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS) over the Amazon and Congo rainforests. The remote sensing data used were collected from 1996 to 2012 using two separate satellites—F13 (1996-2009) and F17 (2007-2012). An inter-sensor calibration between the brightness temperatures collected by the two satellites was conducted in order to ensure consistency amongst the instruments. The interannual changes of LST associated with the dry and wet anomalies were investigated in both regions. The dominant spatial and temporal patterns for inter-seasonal variations of the LST over the tropical rainforest were analyzed, and the impacts of droughts and El Niños (on LST) were also investigated. The remote sensing results suggest that the morning LST is mainly controlled by atmospheric humidity (which controls longwave radiation) whereas the late afternoon LST is controlled by solar radiation.

Observed impacts of wind farms on land surface temperature in Inner Mongolia

NASA Astrophysics Data System (ADS)

Tang, B.; Zhao, X.; Wu, D.; Zhao, W.; Wei, H.

2015-12-01

Abstract: The wind turbine industry in china has experienced a dramatic increase in recent years and wind farms (WFs) have an impact on the underlying surface conditions of climate system. This paper assesses the impacts of wind farms by analyzing the variations of the land surface temperature (LST) data for the period of 2003-2014 over a region consisted of 1097 turbines in the Huitengxile Wind Farm, the largest wind farm in Asia. We first compare the spatial coupling between the geographic layouts of the WFs and the spatial patterns of LST changes of two periods (post- versus pre- wind turbines construction) and then employ the difference of LST between WF pixels and surrounding non-WF pixels to quantify the effects of WFs. The results reveal that the LST at daytime increases by 0.52-0.86°C in winter, spring and autumn and decreases by about 0.56°C in summer over the WFs on average, with the spatial pattern of this warming or cooling generally coupled with the geographic distribution of the wind turbines, while the changes in LST at nighttime are much noisier. The daytime LST warming or cooling effects vary with seasons, and the strongest warming and tightest spatial coupling are in autumn months of September-November. The seasonal variations in albedo due to the construction of wind turbines are primarily responsible for the daytime LST changes. Areal mean decreases in winter, spring and autumn and increase in summer in albedo are observed over the WFs and the spatial pattern and magnitude of the changes in albedo couple very well with the layouts of the wind turbines. The increase (decrease) in albedo over the WFs indicates that WFs across the Huitengxile grassland absorb less (more) incoming radiation, thus resulting in a decrease (increase) in LST at daytime. The inter-annual variations in areal mean LST differences at daytime are highly correlated with those in areal mean albedo differences for all four seasons (R2=0.48~0.67). Our findings are in contrast with those studies, which show a warming effect at nighttime and no apparent effect on LST at daytime over the WFs in the United States. Keywords: Wind farm impacts; land surface temperature; albedo; warming and cooling

Impact of the assimilation of satellite soil moisture and LST on the hydrological cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Delogu, Fabio; Silvestro, Francesco; Rudari, Roberto; Campo, Lorenzo; Boni, Giorgio

2014-05-01

The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce ground based data. The aim of this work is to investigate the impacts on the performances of a distributed hydrological model (Continuum) of the assimilation of satellite-derived soil moisture products and Land Surface (LST). In this work three different soil moisture (SM) products, derived by ASCAT sensor, are used. These data are provided by the EUMETSAT's H-SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) program. The considered soil moisture products are: large scale surface soil moisture (SM OBS 1 - H07), small scale surface soil moisture (SM OBS 2 - H08) and profile index in the roots region (SM DAS 2 - H14). These data are compared with soil moisture estimated by Continuum model on the Orba catchment (800 km2), in the northern part of Italy, for the period July 2012-June 2013. Different assimilation experiments have been performed. The first experiment consists in the assimilation of the SM products by using a simple Nudging technique; the second one is the assimilation of only LST data, derived from MSG satellite, and the third is the assimilation of both SM products and LST. The benefits on the model predictions of discharge, LST and soil moisture dynamics were tested.

Spring Soil Temperature Anomalies over Tibetan Plateau and Summer Droughts/Floods in East Asia

NASA Astrophysics Data System (ADS)

Xue, Y.; Li, W.; LI, Q.; Diallo, I.; Chu, P. C.; Guo, W.; Fu, C.

2017-12-01

Recurrent extreme climate events, such as droughts and floods, are important features of the climate of East Asia, especially over the Yangtze River basin. Many studies have attributed these episodes to variability and anomaly of global sea surface temperatures (SST) anomaly. In addition, snow in the Tibetan Plateau has also been considered as one of the factors affecting the Asian monsoon variability. However, studies have consistently shown that SST along is unable to explain the extreme climate events fully and snow has difficulty to use as a predictor. Remote effects of observed large-scale land surface temperature (LST) and subsurface temperature variability in Tibetan Plateau (TP) on East Asian regional droughts/floods, however, have been largely ignored. We conjecture that a temporally filtered response to snow anomalies may be preserved in the LST anomaly. In this study, evidence from climate observations and model simulations addresses the LST/SUBT effects. The Maximum Covariance Analysis (MCA) of observational data identifies that a pronounce spring LST anomaly pattern over TP is closely associated with precipitation anomalies in East Asia with a dipole pattern, i.e., negative/positive TP spring LST anomaly is associated with the summer drought/flood over the region south of the Yangtze River and wet/dry conditions to the north of the Yangtze River. Climate models were used to demonstrate a causal relationship between spring cold LST anomaly in the TP and the severe 2003 drought over the southern part of the Yangtze River in eastern Asia. This severe drought resulted in 100 x 106 kg crop yield losses and an economic loss of 5.8 billion Chinese Yuan. The modeling study suggests that the LST effect produced about 58% of observed precipitation deficit; while the SST effect produced about 32% of the drought conditions. Meanwhile, the LST and SST effects also simulated the observed flood over to the north of the Yangtze River. This suggests that inclusion of this LST effect is essential to make reliable East Asian drought/flood predictions.

Characterizing an Integrated Annual Global Measure of the Earth's Maximum Land Surface Temperatures from 2003 to 2012 Reveals Strong Biogeographic Influences

NASA Astrophysics Data System (ADS)

Mildrexler, D. J.; Zhao, M.; Running, S. W.

2014-12-01

Land Surface Temperature (LST) is a good indicator of the surface energy balance because it is determined by interactions and energy fluxes between the atmosphere and the ground. The variability of land surface properties and vegetation densities across the Earth's surface changes these interactions and gives LST a unique biogeographic influence. Natural and human-induced disturbances modify the surface characteristics and alter the expression of LST. This results in a heterogeneous and dynamic thermal environment. Measurements that merge these factors into a single global metric, while maintaining the important biophysical and biogeographical factors of the land surface's thermal environment are needed to better understand integrated temperature changes in the Earth system. Using satellite-based LST we have developed a new global metric that focuses on one critical component of LST that occurs when the relationship between vegetation density and surface temperature is strongly coupled: annual maximum LST (LSTmax). A 10 year evaluation of LSTmax histograms that include every 1-km pixel across the Earth's surface reveals that this integrative measurement is strongly influenced by the biogeographic patterns of the Earth's ecosystems, providing a unique comparative view of the planet every year that can be likened to the Earth's thermal maximum fingerprint. The biogeographical component is controlled by the frequency and distribution of vegetation types across the Earth's land surface and displays a trimodal distribution. The three modes are driven by ice covered polar regions, forests, and hot desert/shrubland environments. In ice covered areas the histograms show that the heat of fusion results in a convergence of surface temperatures around the melting point. The histograms also show low interannual variability reflecting two important global land surface dynamics; 1) only a small fraction of the Earth's surface is disturbed in any given year, and 2) when considered at the global scale, the positive and negative climate forcings resulting from the aggregate effects of the loss of vegetation to disturbances and the regrowth from natural succession are roughly in balance. Changes in any component of the histogram can be tracked and would indicate a major change in the Earth system.

Improving soil moisture simulation to support Agricultural Water Resource Management using Satellite-based water cycle observations

NASA Astrophysics Data System (ADS)

Gupta, Manika; Bolten, John; Lakshmi, Venkat

2016-04-01

Efficient and sustainable irrigation systems require optimization of operational parameters such as irrigation amount which are dependent on the soil hydraulic parameters that affect the model's accuracy in simulating soil water content. However, it is a scientific challenge to provide reliable estimates of soil hydraulic parameters and irrigation estimates, given the absence of continuously operating soil moisture and rain gauge network. For agricultural water resource management, the in-situ measurements of soil moisture are currently limited to discrete measurements at specific locations, and such point-based measurements do not represent the spatial distribution at a larger scale accurately, as soil moisture is highly variable both spatially and temporally (Wang and Qu 2009). In the current study, flood irrigation scheme within the land surface model is triggered when the root-zone soil moisture deficit reaches below a threshold of 25%, 50% and 75% with respect to the maximum available water capacity (difference between field capacity and wilting point) and applied until the top layer is saturated. An additional important criterion needed to activate the irrigation scheme is to ensure that it is irrigation season by assuming that the greenness vegetation fraction (GVF) of the pixel exceed 0.40 of the climatological annual range of GVF (Ozdogan et al. 2010). The main hypothesis used in this study is that near-surface remote sensing soil moisture data contain useful information that can describe the effective hydrological conditions of the basin such that when appropriately inverted, it would provide field capacity and wilting point soil moisture, which may be representative of that basin. Thus, genetic algorithm inverse method is employed to derive the effective parameters and derive the soil moisture deficit for the root zone by coupling of AMSR-E soil moisture with the physically based hydrological model. Model performance is evaluated using MODIS-evapotranspiration (ET) and MODIS land surface temperature (LST) products. The soil moisture estimates for the root zone are also validated with the in-situ field data, for three sites (2- irrigated and 1- rainfed) located at the University of Nebraska Agricultural Research and Development Center near Mead, NE and monitored by three AmeriFlux installations (Verma et al., 2005) by evaluating the root mean square error (RMSE) and Mean Bias error (MBE).

Satellite-derived, melt-season surface temperature of the Greenland Ice Sheet (2000-2005) and its relationship to mass balance

USGS Publications Warehouse

Hall, D.K.; Williams, R.S.; Casey, K.A.; DiGirolamo, N.E.; Wan, Z.

2006-01-01

Mean, clear-sky surface temperature of the Greenland Ice Sheet was measured for each melt season from 2000 to 2005 using Moderate-Resolution Imaging Spectroradiometer (MODIS)–derived land-surface temperature (LST) data-product maps. During the period of most-active melt, the mean, clear-sky surface temperature of the ice sheet was highest in 2002 (−8.29 ± 5.29°C) and 2005 (−8.29 ± 5.43°C), compared to a 6-year mean of −9.04 ± 5.59°C, in agreement with recent work by other investigators showing unusually extensive melt in 2002 and 2005. Surface-temperature variability shows a correspondence with the dry-snow facies of the ice sheet; a reduction in area of the dry-snow facies would indicate a more-negative mass balance. Surface-temperature variability generally increased during the study period and is most pronounced in the 2005 melt season; this is consistent with surface instability caused by air-temperature fluctuations.

Performance measures from the explorer platform berthing experiment

NASA Technical Reports Server (NTRS)

Leake, Stephen

1993-01-01

The Explorer Platform is a Modular Mission Spacecraft: it has several subunits that are designed to be replaced on orbit. The Goddard Space Flight Center Robotics Lab undertook an experiment to evaluate various robotic approaches to replacing one of the units; a large (approximately 1 meter by 1 meter by 0.5 meter) power box. The hardware consists of a Robotics Research Corporation K-1607 (RRC) manipulator mounted on a large gantry robot, a Kraft handcontroller for teleoperation of RRC, a Lightweight Servicing Tool (LST) mounted on the RRC, and an Explorer Platform mockup (EP) with a removable box (MMS) that has fixtures that mate with the LST. Sensors include a wrist wrench sensor on the RRC and Capaciflectors mounted on the LST and the MMS. There are also several cameras, but no machine vision is used. The control system for the RRC is entirely written by Goddard; it consists of Ada code on three Multibus I 386/387 CPU boards doing the real-time robot control, and C on a 386 PC processing Capaciflector data. The gantry is not moved during this experiment. The task is the exchange of the MMS; it is removed and replaced. This involves four basic steps: mating the LST to the MMS, demating the MMS from the EP, mating the MMS to the EP, and demating the LST form the MMS. Each of the mating steps must be preceeded by an alignment to bring the mechanical fixtures within their capture range. Two basic approaches to alignment are explored: teleoperation with the operator viewing thru cameras, and Capaciflector based autonomy. To evaluate the two alignment approaches, several runs were run with each approach and the final pose was recorded. Comparing this to the ideal alignment pose gives accuracy and repeatability data. In addition the wrenches exerted during the mating tasks were recorded; this gives information on how the alignment step affects the mating step. There are also two approaches to mating; teleoperation, and impedance based autonomy. The wrench data taken during mating using these two approaches is used to evaluate them. Section 2 describes the alignment results, section 3 describes the mating results, and finally Section 4 gives some conclusions.

Quality Evaluation of Herbs and Spices in The Military Food System

DTIC Science & Technology

1976-06-01

tested would inhibit bacterial growth, Eseheric- hia coli and Staphyloeoeeus aureus were inoculated in tubes of lauryl sulfate tryptose (LST) broth... lauryl sulfate tryptose (LST) broth by standard methods**. Gas producing LST tubes were confirmed in brilliant green lactose bile (2%) broth (BGLB...and trypticase soy broth (TSB) containing 10% sodium chloride (NaCl), respectively, to which was added 1 ml of 1:10 dilution of each spice

Tropical Cyclone Diurnal Cycle as Observed by TRMM

PubMed Central

Leppert, Kenneth D.; Cecil, Daniel J.

2018-01-01

Previous work has indicated a clear, consistent diurnal cycle in rainfall and cold cloudiness coverage around tropical cyclones. This cycle may have important implications for structure and intensity changes of these storms and the forecasting of such changes. The goal of this paper is to use passive and active microwave measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR), respectively, to better understand the tropical cyclone diurnal cycle throughout a deep layer of a tropical cyclone’s clouds. The composite coverage by PR reflectivity ≥20 dBZ at various heights as a function of local standard time (LST) and radius suggests the presence of a diurnal signal for radii <500 km through a deep layer (2–10 km height) of the troposphere using 1998–2011 Atlantic tropical cyclones of at least tropical storm strength. The area covered by reflectivity ≥20 dBZ at radii 100–500 km peaks in the morning (0130–1030 LST) and reaches a minimum 1030–1930 LST. Radii between 300–500 km tend to reach a minimum in coverage closer to 1200 LST before reaching another peak at 2100 LST. The inner core (0–100 km) appears to be associated with a single-peaked diurnal cycle only at upper levels (8–10 km) with a maximum at 2230−0430 LST. The TMI rainfall composites suggest a clear diurnal cycle at all radii between 200 and 1000 km with peak rainfall coverage and rain rate occurring in the morning (0130−0730 LST). PMID:29371745

Land surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems

NASA Astrophysics Data System (ADS)

Quintano, C.; Fernández-Manso, A.; Calvo, L.; Marcos, E.; Valbuena, L.

2015-04-01

Forest fires are one of the most important causes of environmental alteration in Mediterranean countries. Discrimination of different degrees of burn severity is critical for improving management of fire-affected areas. This paper aims to evaluate the usefulness of land surface temperature (LST) as potential indicator of burn severity. We used a large convention-dominated wildfire, which occurred on 19-21 September, 2012 in Northwestern Spain. From this area, a 1-year series of six LST images were generated from Landsat 7 Enhanced Thematic Mapper (ETM+) data using a single channel algorithm. Further, the Composite Burn Index (CBI) was measured in 111 field plots to identify the burn severity level (low, moderate, and high). Evaluation of the potential relationship between post-fire LST and ground measured CBI was performed by both correlation analysis and regression models. Correlation coefficients were higher in the immediate post-fire LST images, but decreased during the fall of 2012 and increased again with a second maximum value in summer, 2013. A linear regression model between post-fire LST and CBI allowed us to represent spatially predicted CBI (R-squaredadj > 85%). After performing an analysis of variance (ANOVA) between post-fire LST and CBI, a Fisher's least significant difference test determined that two burn severity levels (low-moderate and high) could be statistically distinguished. The identification of such burn severity levels is sufficient and useful to forest managers. We conclude that summer post-fire LST from moderate resolution satellite data may be considered as a valuable indicator of burn severity for large fires in Mediterranean forest ecosytems.

Developing a confidence metric for the Landsat land surface temperature product

NASA Astrophysics Data System (ADS)

Laraby, Kelly G.; Schott, John R.; Raqueno, Nina

2016-05-01

Land Surface Temperature (LST) is an important Earth system data record that is useful to fields such as change detection, climate research, environmental monitoring, and smaller scale applications such as agriculture. Certain Earth-observing satellites can be used to derive this metric, and it would be extremely useful if such imagery could be used to develop a global product. Through the support of the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), a LST product for the Landsat series of satellites has been developed. Currently, it has been validated for scenes in North America, with plans to expand to a trusted global product. For ideal atmospheric conditions (e.g. stable atmosphere with no clouds nearby), the LST product underestimates the surface temperature by an average of 0.26 K. When clouds are directly above or near the pixel of interest, however, errors can extend to several Kelvin. As the product approaches public release, our major goal is to develop a quality metric that will provide the user with a per-pixel map of estimated LST errors. There are several sources of error that are involved in the LST calculation process, but performing standard error propagation is a difficult task due to the complexity of the atmospheric propagation component. To circumvent this difficulty, we propose to utilize the relationship between cloud proximity and the error seen in the LST process to help develop a quality metric. This method involves calculating the distance to the nearest cloud from a pixel of interest in a scene, and recording the LST error at that location. Performing this calculation for hundreds of scenes allows us to observe the average LST error for different ranges of distances to the nearest cloud. This paper describes this process in full, and presents results for a large set of Landsat scenes.

Global Land Surface Temperature From the Along-Track Scanning Radiometers

NASA Astrophysics Data System (ADS)

Ghent, D. J.; Corlett, G. K.; Göttsche, F.-M.; Remedios, J. J.

2017-11-01

The Leicester Along-Track Scanning Radiometer (ATSR) and Sea and Land Surface Temperature Radiometer (SLSTR) Processor for LAnd Surface Temperature (LASPLAST) provides global land surface temperature (LST) products from thermal infrared radiance data. In this paper, the state-of-the-art version of LASPLAST, as deployed in the GlobTemperature project, is described and applied to data from the Advanced Along-Track Scanning Radiometer (AATSR). The LASPLAST retrieval formulation for LST is a nadir-only, two-channel, split-window algorithm, based on biome classification, fractional vegetation, and across-track water vapor dependences. It incorporates globally robust retrieval coefficients derived using highly sampled atmosphere profiles. LASPLAST benefits from appropriate spatial resolution auxiliary information and a new probabilistic-based cloud flagging algorithm. For the first time for a satellite-derived LST product, pixel-level uncertainties characterized in terms of random, locally correlated, and systematic components are provided. The new GlobTemperature GT_ATS_2P Version 1.0 product has been validated for 1 year of AATSR data (2009) against in situ measurements acquired from "gold standard reference" stations: Gobabeb, Namibia, and Evora, Portugal; seven Surface Radiation Budget stations, and the Atmospheric Radiation Measurement station at Southern Great Plains. These data show average absolute biases for the GT_ATS_2P Version 1.0 product of 1.00 K in the daytime and 1.08 K in the nighttime. The improvements in data provenance including better accuracy, fully traceable retrieval coefficients, quantified uncertainty, and more detailed information in the new harmonized format of the GT_ATS_2P product will allow for more significant exploitation of the historical LST data record from the ATSRs and a valuable near-real-time service from the Sea and Land Surface Temperature Radiometers (SLSTRs).

A scientific operations plan for the large space telescope. [ground support system design

NASA Technical Reports Server (NTRS)

West, D. K.

1977-01-01

The paper describes an LST ground system which is compatible with the operational requirements of the LST. The goal of the approach is to minimize the cost of post launch operations without seriously compromising the quality and total throughput of LST science. Attention is given to cost constraints and guidelines, the telemetry operations processing systems (TELOPS), the image processing facility, ground system planning and data flow, and scientific interfaces.

Estimating Air Temperature over the Tibetan Plateau Using MODIS Data

NASA Astrophysics Data System (ADS)

Huang, Fangfang; Ma, Weiqiang; Ma, Yaoming; Li, Maoshan; Hu, Zeyong

2016-04-01

Time series of MODIS land surface temperature (LST) data and normalized difference vegetation index (NDVI) data, combined with digital elevation model (DEM) and meterological data for 2001-2012, were used to estimate and map the spatial distribution of monthly mean air temperature over the Tibatan Plateau (TP). Time series and regression analysis of monthly mean land surface temperature (Ts) and air temperature (Ta) were both conducted by ordinary liner regression (OLR) and geographical weighted regression (GWR) methods. Analysis showed that GWR method had much better result (Adjusted R2 > 0.79, root mean square error (RMSE) is between 0.51° C and 1.12° C) for estimating Ta than OLR method. The GWR model, with MODIS LST, NDVI and altitude as independent variables, was used to estimate Ta over the Tibetan Plateau. All GWR models in each month were tested by F-test with significant level of α=0.01 and the regression coefficients were all tested by T-test with significant level of α=0.01. This illustrated that Ts, NDVI and altitude play an important role on estimating Ta over the Tibetan Plateau. Finally, the major conclusions are as follows: (1) GWR method has higher accuracy for estimating Ta than OLR (Adjusted R2=0.40˜0.78, RMSE=1.60˜4.38° C), and the Ta control precision can be up to 1.12° C. (2) Over the Northern TP, the range of Ta variation in January is -29.28 ˜ -5.0° C, and that in July is -0.53 ˜ 14.0° C. Ta in summer half year (from May to October) is between -15.92 ˜ 14.0° C. From October on, 0° C isothermal level is gradually declining from the altitude of 4˜5 kilometers, and hits the bottom with altitude of 3200 meters in December, and Ta is all under 0° C in January. 10° C isothermal level gradually starts rising from the altitude of 3200 meters from May, and reaches the highest level with altitude of 4˜5 kilometers in July. In addition, Ta in south slope of the Tanggula Mountains is obviously higher than that in the north slope. Ta in east of Qinghai-Tibet Railway is higher than that in the west, and Ta shows an increasing tendency from northwest to southeast. (3) Over the Northern TP, the variation range of the difference between surface and air temperature (DT) in January is -6.52˜6.0° C, and that in July is -6.32˜6.0° C. DT in summer half year (from May to October) is between -6.34˜8.0° C, and DT along Qinghai-Tibet Railway is greater than that in the east and west areas of Qinghai-Tibet Railway. However, except of the southeastern area of the Northern TP, where DT is under 0° C, DT values in other areas are all more than 0° C in winter half year. In summer half year, the altitude of the area lower than 0° C rises to 4˜5 kilometers, and DT in south of the Tanggula Mountains is between -2.0° C and 0° C.

Dynamic linear models using the Kalman filter for early detection and early warning of malaria outbreaks

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Liu, Y.; DeVos, M.; Wimberly, M. C.

2015-12-01

Malaria early detection and early warning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (early warning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

Effects of urban form on the urban heat island effect based on spatial regression model.

PubMed

Yin, Chaohui; Yuan, Man; Lu, Youpeng; Huang, Yaping; Liu, Yanfang

2018-09-01

The urban heat island (UHI) effect is becoming more of a concern with the accelerated process of urbanization. However, few studies have examined the effect of urban form on land surface temperature (LST) especially from an urban planning perspective. This paper used spatial regression model to investigate the effects of both land use composition and urban form on LST in Wuhan City, China, based on the regulatory planning management unit. Landsat ETM+ image data was used to estimate LST. Land use composition was calculated by impervious surface area proportion, vegetated area proportion, and water proportion, while urban form indicators included sky view factor (SVF), building density, and floor area ratio (FAR). We first tested for spatial autocorrelation of urban LST, which confirmed that a traditional regression method would be invalid. A spatial error model (SEM) was chosen because its parameters were better than a spatial lag model (SLM). The results showed that urban form metrics should be the focus for mitigation efforts of UHI effects. In addition, analysis of the relationship between urban form and UHI effect based on the regulatory planning management unit was helpful for promoting corresponding UHI effect mitigation rules in practice. Finally, the spatial regression model was recommended to be an appropriate method for dealing with problems related to the urban thermal environment. Results suggested that the impact of urbanization on the UHI effect can be mitigated not only by balancing various land use types, but also by optimizing urban form, which is even more effective. This research expands the scientific understanding of effects of urban form on UHI by explicitly analyzing indicators closely related to urban detailed planning at the level of regulatory planning management unit. In addition, it may provide important insights and effective regulation measures for urban planners to mitigate future UHI effects. Copyright © 2018 Elsevier B.V. All rights reserved.

Development of a Global Evaporative Stress Index Based on Thermal and Microwave LST towards Improved Monitoring of Agricultural Drought

NASA Astrophysics Data System (ADS)

Hain, C.; Anderson, M. C.; Otkin, J.; Holmes, T. R.; Gao, F.

2017-12-01

This presentation will describe the development of a global agricultural monitoring tool, with a focus on providing early warning of developing vegetation stress for agricultural decision-makers and stakeholders at relatively high spatial resolution (5-km). The tool is based on remotely sensed estimates of evapotranspiration, retrieved via energy balance principals using observations of land surface temperature. The Evaporative Stress Index (ESI) represents anomalies in the ratio of actual-to-potential ET generated with the ALEXI surface energy balance model. The LST inputs to ESI have been shown to provide early warning information about the development of vegetation stress with stress-elevated canopy temperatures observed well before a decrease in greenness is detected in remotely sensed vegetation indices. As a diagnostic indicator of actual ET, the ESI requires no information regarding antecedent precipitation or soil moisture storage capacity - the current available moisture to vegetation is deduced directly from the remotely sensed LST signal. This signal also inherently accounts for both precipitation and non-precipitation related inputs/sinks to the plant-available soil moisture pool (e.g., irrigation) which can modify crop response to rainfall anomalies. Independence from precipitation data is a benefit for global agricultural monitoring applications due to sparseness in existing ground-based precipitation networks, and time delays in public reporting. Several enhancements to the current ESI framework will be addressed as requested from project stakeholders: (a) integration of "all-sky" MW Ka-band LST retrievals to augment "clear-sky" thermal-only ESI in persistently cloudy regions; (b) operational production of ESI Rapid Change Indices which provide important early warning information related to onset of actual vegetation stress; and (c) assessment of ESI as a predictor of global yield anomalies; initial studies have shown the ability of intra-seasonal ESI to provide an early indication of at-harvest agricultural yield anomalies.

Preferences for Life-Sustaining Treatments and Associations With Accurate Prognostic Awareness and Depressive Symptoms in Terminally Ill Cancer Patients' Last Year of Life.

PubMed

Tang, Siew Tzuh; Wen, Fur-Hsing; Hsieh, Chia-Hsun; Chou, Wen-Chi; Chang, Wen-Cheng; Chen, Jen-Shi; Chiang, Ming-Chu

2016-01-01

The stability of life-sustaining treatment (LST) preferences at end of life (EOL) has been established. However, few studies have assessed preferences more than two times. Furthermore, associations of LST preferences with modifiable variables of accurate prognostic awareness, physician-patient EOL care discussions, and depressive symptoms have been investigated in cross-sectional studies only. To explore longitudinal changes in LST preferences and their associations with accurate prognostic awareness, physician-patient EOL care discussions, and depressive symptoms in terminally ill cancer patients' last year. LST preferences (cardiopulmonary resuscitation, intensive care unit [ICU] care, intubation, and mechanical ventilation) were measured approximately every two weeks. Changes in LST preferences and their associations with independent variables were examined by hierarchical generalized linear modeling with logistic regression. Participants (n = 249) predominantly rejected cardiopulmonary resuscitation, ICU care, intubation, and mechanical ventilation at EOL without significant changes as death approached. Patients with inaccurate prognostic awareness were significantly more likely than those with accurate understanding to prefer ICU care, intubation, and mechanical ventilation than to reject these LSTs. Patients with more severe depressive symptoms were less likely to prefer ICU care and to be undecided about wanting ICU care and mechanical ventilation than to reject such LSTs. LST preferences were not associated with physician-patient EOL care discussions, which were rare in our sample. LST preferences are stable in cancer patients' last year. Facilitating accurate prognostic awareness and providing adequate psychological support may counteract the increasing trend for aggressive EOL care and minimize emotional distress during EOL care decisions. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Modeling seasonal surface temperature variations in secondary tropical dry forests

NASA Astrophysics Data System (ADS)

Cao, Sen; Sanchez-Azofeifa, Arturo

2017-10-01

Secondary tropical dry forests (TDFs) provide important ecosystem services such as carbon sequestration, biodiversity conservation, and nutrient cycle regulation. However, their biogeophysical processes at the canopy-atmosphere interface remain unknown, limiting our understanding of how this endangered ecosystem influences, and responds to the ongoing global warming. To facilitate future development of conservation policies, this study characterized the seasonal land surface temperature (LST) behavior of three successional stages (early, intermediate, and late) of a TDF, at the Santa Rosa National Park (SRNP), Costa Rica. A total of 38 Landsat-8 Thermal Infrared Sensor (TIRS) data and the Surface Reflectance (SR) product were utilized to model LST time series from July 2013 to July 2016 using a radiative transfer equation (RTE) algorithm. We further related the LST time series to seven vegetation indices which reflect different properties of TDFs, and soil moisture data obtained from a Wireless Sensor Network (WSN). Results showed that the LST in the dry season was 15-20 K higher than in the wet season at SRNP. We found that the early successional stages were about 6-8 K warmer than the intermediate successional stages and were 9-10 K warmer than the late successional stages in the middle of the dry season; meanwhile, a minimum LST difference (0-1 K) was observed at the end of the wet season. Leaf phenology and canopy architecture explained most LST variations in both dry and wet seasons. However, our analysis revealed that it is precipitation that ultimately determines the LST variations through both biogeochemical (leaf phenology) and biogeophysical processes (evapotranspiration) of the plants. Results of this study could help physiological modeling studies in secondary TDFs.

Estimation and Modelling of Land Surface Temperature Using Landsat 7 ETM+ Images and Fuzzy System Techniques

NASA Astrophysics Data System (ADS)

Bisht, K.; Dodamani, S. S.

2016-12-01

Modelling of Land Surface Temperature is essential for short term and long term management of environmental studies and management activities of the Earth's resources. The objective of this research is to estimate and model Land Surface Temperatures (LST). For this purpose, Landsat 7 ETM+ images period from 2007 to 2012 were used for retrieving LST and processed through MATLAB software using Mamdani fuzzy inference systems (MFIS), which includes pre-monsoon and post-monsoon LST in the fuzzy model. The Mangalore City of Karnataka state, India has been taken for this research work. Fuzzy model inputs are considered as the pre-monsoon and post-monsoon retrieved temperatures and LST was chosen as output. In order to develop a fuzzy model for LST, seven fuzzy subsets, nineteen rules and one output are considered for the estimation of weekly mean air temperature. These are very low (VL), low (L), medium low (ML), medium (M), medium high (MH), high (H) and very high (VH). The TVX (Surface Temperature Vegetation Index) and the empirical method have provided estimated LST. The study showed that the Fuzzy model M4/7-19-1 (model 4, 7 fuzzy sets, 19 rules and 1 output) which developed over Mangalore City has provided more accurate outcomes than other models (M1, M2, M3, M5). The result of this research was evaluated according to statistical rules. The best correlation coefficient (R) and root mean squared error (RMSE) between estimated and measured values for pre-monsoon and post-monsoon LST found to be 0.966 - 1.607 K and 0.963- 1.623 respectively.

Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery

NASA Astrophysics Data System (ADS)

Weng, Qihao; Fu, Peng

2014-11-01

Land surface temperature is a key parameter for monitoring urban heat islands, assessing heat related risks, and estimating building energy consumption. These environmental issues are characterized by high temporal variability. A possible solution from the remote sensing perspective is to utilize geostationary satellites images, for instance, images from Geostationary Operational Environmental System (GOES) and Meteosat Second Generation (MSG). These satellite systems, however, with coarse spatial but high temporal resolution (sub-hourly imagery at 3-10 km resolution), often limit their usage to meteorological forecasting and global climate modeling. Therefore, how to develop efficient and effective methods to disaggregate these coarse resolution images to a proper scale suitable for regional and local studies need be explored. In this study, we propose a least square support vector machine (LSSVM) method to achieve the goal of downscaling of GOES image data to half-hourly 1-km LSTs by fusing it with MODIS data products and Shuttle Radar Topography Mission (SRTM) digital elevation data. The result of downscaling suggests that the proposed method successfully disaggregated GOES images to half-hourly 1-km LSTs with accuracy of approximately 2.5 K when validated against with MODIS LSTs at the same over-passing time. The synthetic LST datasets were further explored for monitoring of surface urban heat island (UHI) in the Los Angeles region by extracting key diurnal temperature cycle (DTC) parameters. It is found that the datasets and DTC derived parameters were more suitable for monitoring of daytime- other than nighttime-UHI. With the downscaled GOES 1-km LSTs, the diurnal temperature variations can well be characterized. An accuracy of about 2.5 K was achieved in terms of the fitted results at both 1 km and 5 km resolutions.

Imposed Thermal Fatigue and Post-Thermal-Cycle Wear Resistance of Biomimetic Gray Cast Iron by Laser Treatment

NASA Astrophysics Data System (ADS)

Sui, Qi; Zhou, Hong; Zhang, Deping; Chen, Zhikai; Zhang, Peng

2017-08-01

The present study aims to create coupling biomimetic units on gray cast iron substrate by laser surface treatment (LST). LSTs for single-step (LST1) and two-step (LST2) processes, were carried out on gray cast iron in different media (air and water). Their effects on microstructure, thermal fatigue, and post-thermal-cycle wear (PTW) resistance on the specimens were studied. The tests were carried out to examine the influence of crack-resistance behavior as well as the biomimetic surface on its post-thermal-cycle wear behavior and different units, with different laser treatments for comparison. Results showed that LST2 enhanced the PTW behaviors of gray cast iron, which then led to an increase in its crack resistance. Among the treated cast irons, the one treated by LST2 in air showed the lowest residual stress, due to the positive effect of the lower steepness of the thermal gradient. Moreover, the same specimen showed the best PTW performance, due to its superior crack resistance and higher hardness as a result of it.

Automating data analysis during the inspection of boiler tubes using line scanning thermography

NASA Astrophysics Data System (ADS)

Ley, Obdulia; Momeni, Sepand; Ostroff, Jason; Godinez, Valery

2012-05-01

Failures in boiler waterwalls can occur when a relatively small amount of corrosion and loss of metal have been experienced. This study presents our efforts towards the application of Line Scanning Thermography (LST) for the analysis of thinning in boiler waterwall tubing. LST utilizes a line heat source to thermally excite the surface to be inspected and an infrared detector to record the transient surface temperature increase observed due to the presence of voids, thinning or other defects. In waterwall boiler tubes the defects that can be detected using LST correspond to corrosion pitting, hydrogen damage and wall thinning produced by inadequate burner heating or problems with the water chemistry. In this paper we discuss how the LST technique is implemented to determine thickness from the surface temperature data, and we describe our efforts towards developing a semiautomatic analysis tool to speed up the time between scanning, reporting and implementing repairs. We compare the density of data produced by the common techniques used to assess wall thickness and the data produced by LST.

Challenges to quantitative applications of Landsat observations for the urban thermal environment.

PubMed

Chen, Feng; Yang, Song; Yin, Kai; Chan, Paul

2017-09-01

Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program significantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature (LST). However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM+ and channel differences among sensors. Based on these investigations, the concerns are to: (1) show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors; (2) emphasize efforts which should be done for the quantitative applications of Landsat data; and (3) understand the potential challenges for the continuity of Landsat observation (i.e., thermal infrared) for global change monitoring, while several climate data record programs being in progress. Copyright © 2017. Published by Elsevier B.V.

Landsat science team meeting: Summer 2015

USGS Publications Warehouse

Schroeder, Todd; Loveland, Thomas; Wulder, Michael A.; Irons, James R.

2015-01-01

With over 60 participants in attendance, this was the largest LST meeting ever held. Meeting topics on the first day included Sustainable Land Imaging and Landsat 9 development, Landsat 7 and 8 operations and data archiving, the Landsat 8 Thermal Infrared Sensor (TIRS) stray-light issue, and the successful Sentinel-2 launch. In addition, on days two and three the LST members presented updates on their Landsat science and applications research. All presentations are available at landsat.usgs.gov/science_LST_Team_ Meetings.php.

Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

NASA Astrophysics Data System (ADS)

Padhee, S. K.; Nikam, B. R.; Aggarwal, S. P.; Garg, V.

2014-11-01

Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area). The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (October-April) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct-Dec of 2010).The predictions of the model were based on future meteorological data (year 2010) and were found to be yielding good results. The developed model have good predictive capability based on future meteorological data (rainfall data) availability, which enhances its utility in analyzing future Agricultural conditions if meteorological data is available.

Exploring the water storage changes in the largest lake (Selin Co) over the Tibetan Plateau during 2003-2012 from a basin-wide hydrological modeling

NASA Astrophysics Data System (ADS)

Zhou, Jing; Wang, Lei; Zhang, Yinsheng; Guo, Yanhong

2016-04-01

Lake water storage change (DSw) is an important indicator of the hydrologic cycle and greatly influences lake expansion/shrinkage over the Tibetan Plateau (TP). Accurate estimation of DSw will contribute to improved understanding of lake variations in the TP. Based on a water balance, this study explored the variations of DSw for the Lake Selin Co (the largest closed lake on the TP) during 2003-2012 using the Water and Energy Budget-based Distributed Hydrological Model (WEB-DHM) together with two different evapotranspiration (ET) algorithms (the Penman-Monteith method and a simple sublimation estimation approach for water area in unfrozen and frozen period). The contributions of basin discharge and climate causes to the DSw are also quantitatively analyzed. The results showed that WEB-DHM could well reproduce daily discharge, the spatial pattern, and basin-averaged values of MODIS land surface temperature (LST) during nighttime and daytime. Compared with the ET reference values estimated from the basin-wide water balance, our ET estimates showed better performance than three global ET products in reproducing basin-averaged ET. The modeled ET at point scale matches well with short-term in situ daily measurements (RMSE=0.82 mm/d). Lake inflows and precipitation over the water area had stronger relationships with DSw in the warm season and monthly scale, whereas evaporation from the water area had remarkable effects on DSw in the cold season. The total contribution of the three factors to DSw was about 90%, and accounting for 49.5%, 22.1%, and 18.3%, respectively.

Colorectal laterally spreading tumors show characteristic expression of cell polarity factors, including atypical protein kinase C λ/ι, E-cadherin, β-catenin and basement membrane component.

PubMed

Ichikawa, Yasushi; Nagashima, Yoji; Morioka, Kaori; Akimoto, Kazunori; Kojima, Yasuyuki; Ishikawa, Takashi; Goto, Ayumu; Kobayashi, Noritoshi; Watanabe, Kazuteru; Ota, Mitsuyoshi; Fujii, Shoichi; Kawamata, Mayumi; Takagawa, Ryo; Kunizaki, Chikara; Takahashi, Hirokazu; Nakajima, Atsushi; Maeda, Shin; Shimada, Hiroshi; Inayama, Yoshiaki; Ohno, Shigeo; Endo, Itaru

2014-09-01

Colorectal flat-type tumors include laterally spreading tumors (LSTs) and flat depressed-type tumors. The former of which shows a predominant lateral spreading growth rather than an invasive growth. The present study examined the morphological characteristics of LSTs, in comparison with polypoid- or flat depressed-type tumors, along with the expression of atypical protein kinase C (aPKC) λ/ι, a pivotal cell polarity regulator, and the hallmarks of cell polarity, as well as with type IV collagen, β-catenin and E-cadherin. In total, 37 flat-type (24 LSTs and 13 flat depressed-type tumors) and 20 polypoid-type colorectal tumors were examined. The LSTs were classified as 15 LST adenoma (LST-A) and nine LST cancer in adenoma (LST-CA). An immunohistochemical examination was performed on aPKC λ/ι, type IV collagen, β-catenin and E-cadherin. The LST-A and -CA showed a superficial replacing growth pattern, with expression of β-catenin and E-cadherin in the basolateral membrane and type IV collagen along the basement membrane. In addition, 86.6% of LST-A and 55.6% of LST-CA showed aPKC λ/ι expression of 1+ (weak to normal intensity staining in the cytoplasm compared with the normal epithelium). Furthermore, ~45% of the polypoid-type adenomas showed 2+ (moderate intensity staining in the cytoplasm and/or nucleus) and 66.7% of the polypoid-type cancer in adenoma were 3+ (strong intensity staining in the cytoplasm and nucleus). A statistically significant positive correlation was observed between the expression of aPKC λ/ι and β-catenin (r=0.842; P<0.001), or type IV collagen (r=0.823; P<0.001). The LSTs showed a unique growth pattern, different from the expanding growth pattern presented by a polypoid tumor and invasive cancer. The growth characteristics of LST appear to be caused by adequate coexpression of β-catenin, type IV collagen and aPKC λ/ι.

Cloud Retrieval Intercomparisons Between SEVIRI, MODIS and VIIRS with CHIMAERA PGE06 Data Collection 6 Products

NASA Technical Reports Server (NTRS)

Wind, Galina; Riedi, Jerome; Platnick, Steven; Heidinger, Andrew

2014-01-01

The Cross-platform HIgh resolution Multi-instrument AtmosphEric Retrieval Algorithms (CHIMAERA) system allows us to perform MODIS-like cloud top, optical and microphysical properties retrievals on any sensor that possesses a minimum set of common spectral channels. The CHIMAERA system uses a shared-core architecture that takes retrieval method out of the equation when intercomparisons are made. Here we show an example of such retrieval and a comparison of simultaneous retrievals done using SEVIRI, MODIS and VIIRS sensors. All sensor retrievals are performed using CLAVR-x (or CLAVR-x based) cloud top properties algorithm. SEVIRI uses the SAF_NWC cloud mask. MODIS and VIIRS use the IFF-based cloud mask that is a shared algorithm between MODIS and VIIRS. The MODIS and VIIRS retrievals are performed using a VIIRS branch of CHIMAERA that limits available MODIS channel set. Even though in that mode certain MODIS products such as multilayer cloud map are not available, the cloud retrieval remains fully equivalent to operational Data Collection 6.

Effect of Cloud Fraction on Near-Cloud Aerosol Behavior Based on MODIS and CALIPSO Observations

NASA Technical Reports Server (NTRS)

Marshak, A.; Varnai, T.; Yang, W.

2015-01-01

Organizers of the MODIS-VIIRS Science Team Meeting, held May 18-22, 2015 in Silver Spring, MD plan to post the presentations and posters to the NASA MODIS website: http:modis.gsfc.nasa.govsci_teammeetings201505index.php. The MODIS Science Team Meeting is held twice a year, so that the members of the science team may assemble and discuss data they have collected, ideas they have formed, and future issues that apply to the MODIS Mission.

The role of religious beliefs in ethics committee consultations for conflict over life-sustaining treatment.

PubMed

Bandini, Julia I; Courtwright, Andrew; Zollfrank, Angelika A; Robinson, Ellen M; Cadge, Wendy

2017-06-01

Previous research has suggested that individuals who identify as being more religious request more aggressive medical treatment at end of life. These requests may generate disagreement over life-sustaining treatment (LST). Outside of anecdotal observation, however, the actual role of religion in conflict over LST has been underexplored. Because ethics committees are often consulted to help mediate these conflicts, the ethics consultation experience provides a unique context in which to investigate this question. The purpose of this paper was to examine the ways religion was present in cases involving conflict around LST. Using medical records from ethics consultation cases for conflict over LST in one large academic medical centre, we found that religion can be central to conflict over LST but was also present in two additional ways through (1) religious coping, including a belief in miracles and support from a higher power, and (2) chaplaincy visits. In-hospital mortality was not different between patients with religiously versus non-religiously centred conflict. In our retrospective cohort study, religion played a variety of roles and did not lead to increased treatment intensity or prolong time to death. Ethics consultants and healthcare professionals involved in these cases should be cognisant of the complex ways that religion can manifest in conflict over LST. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Fabrication and electrochemical performance of nickel- and gadolinium-doped ceria-infiltrated La0·2Sr0·8TiO3 anodes for solid oxide fuel cells

NASA Astrophysics Data System (ADS)

Lee, Min-Jin; Shin, Jae-Hwa; Ji, Mi-Jung; Hwang, Hae-Jin

2018-01-01

In this work, nickel and gadolinium-doped ceria (GDC)-infiltrated lanthanum strontium titanate (LST) anodes are fabricated, and their electrode performances under a hydrogen atmosphere is investigated in terms of the Ni:GDC ratios and cell operating temperature. The Ni/GDC-infiltrated LST anode exhibits excellent electrode performance in comparison with the Ni- or GDC-infiltrated anodes, which is attributed to the synergistic effect of an extended triple-phase boundary length by GDC and good catalytic activity for hydrogen oxidation because of the Ni particles. The polarization resistances (Rp) of Ni/GDC-infiltrated LST are 0.07, 0.08, and 0.12 Ω cm2 at 800, 750, and 700 °C, respectively, which are approximately three orders of magnitude lower than that of the LST anode (68.5 Ω cm2 at 700 °C). The effect of Ni and GDC on the electrochemical performance of LST was also investigated by using electrochemical impedance spectroscopy (EIS). The anode polarization resistance (Rp) is confirmed to be dependent on the content and dispersion state (microstructure) of the Ni and GDC nanoparticles.

Remote sensing study of the impact of vegetation on thermal environment in different contexts

NASA Astrophysics Data System (ADS)

Xie, Qijiao; Wu, Yingjiao; Zhou, Zhixiang; Wang, Zhengxiang

2018-02-01

Satellite remote sensing technology provides informative data for detecting the land surface temperature (LST) distribution and urban heat island (UHI) effect remotely and regionally. In this study, two Landsat Thematic Mapper (TM) images acquired on September 26, 1987 and September 17, 2013 were used to derive LST and the normalized difference vegetation index (NDVI) values in Wuhan, China. The relationships between NDVI and LST were examined in different contexts, namely built-up area, farmland, grassland and forest. Results showed that negative correlations between the mean NDVI and LST were detected in all observed land covers, which meant that vegetation was efficient in decreasing surface temperatures and mitigating UHI effect. The cooling efficiency of vegetation on thermal environment varied with different contexts. As mean NDVI increased at each 0.1, the decreased LST values in built-up area, farmland, grassland and forest were 1.4 °C, 1.4 °C, 1.1 °C, 1.9 °C in 1987 and 1.4 °C, 1.7 °C, 1.3 °C, 1.8 °C in 2013, respectively. This finding encourages urban planners and greening designers to devote more efforts in protecting urban forests.

Landsat Science Team meeting: Winter 2015

USGS Publications Warehouse

Schroeder, Todd A.; Loveland, Thomas; Wulder, Michael A.; Irons, James R.

2015-01-01

The summer meeting of the joint U.S. Geological Survey (USGS)–NASA Landsat Science Team (LST) was held at the USGS’s Earth Resources Observation and Science (EROS) Center July 7-9, 2015, in Sioux Falls, SD. The LST co-chairs, Tom Loveland [EROS—Senior Scientist] and Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Landsat 8 Project Scientist], opened the three-day meeting on an upbeat note following the recent successful launch of the European Space Agency’s Sentinel-2 mission on June 23, 2015 (see image on page 14), and the news that work on Landsat 9 has begun, with a projected launch date of 2023.With over 60 participants in attendance, this was the largest LST meeting ever held. Meeting topics on the first day included Sustainable Land Imaging and Landsat 9 development, Landsat 7 and 8 operations and data archiving, the Landsat 8 Thermal Infrared Sensor (TIRS) stray-light issue, and the successful Sentinel-2 launch. In addition, on days two and three the LST members presented updates on their Landsat science and applications research. All presentations are available at landsat.usgs.gov/science_LST_Team_ Meetings.php.

Cost-effectiveness of MODY genetic testing: translating genomic advances into practical health applications.

PubMed

Naylor, Rochelle N; John, Priya M; Winn, Aaron N; Carmody, David; Greeley, Siri Atma W; Philipson, Louis H; Bell, Graeme I; Huang, Elbert S

2014-01-01

OBJECTIVE To evaluate the cost-effectiveness of a genetic testing policy for HNF1A-, HNF4A-, and GCK-MODY in a hypothetical cohort of type 2 diabetic patients 25-40 years old with a MODY prevalence of 2%. RESEARCH DESIGN AND METHODS We used a simulation model of type 2 diabetes complications based on UK Prospective Diabetes Study data, modified to account for the natural history of disease by genetic subtype to compare a policy of genetic testing at diabetes diagnosis versus a policy of no testing. Under the screening policy, successful sulfonylurea treatment of HNF1A-MODY and HNF4A-MODY was modeled to produce a glycosylated hemoglobin reduction of -1.5% compared with usual care. GCK-MODY received no therapy. Main outcome measures were costs and quality-adjusted life years (QALYs) based on lifetime risk of complications and treatments, expressed as the incremental cost-effectiveness ratio (ICER) (USD/QALY). RESULTS The testing policy yielded an average gain of 0.012 QALYs and resulted in an ICER of 205,000 USD. Sensitivity analysis showed that if the MODY prevalence was 6%, the ICER would be ~50,000 USD. If MODY prevalence was >30%, the testing policy was cost saving. Reducing genetic testing costs to 700 USD also resulted in an ICER of ~50,000 USD. CONCLUSIONS Our simulated model suggests that a policy of testing for MODY in selected populations is cost-effective for the U.S. based on contemporary ICER thresholds. Higher prevalence of MODY in the tested population or decreased testing costs would enhance cost-effectiveness. Our results make a compelling argument for routine coverage of genetic testing in patients with high clinical suspicion of MODY.

Intercomparisons of Marine Boundary Layer Cloud Properties from the ARM CAP-MBL Campaign and Two MODIS Cloud Products

NASA Technical Reports Server (NTRS)

Zhang, Zhibo; Dong, Xiquan; Xi, Baike; Song, Hua; Ma, Po-Lun; Ghan, Steven J.; Platnick, Steven; Minnis, Patrick

2017-01-01

From April 2009 to December 2010, the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program carried out an observational field campaign on Graciosa Island, targeting the marine boundary layer (MBL) clouds over the Azores region. In this paper, we present an inter-comparison of the MBL cloud properties, namely, cloud liquid water path (LWP), cloud optical thickness (COT) and cloud-droplet effective radius (CER), among retrievals from the ARM mobile facility (AMF) and two Moderate Resolution Spectroradiometer (MODIS) cloud products (GSFC-MODIS and CERES-MODIS). A total of 63 daytime single-layer MBL cloud cases are selected for inter-comparison. Comparison of collocated retrievals indicates that the two MODIS cloud products agree well on both COT and CER retrievals, with the correlation coefficient R greater than 0.95 despite their significant difference in spatial sampling. In both MODIS products, the CER retrievals based on the 2.1 micrometers band (CER(sub 2.1)) is significantly smaller than that based on the 3.7 micrometers band (CER(sub 3.7)). The GSFC-MODIS cloud product is collocated and compared with ground-based ARM observations at several temporal spatial scales. In general, the correlation increases with more precise collocation. For the 63 selected MBL cloud cases, the GSFC-MODIS LWP and COT retrievals agree reasonably well with the ground-based observations with no apparent bias and correlation coefficient R around 0.85 and 0.70, respectively. However, GSFC-MODIS CER(sub 3.7) and CER(sub 2.1) retrievals have a lower correlation (R is approximately 0.5) with the ground-based retrievals. For the 63 selected cases, they are on average larger than ground observations by about 1.5 micrometers and 3.0 micrometers, respectively. Taking into account that the MODIS CER retrievals are only sensitive to cloud top reduces the bias only by 0.5 micrometers.

Intercomparisons of marine boundary layer cloud properties from the ARM CAP-MBL campaign and two MODIS cloud products

NASA Astrophysics Data System (ADS)

Zhang, Zhibo; Dong, Xiquan; Xi, Baike; Song, Hua; Ma, Po-Lun; Ghan, Steven J.; Platnick, Steven; Minnis, Patrick

2017-02-01

From April 2009 to December 2010, the Department of Energy Atmospheric Radiation Measurement (ARM) program carried out an observational field campaign on Graciosa Island, targeting the marine boundary layer (MBL) clouds over the Azores region. In this paper, we present an intercomparison of the MBL cloud properties, namely, cloud liquid water path (LWP), cloud optical thickness (COT), and cloud-droplet effective radius (CER), among retrievals from the ARM mobile facility and two Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products (Goddard Space Flight Center (GSFC)-MODIS and Clouds and Earth's Radiant Energy System-MODIS). A total of 63 daytime single-layer MBL cloud cases are selected for intercomparison. Comparison of collocated retrievals indicates that the two MODIS cloud products agree well on both COT and CER retrievals, with the correlation coefficient R > 0.95, despite their significant difference in spatial sampling. In both MODIS products, the CER retrievals based on the 2.1 µm band (CER2.1) are significantly larger than those based on the 3.7 µm band (CER3.7). The GSFC-MODIS cloud product is collocated and compared with ground-based ARM observations at several temporal-spatial scales. In general, the correlation increases with more precise collocation. For the 63 selected MBL cloud cases, the GSFC-MODIS LWP and COT retrievals agree reasonably well with the ground-based observations with no apparent bias and correlation coefficient R around 0.85 and 0.70, respectively. However, GSFC-MODIS CER3.7 and CER2.1 retrievals have a lower correlation (R 0.5) with the ground-based retrievals. For the 63 selected cases, they are on average larger than ground observations by about 1.5 µm and 3.0 µm, respectively. Taking into account that the MODIS CER retrievals are only sensitive to cloud top reduces the bias only by 0.5 µm.

Evaluating Vegetation Type Effects on Land Surface Temperature at the City Scale

NASA Astrophysics Data System (ADS)

Wetherley, E. B.; McFadden, J. P.; Roberts, D. A.

2017-12-01

Understanding the effects of different plant functional types and urban materials on surface temperatures has significant consequences for climate modeling, water management, and human health in cities. To date, doing so at the urban scale has been complicated by small-scale surface heterogeneity and limited data. In this study we examined gradients of land surface temperature (LST) across sub-pixel mixtures of different vegetation types and urban materials across the entire Los Angeles, CA, metropolitan area (4,283 km2). We used AVIRIS airborne hyperspectral imagery (36 m resolution, 224 bands, 0.35 - 2.5 μm) to estimate sub-pixel fractions of impervious, pervious, tree, and turfgrass surfaces, validating them with simulated mixtures constructed from image spectra. We then used simultaneously imaged LST retrievals collected at multiple times of day to examine how temperature changed along gradients of the sub-pixel mixtures. Diurnal in situ LST measurements were used to confirm image values. Sub-pixel fractions were well correlated with simulated validation data for turfgrass (r2 = 0.71), tree (r2 = 0.77), impervious (r2 = 0.77), and pervious (r2 = 0.83) surfaces. The LST of pure pixels showed the effects of both the diurnal cycle and the surface type, with vegetated classes having a smaller diurnal temperature range of 11.6°C whereas non-vegetated classes had a diurnal range of 16.2°C (similar to in situ measurements collected simultaneously with the imagery). Observed LST across fractional gradients of turf/impervious and tree/impervious sub-pixel mixtures decreased linearly with increasing vegetation fraction. The slopes of decreasing LST were significantly different between tree and turf mixtures, with steeper slopes observed for turf (p < 0.05). These results suggest that different physiological characteristics and different access to irrigation water of urban trees and turfgrass results in significantly different LST effects, which can be detected at large scales in fractional mixture analysis.

The Lyman-alpha Solar Telescope for the ASO-S

NASA Astrophysics Data System (ADS)

Li, Hui

2015-08-01

The Lyman-alpha Solar Telescope (LST) is one of the payloads for the proposed Space-Borne Advanced Solar Observatory (ASO-S). LST consists of a Solar Disk Imager (SDI) with a field-of-view (FOV) of 1.2 Rsun, a Solar Corona Imager (SCI) with an FOV of 1.1 - 2.5 Rsun, and a full-disk White-light Solar Telescope (WST) with an FOV of 1.2 Rsun, which also serves as the guiding telescope. The SCI is designed to work at the Lyman-alpha waveband and white-light, while the SDI will work at the Lyman-alpha waveband only. The WST works both in visible (for guide) and ultraviolet (for science) white-light. The LST will observe the Sun from disk-center up to 2.5 solar radii for both solar flares and coronal mass ejections. In this presentation, I will give an introduction to LST, including scientific objectives, science requirement, instrument design and current status.

The Lyman-α Solar Telescope (LST) for the ASO-S mission

NASA Astrophysics Data System (ADS)

Li, Hui

The Lyman-α (Lyα) Solar Telescope (LST) is one of the payloads for the proposed Space-Borne Advanced Solar Observatory (ASO-S). LST consists of a Solar Disk Imager (SDI) with a field-of-view (FOV) of 1.2 R⊙ (R⊙ = solar radius), a Solar Corona Imager (SCI) with an FOV of 1.1 - 2.5 R⊙, and a full-disk White-light Solar Telescope (WST) with the same FOV as the SDI, which also serves as the guiding telescope. The SCI is designed to work in the Lyα (121.6 nm) waveband and white-light (for polarization brightness observation), while the SDI will work in the Lyα waveband only. The WST works in both visible (for guide) and ultraviolet (for science) broadband. The LST will observe the Sun from disk-center up to 2.5 R⊙ for both solar flares and coronal mass ejections with high tempo-spatial resolution

Emission of fermions in little string theory

NASA Astrophysics Data System (ADS)

Lorente-Espín, Oscar

2013-03-01

It is well known that little string theory (LST) black holes radiate a purely thermal spectrum of scalar particles. This theory lives in a Hagedorn phase with a fixed Hagedorn temperature that does not depend on its mass. Therefore, the theory keeps a thermal profile even taking into account self-gravitating effects and the backreaction of the metric. This has implications concerning the information loss paradox; one would not be able to recover any information from the LST black hole since the emission of scalar particles is totally uncorrelated. Several studies of the emission spectrum in LST concern scalar fields; it is our aim in this work to extend the study to the emission of fermions in order to verify that the most relevant conclusion for the scalar field remains valid for the fermion fields. Thus, we have calculated the emission probability, the flux, and also the greybody factor corresponding to a fermion field in LST background.

Applications of Land Surface Temperature from Microwave Observations

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST) is a key input for physically-based retrieval algorithms of hydrological states and fluxes. Yet, it remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observation...

Diagnostic and ethical challenges in disorders of consciousness and locked-in syndrome: a survey of German neurologists.

PubMed

Kuehlmeyer, Katja; Racine, Eric; Palmour, Nicole; Hoster, Eva; Borasio, Gian Domenico; Jox, Ralf J

2012-10-01

Diagnosis and decisions on life-sustaining treatment (LST) in disorders of consciousness, such as the vegetative state (VS) and the minimally conscious state (MCS), are challenging for neurologists. The locked-in syndrome (LiS) is sometimes confounded with these disorders by less experienced physicians. We aimed to investigate (1) the application of diagnostic knowledge, (2) attitudes concerning limitations of LST, and (3) further challenging aspects in the care of patients. A vignette-based online survey with a randomized presentation of a VS, MCS, or LiS case scenario was conducted among members of the German Society for Neurology. A sample of 503 neurologists participated (response rate 16.4%). An accurate diagnosis was given by 86% of the participants. The LiS case was diagnosed more accurately (94%) than the VS case (79%) and the MCS case (87%, p < 0.001). Limiting LST for the patient was considered by 92, 91, and 84% of the participants who accurately diagnosed the VS, LiS, and MCS case (p = 0.09). Overall, most participants agreed with limiting cardiopulmonary resuscitation; a minority considered limiting artificial nutrition and hydration. Neurologists regarded the estimation of the prognosis and determination of the patients' wishes as most challenging. The majority of German neurologists accurately applied the diagnostic categories VS, MCS, and LiS to case vignettes. Their attitudes were mostly in favor of limiting life-sustaining treatment and slightly differed for MCS as compared to VS and LiS. Attitudes toward LST strongly differed according to circumstances (e.g., patient's will opposed treatment) and treatment measures.

Historical GIS Data and Changes in Urban Morphological Parameters for the Analysis of Urban Heat Islands in Hong Kong

NASA Astrophysics Data System (ADS)

Peng, F.; Wong, M. S.; Nichol, J. E.; Chan, P. W.

2016-06-01

Rapid urban development between the 1960 and 2010 decades have changed the urban landscape and pattern in the Kowloon Peninsula of Hong Kong. This paper aims to study the changes of urban morphological parameters between the 1985 and 2010 and explore their influences on the urban heat island (UHI) effect. This study applied a mono-window algorithm to retrieve the land surface temperature (LST) using Landsat Thematic Mapper (TM) images from 1987 to 2009. In order to estimate the effects of local urban morphological parameters to LST, the global surface temperature anomaly was analysed. Historical 3D building model was developed based on aerial photogrammetry technique using aerial photographs from 1964 to 2010, in which the urban digital surface models (DSMs) including elevations of infrastructures and buildings have been generated. Then, urban morphological parameters (i.e. frontal area index (FAI), sky view factor (SVF)), vegetation fractional cover (VFC), global solar radiation (GSR), Normalized Difference Built-Up Index (NDBI), wind speed were derived. Finally, a linear regression method in Waikato Environment for Knowledge Analysis (WEKA) was used to build prediction model for revealing LST spatial patterns. Results show that the final apparent surface temperature have uncertainties less than 1 degree Celsius. The comparison between the simulated and actual spatial pattern of LST in 2009 showed that the correlation coefficient is 0.65, mean absolute error (MAE) is 1.24 degree Celsius, and root mean square error (RMSE) is 1.51 degree Celsius of 22,429 pixels.

Investigation of the scaling characteristics of LANDSAT temperature and vegetation data: a wavelet-based approach

NASA Astrophysics Data System (ADS)

Rathinasamy, Maheswaran; Bindhu, V. M.; Adamowski, Jan; Narasimhan, Balaji; Khosa, Rakesh

2017-10-01

An investigation of the scaling characteristics of vegetation and temperature data derived from LANDSAT data was undertaken for a heterogeneous area in Tamil Nadu, India. A wavelet-based multiresolution technique decomposed the data into large-scale mean vegetation and temperature fields and fluctuations in horizontal, diagonal, and vertical directions at hierarchical spatial resolutions. In this approach, the wavelet coefficients were used to investigate whether the normalized difference vegetation index (NDVI) and land surface temperature (LST) fields exhibited self-similar scaling behaviour. In this study, l-moments were used instead of conventional simple moments to understand scaling behaviour. Using the first six moments of the wavelet coefficients through five levels of dyadic decomposition, the NDVI data were shown to be statistically self-similar, with a slope of approximately -0.45 in each of the horizontal, vertical, and diagonal directions of the image, over scales ranging from 30 to 960 m. The temperature data were also shown to exhibit self-similarity with slopes ranging from -0.25 in the diagonal direction to -0.20 in the vertical direction over the same scales. These findings can help develop appropriate up- and down-scaling schemes of remotely sensed NDVI and LST data for various hydrologic and environmental modelling applications. A sensitivity analysis was also undertaken to understand the effect of mother wavelets on the scaling characteristics of LST and NDVI images.

Land surface temperature distribution and development for green open space in Medan city using imagery-based satellite Landsat 8

NASA Astrophysics Data System (ADS)

Sulistiyono, N.; Basyuni, M.; Slamet, B.

2018-03-01

Green open space (GOS) is one of the requirements where a city is comfortable to stay. GOS might reduce land surface temperature (LST) and air pollution. Medan is one of the biggest towns in Indonesia that experienced rapid development. However, the early development tends to neglect the GOS existence for the city. The objective of the study is to determine the distribution of land surface temperature and the relationship between the normalized difference vegetation index (NDVI) and the priority of GOS development in Medan City using imagery-based satellite Landsat 8. The method approached to correlate the distribution of land surface temperature derived from the value of digital number band 10 with the NDVI which was from the ratio of groups five and four on satellite images of Landsat 8. The results showed that the distribution of land surface temperature in the Medan City in 2016 ranged 20.57 - 33.83 °C. The relationship between the distribution of LST distribution with NDVI was reversed with a negative correlation of -0.543 (sig 0,000). The direction of GOS in Medan City is therefore developed on the allocation of LST and divided into three priority classes namely first priority class had 5,119.71 ha, the second priority consisted of 16,935.76 ha, and third priority of 6,118.50 ha.

[Applicability of traditional landscape metrics in evaluating urban heat island effect].

PubMed

Chen, Ai-Lian; Sun, Ran-Hao; Chen, Li-Ding

2012-08-01

By using 24 landscape metrics, this paper evaluated the urban heat island effect in parts of Beijing downtown area. QuickBird (QB) images were used to extract the landscape type information, and the thermal bands from Landsat Enhanced Thematic Mapper Plus (ETM+) images were used to extract the land surface temperature (LST) in four seasons of the same year. The 24 landscape pattern metrics were calculated at landscape and class levels in a fixed window with 120 mx 120 m in size, with the applicability of these traditional landscape metrics in evaluating the urban heat island effect examined. Among the 24 landscape metrics, only the percentage composition of landscape (PLAND), patch density (PD), largest patch index (LPI), coefficient of Euclidean nearest-neighbor distance variance (ENN_CV), and landscape division index (DIVISION) at landscape level were significantly correlated with the LST in March, May, and November, and the PLAND, LPI, DIVISION, percentage of like adjacencies, and interspersion and juxtaposition index at class level showed significant correlations with the LST in March, May, July, and December, especially in July. Some metrics such as PD, edge density, clumpiness index, patch cohesion index, effective mesh size, splitting index, aggregation index, and normalized landscape shape index showed varying correlations with the LST at different class levels. The traditional landscape metrics could not be appropriate in evaluating the effects of river on LST, while some of the metrics could be useful in characterizing urban LST and analyzing the urban heat island effect, but screening and examining should be made on the metrics.

AATSR land surface temperature product algorithm verification over a WATERMED site

NASA Astrophysics Data System (ADS)

Noyes, E. J.; Sòria, G.; Sobrino, J. A.; Remedios, J. J.; Llewellyn-Jones, D. T.; Corlett, G. K.

A new operational Land Surface Temperature (LST) product generated from data acquired by the Advanced Along-Track Scanning Radiometer (AATSR) provides the opportunity to measure LST on a global scale with a spatial resolution of 1 km2. The target accuracy of the product, which utilises nadir data from the AATSR thermal channels at 11 and 12 μm, is 2.5 K for daytime retrievals and 1.0 K at night. We present the results of an experiment where the performance of the algorithm has been assessed for one daytime and one night time overpass occurring over the WATERMED field site near Marrakech, Morocco, on 05 March 2003. Top of atmosphere (TOA) brightness temperatures (BTs) are simulated for 12 pixels from each overpass using a radiative transfer model, with the LST product and independent emissivity values and atmospheric data as inputs. We have estimated the error in the LST product over this biome for this set of conditions by applying the operational AATSR LST retrieval algorithm to the modelled BTs and comparing the results with the original AATSR LSTs input into the model. An average bias of -1.00 K (standard deviation 0.07 K) for the daytime data, and -1.74 K (standard deviation 0.02 K) for the night time data is obtained, which indicates that the algorithm is yielding an LST that is too cold under these conditions. While these results are within specification for daytime retrievals, this suggests that the target accuracy of 1.0 K at night is not being met within this biome.

Sequence stratigraphic controls on reservoir characterization and architecture: case study of the Messinian Abu Madi incised-valley fill, Egypt

NASA Astrophysics Data System (ADS)

Abdel-Fattah, Mohamed I.; Slatt, Roger M.

2013-12-01

Understanding sequence stratigraphy architecture in the incised-valley is a crucial step to understanding the effect of relative sea level changes on reservoir characterization and architecture. This paper presents a sequence stratigraphic framework of the incised-valley strata within the late Messinian Abu Madi Formation based on seismic and borehole data. Analysis of sand-body distribution reveals that fluvial channel sandstones in the Abu Madi Formation in the Baltim Fields, offshore Nile Delta, Egypt, are not randomly distributed but are predictable in their spatial and stratigraphic position. Elucidation of the distribution of sandstones in the Abu Madi incised-valley fill within a sequence stratigraphic framework allows a better understanding of their characterization and architecture during burial. Strata of the Abu Madi Formation are interpreted to comprise two sequences, which are the most complex stratigraphically; their deposits comprise a complex incised valley fill. The lower sequence (SQ1) consists of a thick incised valley-fill of a Lowstand Systems Tract (LST1)) overlain by a Transgressive Systems Tract (TST1) and Highstand Systems Tract (HST1). The upper sequence (SQ2) contains channel-fill and is interpreted as a LST2 which has a thin sandstone channel deposits. Above this, channel-fill sandstone and related strata with tidal influence delineates the base of TST2, which is overlain by a HST2. Gas reservoirs of the Abu Madi Formation (present-day depth ˜3552 m), the Baltim Fields, Egypt, consist of fluvial lowstand systems tract (LST) sandstones deposited in an incised valley. LST sandstones have a wide range of porosity (15 to 28%) and permeability (1 to 5080mD), which reflect both depositional facies and diagenetic controls. This work demonstrates the value of constraining and evaluating the impact of sequence stratigraphic distribution on reservoir characterization and architecture in incised-valley deposits, and thus has an important impact on reservoir quality evolution in hydrocarbon exploration in such settings.

An Examination of Intertidal Temperatures Through Remotely Sensed Satellite Observations

NASA Astrophysics Data System (ADS)

Lakshmi, V.

2010-12-01

MODIS Aqua and Terra satellites produce both land surface temperatures and sea surface temperatures using calibrated algorithms. In this study, the land surface temperatures were retrieved during clear-sky (non-cloudy) conditions at a 1 km2 resolution (overpass time at 10:30 am) whereas the sea surface temperatures are also retrieved during clear-sky conditions at approximately 4 km resolution (overpass time at 1:30 pm). The purpose of this research was to examine remotely sensed sea surface (SST), intertidal (IST), and land surface temperatures (LST), in conjunction with observed in situ mussel body temperatures, as well as associated weather and tidal data. In Strawberry Hill, Oregon, it was determined that intertidal surface temperatures are similar to but distinctly different from land surface temperatures although influenced by sea surface temperatures. The air temperature and differential heating throughout the day, as well as location in relation to the shore, can greatly influence the remotely sensed surface temperatures. Therefore, remotely sensed satellite data is a very useful tool in examining intertidal temperatures for regional climatic changes over long time periods and may eventually help researchers forecast expected climate changes and help determine associated biological implications.

Using microwave observations to estimate land surface temperature during cloudy conditions

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST), a key ingredient for physically-based retrieval algorithms of hydrological states and fluxes, remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observations and...

Evaluation of Potential Exposure to Metals in Laundered Shop Towels

PubMed Central

Greenberg, Grace; Beck, Barbara D.

2013-01-01

We reported in 2003 that exposure to metals on laundered shop towels (LSTs) could exceed toxicity criteria. New data from LSTs used by workers in North America document the continued presence of metals in freshly laundered towels. We assessed potential exposure to metals based on concentrations of metals on the LSTs, estimates of LST usage by employees, and the transfer of metals from LST-to-hand, hand-to-mouth, and LST-to-lip, under average- or high-exposure scenarios. Exposure estimates were compared to toxicity criteria. Under an average-exposure scenario (excluding metals' data outliers), exceedances of the California Environmental Protection Agency, U.S. Environmental Protection Agency, and the Agency for Toxic Substances and Disease Registry toxicity criteria may occur for aluminum, cadmium, cobalt, copper, iron, and lead. Calculated intakes for these metals were up to more than 400-fold higher (lead) than their respective toxicity criterion. For the high-exposure scenario, additional exceedances may occur, and high-exposure intakes were up to 1,170-fold higher (lead) than their respective toxicity criterion. A sensitivity analysis indicated that alternate plausible assumptions could increase or decrease the magnitude of exceedances, but were unlikely to eliminate certain exceedances, particularly for lead. PMID:24453472

Geothermal area detection using Landsat ETM+ thermal infrared data and its mechanistic analysis—A case study in Tengchong, China

NASA Astrophysics Data System (ADS)

Qin, Qiming; Zhang, Ning; Nan, Peng; Chai, Leilei

2011-08-01

Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using TIR data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. Based on radiometric calibration, atmospheric correction and emissivity calculation, a simple but efficient single channel algorithm with acceptable precision is applied to retrieve the land surface temperature (LST) of study area. The LST anomalous areas with temperature about 4-10 K higher than background area are discovered. Four geothermal areas are identified with the discussion of geothermal mechanism and the further analysis of regional geologic structure. The research reveals that the distribution of geothermal areas is consistent with the fault development in study area. Magmatism contributes abundant thermal source to study area and the faults provide thermal channels for heat transfer from interior earth to land surface and facilitate the present of geothermal anomalies. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect LST anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.

2017 Landsat Science Team Summer Meeting Summary

USGS Publications Warehouse

Crawford, Christopher J.; Loveland, Thomas R.; Wulder, Michael A.; Irons, James R.

2018-01-01

The summer meeting of the U.S. Geological Survey (USGS)-NASA Landsat Science Team (LST) was held June 11-13, 2017, at the USGS’s Earth Resources Observation and Science (EROS) Center near Sioux Falls, SD. This was the final meeting of the Second (2012-2017) LST.1 Frank Kelly [EROS—Center Director] welcomed the attendees and expressed his thanks to the LST members for their contributions. He then introduced video-recorded messages from South Dakota’s U.S. senators, John Thune and Mike Rounds, in which they acknowledged the efforts of the team in advancing the societal impacts of the Landsat Program.

Preparation and Electrical Properties of La0.9Sr0.1TiO3+δ

PubMed Central

Li, Wenzhi; Ma, Zhuang; Gao, Lihong; Wang, Fuchi

2015-01-01

La1−xSrxTiO3+δ (LST) has been studied in many fields, especially in the field of microelectronics due to its excellent electrical performance. Our previous theoretical simulated work has suggested that LST has good dielectric properties, but there are rare reports about this, especially experimental reports. In this paper, LST was prepared using a solid-state reaction method. The X-rays diffraction (XRD), scanning electron microscope (SEM), broadband dielectric spectroscopy, impedance spectroscopy and photoconductive measurement were used to characterize the sample. The results show that the values of dielectric parameters (the relative dielectric constant εr and dielectric loss tanδ), dependent on temperature, are stable under 350 °C and the value of the relative dielectric constant and dielectric loss are about 52–88 and 6.5 × 10−3, respectively. Its value of conductivity increases with rise in temperature, which suggests its negative temperature coefficient of the resistance. In addition, the band gap of LST is about 3.39 eV, so it belongs to a kind of wide-band-gap semiconductor materials. All these indicate that LST has anti-interference ability and good dielectric properties. It could have potential applications as an electronic material. PMID:28787995

Laser Science & Technology Program Annual Report - 2000

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, H-L

2001-03-20

The Laser Science and Technology (LS&T) Program Annual Report 2001 provides documentation of the achievements of the LLNL LS&T Program during the April 2001 to March 2002 period using three formats: (1) an Overview that is a narrative summary of important results for the year; (2) brief summaries of research and development activity highlights within the four Program elements: Advanced Lasers and Components (AL&C), Laser Optics and Materials (LO&M), Short Pulse Laser Applications and Technologies (SPLAT), and High-Energy Laser System and Tests (HELST); and (3) a compilation of selected articles and technical reports published in reputable scientific or technology journalsmore » in this period. All three elements (Annual Overview, Activity Highlights, and Technical Reports) are also on the Web: http://laser.llnl.gov/lasers/pubs/icfq.html. The underlying mission for the LS&T Program is to develop advanced lasers, optics, and materials technologies and applications to solve problems and create new capabilities of importance to the Laboratory and the nation. This mission statement has been our guide for defining work appropriate for our Program. A major new focus of LS&T beginning this past year has been the development of high peak power short-pulse capability for the National Ignition Facility (NIF). LS&T is committed to this activity.« less

Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India

NASA Astrophysics Data System (ADS)

Guha, Subhanil; Govil, Himanshu; Mukherjee, Sandip

2017-07-01

Spatial-temporal distribution of the urban heat islands (UHI) and their changes over Raipur city have been analyzed using multitemporal Landsat satellite data from 1995 to 2016. Land surface temperature (LST) was retrieved through a mono-window algorithm. Some selected land use/land cover (LU-LC) indices were analyzed with LST using linear regression. The urban thermal field variance index (UTFVI) was applied to measure the thermal comfort level of the city. Results show that during the observed period, the study area experienced a gradual increasing rate in mean LST (>1% per annum). The UHI developed especially along the north-western industrial area and south-eastern bare land of the city. A difference in mean LST between UHI and non-UHI for different time periods (2.6°C in 1995, 2.85°C in 2006, 3.42°C in 2009, and 3.63°C in 2016) reflects the continuous warming status of the city. The LST map also shows the existence of a few urban hot spots near the industrial areas, metal roofs, and high density transport parking lots, which are more abundant in the north-western part of the city. The UTFVI map associated with UHI indicates that the inner parts of the city are ecologically more comfortable than the outer peripheries.

Effects of urban tree canopy loss on land surface temperature magnitude and timing

NASA Astrophysics Data System (ADS)

Elmes, Arthur; Rogan, John; Williams, Christopher; Ratick, Samuel; Nowak, David; Martin, Deborah

2017-06-01

Urban Tree Canopy (UTC) plays an important role in moderating the Surface Urban Heat Island (SUHI) effect, which poses threats to human health due to substantially increased temperatures relative to rural areas. UTC coverage is associated with reduced urban temperatures, and therefore benefits both human health and reducing energy use in cities. Measurement of this relationship relies on accurate, fine spatial resolution UTC mapping, and on time series analysis of Land Surface Temperatures (LST). The City of Worcester, Massachusetts underwent extensive UTC loss and gain during the relatively brief period from 2008 to 2015, providing a natural experiment to measure the UTC/LST relationship. This paper consists of two elements to this end. First, it presents methods to map UTC in urban and suburban locations at fine spatial resolution (∼0.5 m) using image segmentation of a fused Lidar/WorldView-2 dataset, in order to show UTC change over time. Second, the areas of UTC change are used to explore changes in LST magnitude and seasonal variability using a time series of all available Landsat data for the study area over the eight-year period from 2007 to 2015. Fractional UTC change per unit area was determined using fine resolution UTC maps for 2008, 2010, and 2015, covering the period of large-scale tree loss and subsequent planting. LST changes were measured across a series of net UTC change bins, providing a relationship between UTC net change and LST trend. LST was analyzed for both monotonic trends over time and changes to seasonal magnitude and timing, using Theil-Sen slopes and Seasonal Trend Analysis (STA), respectively. The largest magnitudes of UTC loss occurred in residential neighborhoods, causing increased exposure of impervious (road) and pervious (grass) surfaces. Net UTC loss showed higher monotonic increases in LST than persistence and gain areas. STA indicated that net UTC loss was associated greater difference between 2008 and 2015 seasonal temperature curves than persistence areas, and also larger peak LST values, with peak increases ranging from 1 to 6 °C. Timing of summer warm period was extended in UTC loss areas by up to 15 days. UTC gain provided moderate LST mitigation, with lower monotonic trends, lower peak temperatures, and smaller seasonal curve changes than both persistence and loss locations. This study shows that urban trees mitigate the magnitude and timing of the surface urban heat island effect, even in suburban areas with less proportional impervious coverage than the dense urban areas traditionally associated with SUHI. Trees can therefore be seen as an effective means of offsetting the energy-intensive urban heat island effect.

Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands

USGS Publications Warehouse

Zhang, Li; Wylie, Bruce K.; Loveland, Thomas R.; Fosnight, Eugene A.; Tieszen, Larry L.; Ji, Lei; Gilmanov, Tagir

2007-01-01

Two spatially-explicit estimates of gross primary production (GPP) are available for the Northern Great Plains. An empirical piecewise regression (PWR) GPP model was developed from flux tower measurements to map carbon flux across the region. The Moderate Resolution Imaging Spectrometer (MODIS) GPP model is a process-based model that uses flux tower data to calibrate its parameters. Verification and comparison of the regional PWR GPP and the global MODIS GPP are important for the modeling of grassland carbon flux. This study compared GPP estimates from PWR and MODIS models with five towers in the grasslands. Among them, PWR GPP and MODIS GPP showed a good agreement with tower-based GPP at three towers. The global MODIS GPP, however, did not agree well with tower-based GPP at two other towers, probably because of the insensitivity of MODIS model to regional ecosystem and climate change and extreme soil moisture conditions. Cross-validation indicated that the PWR model is relatively robust for predicting regional grassland GPP. However, the PWR model should include a wide variety of flux tower data as the training data sets to obtain more accurate results.In addition, GPP maps based on the PWR and MODIS models were compared for the entire region. In the northwest and south, PWR GPP was much higher than MODIS GPP. These areas were characterized by the higher water holding capacity with a lower proportion of C4 grasses in the northwest and a higher proportion of C4 grasses in the south. In the central and southeastern regions, PWR GPP was much lower than MODIS GPP under complicated conditions with generally mixed C3/C4 grasses. The analysis indicated that the global MODIS GPP model has some limitations on detecting moisture stress, which may have been caused by the facts that C3 and C4 grasses are not distinguished, water stress is driven by vapor pressure deficit (VPD) from coarse meteorological data, and MODIS land cover data are unable to differentiate the sub-pixel cropland components.

Downscaling Land Surface Temperature in an Urban Area: A Case Study for Hamburg, Germany

NASA Astrophysics Data System (ADS)

Bechtel, Benjamin; Zakšek, Klemen

2013-04-01

Land surface temperature (LST) is an important parameter for the urban radiation and heat balance and a boundary condition for the atmospheric urban heat island (UHI). The increase in urban surface temperatures compared to the surrounding area (surface urban heat island, SUHI) has been described and analysed with satellite-based measurements for several decades. Besides continuous progress in the development of new sensors, an operational monitoring is still severely limited by physical constraints regarding the spatial and temporal resolution of the satellite data. Essentially, two measurement concepts must be distinguished: Sensors on geostationary platforms have high temporal (several times per hour) and poor spatial resolution (~ 5 km) while those on low earth orbiters have high spatial (~ 100-1000 m) resolution and a long return period (one day to several weeks). To enable an observation with high temporal and spatial resolution, a downscaling scheme for LST from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard the geostationary meteorological Meteosat 9 to spatial resolutions between 100 and 1000 m was developed and tested for Hamburg in this case study. Therefore, various predictor sets (including parameters derived from multi-temporal thermal data, NDVI, and morphological parameters) were tested. The relationship between predictors and LST was empirically calibrated in the low resolution domain and then transferred to the high resolution domain. The downscaling was validated with LST data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) for the same time. Aggregated parameters from multi-temporal thermal data (in particular annual cycle parameters and principal components) proved particularly suitable. The results for the highest resolution of 100 m showed a high explained variance (R² = 0.71) and relatively low root mean square errors (RMSE = 2.2 K). Larger predictor sets resulted in higher errors, because they tended to overfit. As expected the results were better for coarser spatial resolutions (R² = 0.80, RMSE = 1.8 K for 500 m). These results are similar or slightly better than in previous studies, although we are not aware of any study with a comparably large downscaling factor. A considerable percentage of the error is systematic due to the different viewing geometry of the sensors (the high resolution LST was overestimated about 1.3 K). The study shows that downscaling of SEVIRI LST is possible up to a resolution of 100 m for urban areas and that multi-temporal thermal data are particularly suitable as predictors.

Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites

USGS Publications Warehouse

Chen, Mingshi; Senay, Gabriel B.; Singh, Ramesh K.; Verdin, James P.

2016-01-01

Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET (ETo); and parameters, differential temperature (dT), and maximum ET scalar (Kmax), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables (ETo and LST) and two key parameters (Kmax and dT).

MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data

Treesearch

S. E. Lobser; W. B. Cohen

2007-01-01

The tasselled cap concept is extended to Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-Adjusted Reflectance (NBAR, MOD43) data. The transformation is based on a rigid rotation of principal component axes (PCAs) derived from a global sample spanning one full year of NBAR 16-day composites. To provide a standard for MODIS tasselled cap axes, we...

A Model-based Approach to Scaling GPP and NPP in Support of MODIS Land Product Validation

NASA Astrophysics Data System (ADS)

Turner, D. P.; Cohen, W. B.; Gower, S. T.; Ritts, W. D.

2003-12-01

Global products from the Earth-orbiting MODIS sensor include land cover, leaf area index (LAI), FPAR, 8-day gross primary production (GPP), and annual net primary production (NPP) at the 1 km spatial resolution. The BigFoot Project was designed specifically to validate MODIS land products, and has initiated ground measurements at 9 sites representing a wide array of vegetation types. An ecosystem process model (Biome-BGC) is used to generate estimates of GPP and NPP for each 5 km x 5 km BigFoot site. Model inputs include land cover and LAI (from Landsat ETM+), daily meteorological data (from a centrally located eddy covariance flux tower), and soil characteristics. Model derived outputs are validated against field-measured NPP and flux tower-derived GPP. The resulting GPP and NPP estimates are then aggregated to the 1 km resolution for direct spatial comparison with corresponding MODIS products. At the high latitude sites (tundra and boreal forest), the MODIS GPP phenology closely tracks the BigFoot GPP, but there is a high bias in the MODIS GPP. In the temperate zone sites, problems with the timing and magnitude of the MODIS FPAR introduce differences in MODIS GPP compared to the validation data at some sites. However, the MODIS LAI/FPAR data are currently being reprocessed (=Collection 4) and new comparisons will be made for 2002. The BigFoot scaling approach permits precise overlap in spatial and temporal resolution between the MODIS products and BigFoot products, and thus permits the evaluation of specific components of the MODIS NPP algorithm. These components include meteorological inputs from the NASA Data Assimilation Office, LAI and FPAR from other MODIS algorithms, and biome-specific parameters for base respiration rate and light use efficiency.

Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2015-01-01

The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS land surface products robust and reliable. The long-term 30-m Landsat data provide spatial detailed information for characterizing human-scale processes and have been used for land cover and land change studies. The main goal of this study is to combine 250-m MODIS GSN and 30-m Landsat observations to generate a quality-improved high spatial resolution (30-m) GSN database. A rule-based piecewise regression GSN model based on MODIS and Landsat data was developed. Results show a strong correlation between predicted GSN and actual GSN (r = 0.97, average error = 0.026). The most important Landsat variables in the GSN model are Normalized Difference Vegetation Indices (NDVIs) in May and August. The derived MODIS-Landsat-based 30-m GSN map provides biophysical information for moderate-scale ecological features. This multiple sensor study retains the detailed seasonal dynamic information captured by MODIS and leverages the high-resolution information from Landsat, which will be useful for regional ecosystem studies.

Niche modeling predictions of the potential distribution of Marmota himalayana, the host animal of plague in Yushu County of Qinghai.

PubMed

Lu, Liang; Ren, Zhoupeng; Yue, Yujuan; Yu, Xiaotao; Lu, Shan; Li, Guichang; Li, Hailong; Wei, Jianchun; Liu, Jingli; Mu, You; Hai, Rong; Yang, Yonghai; Wei, Rongjie; Kan, Biao; Wang, Hu; Wang, Jinfeng; Wang, Zuyun; Liu, Qiyong; Xu, Jianguo

2016-02-24

After the earthquake on 14, April 2010 at Yushu in China, a plague epidemic hosted by Himalayan marmot (Marmota himalayana) became a major public health concern during the reconstruction period. A rapid assessment of the distribution of Himalayan marmot in the area was urgent. The aims of this study were to analyze the relationship between environmental factors and the distribution of burrow systems of the marmot and to predict the distribution of marmots. Two types of marmot burrows (hibernation and temporary) in Yushu County were investigated from June to September in 2011. The location of every burrow was recorded with a global positioning system receiver. An ecological niche model was used to determine the relationship between the burrow occurrence data and environmental variables, such as land surface temperature (LST) in winter and summer, normalized difference vegetation index (NDVI) in winter and summer, elevation, and soil type. The predictive accuracies of the models were assessed by the area under the curve of the receiving operator curve. The models for hibernation and temporary burrows both performed well. The contribution orders of the variables were LST in winter and soil type, NDVI in winter and elevation for the hibernation burrow model, and LST in summer, NDVI in summer, soil type and elevation in the temporary burrow model. There were non-linear relationships between the probability of burrow presence and LST, NDVI and elevation. LST of 14 and 23 °C, NDVI of 0.22 and 0.60, and 4100 m were inflection points. A substantially higher probability of burrow presence was observed in swamp soil and dark felty soil than in other soil types. The potential area for hibernation burrows was 5696 km(2) (37.7% of Yushu County), and the area for temporary burrows was 7711 km(2) (51.0% of Yushu County). The results suggested that marmots preferred warm areas with relatively low altitudes and good vegetation conditions in Yushu County. Based on these results, the present research is useful in understanding the niche selection and distribution pattern of marmots in this region.

[Temporal and spatial variation of MODIS vegetation indices in Hunan Province].

PubMed

Lin, Hui; Xiong, Yu-Jiu; Wan, Ling-Feng; Mo, Deng-Kui; Sun, Hua

2007-03-01

Based on MODIS images and by using the algorithm of maximum value composite (MVC), the monthly vegetation indices (VIs) in 2005 in Hunan Province were obtained. Through the analysis of the MODIS VIs, Hunan Province was divided into six districts to describe the spatial distribution of the VIs, and by using the monthly mean temperature and rainfall data collected from 5 climatic monitoring stations in this province, the temporal variation of the VIs was analyzed. The results showed that the spatial distribution of MODIS VIs was positively correlated with vegetation cover, and appeared regional characteristics. The MODIS VIs varied with season, and the curves of their monthly mean values were downwards opening quadratic parabolas, with the maximum appeared in July. The value of MODIS EVI was smaller than that of MODIS NDVI. MODIS VI was mainly affected by monthly mean temperature, but this effect was decreased with decreasing latitude. The variation pattern of MODIS EVI was more apparent than that of MODIS NDVI, i. e. , the quadratic parabola of MODIS EVI was smoother, going gradually from minimum to maximum and then going down, while that of MODIS NDVI had tiny fluctuations on both sides of the maximum point.

Narcolepsy with and without cataplexy, idiopathic hypersomnia with and without long sleep time: a cluster analysis.

PubMed

Šonka, Karel; Šusta, Marek; Billiard, Michel

2015-02-01

The successive editions of the International Classification of Sleep Disorders (ICSD) reflect the evolution of the concepts of various sleep disorders. This is particularly the case for central disorders of hypersomnolence, with continuous changes in terminology and divisions of narcolepsy, idiopathic hypersomnia, and recurrent hypersomnia. According to the ICSD 2nd Edition (ICSD-2), narcolepsy with cataplexy (NwithC), narcolepsy without cataplexy (Nw/oC), idiopathic hypersomnia with long sleep time (IHwithLST), and idiopathic hypersomnia without long sleep time (IHw/oLST) are four, well-defined hypersomnias of central origin. However, in the absence of biological markers, doubts have been raised as to the relevance of a division of idiopathic hypersomnia into two forms, and it is not yet clear whether Nw/oC and IHw/oLST are two distinct entities. With this in mind, it was decided to empirically review the ICSD-2 classification by using a hierarchical cluster analysis to see whether this division has some relevance, even though the terms "with long sleep time" and "without long sleep time" are inappropriate. The cluster analysis differentiated three main clusters: Cluster 1, "combined monosymptomatic hypersomnia/narcolepsy type 2" (people initially diagnosed with IHw/oLST and Nw/oC); Cluster 2 "polysymptomatic hypersomnia" (people initially diagnosed with IHwithLST); and Cluster 3, narcolepsy type 1 (people initially diagnosed with NwithC). Cluster analysis confirmed that narcolepsy type 1 and polysymptomatic hypersomnia are independent sleep disorders. People who were initially diagnosed with Nw/oC and IHw/oLST formed a single cluster, referred to as "combined monosymptomatic hypersomnia/narcolepsy type 2." Copyright © 2014 Elsevier B.V. All rights reserved.

A Global Characterization of Urban Heat Islands

NASA Astrophysics Data System (ADS)

Chakraborty, T.; Lee, X.

2017-12-01

The urban heat island (UHI) effect refers to the higher temperatures in urban areas, and it is one of the most well-known consequences of urbanization on local climate. In the present study, we define a new simplified urban-boundary (SUB) algorithm to quantify the daytime and nighttime surface UHIs on a global scale based on 16 years of MODIS Land Surface Temperature (LST) data. The results from the algorithm are validated against previous studies and used to determine the diurnal, monthly, and long-term variation in the surface UHI for over 9000 urban clusters situated in the different Koppen-Geiger climate zones,namely equatorial, arid, warm temperate, snow, and polar. Thus, the variability of the surface UHI for each climate class is determined using a consistent methodology for the first time. The 16-year mean global daytime surface UHI is 0.71 ± 0.93 °C at 1030 LT and 1.00 ± 1.17 °C at 1330 LT, while the nighttime surface UHI is 0.51 ± 0.50 °C at 2230 LT and 0.42 ± 0.52 °C at 0130 LT. This is in good agreement with the results from previous studies, which have looked at the UHI for multiple cities. Summer surface UHI is larger than winter surface UHI across all climate zones. The annual daytime surface UHI is highest in the polar urban clusters (1.77 ± 1.61 °C), followed by snow (1.39 ± 1.17 °C), equatorial (1.21 ± 1.32 °C), warm temperate (1.02 ± 0.98 °C), and arid (0.18 ± 1.27 °C). Urban clusters in the arid climate are found to show different diurnal and seasonal patterns, with higher nighttime surface UHI (0.65 ± 0.58 °C) and two seasonal peaks during the year. The diurnal variation in surface UHI is highest in the polar zone (1.16 °C) and lowest in the arid zone (0.57 °C). The inter-seasonality is also highest in the polar Zone (2.20 °C) and lowest in the arid zone (0.80 °C). Finally, we investigate the change in the surface UHI in more than a decade (2001 to 2013 for MODIS TERRA and 2003 to 2013 for MODIS AQUA) and find a gradual increase in the UHI magnitude in the equatorial (0.05 °C/decade) and snow (0.12 °C/decade) climate zones. Our results imply that city planners and policy makers should take the background climate zone of a city into account when trying to mitigate the impact of thermal stress in urban areas.

Influence of Scale Effect and Model Performance in Downscaling ASTER Land Surface Temperatures to a Very High Spatial Resolution in an Agricultural Area

NASA Astrophysics Data System (ADS)

Zhou, J.; Li, G.; Liu, S.; Zhan, W.; Zhang, X.

2015-12-01

At present land surface temperatures (LSTs) can be generated from thermal infrared remote sensing with spatial resolutions from ~100 m to tens of kilometers. However, LSTs with high spatial resolution, e.g. tens of meters, are still lack. The purpose of LST downscaling is to generate LSTs with finer spatial resolutions than their native spatial resolutions. The statistical linear or nonlinear regression models are most frequently used for LST downscaling. The basic assumption of these models is the scale-invariant relationships between LST and its descriptors, which is questioned but rare researches have been reported. In addition, few researches can be found for downscaling satellite LST or TIR data to a high spatial resolution, i.e. better than 100 m or even finer. The lack of LST with high spatial resolution cannot satisfy the requirements of applications such as evapotranspiration mapping at the field scale. By selecting a dynamically developing agricultural oasis as the study area, the aim of this study is to downscale the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LSTs to 15 m, to satisfy the requirement of evapotranspiration mapping at the field scale. Twelve ASTER images from May to September in 2012, covering the entire growth stage of maize, were selected. Four statistical models were evaluated, including one global model, one piecewise model, and two local models. The influence from scale effect in downscaling LST was quantified. The downscaled LSTs are evaluated from accuracy and image quality. Results demonstrate that the influence from scale effect varies according to models and the maize growth stage. Significant influence about -4 K to 6 K existed at the early stage and weaker influence existed in the middle stage. When compared with the ground measured LSTs, the downscaled LSTs resulted from the global and local models yielded higher accuracies and better image qualities than the local models. In addition to the vegetation indices, the surface albedo is an important descriptor for downscaling LST through explaining its spatial variation induced by soil moisture.

Evaluation of leishmanin skin test and its relationship with the clinical form and duration of cutaneous leishmaniasis.

PubMed

Sadeghian, Giti; Momeni, Ali; Siadat, Amir Hossein; Yousefi, Pedram

2006-12-10

Cellular immunity plays a major role in natural defense against cutaneous leishmaniasis. The leishmanin skin test (LST) is one method of evaluating the infected individual's immune response to leishmania. Our objective in this study was to evaluate the relationship between positivity of the LST with duration of disease, clinical form, number of lesions, and age and gender of the patient. This open study was performed on 198 patients who were affected by cutaneous leishmaniasis before any treatment was administered. Following confirmation of the diagnosis of cutaneous leishmaniasis, relevant data were recorded, including age, gender, occupation, address, duration of disease, clinical form, location of the lesions, and the number of the lesions. After performing the leishmanin skin test, patients were treated for leishmaniasis according to the type and severity of the disease. For patients whose LST was initially negative, the test was repeated every 15 days. If the LST was still negative after 4 months, the test was repeated every 3 months; if the LST remained negative 12 months after the first test, the result was considered negative. The collected data were statistically analyzed using the SPSS program. In 179 patients (90.4%) the test was positive at the time of the first test. In 7 patients (3.8%) it became positive during treatment, and in 12 patients (6 percent) the test remained negative until the end of study. There was no significant relationship between the skin lesion number and the positivity of the leishmanin skin test (p = 0.98). There was no significant relationship between age group and diameter of the induration. All of the patients who had negative leishmnanin test at the 12 months followup visit had one lesion only. This study showed that there is no relationship between age, gender, or duration of disease with positivity of the LST or degree of positivity, but there is a significant relationship with the clinical form of cutaneous leishmaniasis at the final test (12 patients). This study showed that there is no significant relationship between positivity of LST and the type of treatment.

Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, S.; Kim, H.; Choi, M.; Kim, K.

2016-12-01

Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

Station Climatic Summaries, Latin America

DTIC Science & Technology

1990-06-01

GERONA, CUBA STATION 4: 782210 ICAO ID: MUNG LOCATION: 21 50’N, 82 47’W ELEVATION (FEET): 75 LST - GMT: -5 PREPARED BY: USAFETAC/ECR, MAR 1987 PERIOD...AUG SFP ocr Nt;V DEC ANN 00=02 [, sTr "• -€ -• : € : . : ’ " ’ 06-08 LST # # # 1 0 2 1 #i 1 1 1 2 1 09-1 [IST # 0 i 2 1 3 2 ! 1 # I 13 12-14 LST I # # 2...A N S STATIO .NAME ASU-N-CIU.N./.P.R.ES.- STR SSIR PARAG STATION MSC: 862180 LATITUDE/LOITITE: S 25 14 I 057 31 FIELD ELEVATION: 292 FEET CALL

N-MODY: A Code for Collisionless N-body Simulations in Modified Newtonian Dynamics

NASA Astrophysics Data System (ADS)

Londrillo, Pasquale; Nipoti, Carlo

2011-02-01

N-MODY is a parallel particle-mesh code for collisionless N-body simulations in modified Newtonian dynamics (MOND). N-MODY is based on a numerical potential solver in spherical coordinates that solves the non-linear MOND field equation, and is ideally suited to simulate isolated stellar systems. N-MODY can be used also to compute the MOND potential of arbitrary static density distributions. A few applications of N-MODY indicate that some astrophysically relevant dynamical processes are profoundly different in MOND and in Newtonian gravity with dark matter.

Global Clear-Sky Surface Skin Temperature from Multiple Satellites Using a Single-Channel Algorithm with Angular Anisotropy Corrections

NASA Technical Reports Server (NTRS)

Scarino, Benjamin R.; Minnis, Patrick; Chee, Thad; Bedka, Kristopher M.; Yost, Christopher R.; Palikonda, Rabindra

2017-01-01

Surface skin temperature (T(sub s)) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared (TIR) method to retrieve T(sub s) over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of T(sub s) over the diurnal cycle in non-polar regions, while polar T(sub s) retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR), can complement the GEO measurements. The combined global coverage of remotely sensed T(sub s), along with accompanying cloud and surface radiation parameters, produced in near-realtime and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-realtime hourly T(sub s) observations can be assimilated in high-temporal-resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived T(sub s) data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing and illumination angles. Therefore, T(sub s) validation with established references is essential, as is proper evaluation of T(sub s) sensitivity to atmospheric correction source. This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based T(sub s) product that is derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirically adjusted theoretical model of satellite land surface temperature (LST) angular anisotropy is tested to improve satellite LST retrievals. Application of the anisotropic correction yields reduced mean bias and improved precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program ground station measurements. It also significantly reduces inter-satellite differences between LSTs retrieved simultaneously from two different imagers. The implementation of these universal corrections into the SatCORPS product can yield significant improvement in near-global-scale, near-realtime, satellite-based LST measurements. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

Tribological Behavior of Oil-Lubricated Laser Textured Steel Surfaces in Conformal Flat and Non-Conformal Contacts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kovalchenko, A. M.; Erdemir, A.; Ajayi, O. O.

Changing the surface texture of sliding surfaces is an effective way to manipulate friction and wear of lubricated surfaces. Having realized its potential, we have done very extensive studies on the effects of laser surface texturing (LST, which involves the creation of an array of microdimples on a surface) on friction and wear behavior of oil-lubricated steel surfaces in the early 2000s. In this paper, we reviewed some of our research accomplishments and assessed future directions of the laser texturing field in many diverse industrial applications. Our studies specifically addressed the impact of laser texturing on friction and wear ofmore » both the flat conformal and initial non-conformal point contact configurations using a pin-on-disk test rig under fully-flooded synthetic oil lubricants with different viscosities. Electrical resistance measurement between pin and LST disks was also used to determine the operating lubrication regimes in relation to friction. In conformal contact, we confirmed that LST could significantly expand the operating conditions for hydrodynamic lubrication to significantly much higher loads and slower speeds. In particular, with LST and higher viscosity oils, the low-friction full hydrodynamic regime was shifted to the far left in the Stribeck diagram. Overall, the beneficial effects of laser surface texturing were more pronounced at higher speeds and loads and with higher viscosity oil. LST was also observed to reduce the magnitude of friction coefficients in the boundary regime. For the non-conformal contact configuration, we determined that LST would produce more abrasive wear on the rubbing counterface compared to the untreated surfaces due to a reduction in lubricant fluid film thickness, as well as the highly uneven and rough nature of the textured surfaces. However, this higher initial wear rate has led to faster generation of a conformal contact, and thus transition from the high-friction boundary to lower friction mixed lubrication regime, resulting in a rapid reduction in the friction coefficient with increased ball wear. Higher density of LST, lower oil viscosity, and hardness of counterface steel surface facilitate an increase of the initial wear, which promotes friction reduction. This phenomenon can be beneficial if the initial accelerated wear on the counterface is acceptable in intended applications. This paper summarizes our experimental investigation of the effect of LST on friction properties and lubrication regime transitions in a unidirectional sliding contact.« less

Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

NASA Astrophysics Data System (ADS)

Şekertekin, A.; Kutoglu, Ş. H.; Kaya, S.; Marangoz, A. M.

2015-12-01

Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

Mapping Irrigated Areas in the Tunisian Semi-Arid Context with Landsat Thermal and VNIR Data Imagery

NASA Astrophysics Data System (ADS)

Rivalland, Vincent; Drissi, Hsan; Simonneaux, Vincent; Tardy, Benjamin; Boulet, Gilles

2016-04-01

Our study area is the Merguellil semi-arid irrigated plain in Tunisia, where the water resource management is an important stake for governmental institutions, farmer communities and more generally for the environment. Indeed, groundwater abstraction for irrigation is the primary cause of aquifer depletion. Moreover, unregistered pumping practices are widespread and very difficult to survey by authorities. Thus, the identification of areas actually irrigated in the whole plain is of major interest. In order to map the irrigated areas, we tried out a methodology based on the use of Landsat 7 and 8 Land Surface Temperature (LST) data issued from atmospherically corrected thermal band using the LANDARTs Tool jointly with the NDVI vegetation indices obtained from visible ane near infrared (VNIR) bands. For each Landsat acquisition during the years 2012 to 2014, we computed a probability of irrigation based on the location of the pixel in the NDVI - LST space. Basically for a given NDVI value, the cooler the pixel the higher its probability to be irrigated is. For each date, pixels were classified in seven bins of irrigation probability ranges. Pixel probabilities for each date were then summed over the study period resulting in a probability map of irrigation. Comparison with ground data shows a consistent identification of irrigated plots and supports the potential operational interest of the method. However, results were hampered by the low Landsat LST data availability due to clouds and the inadequate revisit frequency of the sensor.

Landsat Science Team meeting—first Landsat 8 evaluations

USGS Publications Warehouse

Loveland, Thomas R.; Wulder, Michael A.; Irons, James R.

2014-01-01

The U.S. Geological Survey (USGS)-NASA Landsat Science Team (LST) met at the USGS’ Earth Resources Observation and Science (EROS) Center near Sioux Falls, SD, from October 29-31, 2013. All meeting presentations can be downloaded from landsat.usgs.gov/science_LST_October_29_31_2013.php.

Mapping Daily Evapotranspiration at Field to Global Scales using Geostationary and Polar Orbiting Satellite Imagery

USDA-ARS?s Scientific Manuscript database

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetati...

A review on remotely sensed land surface temperature anomaly as an earthquake precursor

NASA Astrophysics Data System (ADS)

Bhardwaj, Anshuman; Singh, Shaktiman; Sam, Lydia; Joshi, P. K.; Bhardwaj, Akanksha; Martín-Torres, F. Javier; Kumar, Rajesh

2017-12-01

The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors.

Sprite climatology in the Eastern Mediterranean Region

NASA Astrophysics Data System (ADS)

Yair, Yoav; Price, Colin; Katzenelson, Dor; Rosenthal, Neta; Rubanenko, Lior; Ben-Ami, Yuval; Arnone, Enrico

2015-04-01

We present statistical analysis of 436 sprites observed in 7 winter campaigns from 2006/7-2012/13. Results show a clear peak in the frequency of sprite detections, with maximum values (< 40% of events) between 00:30 and 02:15 LST (22:30-00:15 UT; LST = UT + 2). The detection times of sprites are well-correlated with a relative increase in the fraction of + CG strokes, which exhibit maxima between 00:00 and 02:00 LST. The morphological distribution of 339 sprites, that we were able to clearly identify, is dominated by column sprites (49.3%), with angels (33.0%) and carrots (25.7%) being less frequent. This is similar to reports of winter sprites over the Sea of Japan and summer ones in Central Europe. Other shapes such as trees, wishbones, etc. appear quite rarely. Single element events constitute 16.5% of observations, with 83.5% containing 2 elements or more. Clusters of homogenous types are slightly more frequent than mixed ones (55%). Our observations suggest winter Mediterranean thunderstorms to have a vertical structure in between high tropical convective systems and the lower cloud-top cells in Japan. The climatology shows the Eastern Mediterranean to be a major sprite producer in Northern Hemisphere winter, and offers ground-based coverage for future space missions.

Utilizing Hierarchical Segmentation to Generate Water and Snow Masks to Facilitate Monitoring Change with Remotely Sensed Image Data

NASA Technical Reports Server (NTRS)

Tilton, James C.; Lawrence, William T.; Plaza, Antonio J.

2006-01-01

The hierarchical segmentation (HSEG) algorithm is a hybrid of hierarchical step-wise optimization and constrained spectral clustering that produces a hierarchical set of image segmentations. This segmentation hierarchy organizes image data in a manner that makes the image's information content more accessible for analysis by enabling region-based analysis. This paper discusses data analysis with HSEG and describes several measures of region characteristics that may be useful analyzing segmentation hierarchies for various applications. Segmentation hierarchy analysis for generating landwater and snow/ice masks from MODIS (Moderate Resolution Imaging Spectroradiometer) data was demonstrated and compared with the corresponding MODIS standard products. The masks based on HSEG segmentation hierarchies compare very favorably to the MODIS standard products. Further, the HSEG based landwater mask was specifically tailored to the MODIS data and the HSEG snow/ice mask did not require the setting of a critical threshold as required in the production of the corresponding MODIS standard product.

N-MODY: a code for collisionless N-body simulations in modified Newtonian dynamics.

NASA Astrophysics Data System (ADS)

Londrillo, P.; Nipoti, C.

We describe the numerical code N-MODY, a parallel particle-mesh code for collisionless N-body simulations in modified Newtonian dynamics (MOND). N-MODY is based on a numerical potential solver in spherical coordinates that solves the non-linear MOND field equation, and is ideally suited to simulate isolated stellar systems. N-MODY can be used also to compute the MOND potential of arbitrary static density distributions. A few applications of N-MODY indicate that some astrophysically relevant dynamical processes are profoundly different in MOND and in Newtonian gravity with dark matter.

Station Climatic Summaries, Europe

DTIC Science & Technology

1989-01-01

ICAO ID: BIHN LOCATION: 64118’N, 15󈧑’W ELEVATION (FEET): 30 LST = GMT: +1 PREPARED BY: USAFETAC/ECR, OCT 1986 PERIOD: 8007-8512 SOURCE NO. JAN FEB...SUMMARY SrATION: HOFN, ICELAND STATION #: 040820 ICAO ID: BIHN LOCATION: 64018’N, 15013’W ELEVATION (FEET): 30 LST = GT: +1 PREPARED BY: USAFETAC/ECR

A Comparison of Four Approaches to Account for Method Effects in Latent State-Trait Analyses

ERIC Educational Resources Information Center

Geiser, Christian; Lockhart, Ginger

2012-01-01

Latent state-trait (LST) analysis is frequently applied in psychological research to determine the degree to which observed scores reflect stable person-specific effects, effects of situations and/or person-situation interactions, and random measurement error. Most LST applications use multiple repeatedly measured observed variables as indicators…

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas.

PubMed

Kim, Jun-Hyun; Gu, Donghwan; Sohn, Wonmin; Kil, Sung-Ho; Kim, Hwanyong; Lee, Dong-Kun

2016-09-02

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas

PubMed Central

Kim, Jun-Hyun; Gu, Donghwan; Sohn, Wonmin; Kil, Sung-Ho; Kim, Hwanyong; Lee, Dong-Kun

2016-01-01

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST. PMID:27598186

Continental-Scale Validation of Modis-Based and LEDAPS Landsat ETM + Atmospheric Correction Methods

NASA Technical Reports Server (NTRS)

Ju, Junchang; Roy, David P.; Vermote, Eric; Masek, Jeffrey; Kovalskyy, Valeriy

2012-01-01

The potential of Landsat data processing to provide systematic continental scale products has been demonstratedby several projects including the NASA Web-enabled Landsat Data (WELD) project. The recent freeavailability of Landsat data increases the need for robust and efficient atmospheric correction algorithms applicableto large volume Landsat data sets. This paper compares the accuracy of two Landsat atmospheric correctionmethods: a MODIS-based method and the Landsat Ecosystem Disturbance Adaptive ProcessingSystem (LEDAPS) method. Both methods are based on the 6SV radiative transfer code but have different atmosphericcharacterization approaches. The MODIS-based method uses the MODIS Terra derived dynamicaerosol type, aerosol optical thickness, and water vapor to atmospherically correct ETM+ acquisitions ineach coincident orbit. The LEDAPS method uses aerosol characterizations derived independently from eachLandsat acquisition and assumes a fixed continental aerosol type and uses ancillary water vapor. Validationresults are presented comparing ETM+ atmospherically corrected data generated using these two methodswith AERONET corrected ETM+ data for 95 10 km10 km 30 m subsets, a total of nearly 8 million 30 mpixels, located across the conterminous United States. The results indicate that the MODIS-based methodhas better accuracy than the LEDAPS method for the ETM+ red and longer wavelength bands.

The Blue Marble

NASA Technical Reports Server (NTRS)

2002-01-01

This spectacular Moderate Resolution Imaging Spectroradiometer (MODIS) 'blue marble' image is based on the most detailed collection of true-color imagery of the entire Earth to date. Using a collection of satellite-based observations, scientists and visualizers stitched together months of observations of the land surface, oceans, sea ice, and clouds into a seamless, true-color mosaic of every square kilometer (.386 square mile) of our planet. Most of the information contained in this image came from MODIS, illustrating MODIS' outstanding capacity to act as an integrated tool for observing a variety of terrestrial, oceanic, and atmospheric features of the Earth. The land and coastal ocean portions of this image is based on surface observations collected from June through September 2001 and combined, or composited, every eight days to compensate for clouds that might block the satellite's view on any single day. Global ocean color (or chlorophyll) data was used to simulate the ocean surface. MODIS doesn't measure 3-D features of the Earth, so the surface observations were draped over topographic data provided by the U.S. Geological Survey EROS Data Center. MODIS observations of polar sea ice were combined with observations of Antarctica made by the National Oceanic and Atmospheric Administration's AVHRR sensor-the Advanced Very High Resolution Radiometer. The cloud image is a composite of two days of MODIS imagery collected in visible light wavelengths and a third day of thermal infra-red imagery over the poles. A large collection of imagery based on the blue marble in a variety of sizes and formats, including animations and the full (1 km) resolution imagery, is available at the Blue Marble page. Image by Reto Stockli, Render by Robert Simmon. Based on data from the MODIS Science Team

Changes of paddy rice planting areas in Northeastern Asia from 1986 to 2014 based on Landsat data

NASA Astrophysics Data System (ADS)

Dong, J.; Xiao, X.; Kou, W.; Qin, Y.; Wang, J.; Zhang, G.; Jin, C.; Zhou, Y.; Menarguez, M. A.; Moore, B., III

2014-12-01

Paddy rice is an important cereal crop and main grain source for more than half of the global human population. However, knowledge about its area and spatial pattern is still limited due to large changes in agriculture in different regions; for example, higher latitude areas underwent increase (e.g., northeastern China) and decrease (e.g., South Korea) of paddy rice planting areas due to climatic warming, urbanization and other drivers. It is necessary to track paddy rice planting area changes in these regions in the past decades. We developed a pixel- and phenology-based image analysis system, Landsat-RICE, to map the paddy rice by using Landsat imagery. The algorithm was based on the unique physical and spectral characteristics of paddy rice fields during the flooding and transplanting phases. First, Landsat images are preprocessed and time series vegetation indices (NDVI, EVI, and LSWI) are generated. Second, MODIS Land Surface Temperature (LST) data were used to define thermal plant growing season (0 oC, 5 oC and 10 oC), which provides a guide for selection of Landsat images within the period of flooding and transplanting. Third, several non-cropland land cover maps (e.g., permanent water bodies, built-up and barren lands, sparsely vegetated lands, and evergreen vegetation) are produced through analysis of Landsat-based vegetation indices within the plant growing season and combined as a mask. Fourthly, vegetation index data within the time window of flooded and rice transplanting were analyzed to identify flood/transplanting signals. Finally, the maps of paddy rice planting areas were generated through overlying the results from Step 3 and 4. Paddy rice planting area changes were investigated in some hotspots of Northeastern Asia from 1986 to 2014 at 30-m spatial resolution and 5-year interval. This study has demonstrated that our newly developed Landsat-Rice system is robust and effective for tracking paddy rice changes in cold temperate and temperate zones.

Evaluation of Crop-Water Consumption Simulation to Support Agricultural Water Resource Management using Satellite-based Water Cycle Observations

NASA Astrophysics Data System (ADS)

Gupta, M.; Bolten, J. D.; Lakshmi, V.

2016-12-01

Water scarcity is one of the main factors limiting agricultural development. Numerical models integrated with remote sensing datasets are increasingly being used operationally as inputs for crop water balance models and agricultural forecasting due to increasing availability of high temporal and spatial resolution datasets. However, the model accuracy in simulating soil water content is affected by the accuracy of the soil hydraulic parameters used in the model, which are used in the governing equations. However, soil databases are known to have a high uncertainty across scales. Also, for agricultural sites, the in-situ measurements of soil moisture are currently limited to discrete measurements at specific locations, and such point-based measurements do not represent the spatial distribution at a larger scale accurately, as soil moisture is highly variable both spatially and temporally. The present study utilizes effective soil hydraulic parameters obtained using a 1-km downscaled microwave remote sensing soil moisture product based on the NASA Advanced Microwave Scanning Radiometer (AMSR-E) using the genetic algorithm inverse method within the Catchment Land Surface Model (CLSM). Secondly, to provide realistic irrigation estimates for agricultural sites, an irrigation scheme within the land surface model is triggered when the root-zone soil moisture deficit reaches the threshold, 50% with respect to the maximum available water capacity obtained from the effective soil hydraulic parameters. An additional important criterion utilized is the estimation of crop water consumption based on dynamic root growth and uptake in root zone layer. Model performance is evaluated using MODIS land surface temperature (LST) product. The soil moisture estimates for the root zone are also validated with the in situ field data, for three sites (2- irrigated and 1- rainfed) located at the University of Nebraska Agricultural Research and Development Center near Mead, NE and monitored by three AmeriFlux installations (Verma et al., 2005).

Evolution of sedimentary architecture in retro-foreland basin: Aquitaine basin example from Paleocene to lower Eocene.

NASA Astrophysics Data System (ADS)

Ortega, Carole; Lasseur, Eric; Guillocheau, François; Serrano, Olivier; Malet, David

2017-04-01

The Aquitaine basin located in south western Europe, is a Pyrenean retro-foreland basin. Two main phases of compression are recorded in this retro-foreland basin during the Pyrenean orogeny. A first upper Cretaceous phase corresponding to the early stage of the orogeny, and a second one usually related to a Pyrenean paroxysmal phase during the middle Eocene. During Paleocene to lower Eocene deformations are less pronounced, interpreted as a tectonically quiet period. The aim of the study is to better constrain the sedimentary system of the Aquitaine basin during this period of Paleocene-lower Eocene, in order to discuss the evolution of the sedimentary architecture in response of the Pyrenean compression. This work is based on a compilation of a large set of subsurface data (wells logs, seismic lines and cores logs) represented by isopachs and facies map. Three main cycles were identified during this structural quiet period: (1) The Danian cycle, is recorded by the aggradation of carbonate reef-rimmed platform. This platform is characterized by proximal facies (oncoid carbonate and mudstone with thalassinoides) to the north, which leads to distal deposit facies southern (pelagic carbonate with globigerina and slump facies) and present a significant thickness variation linked to the platform-slope-basin morphology. (2) The upper Selandian-Thanetian cycle follows a non-depositional/erosional surface associated with a Selandian hiatus. The base of this cycle marked the transition between the last reef rimmed platform and a carbonate ramp. The transgressive cycle is characterized by proximal lagoon facies to the north that leads southward to distal hemipelagic facies interfingered by turbiditic Lowstand System Tracks (LST). The location of these LST is strongly controlled by inherited Danian topography. The regressive cycle ends with a major regression associated with an erosional surface. This surface is linked with a network of canyons in the north, an important terrigeneous LST and a massive erosional surface in deep basin. We correlated this upper Thanetian major regression with a flexural deformation of the basin. In this context, the importance of terrigeneous LST could be explained by the erosion of the East Pyrenean range. (3) The lower Ypresian records the installation of mixed terrigenous-carbonated system. While the East-West progradation of siliciclastic deltas is drained into foreland basin, a carbonates condensation are developed on structural ridges, attesting the structural activation of foreland basin during lower Ypresian. This study shows that Danian to middle Thanetian time represents a quiet tectonic period in the retro-foreland basin. During the upper Thanetian period, the compressive deformation is increasing, marked by the emersion of the northern platform, a massive LST in distal environment and a rise of terrigenous input in flexural basin (LST). This deformation associated with the Pyrenean compression continues during the Ypresian and highlights the paroxysm of the Pyrenean orogeny. This work is included in the Gaia project founded by TIGF, BRGM and Agence de l'Eau Adour/Garonne whose aim at constrain the nature and dynamics of deep Upper cretaceous and Tertiary aquifers of the Aquitaine basin.

Phenotypical aspects of maturity-onset diabetes of the young (MODY diabetes) in comparison with Type 2 diabetes mellitus (T2DM) in children and adolescents: experience from a large multicentre database.

PubMed

Schober, E; Rami, B; Grabert, M; Thon, A; Kapellen, Th; Reinehr, Th; Holl, R W

2009-05-01

To analyse and compare clinical characteristics in young patients with maturity-onset diabetes of the young (MODY) and Type 2 diabetes mellitus (T2DM). We conducted an observational investigation using the DPV-Wiss database containing clinical data on 40 757 diabetic patients < 20 years of age from Germany and Austria. Three hundred and thirty-nine cases were clinically categorized as MODY (0.83%); 562 patients were diagnosed as T2DM (1.4%). In 20% of cases, the diagnosis of MODY was based on clinical findings only. Of the 272 subjects where genetic testing was available, 3% did not carry mutations in the three examined MODY genes. Glucokinase-MODY was commoner than HNF1A-MODY and HNF4A-MODY. Age at diagnosis was younger in MODY patients. The body mass index of T2DM was significantly higher compared with all MODY subgroups. Macrovascular risk factors such as dyslipidaemia and hypertension were commoner in T2DM, but 23% of MODY patients had dyslipidaemia and 10% hypertension. Glycaemic control was within the therapeutic target (HbA(1c) < 7.5%) in 86% of MODY and 70% of T2DM patients. The prevalence of MODY in children and adolescents in Germany and Austria is lower than that of T2DM in this age group. Dyslipidaemia and hypertension are less frequent in MODY compared with T2DM patients, but do occur.

Hookworm Infection and Environmental Factors in Mbeya Region, Tanzania: A Cross-Sectional, Population-Based Study

PubMed Central

Riess, Helene; Clowes, Petra; Kroidl, Inge; Kowuor, Dickens O.; Nsojo, Anthony; Mangu, Chacha; Schüle, Steffen A.; Mansmann, Ulrich; Geldmacher, Christof; Mhina, Seif; Maboko, Leonard; Hoelscher, Michael; Saathoff, Elmar

2013-01-01

Background Hookworm disease is one of the most common infections and cause of a high disease burden in the tropics and subtropics. Remotely sensed ecological data and model-based geostatistics have been used recently to identify areas in need for hookworm control. Methodology Cross-sectional interview data and stool samples from 6,375 participants from nine different sites in Mbeya region, south-western Tanzania, were collected as part of a cohort study. Hookworm infection was assessed by microscopy of duplicate Kato-Katz thick smears from one stool sample from each participant. A geographic information system was used to obtain remotely sensed environmental data such as land surface temperature (LST), vegetation cover, rainfall, and elevation, and combine them with hookworm infection data and with socio-demographic and behavioral data. Uni- and multivariable logistic regression was performed on sites separately and on the pooled dataset. Principal Findings Univariable analyses yielded significant associations for all ecological variables. Five ecological variables stayed significant in the final multivariable model: population density (odds ratio (OR) = 0.68; 95% confidence interval (CI) = 0.63–0.73), mean annual vegetation density (OR = 0.11; 95% CI = 0.06–0.18), mean annual LST during the day (OR = 0.81; 95% CI = 0.75–0.88), mean annual LST during the night (OR = 1.54; 95% CI = 1.44–1.64), and latrine coverage in household surroundings (OR = 1.02; 95% CI = 1.01–1.04). Interaction terms revealed substantial differences in associations of hookworm infection with population density, mean annual enhanced vegetation index, and latrine coverage between the two sites with the highest prevalence of infection. Conclusion/Significance This study supports previous findings that remotely sensed data such as vegetation indices, LST, and elevation are strongly associated with hookworm prevalence. However, the results indicate that the influence of environmental conditions can differ substantially within a relatively small geographic area. The use of large-scale associations as a predictive tool on smaller scales is therefore problematic and should be handled with care. PMID:24040430

Estimating the Effect of Gypsy Moth Defloiation Using MODIS

NASA Technical Reports Server (NTRS)

deBeurs, K. M.; Townsend, P. A.

2008-01-01

The area of North American forests affected by gypsy moth defoliation continues to expand despite efforts to slow the spread. With the increased area of infestation, ecological, environmental and economic concerns about gypsy moth disturbance remain significant, necessitating coordinated, repeatable and comprehensive monitoring of the areas affected. In this study, our primary objective was to estimate the magnitude of defoliation using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for a gypsy moth outbreak that occurred in the US central Appalachian Mountains in 2000 and 2001. We focused on determining the appropriate spectral MODIS indices and temporal compositing method to best monitor the effects of gypsy moth defoliation. We tested MODIS-based Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and two versions of the Normalized Difference Infrared index (NDIIb6 and NDIIb7, using the channels centered on 1640 nm and 2130 nm respectively) for their capacity to map defoliation as estimated by ground observations. In addition, we evaluated three temporal resolutions: daily, 8-day and 16-day data. We validated the results through quantitative comparison to Landsat based defoliation estimates and traditional sketch maps. Our MODIS based defoliation estimates based on NDIIb6 and NDIIb7 closely matched Landsat defoliation estimates derived from field data as well as sketch maps. We conclude that daily MODIS data can be used with confidence to monitor insect defoliation on an annual time scale, at least for larger patches (greater than 0.63 km2). Eight-day and 16-day MODIS composites may be of lesser use due to the ephemeral character of disturbance by the gypsy moth.

Analyzing the effects of urban expansion on land surface temperature patterns by landscape metrics: a case study of Isfahan city, Iran.

PubMed

Madanian, Maliheh; Soffianian, Ali Reza; Koupai, Saeid Soltani; Pourmanafi, Saeid; Momeni, Mehdi

2018-03-03

Urban expansion can cause extensive changes in land use and land cover (LULC), leading to changes in temperature conditions. Land surface temperature (LST) is one of the key parameters that should be considered in the study of urban temperature conditions. The purpose of this study was, therefore, to investigate the effects of changes in LULC due to the expansion of the city of Isfahan on LST using landscape metrics. To this aim, two Landsat 5 and Landsat 8 images, which had been acquired, respectively, on August 2, 1985, and July 4, 2015, were used. The support vector machine method was then used to classify the images. The results showed that Isfahan city had been encountered with an increase of impervious surfaces; in fact, this class covered 15% of the total area in 1985, while this value had been increased to 30% in 2015. Then LST zoning maps were created, indicating that the bare land and impervious surfaces categories were dominant in high temperature zones, while in the zones where water was present or NDVI was high, LST was low. Then, the landscape metrics in each of the LST zones were analyzed in relation to the LULC changes, showing that LULC changes due to urban expansion changed such landscape properties as the percentage of landscape, patch density, large patch index, and aggregation index. This information could be beneficial for urban planners to monitor and manage changes in the LULC patterns.

Linking potential heat source and sink to urban heat island: Heterogeneous effects of landscape pattern on land surface temperature.

PubMed

Li, Weifeng; Cao, Qiwen; Lang, Kun; Wu, Jiansheng

2017-05-15

Rapid urbanization has significantly contributed to the development of urban heat island (UHI). Regulating landscape composition and configuration would help mitigate the UHI in megacities. Taking Shenzhen, China, as a case study area, we defined heat source and heat sink and identified strong and weak sources as well as strong and weak sinks according to the natural and socioeconomic factors influencing land surface temperature (LST). Thus, the potential thermal contributions of heat source and heat sink patches were differentiated. Then, the heterogeneous effects of landscape pattern on LST were examined by using semiparametric geographically weighted regression (SGWR) models. The results showed that landscape composition has more significant effects on thermal environment than configuration. For a strong source, the percentage of patches has a positive impact on LST. Additionally, when mosaicked with some heat sink, even a small improvement in the degree of dispersion of a strong source helps to alleviate UHI. For a weak source, the percentage and density of patches have positive impacts on LST. For a strong sink, the percentage, density, and degree of aggregation of patches have negative impacts on LST. The effects of edge density and patch shape complexity vary spatially with the fragmentation of a strong sink. Similarly, the impacts of a weak sink are mainly exerted via the characteristics of percent, density, and shape complexity of patches. Copyright © 2017 Elsevier B.V. All rights reserved.

New 50-m-class single-dish telescope: Large Submillimeter Telescope (LST)

NASA Astrophysics Data System (ADS)

Kawabe, Ryohei; Kohno, Kotaro; Tamura, Yoichi; Takekoshi, Tatsuya; Oshima, Tai; Ishii, Shun

2016-08-01

We report on a plan to construct a 50-m-class single-dish telescope, the Large Submillimeter Telescope (LST). The conceptual design and key science behind the LST are presented, together with its tentative specifications. This telescope is optimized for wide-area imaging and spectroscopic surveys in the 70-420 GHz frequency range, which spans the main atmospheric windows at millimeter and submillimeter wavelengths for good observation sites such as the Atacama Large Millimeter/submillimeter Array (ALMA) site in Chile. We also target observations at higher frequencies of up to 1 THz, using an inner high-precision surface. Active surface control is required in order to correct gravitational and thermal deformations of the surface, and will be useful for correction of the wind-load deformation. The LST will facilitate new discovery spaces such as wide-field imaging with both continuum and spectral lines, along with new developments for time-domain science. Through exploitation of its synergy with ALMA and other telescopes, the LST will contribute to research on a wide range of topics in the fields of astronomy and astrophysics, e.g., astrochemistry, star formation in our Galaxy and galaxies, the evolution of galaxy clusters via the Sunyaev-Zel'dovich (SZ) effect, the search for transients such as γ-ray burst reverse shocks produced during the epoch of re-ionization, electromagnetic follow up of detected gravitational wave sources, and examination of general relativity in the vicinity of super massive black holes via submillimeter very-long-baseline interferometry (VLBI).

Global Space-Based Inter-Calibration System Reflective Solar Calibration Reference: From Aqua MODIS to S-NPP VIIRS

NASA Technical Reports Server (NTRS)

Xiong, Xiaoxiong; Angal, Amit; Butler, James; Cao, Changyong; Doelling, Daivd; Wu, Aisheng; Wu, Xiangqian

2016-01-01

The MODIS has successfully operated on-board the NASA's EOS Terra and Aqua spacecraft for more than 16 and 14 years, respectively. MODIS instrument was designed with stringent calibration requirements and comprehensive on-board calibration capability. In the reflective solar spectral region, Aqua MODIS has performed better than Terra MODIS and, therefore, has been chosen by the Global Space-based Inter-Calibration System (GSICS) operational community as the calibration reference sensor in cross-sensor calibration and calibration inter-comparisons. For the same reason, it has also been used by a number of earth observing sensors as their calibration reference. Considering that Aqua MODIS has already operated for nearly 14 years, it is essential to transfer its calibration to a follow-on reference sensor with a similar calibration capability and stable performance. The VIIRS is a follow-on instrument to MODIS and has many similar design features as MODIS, including their on-board calibrators (OBC). As a result, VIIRS is an ideal candidate to replace MODIS to serve as the future GSICS reference sensor. Since launch, the S-NPP VIIRS has already operated for more than 4 years and its overall performance has been extensively characterized and demonstrated to meet its overall design requirements. This paper provides an overview of Aqua MODIS and S-NPP VIIRS reflective solar bands (RSB) calibration methodologies and strategies, traceability, and their on-orbit performance. It describes and illustrates different methods and approaches that can be used to facilitate the calibration reference transfer, including the use of desert and Antarctic sites, deep convective clouds (DCC), and the lunar observations.

Evaluation and Validation of Updated MODIS C6 and VIIRS LAI/FPAR

NASA Astrophysics Data System (ADS)

Yan, K.; Park, T.; Chen, C.; Yang, B.; Yan, G.; Knyazikhin, Y.; Myneni, R. B.; CHOI, S.

2015-12-01

Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (0.4-0.7 μm) absorbed by vegetation (FPAR) play a key role in characterizing vegetation canopy functioning and energy absorption capacity. With radiative transfer realization, MODIS onboard NASA EOS Terra and Aqua satellites has provided globally continuous LAI/FPAR since 2000 and continuously updated the products with better quality. And NPP VIIRS shows the measurement capability to extend high-quality LAI/FPAR time series data records as a successor of MODIS. The primary objectives of this study are 1) to evaluate and validate newly updated MODIS Collection 6 (C6) LAI/FPAR product which has finer resolution (500m) and improved biome type input, and 2) to examine and adjust VIIRS LAI/FPAR algorithm for continuity with MODIS'. For MODIS C6 investigation, we basically measure the spatial coverage (i.e., main radiative transfer algorithm execution), continuity and consistency with Collection 5 (C5), and accuracy with field measured LAI/FPAR. And we also validate C6 LAI/FPAR via comparing other possible global LAI/FPAR products (e.g., GLASS and CYCLOPES) and capturing co-varying seasonal signatures with climatic variables (e.g., temperature and precipitation). For VIIRS evaluation and adjustment, we first quantify possible difference between C5 and MODIS heritage based VIIRS LAI/FPAR. Then based on the radiative transfer theory of canopy spectral invariants, we find VIIRS- and biome-specific configurable parameters (single scattering albedo and uncertainty). These two practices for MODIS C6 and VIIRS LAI/FPAR products clearly suggest that (a) MODIS C6 has better coverage and accuracy than C5, (b) C6 shows consistent spatiotemporal pattern with C5, (c) VIIRS has the potential for producing MODIS-like global LAI/FPAR Earth System Data Records.

Associations of Low-Intensity Resistance Training with Body Composition and Lipid Profile in Obese Patients with Type 2 Diabetes.

PubMed

Hamasaki, Hidetaka; Kawashima, Yu; Tamada, Yoshiki; Furuta, Masashi; Katsuyama, Hisayuki; Sako, Akahito; Yanai, Hidekatsu

2015-01-01

Resistance training to increase muscle mass and functional capacity is an integral part of diet and exercise programs for the management of obesity and type 2 diabetes. Low-intensity resistance training with slow movement and tonic force generation (LST) may be a practical and safe regimen for elderly obese individuals but the health benefits are uncertain. This study investigated the effects of LST on body composition and metabolic parameters in obese patients with type 2 diabetes. Twenty-six obese patients with type 2 diabetes engaged in LST training during hospitalization and were advised to maintain this regimen for 12 weeks after discharge. We compared lipid profile, arterial stiffness, and body composition before and after LST training. After 12 weeks of LST training, the ratio of lower extremity muscle mass to body weight increased significantly (0.176 ± 0.028 to 0.184 ± 0.023, mean ± SD), while body fat mass and body fat percentage decreased significantly (36.2 ± 10.9 kg to 34.3 ± 9.4 kg and 41.2 ± 8.6% to 40.1 ± 7.7%, respectively). Moreover, high-density lipoprotein cholesterol was significantly increased (42.2 ± 14 mg/dl to 46.3 ± 12.4 mg/dl) and both free fatty acids and lipoprotein(a) were decreased (665.2 ± 212.1 μEq/l to 525.4 ± 231.3 μEq/l and 15.4 ± 18 mg/dl to 13.8 ± 18 mg/dl, respectively). No significant change was observed in arterial stiffness. Although this study was a non-controlled investigation and some confounding factors including dietary intake, medication and compliance with training might affect the study result, a brief (12-week) LST training program may be a safe and effective strategy for the management of obesity and type 2 diabetes.

Prediction Equation for Lower Limbs Lean Soft Tissue in Circumpubertal Boys Using Anthropometry and Biological Maturation

PubMed Central

Valente-dos-Santos, João; Coelho-e-Silva, Manuel J.; Machado-Rodrigues, Aristides M.; Elferink-Gemser, Marije T.; Malina, Robert M.; Petroski, Édio L.; Minderico, Cláudia S.; Silva, Analiza M.; Baptista, Fátima; Sardinha, Luís B.

2014-01-01

Lean soft tissue (LST), a surrogate of skeletal muscle mass, is largely limited to appendicular body regions. Simple and accurate methods to estimate lower limbs LST are often used in attempts to partition out the influence of body size on performance outputs. The aim of the current study was to develop and cross-validate a new model to predict lower limbs LST in boys aged 10–13 years, using dual-energy X-ray absorptiometry (DXA) as the reference method. Total body and segmental (lower limbs) composition were assessed with a Hologic Explorer-W QDR DXA scanner in a cross-sectional sample of 75 Portuguese boys (144.8±6.4 cm; 40.2±9.0 kg). Skinfolds were measured at the anterior and posterior mid-thigh, and medial calf. Circumferences were measured at the proximal, mid and distal thigh. Leg length was estimated as stature minus sitting height. Current stature expressed as a percentage of attained predicted mature stature (PMS) was used as an estimate of biological maturity status. Backward proportional allometric models were used to identify the model with the best statistical fit: ln (lower limbs LST)  = 0.838× ln (body mass) +0.476× ln (leg length) – 0.135× ln (mid-thigh circumference) – 0.053× ln (anterior mid-thigh skinfold) – 0.098× ln (medial calf skinfold) – 2.680+0.010× (percentage of attained PMS) (R = 0.95). The obtained equation was cross-validated using the predicted residuals sum of squares statistics (PRESS) method (R 2 PRESS = 0.90). Deming repression analysis between predicted and current lower limbs LST showed a standard error of estimation of 0.52 kg (95% limits of agreement: 0.77 to −1.27 kg). The new model accurately predicts lower limbs LST in circumpubertal boys. PMID:25229472

Validation of MODIS Aerosol Retrieval Over Ocean

NASA Technical Reports Server (NTRS)

Remer, Lorraine A.; Tanre, Didier; Kaufman, Yoram J.; Ichoku, Charles; Mattoo, Shana; Levy, Robert; Chu, D. Allen; Holben, Brent N.; Dubovik, Oleg; Ahmad, Ziauddin;

A Thermal-based Two-Source Energy Balance Model for Estimating Evapotranspiration over Complex Canopies

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation describes a robust but relatively simple LS...

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

PubMed Central

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-01-01

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting. PMID:29883381

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

PubMed

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-05-21

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

Comparing Stream Discharge, Dissolved Organic Carbon, and Selected MODIS Indices in Freshwater Basins

NASA Astrophysics Data System (ADS)

Shaver, W. T.; Wollheim, W. M.

2009-12-01

In a preliminary study of the Ipswich Basin in Massachusetts, a good correlation was found to exist between the MODIS (Moderate Resolution Imaging Spectroradiometer) Enhanced Vegetation Index and stream dissolved organic carbon (DOC). Further study was warranted to determine the utility of MODIS indices in predicting temporal stream DOC. Stream discharge rates and DOC data were obtained from the USGS National Water Quality Assessment Program (NAWQA) database. Twelve NAWQA monitoring sites were selected for evaluation based on the criteria of having drainage basin sizes less than 600 km2 with relatively continuous, long-term DOC and discharge data. MODIS indices were selected based on their connections with terrestrial DOC and were obtained for each site's catchment area. These included the Normalized Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Daily Photosynthesis (PSN) and the Leaf Area Index (LAI). Regression analysis was used to evaluate the relationships between DOC, discharge and MODIS products. Data analysis revealed several important trends. Sites with strong positive correlation coefficients (r values ranging from 0.462 to 0.831) between DOC and discharge displayed weak correlations with all of the MODIS indices (r values ranging from 0 to 0.322). For sites where the DOC/discharge correlation was weak or negative, MODIS indices were moderately correlated, with r values ranging from 0.35 to 0.647, all of which were significant at less than 1 percent. Some sites that had weak positive correlations with MODIS indices displayed a lag time, that is, the MODIS index rose and fell shortly before the DOC concentration rose and fell. Shifting the MODIS data forward in time by roughly one month significantly increased the DOC/MODIS r values by about 10%. NDVI and EVI displayed the strongest correlations with temporal DOC variability (r values ranging from 0.471 to 0.647), and therefore these indices are the most promising for being incorporated into a model for remotely sensing terrestrial DOC.

The LST scientific instruments

NASA Technical Reports Server (NTRS)

Levin, G. M.

1975-01-01

Seven scientific instruments are presently being studied for use with the Large Space Telescope (LST). These instruments are the F/24 Field Camera, the F/48-F/96 Planetary Camera, the High Resolution Spectrograph, the Faint Object Spectrograph, the Infrared Photometer, and the Astrometer. These instruments are being designed as facility instruments to be replaceable during the life of the Observatory.

Mapping Snow Grain Size over Greenland from MODIS

NASA Technical Reports Server (NTRS)

Lyapustin, Alexei; Tedesco, Marco; Wang, Yujie; Kokhanovsky, Alexander

2008-01-01

This paper presents a new automatic algorithm to derive optical snow grain size (SGS) at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. Differently from previous approaches, snow grains are not assumed to be spherical but a fractal approach is used to account for their irregular shape. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived SGS shows a good sensitivity to snow metamorphism, including melting and snow precipitation events. Preprocessing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask (CM) is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust performance of CM algorithm in discrimination of clouds over bright snow and ice. As part of the validation analysis, SGS derived from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM\\I radiometer, which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS SGS was compared with predictions of the snow model CROCUS driven by measurements of the automatic whether stations of the Greenland Climate Network. We found that CROCUS grain size is on average a factor of two larger than MODIS-derived SGS. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of SGS for four permanent sites, the paper presents SGS maps over the Greenland ice sheet for the March-September period of 2004.

Development of Modal Analysis for the Study of Global Modes in High Speed Boundary Layer Flows

NASA Astrophysics Data System (ADS)

Brock, Joseph Michael

Boundary layer transition for compressible flows remains a challenging and unsolved problem. In the context of high-speed compressible flow, transitional and turbulent boundary-layers produce significantly higher surface heating caused by an increase in skin-friction. The higher heating associated with transitional and turbulent boundary layers drives thermal protection systems (TPS) and mission trajectory bounds. Proper understanding of the mechanisms that drive transition is crucial to the successful design and operation of the next generation spacecraft. Currently, prediction of boundary-layer transition is based on experimental efforts and computational stability analysis. Computational analysis, anchored by experimental correlations, offers an avenue to assess/predict stability at a reduced cost. Classical methods of Linearized Stability Theory (LST) and Parabolized Stability Equations (PSE) have proven to be very useful for simple geometries/base flows. Under certain conditions the assumptions that are inherent to classical methods become invalid and the use of LST/PSE is inaccurate. In these situations, a global approach must be considered. A TriGlobal stability analysis code, Global Mode Analysis in US3D (GMAUS3D), has been developed and implemented into the unstructured solver US3D. A discussion of the methodology and implementation will be presented. Two flow configurations are presented in an effort to validate/verify the approach. First, stability analysis for a subsonic cylinder wake is performed and results compared to literature. Second, a supersonic blunt cone is considered to directly compare LST/PSE analysis and results generated by GMAUS3D.

The urban heat island in the city of Poznań as derived from Landsat 5 TM

NASA Astrophysics Data System (ADS)

Majkowska, Agnieszka; Kolendowicz, Leszek; Półrolniczak, Marek; Hauke, Jan; Czernecki, Bartosz

2017-05-01

To study urban heat island (UHI), Landsat 5 TM data and in situ measurements of air temperature from nine points in Poznań (Poland) for the period June 2008-May 2013 were used. Based on data from measurement points located in different types of land use, the surface urban heat island (SUHI) maps were created. All available and quality-controlled Landsat 5 TM images from 15 unique days were used to obtain the characteristics of land surface temperature (LST) and UHI intensity. In addition, spatial analysis of UHI was conducted on the basis of Corine Land Cover 2006 dataset. In situ measurements at a height of 2 m above ground level show that the UHI is a common occurrence in Poznań with a mean annual intensity of 1.0 °C. The UHI intensity is greater during the warm half of the year. Moreover, results based on the remote sensing data and the Corine Land Cover 2006 indicate that the highest value of the mean LST anomalies (3.4 °C) is attained by the continuous urban fabric, while the lowest value occurs within the broad-leaved forests (-3.1 °C). To re-count from LST to the air temperature at a height of 2 m above ground level ( T agl), linear and non-linear regression models were created. For both models, coefficients of determination equal about 0.80, with slightly higher value for the non-linear approach, which was applied to estimate the T agl spatial variability over the city of Poznań.

Empirical model of atomic nitrogen in the upper thermosphere

NASA Technical Reports Server (NTRS)

Engebretson, M. J.; Mauersberger, K.; Kayser, D. C.; Potter, W. E.; Nier, A. O.

1977-01-01

Atomic nitrogen number densities in the upper thermosphere measured by the open source neutral mass spectrometer (OSS) on Atmosphere Explorer-C during 1974 and part of 1975 have been used to construct a global empirical model at an altitude of 375 km based on a spherical harmonic expansion. The most evident features of the model are large diurnal and seasonal variations of atomic nitrogen and only a moderate and latitude-dependent density increase during periods of geomagnetic activity. Maximum and minimum N number densities at 375 km for periods of low solar activity are 3.6 x 10 to the 6th/cu cm at 1500 LST (local solar time) and low latitude in the summer hemisphere and 1.5 x 10 to the 5th/cu cm at 0200 LST at mid-latitudes in the winter hemisphere.

Spatial prediction of wheat Septoria leaf blotch (Septoria tritici) disease severity in central Ethiopia

USGS Publications Warehouse

Wakie, Tewodros; Kumar, Sunil; Senay, Gabriel; Takele, Abera; Lencho, Alemu

2016-01-01

A number of studies have reported the presence of wheat septoria leaf blotch (Septoria tritici; SLB) disease in Ethiopia. However, the environmental factors associated with SLB disease, and areas under risk of SLB disease, have not been studied. Here, we tested the hypothesis that environmental variables can adequately explain observed SLB disease severity levels in West Shewa, Central Ethiopia. Specifically, we identified 50 environmental variables and assessed their relationships with SLB disease severity. Geographically referenced disease severity data were obtained from the field, and linear regression and Boosted Regression Trees (BRT) modeling approaches were used for developing spatial models. Moderate-resolution imaging spectroradiometer (MODIS) derived vegetation indices and land surface temperature (LST) variables highly influenced SLB model predictions. Soil and topographic variables did not sufficiently explain observed SLB disease severity variation in this study. Our results show that wheat growing areas in Central Ethiopia, including highly productive districts, are at risk of SLB disease. The study demonstrates the integration of field data with modeling approaches such as BRT for predicting the spatial patterns of severity of a pathogenic wheat disease in Central Ethiopia. Our results can aid Ethiopia's wheat disease monitoring efforts, while our methods can be replicated for testing related hypotheses elsewhere.

A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared (TIR) remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation d...

Statistical Inter-comparison Analysis of MODIS, MISR, and AERONET Over the Middle East and North Africa

NASA Astrophysics Data System (ADS)

Farahat, A.; El-Askary, H. M.; Kalashnikova, O. V.; Garay, M. J.

2016-12-01

Several space-borne and ground based sensors can provide long-standing monitoring of aerosols characteristics, but inconsistencies among different sensors reduce data reliability and lead to uncertainty in analysing long-term data. In this study, we perform statistical inter-comparison of the Aerosol Optical Depth (AOD) among MISR, MODIS/Terra, MODIS/Aqua and Aerosol Robotic Network (AERONET) over seven sites located in the Middle East and North Africa during the period (1995 -2015). The sites are categorized into two regions based on their geographic location and possible dominate particles composition. Compared to MISR, MODIS and AERONET AOD data retrievals indicate larger uncertainty over all sites with a larger daily variability in MODIS measurements. In general, MISR and MODIS AOD matches during high dust seasons but MODIS tends to under estimate the AOD values on low dust seasons. While Terra measurements give a negative trend over the time series at the dust-dominated sites, Aqua, MISR and AERONET show a positive trend. In general, MODIS/Aqua displays stable measurements over the time line at the dust dominated sites. MODIS/Terra, MODIS/Aqua and MISR display a positive trend over Cairo_EMA site while AERONET shows a negative trend over the time line. Terra was found to overestimate AOD during 2002 - 2004 and underestimates it after 2004. We also observe a deviation between Aqua and Terra regardless of the region and data sampling. Excluding Bahrain and Cairo_EMA for low data retrievals the performance of MODIS tends to be similar over all region with 68 % of the retrieved AOD values fall within the confidence range of the AERONET matched data, within global averaged level (> 66 %). MISR indicated better data performance with 72 % falls within the same confidence range. Complimentary MISR and MODIS data was found to provide a better picture of dust storms evolution over Arabian Peninsula and the Middle East. Acknowledgement The authors would like to acknowledge the support provided by the Deanship of Scientific Research (DSR) at the King Fahd University of Petroleum and Minerals (KFUPM) for funding this work through project No. IN141051.

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

Treesearch

Joseph P. Spruce; Steven Sader; Robert E. Ryan; James Smoot; Philip Kuper; al. et.

2011-01-01

This paper discusses an assessment of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data products for detecting forest defoliation from European gypsy moth (Lymantria dispar). This paper describes an effort to aid the United States Department of Agriculture (USDA) Forest Service in developing and assessing MODIS-based gypsy moth defoliation...

eMODIS: A User-Friendly Data Source

USGS Publications Warehouse

Jenkerson, Calli B.; Maiersperger, Thomas; Schmidt, Gail

2010-01-01

The U.S. Geological Survey's (USGS) Earth Resources Observation and Science (EROS) Center is generating a suite of products called 'eMODIS' based on Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired by the National Aeronautics and Space Administration's (NASA) Earth Observing System (EOS). With a more frequent repeat cycle than Landsat and higher spatial resolutions than the Advanced Very High Resolution Spectroradiometer (AVHRR), MODIS is well suited for vegetation studies. For operational monitoring, however, the benefits of MODIS are counteracted by usability issues with the standard map projection, file format, composite interval, high-latitude 'bow-tie' effects, and production latency. eMODIS responds to a community-specific need for alternatively packaged MODIS data, addressing each of these factors for real-time monitoring and historical trend analysis. eMODIS processes calibrated radiance data (level-1B) acquired by the MODIS sensors on the EOS Terra and Aqua satellites by combining MODIS Land Science Collection 5 Atmospherically Corrected Surface Reflectance production code and USGS EROS MODIS Direct Broadcast System (DBS) software to create surface reflectance and Normalized Difference Vegetation Index (NDVI) products. eMODIS is produced over the continental United States and over Alaska extending into Canada to cover the Yukon River Basin. The 250-meter (m), 500-m, and 1,000-m products are delivered in Geostationary Earth Orbit Tagged Image File Format (Geo- TIFF) and composited in 7-day intervals. eMODIS composites are projected to non-Sinusoidal mapping grids that best suit the geography in their areas of application (see eMODIS Product Description below). For eMODIS products generated over the continental United States (eMODIS CONUS), the Terra (from 2000) and Aqua (from 2002) records are available and continue through present time. eMODIS CONUS also is generated in an expedited process that delivers a 7-day rolling composite, created daily with the most recent 7 days of acquisition, to users monitoring real-time vegetation conditions. eMODIS Alaska is not part of expedited processing, but does cover the Terra mission life (2000-present). A simple file transfer protocol (FTP) distribution site currently is enabled on the Internet for direct download of eMODIS products (ftp://emodisftp.cr.usgs.gov/eMODIS), with plans to expand into an interactive portal environment.

Continental-scale Validation of MODIS-based and LEDAPS Landsat ETM+ Atmospheric Correction Methods

NASA Technical Reports Server (NTRS)

Ju, Junchang; Roy, David P.; Vermote, Eric; Masek, Jeffrey; Kovalskyy, Valeriy

2012-01-01

The potential of Landsat data processing to provide systematic continental scale products has been demonstrated by several projects including the NASA Web-enabled Landsat Data (WELD) project. The recent free availability of Landsat data increases the need for robust and efficient atmospheric correction algorithms applicable to large volume Landsat data sets. This paper compares the accuracy of two Landsat atmospheric correction methods: a MODIS-based method and the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) method. Both methods are based on the 6SV radiative transfer code but have different atmospheric characterization approaches. The MODIS-based method uses the MODIS Terra derived dynamic aerosol type, aerosol optical thickness, and water vapor to atmospherically correct ETM+ acquisitions in each coincident orbit. The LEDAPS method uses aerosol characterizations derived independently from each Landsat acquisition and assumes a fixed continental aerosol type and uses ancillary water vapor. Validation results are presented comparing ETM+ atmospherically corrected data generated using these two methods with AERONET corrected ETM+ data for 95 10 km×10 km 30 m subsets, a total of nearly 8 million 30 m pixels, located across the conterminous United States. The results indicate that the MODIS-based method has better accuracy than the LEDAPS method for the ETM+ red and longer wavelength bands.

Estimation of Turbulent Heat Fluxes by Assimilation of Land Surface Temperature Observations From GOES Satellites Into an Ensemble Kalman Smoother Framework

NASA Astrophysics Data System (ADS)

Xu, Tongren; Bateni, S. M.; Neale, C. M. U.; Auligne, T.; Liu, Shaomin

2018-03-01

In different studies, land surface temperature (LST) observations have been assimilated into the variational data assimilation (VDA) approaches to estimate turbulent heat fluxes. The VDA methods yield accurate turbulent heat fluxes, but they need an adjoint model, which is difficult to derive and code. They also cannot directly calculate the uncertainty of their estimates. To overcome the abovementioned drawbacks, this study assimilates LST data from Geostationary Operational Environmental Satellite into the ensemble Kalman smoother (EnKS) data assimilation system to estimate turbulent heat fluxes. EnKS does not need to derive the adjoint term and directly generates statistical information on the accuracy of its predictions. It uses the heat diffusion equation to simulate LST. EnKS with the state augmentation approach finds the optimal values for the unknown parameters (i.e., evaporative fraction and neutral bulk heat transfer coefficient, CHN) by minimizing the misfit between LST observations from Geostationary Operational Environmental Satellite and LST estimations from the heat diffusion equation. The augmented EnKS scheme is tested over six Ameriflux sites with a wide range of hydrological and vegetative conditions. The results show that EnKS can predict not only the model parameters and turbulent heat fluxes but also their uncertainties over a variety of land surface conditions. Compared to the variational method, EnKS yields suboptimal turbulent heat fluxes. However, suboptimality of EnKS is small, and its results are comparable to those of the VDA method. Overall, EnKS is a feasible and reliable method for estimation of turbulent heat fluxes.

Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data

NASA Astrophysics Data System (ADS)

Lu, Yang; Steele-Dunne, Susan C.; Farhadi, Leila; van de Giesen, Nick

2017-12-01

Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large-scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual-source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST-only and a soil moisture-only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.

Improvement of Hungarian Joint Terminal Attack Program

DTIC Science & Technology

2013-06-13

LST Laser Spot Tracker NVG Night Vision Goggle ROMAD Radio Operator Maintainer and Driver ROVER Remotely Operated Video Enhanced Receiver TACP...visual target designation. The other component consists of a laser spot tracker (LST), which identifies targets by tracking laser energy reflecting...capability for every type of night time missions, laser spot tracker for laser spot search missions, remotely operated video enhanced receiver

Utilization of a specific in vitro lymphocyte immunostimulation assay as an aid in detection of brucella-infected cattle not detected by serological tests.

PubMed Central

Kaneene, J M; Johnson, D W; Anderson, R K; Muscoplat, C C

1978-01-01

Studies using the in vitro lymphocyte stimulation test (LST) were conducted with cattle in a dairy herd with a high percentage of reactors to several serological tests for brucellosis. Lymphocytes were prepared from peripheral bovine blood by the Ficoll-diatrizoate technique. Lymphocytes were cultured using microtitration culture plates. Brucella abortus soluble antigen, at a concentration of 4.4 microgram/culture, was added to the appropriate wells of microtitration culture plates and incubated for 6 days. The lymphocyte stimulation responses were measured by assaying for [3H]thymidine incorporation into DNA. Seroagglutination tests were conducted simultaneously with the LST, and tissues were collected after slaughter of the cattle for bacteriological culture to isolate B. abortus. All 21 animals studied were serologically negative for anti-brucella antibodies. Two of the 21 animals were classified as infected with Brucella by the LST, and B. abortus biotype 1 was isolated from tissues of these same two animals. The LST exhibited significant sensitivity and specificity in this study, and more observations of this nature might strengthen the application of this assay as an aid in the diagnosis of brucellosis. PMID:103888

Effects of vegetation types on soil moisture estimation from the normalized land surface temperature versus vegetation index space

NASA Astrophysics Data System (ADS)

Zhang, Dianjun; Zhou, Guoqing

2015-12-01

Soil moisture (SM) is a key variable that has been widely used in many environmental studies. Land surface temperature versus vegetation index (LST-VI) space becomes a common way to estimate SM in optical remote sensing applications. Normalized LST-VI space is established by the normalized LST and VI to obtain the comparable SM in Zhang et al. (Validation of a practical normalized soil moisture model with in situ measurements in humid and semiarid regions [J]. International Journal of Remote Sensing, DOI: 10.1080/01431161.2015.1055610). The boundary conditions in the study were set to limit the point A (the driest bare soil) and B (the wettest bare soil) for surface energy closure. However, no limitation was installed for point D (the full vegetation cover). In this paper, many vegetation types are simulated by the land surface model - Noah LSM 3.2 to analyze the effects on soil moisture estimation, such as crop, grass and mixed forest. The locations of point D are changed with vegetation types. The normalized LST of point D for forest is much lower than crop and grass. The location of point D is basically unchanged for crop and grass.

New 50-M-Class Single Dish Telescope: Large Submillimeter Telescope (LST)

NASA Astrophysics Data System (ADS)

Kawabe, Ryohei

2018-01-01

We report on the plan to construct a 50 m class millimeter (mm) and sub-mm single dish telescope, the Large Submillimeter Telescope (LST). The telescope is optimized for wide-area imaging and spectroscopic surveys in the 70 to 420 GHz main frequency range, which just covers main atmospheric windows at millimeter and submillimeter wavelengths for good observing sites such as the ALMA site in Chile. We also target observations at higher frequencies of up to 1 THz, using an inner part high-precision surface. Active surface control is required in order to correct gravitational and thermal deformations of the surface. The LST will facilitate new discovery spaces such as wide-field imaging with both continuum and spectral lines, along with new developments for time domain science. With exploiting synergy with ALMA and other telescopes, LST can contribute to a wide range of topics in astronomy and astrophysics, e.g., astrochemistry, star formation in the Galaxy and galaxies, evolution of galaxy clusters via SZ effect. We also report the recent progress on the technical study, e.g., the tentative study of the surface error budget and challenges to correction for the wind-load effect.

Quantifying the Contributions of Environmental Parameters to Ceres Surface Net Radiation Error in China

NASA Astrophysics Data System (ADS)

Pan, X.; Yang, Y.; Liu, Y.; Fan, X.; Shan, L.; Zhang, X.

2018-04-01

Error source analyses are critical for the satellite-retrieved surface net radiation (Rn) products. In this study, we evaluate the Rn error sources in the Clouds and the Earth's Radiant Energy System (CERES) project at 43 sites from July in 2007 to December in 2007 in China. The results show that cloud fraction (CF), land surface temperature (LST), atmospheric temperature (AT) and algorithm error dominate the Rn error, with error contributions of -20, 15, 10 and 10 W/m2 (net shortwave (NSW)/longwave (NLW) radiation), respectively. For NSW, the dominant error source is algorithm error (more than 10 W/m2), particularly in spring and summer with abundant cloud. For NLW, due to the high sensitivity of algorithm and large LST/CF error, LST and CF are the largest error sources, especially in northern China. The AT influences the NLW error large in southern China because of the large AT error in there. The total precipitable water has weak influence on Rn error even with the high sensitivity of algorithm. In order to improve Rn quality, CF and LST (AT) error in northern (southern) China should be decreased.

Determination of optimum viewing angles for the angular normalization of land surface temperature over vegetated surface.

PubMed

Ren, Huazhong; Yan, Guangjian; Liu, Rongyuan; Li, Zhao-Liang; Qin, Qiming; Nerry, Françoise; Liu, Qiang

2015-03-27

Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to perform such normalizations on LST. The normally kernel-driven bi-directional reflectance distribution function (BRDF) is first extended to the thermal infrared (TIR) domain as TIR-BRDF model, and its uncertainty is shown to be less than 0.3 K when used to fit the hemispheric directional thermal radiation. A local optimum three-angle combination is found and verified using the TIR-BRDF model based on two patterns: the single-point pattern and the linear-array pattern. The TIR-BRDF is applied to an airborne multi-angular dataset to retrieve LST at nadir (Te-nadir) from different viewing directions, and the results show that this model can obtain reliable Te-nadir from 3 to 4 directional observations with large angle intervals, thus corresponding to large temperature angular variations. The Te-nadir is generally larger than temperature of the slant direction, with a difference of approximately 0.5~2.0 K for vegetated pixels and up to several Kelvins for non-vegetated pixels. The findings of this paper will facilitate the future development of multi-angular thermal infrared sensors.

Determination of Optimum Viewing Angles for the Angular Normalization of Land Surface Temperature over Vegetated Surface

PubMed Central

Ren, Huazhong; Yan, Guangjian; Liu, Rongyuan; Li, Zhao-Liang; Qin, Qiming; Nerry, Françoise; Liu, Qiang

2015-01-01

Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to perform such normalizations on LST. The normally kernel-driven bi-directional reflectance distribution function (BRDF) is first extended to the thermal infrared (TIR) domain as TIR-BRDF model, and its uncertainty is shown to be less than 0.3 K when used to fit the hemispheric directional thermal radiation. A local optimum three-angle combination is found and verified using the TIR-BRDF model based on two patterns: the single-point pattern and the linear-array pattern. The TIR-BRDF is applied to an airborne multi-angular dataset to retrieve LST at nadir (Te-nadir) from different viewing directions, and the results show that this model can obtain reliable Te-nadir from 3 to 4 directional observations with large angle intervals, thus corresponding to large temperature angular variations. The Te-nadir is generally larger than temperature of the slant direction, with a difference of approximately 0.5~2.0 K for vegetated pixels and up to several Kelvins for non-vegetated pixels. The findings of this paper will facilitate the future development of multi-angular thermal infrared sensors. PMID:25825975

Characterization of Bond Strength of U-Mo Fuel Plates Using the Laser Shockwave Technique: Capabilities and Preliminary Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

J. A. Smith; D. L. Cottle; B. H. Rabin

2013-09-01

This report summarizes work conducted to-date on the implementation of new laser-based capabilities for characterization of bond strength in nuclear fuel plates, and presents preliminary results obtained from fresh fuel studies on as-fabricated monolithic fuel consisting of uranium-10 wt.% molybdenum alloys clad in 6061 aluminum by hot isostatic pressing. Characterization involves application of two complementary experimental methods, laser-shock testing and laser-ultrasonic imaging, collectively referred to as the Laser Shockwave Technique (LST), that allows the integrity, physical properties and interfacial bond strength in fuel plates to be evaluated. Example characterization results are provided, including measurement of layer thicknesses, elastic properties ofmore » the constituents, and the location and nature of generated debonds (including kissing bonds). LST provides spatially localized, non-contacting measurements with minimum specimen preparation, and is ideally suited for applications involving radioactive materials, including irradiated materials. The theoretical principles and experimental approaches employed in characterizing nuclear fuel plates are described, and preliminary bond strength measurement results are discussed, with emphasis on demonstrating the capabilities and limitations of these methods. These preliminary results demonstrate the ability to distinguish bond strength variations between different fuel plates. Although additional development work is necessary to validate and qualify the test methods, these results suggest LST is viable as a method to meet fuel qualification requirements to demonstrate acceptable bonding integrity.« less

Moderate Resolution Imaging Spectroradiometer (MODIS) Overview

USGS Publications Warehouse

,

2008-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) is an instrument that collects remotely sensed data used by scientists for monitoring, modeling, and assessing the effects of natural processes and human actions on the Earth's surface. The continual calibration of the MODIS instruments, the refinement of algorithms used to create higher-level products, and the ongoing product validation make MODIS images a valuable time series (2000-present) of geophysical and biophysical land-surface measurements. Carried on two National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) satellites, MODIS acquires morning (EOS-Terra) and afternoon (EOS-Aqua) views almost daily. Terra data acquisitions began in February 2000 and Aqua data acquisitions began in July 2002. Land data are generated only as higher-level products, removing the burden of common types of data processing from the user community. MODIS-based products describing ecological dynamics, radiation budget, and land cover are projected onto a sinusoidal mapping grid and distributed as 10- by 10-degree tiles at 250-, 500-, or 1,000-meter spatial resolution. Some products are also created on a 0.05-degree geographic grid to support climate modeling studies. All MODIS products are distributed in the Hierarchical Data Format-Earth Observing System (HDF-EOS) file format and are available through file transfer protocol (FTP) or on digital video disc (DVD) media. Versions 4 and 5 of MODIS land data products are currently available and represent 'validated' collections defined in stages of accuracy that are based on the number of field sites and time periods for which the products have been validated. Version 5 collections incorporate the longest time series of both Terra and Aqua MODIS data products.

Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area

NASA Astrophysics Data System (ADS)

Kustas, William P.; Alfieri, Joseph G.; Anderson, Martha C.; Colaizzi, Paul D.; Prueger, John H.; Evett, Steven R.; Neale, Christopher M. U.; French, Andrew N.; Hipps, Lawrence E.; Chávez, José L.; Copeland, Karen S.; Howell, Terry A.

2012-12-01

Application and validation of many thermal remote sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal thermal remote sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) [1], and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) [2]. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the remote sensing observations with the local flux measurement footprint.

Progress in Understanding the Impacts of 3-D Cloud Structure on MODIS Cloud Property Retrievals for Marine Boundary Layer Clouds

NASA Technical Reports Server (NTRS)

Zhang, Zhibo; Werner, Frank; Miller, Daniel; Platnick, Steven; Ackerman, Andrew; DiGirolamo, Larry; Meyer, Kerry; Marshak, Alexander; Wind, Galina; Zhao, Guangyu

2016-01-01

Theory: A novel framework based on 2-D Tayler expansion for quantifying the uncertainty in MODIS retrievals caused by sub-pixel reflectance inhomogeneity. (Zhang et al. 2016). How cloud vertical structure influences MODIS LWP retrievals. (Miller et al. 2016). Observation: Analysis of failed MODIS cloud property retrievals. (Cho et al. 2015). Cloud property retrievals from 15m resolution ASTER observations. (Werner et al. 2016). Modeling: LES-Satellite observation simulator (Zhang et al. 2012, Miller et al. 2016).

Acting to let someone die.

PubMed

McGee, Andrew

2015-02-01

This paper examines the recent prominent view in medical ethics that withdrawing life-sustaining treatment (LST) is an act of killing. I trace this view to the rejection of the traditional claim that withdrawing LST is an omission rather than an act. Although that traditional claim is not as problematic as this recent prominent view suggests, my main claim is that even if we accepted that withdrawing LST should be classified as an act rather than as an omission, it could still be classified as letting die rather than killing. Even though omissions are contrasted with acts, letting die need not be, for one can let die by means of acts. The remainder of the paper is devoted to establishing this claim and addresses certain objections to it. © 2013 John Wiley & Sons Ltd.

Landsat Science Team: 2016 winter meeting summary

USGS Publications Warehouse

Schroeder, Todd; Loveland, Thomas; Wulder, Michael A.; Irons, James R.

2016-01-01

The winter meeting of the joint U.S. Geological Survey (USGS)–NASA Landsat Science Team (LST) was held January 12-14, 2016, at Virginia Tech University in Blacksburg, VA. LST co-chairs Tom Loveland [USGS’s Earth Resources Observation and Science Data Center (EROS)—Senior Scientist] and Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Landsat 8 Project Scientist] welcomed more than 50 participants to the three-day meeting. The main objectives of this meeting focused on identifying priorities and approaches to improve the global moderate-resolution satellite record. Overall, the meeting was geared more towards soliciting team member recommendations on several rapidly evolving issues, than on providing updates on individual research activities. All the presentations given at the meeting are available at landsat.usgs. gov//science_LST_january2016.php.

A multilevel control system for the large space telescope. [numerical analysis/optimal control

NASA Technical Reports Server (NTRS)

Siljak, D. D.; Sundareshan, S. K.; Vukcevic, M. B.

1975-01-01

A multilevel scheme was proposed for control of Large Space Telescope (LST) modeled by a three-axis-six-order nonlinear equation. Local controllers were used on the subsystem level to stabilize motions corresponding to the three axes. Global controllers were applied to reduce (and sometimes nullify) the interactions among the subsystems. A multilevel optimization method was developed whereby local quadratic optimizations were performed on the subsystem level, and global control was again used to reduce (nullify) the effect of interactions. The multilevel stabilization and optimization methods are presented as general tools for design and then used in the design of the LST Control System. The methods are entirely computerized, so that they can accommodate higher order LST models with both conceptual and numerical advantages over standard straightforward design techniques.

A browser-based 3D Visualization Tool designed for comparing CERES/CALIOP/CloudSAT level-2 data sets.

NASA Astrophysics Data System (ADS)

Chu, C.; Sun-Mack, S.; Chen, Y.; Heckert, E.; Doelling, D. R.

2017-12-01

In Langley NASA, Clouds and the Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) are merged with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat Cloud Profiling Radar (CPR). The CERES merged product (C3M) matches up to three CALIPSO footprints with each MODIS pixel along its ground track. It then assigns the nearest CloudSat footprint to each of those MODIS pixels. The cloud properties from MODIS, retrieved using the CERES algorithms, are included in C3M with the matched CALIPSO and CloudSat products along with radiances from 18 MODIS channels. The dataset is used to validate the CERES retrieved MODIS cloud properties and the computed TOA and surface flux difference using MODIS or CALIOP/CloudSAT retrieved clouds. This information is then used to tune the computed fluxes to match the CERES observed TOA flux. A visualization tool will be invaluable to determine the cause of these large cloud and flux differences in order to improve the methodology. This effort is part of larger effort to allow users to order the CERES C3M product sub-setted by time and parameter as well as the previously mentioned visualization capabilities. This presentation will show a new graphical 3D-interface, 3D-CERESVis, that allows users to view both passive remote sensing satellites (MODIS and CERES) and active satellites (CALIPSO and CloudSat), such that the detailed vertical structures of cloud properties from CALIPSO and CloudSat are displayed side by side with horizontally retrieved cloud properties from MODIS and CERES. Similarly, the CERES computed profile fluxes whether using MODIS or CALIPSO and CloudSat clouds can also be compared. 3D-CERESVis is a browser-based visualization tool that makes uses of techniques such as multiple synchronized cursors, COLLADA format data and Cesium.

Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI composite data using agricultural measurements: an example at corn fields in western Mexico.

PubMed

Chen, Pei-Yu; Fedosejevs, Gunar; Tiscareño-López, Mario; Arnold, Jeffrey G

2006-08-01

Although several types of satellite data provide temporal information of the land use at no cost, digital satellite data applications for agricultural studies are limited compared to applications for forest management. This study assessed the suitability of vegetation indices derived from the TERRA-Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and SPOT-VEGETATION (VGT) sensor for identifying corn growth in western Mexico. Overall, the Normalized Difference Vegetation Index (NDVI) composites from the VGT sensor based on bi-directional compositing method produced vegetation information most closely resembling actual crop conditions. The NDVI composites from the MODIS sensor exhibited saturated signals starting 30 days after planting, but corresponded to green leaf senescence in April. The temporal NDVI composites from the VGT sensor based on the maximum value method had a maximum plateau for 80 days, which masked the important crop transformation from vegetative stage to reproductive stage. The Enhanced Vegetation Index (EVI) composites from the MODIS sensor reached a maximum plateau 40 days earlier than the occurrence of maximum leaf area index (LAI) and maximum intercepted fraction of photosynthetic active radiation (fPAR) derived from in-situ measurements. The results of this study showed that the 250-m resolution MODIS data did not provide more accurate vegetation information for corn growth description than the 500-m and 1000-m resolution MODIS data.

Validation of MODIS Dust Aerosol Retrieval and Development Ambient Dust Phase Function using PRIDE Data

NASA Technical Reports Server (NTRS)

Remer, Lorraine A.; Lau, William (Technical Monitor)

2002-01-01

The PRIDE data set of MODIS aerosol retrievals co-located with sunphotometer measurements provides the basis of MODIS validation in a dust environment. The sunphotometer measurements include AERONET automatic instruments, land-based Microtops instruments, ship-board Microtops instruments and the AATS-6 aboard the Navajo aircraft. Analysis of these data indicate that the MODIS retrieval is within pre-launch estimates of uncertainty within the spectral range of 600-900 nm. However, the MODIS algorithm consistently retrieves smaller particles than reality thus leading to incorrect spectral response outside of the 600-900 nm range and improper size information. Further analysis of MODIS retrievals in other dust environments shows the inconsistencies are due to nonspherical effects in the phase function. These data are used to develop an ambient phase function for dust aerosol to be used for remote sensing purposes.

Calibration Adjustments to the MODIS Aqua Ocean Color Bands

NASA Technical Reports Server (NTRS)

Meister, Gerhard

2012-01-01

After the end of the SeaWiFS mission in 2010 and the MERIS mission in 2012, the ocean color products of the MODIS on Aqua are the only remaining source to continue the ocean color climate data record until the VIIRS ocean color products become operational (expected for summer 2013). The MODIS on Aqua is well beyond its expected lifetime, and the calibration accuracy of the short wavelengths (412nm and 443nm) has deteriorated in recent years_ Initially, SeaWiFS data were used to improve the MODIS Aqua calibration, but this solution was not applicable after the end of the SeaWiFS mission_ In 2012, a new calibration methodology was applied by the MODIS calibration and support team using desert sites to improve the degradation trending_ This presentation presents further improvements to this new approach. The 2012 reprocessing of the MODIS Aqua ocean color products is based on the new methodology.

Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval

PubMed Central

Liu, Desheng; Pu, Ruiliang

2008-01-01

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods. PMID:27879844

Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval.

PubMed

Liu, Desheng; Pu, Ruiliang

2008-04-06

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods.

Separating vegetation and soil temperature using airborne multiangular remote sensing image data

NASA Astrophysics Data System (ADS)

Liu, Qiang; Yan, Chunyan; Xiao, Qing; Yan, Guangjian; Fang, Li

2012-07-01

Land surface temperature (LST) is a key parameter in land process research. Many research efforts have been devoted to increase the accuracy of LST retrieval from remote sensing. However, because natural land surface is non-isothermal, component temperature is also required in applications such as evapo-transpiration (ET) modeling. This paper proposes a new algorithm to separately retrieve vegetation temperature and soil background temperature from multiangular thermal infrared (TIR) remote sensing data. The algorithm is based on the localized correlation between the visible/near-infrared (VNIR) bands and the TIR band. This method was tested on the airborne image data acquired during the Watershed Allied Telemetry Experimental Research (WATER) campaign. Preliminary validation indicates that the remote sensing-retrieved results can reflect the spatial and temporal trend of component temperatures. The accuracy is within three degrees while the difference between vegetation and soil temperature can be as large as twenty degrees.

Laser Scattering Tomography for the Study of Defects in Protein Crystals

NASA Technical Reports Server (NTRS)

Feigelson, Robert S.; DeLucas, Lawrence; DeMattei, R. C.

1997-01-01

The goal of this research is to explore the application of the non-destructive technique of Laser Scattering Tomography (LST) to study the defects in protein crystals and relate them to the x-ray diffraction performance of the crystals. LST has been used successfully for the study of defects in inorganic crystals and. in the case of lysozyme, for protein crystals.

Using dry spell dynamics of land surface temperature to evaluate large-scale model representation of soil moisture control on evapotranspiration

NASA Astrophysics Data System (ADS)

Taylor, Christopher M.; Harris, Philip P.; Gallego-Elvira, Belen; Folwell, Sonja S.

2017-04-01

The soil moisture control on the partition of land surface fluxes between sensible and latent heat is a key aspect of land surface models used within numerical weather prediction and climate models. As soils dry out, evapotranspiration (ET) decreases, and the excess energy is used to warm the atmosphere. Poor simulations of this dynamic process can affect predictions of mean, and in particular, extreme air temperatures, and can introduce substantial biases into projections of climate change at regional scales. The lack of reliable observations of fluxes and root zone soil moisture at spatial scales that atmospheric models use (typically from 1 to several hundred kilometres), coupled with spatial variability in vegetation and soil properties, makes it difficult to evaluate the flux partitioning at the model grid box scale. To overcome this problem, we have developed techniques to use Land Surface Temperature (LST) to evaluate models. As soils dry out, LST rises, so it can be used under certain circumstances as a proxy for the partition between sensible and latent heat. Moreover, long time series of reliable LST observations under clear skies are available globally at resolutions of the order of 1km. Models can exhibit large biases in seasonal mean LST for various reasons, including poor description of aerodynamic coupling, uncertainties in vegetation mapping, and errors in down-welling radiation. Rather than compare long-term average LST values with models, we focus on the dynamics of LST during dry spells, when negligible rain falls, and the soil moisture store is drying out. The rate of warming of the land surface, or, more precisely, its warming rate relative to the atmosphere, emphasises the impact of changes in soil moisture control on the surface energy balance. Here we show the application of this approach to model evaluation, with examples at continental and global scales. We can compare the behaviour of both fully-coupled land-atmosphere models, and land surface models forced by observed meteorology. This approach provides insight into a fundamental process that affects predictions on multiple time scales, and which has an important impact for society.

Assessing MODIS-based Products and Techniques for Detecting Gypsy Moth Defoliation

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Hargrove, William; Smoot, James C.; Prados, Don; McKellip, Rodney; Sader, Steven A.; Gasser, Jerry; May, George

2008-01-01

The project showed potential of MODIS and VIIRS time series data for contributing defoliation detection products to the USFS forest threat early warning system. This study yielded the first satellite-based wall-to-wall 2001 gypsy moth defoliation map for the study area. Initial results led to follow-on work to map 2007 gypsy moth defoliation over the eastern United States (in progress). MODIS-based defoliation maps offer promise for aiding aerial sketch maps either in planning surveys and/or adjusting acreage estimates of annual defoliation. More work still needs to be done to assess potential of technology for "now casts"of defoliation.

Requirements, Science, and Measurements for Landsat 10 and Beyond: Perspectives from the Landsat Science Team

NASA Astrophysics Data System (ADS)

Crawford, C. J.; Masek, J. G.; Roy, D. P.; Woodcock, C. E.; Wulder, M. A.

2017-12-01

The U.S. Geological Survey (USGS) and NASA are currently prioritizing requirements and investing in technology options for a "Landsat 10 and beyond" mission concept as part of the Sustainable Land Imaging (SLI) architecture. Following the successful February 2013 launch of the Landsat 8, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) have now added over 1 million images to the USGS Landsat archive. The USGS and NASA support and co-lead a Landsat Science Team made up largely of university and government experts to offer independent insight and guidance of program activities and directions. The rapid development of Landsat 9 reflects, in part, strong input from the 2012-2017 USGS Landsat Science Team (LST). During the last two years of the LST's tenure, individual LST members and within LST team working groups have made significant contributions to Landsat 10 and beyond's science traceability and future requirements justification. Central to this input, has been an effort to identify a trade space for enhanced measurement capabilities that maintains mission continuity with eight prior multispectral instruments, and will extend the Landsat Earth observation record beyond 55+ years with an approximate launch date of 2027. The trade space is framed by four fundamental principles in remote sensing theory and practice: (1) temporal resolution, (2) spatial resolution, (3) radiometric resolution, and (4) spectral coverage and resolution. The goal of this communication is to provide a synopsis of past and present 2012-2017 LST contributions to Landsat 10 and beyond measurement science and application priorities. A particular focus will be to document the links between new science and societal benefit areas with potential technical enhancements to the Landsat mission.

Insulinlike growth factor I affects ocular development: a study of untreated and treated patients with Laron syndrome.

PubMed

Bourla, Dan Haim; Laron, Zvi; Snir, Moshe; Lilos, Pearl; Weinberger, Dov; Axer-Siegel, Ruth

2006-07-01

To evaluate the ocular dimensions in patients with primary growth hormone receptor insensitivity (Laron syndrome [LS]) and to study the effect of supplemental insulinlike growth factor I (IGF-I) on ocular growth. Retrospective case series. Twelve patients with LS, 8 untreated (LS group) and 4 treated (LS-T group) with supplemental IGF-I, and 30 healthy controls. Ocular dimensions and refraction were measured, and a full ophthalmologic examination was performed. Differences in the average ocular dimension data among IGF-I-treated patients, untreated ones, and controls. The average axial length of eyes in the LS group was 21.94 mm (standard deviation [SD], 0.81). Corresponding values for the LS-T and control group eyes were 22.53 mm (SD, 1.74) and 23.20 mm (SD, 1.35) respectively. The average anterior chamber depth of eyes in the LS group was 2.55 mm (SD, 0.26). Corresponding values for eyes in the LS-T and control groups were 3.48 mm (SD, 0.09) and 3.84 mm (SD, 0.16) respectively. The average lens thickness of eyes in the LS group was 4.56 mm (SD, 0.36). Corresponding values for the LS-T and control groups were 3.77 mm (SD, 0.23) and 3.51 mm (SD, 0.25), respectively. The average corneal curvature of eyes in the LS group was 46.9 diopters (D) (SD, 2.32). Corresponding values for the LS-T and control groups were 47.6 D (SD, 2.83) and 44.4 D (SD, 1.5), respectively. Insulinlike growth factor I seems to be an important regulator of ocular growth as documented in patients with primary growth hormone insensitivity. The mechanism of this observation should be investigated further.

A case study of effects of atmospheric boundary layer turbulence, wind speed, and stability on wind farm induced temperature changes using observations from a field campaign

NASA Astrophysics Data System (ADS)

Xia, Geng; Zhou, Liming; Freedman, Jeffrey M.; Roy, Somnath Baidya; Harris, Ronald A.; Cervarich, Matthew Charles

2016-04-01

Recent studies using satellite observations show that operational wind farms in west-central Texas increase local nighttime land surface temperature (LST) by 0.31-0.70 °C, but no noticeable impact is detected during daytime, and that the diurnal and seasonal variations in the magnitude of this warming are likely determined by those in the magnitude of wind speed. This paper further explores these findings by using the data from a year-long field campaign and nearby radiosonde observations to investigate how thermodynamic profiles and surface-atmosphere exchange processes work in tandem with the presence of wind farms to affect the local climate. Combined with satellite data analyses, we find that wind farm impacts on LST are predominantly determined by the relative ratio of turbulence kinetic energy (TKE) induced by the wind turbines compared to the background TKE. This ratio explains not only the day-night contrast of the wind farm impact and the warming magnitude of nighttime LST over the wind farms, but also most of the seasonal variations in the nighttime LST changes. These results indicate that the diurnal and seasonal variations in the turbine-induced turbulence relative to the background TKE play an essential role in determining those in the magnitude of LST changes over the wind farms. In addition, atmospheric stability determines the sign and strength of the net downward heat transport as well as the magnitude of the background TKE. The study highlights the need for better understanding of atmospheric boundary layer and wind farm interactions, and for better parameterizations of sub-grid scale turbulent mixing in numerical weather prediction and climate models.

Validation of three-dimensional incompressible spatial direct numerical simulation code: A comparison with linear stability and parabolic stability equation theories for boundary-layer transition on a flat plate

NASA Technical Reports Server (NTRS)

Joslin, Ronald D.; Streett, Craig L.; Chang, Chau-Lyan

1992-01-01

Spatially evolving instabilities in a boundary layer on a flat plate are computed by direct numerical simulation (DNS) of the incompressible Navier-Stokes equations. In a truncated physical domain, a nonstaggered mesh is used for the grid. A Chebyshev-collocation method is used normal to the wall; finite difference and compact difference methods are used in the streamwise direction; and a Fourier series is used in the spanwise direction. For time stepping, implicit Crank-Nicolson and explicit Runge-Kutta schemes are used to the time-splitting method. The influence-matrix technique is used to solve the pressure equation. At the outflow boundary, the buffer-domain technique is used to prevent convective wave reflection or upstream propagation of information from the boundary. Results of the DNS are compared with those from both linear stability theory (LST) and parabolized stability equation (PSE) theory. Computed disturbance amplitudes and phases are in very good agreement with those of LST (for small inflow disturbance amplitudes). A measure of the sensitivity of the inflow condition is demonstrated with both LST and PSE theory used to approximate inflows. Although the DNS numerics are very different than those of PSE theory, the results are in good agreement. A small discrepancy in the results that does occur is likely a result of the variation in PSE boundary condition treatment in the far field. Finally, a small-amplitude wave triad is forced at the inflow, and simulation results are compared with those of LST. Again, very good agreement is found between DNS and LST results for the 3-D simulations, the implication being that the disturbance amplitudes are sufficiently small that nonlinear interactions are negligible.

Annual Changes of Paddy Rice Planting Areas in Northeastern Asia from MODIS images in 2000-2014

NASA Astrophysics Data System (ADS)

Xiao, X.; Zhang, G.; Dong, J.; Menarguez, M. A.; Kou, W.; Jin, C.; Qin, Y.; Zhou, Y.; Wang, J.; Moore, B., III

2014-12-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, estimation of greenhouse gas (methane) emissions, and understanding avian influenza virus transmission. Over the past two decades, paddy rice cultivation has expanded northward in temperate and cold temperate zones, particularly in Northeastern China. There is a need to quantify and map changes in paddy rice planting areas in Northeastern Asia (Japan, North and South Korea, and northeast China) at annual interval. We developed a pixel- and phenology-based image analysis system, MODIS-RICE, to map the paddy rice in Northeastern Asia by using multi-temporal MODIS thermal and surface reflectance imagery. Paddy rice fields during the flooding and transplanting phases have unique physical and spectral characteristics, which make it possible for the development of an automated and robust algorithm to track flooding and transplanting phases of paddy rice fields over time. In this presentation, we will show the MODIS-based annual maps of paddy rice planting area in the Northeastern Asia from 2000-2014 (500-m spatial resolution). Accuracy assessments using high-resolution images show that the resultant paddy rice map of Northeastern Asia had a comparable accuracy to the existing products, including 2010 Landsat-based National Land Cover Dataset (NLCD) of China, the 2010 RapidEye-based paddy rice map in North Korea, and the 2010 AVNIR-2-based National Land Cover Dataset in Japan in terms of both area and spatial pattern of paddy rice. This study has demonstrated that our novel MODIS-Rice system, which use both thermal and optical MODIS data over a year, are simple and robust tools to identify and map paddy rice fields in temperate and cold temperate zones.

[Winter wheat area estimation with MODIS-NDVI time series based on parcel].

PubMed

Li, Le; Zhang, Jin-shui; Zhu, Wen-quan; Hu, Tan-gao; Hou, Dong

2011-05-01

Several attributes of MODIS (moderate resolution imaging spectrometer) data, especially the short temporal intervals and the global coverage, provide an extremely efficient way to map cropland and monitor its seasonal change. However, the reliability of their measurement results is challenged because of the limited spatial resolution. The parcel data has clear geo-location and obvious boundary information of cropland. Also, the spectral differences and the complexity of mixed pixels are weak in parcels. All of these make that area estimation based on parcels presents more advantage than on pixels. In the present study, winter wheat area estimation based on MODIS-NDVI time series has been performed with the support of cultivated land parcel in Tongzhou, Beijing. In order to extract the regional winter wheat acreage, multiple regression methods were used to simulate the stable regression relationship between MODIS-NDVI time series data and TM samples in parcels. Through this way, the consistency of the extraction results from MODIS and TM can stably reach up to 96% when the amount of samples accounts for 15% of the whole area. The results shows that the use of parcel data can effectively improve the error in recognition results in MODIS-NDVI based multi-series data caused by the low spatial resolution. Therefore, with combination of moderate and low resolution data, the winter wheat area estimation became available in large-scale region which lacks completed medium resolution images or has images covered with clouds. Meanwhile, it carried out the preliminary experiments for other crop area estimation.

Application of Modis Data to Assess the Latest Forest Cover Changes of Sri Lanka

NASA Astrophysics Data System (ADS)

Perera, K.; Herath, S.; Apan, A.; Tateishi, R.

2012-07-01

Assessing forest cover of Sri Lanka is becoming important to lower the pressure on forest lands as well as man-elephant conflicts. Furthermore, the land access to north-east Sri Lanka after the end of 30 years long civil war has increased the need of regularly updated land cover information for proper planning. This study produced an assessment of the forest cover of Sri Lanka using two satellite data based maps within 23 years of time span. For the old forest cover map, the study used one of the first island-wide digital land cover classification produced by the main author in 1988. The old land cover classification was produced at 80 m spatial resolution, using Landsat MSS data. A previously published another study by the author has investigated the application feasibility of MODIS and Landsat MSS imagery for a selected sub-section of Sri Lanka to identify the forest cover changes. Through the light of these two studies, the assessment was conducted to investigate the application possibility of MODIS 250 m over a small island like Sri Lanka. The relation between the definition of forest in the study and spatial resolution of the used satellite data sets were considered since the 2012 map was based on MODIS data. The forest cover map of 1988 was interpolated into 250 m spatial resolution to integrate with the GIS data base. The results demonstrated the advantages as well as disadvantages of MODIS data in a study at this scale. The successful monitoring of forest is largely depending on the possibility to update the field conditions at regular basis. Freely available MODIS data provides a very valuable set of information of relatively large green patches on the ground at relatively real-time basis. Based on the changes of forest cover from 1988 to 2012, the study recommends the use of MODIS data as a resalable method to forest assessment and to identify hotspots to be re-investigated. It's noteworthy to mention the possibility of uncounted small isolated pockets of forest, or sub-pixel size forest patches when MODIS 250 m x 250 m data used in small regions.

Development of the quality control system of the readout electronics for the large size telescope of the Cherenkov Telescope Array observatory

NASA Astrophysics Data System (ADS)

Konno, Y.; Kubo, H.; Masuda, S.; Paoletti, R.; Poulios, S.; Rugliancich, A.; Saito, T.

2016-07-01

The Cherenkov Telescope Array (CTA) is the next generation VHE γ-ray observatory which will improve the currently available sensitivity by a factor of 10 in the range 100 GeV to 10 TeV. The array consists of different types of telescopes, called large size telescope (LST), medium size telescope (MST) and small size telescope (SST). A LST prototype is currently being built and will be installed at the Observatorio Roque de los Muchachos, island of La Palma, Canary islands, Spain. The readout system for the LST prototype has been designed and around 300 readout boards will be produced in the coming months. In this note we describe an automated quality control system able to measure basic performance parameters and quickly identify faulty boards.

Synergistic Use of MODIS and AIRS in a Variational Retrieval of Cloud Parameters.

NASA Astrophysics Data System (ADS)

Li, Jun; Menzel, W. Paul; Zhang, Wenjian; Sun, Fengying; Schmit, Timothy J.; Gurka, James J.; Weisz, Elisabeth

2004-11-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from the Earth Observing System's (EOS's) Aqua satellite enable global monitoring of the distribution of clouds. MODIS is able to provide a cloud mask, surface and cloud types, cloud phase, cloud-top pressure (CTP), effective cloud amount (ECA), cloud particle size, and cloud optical thickness at high spatial resolution (1 5 km). The combined MODIS AIRS system offers the opportunity for improved cloud products, better than from either system alone; this improvement is demonstrated in this paper with both simulated and real radiances. A one-dimensional variational (1DVAR) methodology is used to retrieve the CTP and ECA from AIRS longwave (650 790 cm-1 or 15.38 12.65 μm) cloudy radiance measurements (hereinafter referred to as MODIS AIRS 1DVAR). The MODIS AIRS 1DVAR cloud properties show significant improvement over the MODIS-alone cloud properties and slight improvement over AIRS-alone cloud properties in a simulation study, while MODIS AIRS 1DVAR is much more computationally efficient than the AIRS-alone 1DVAR; comparisons with radiosonde observations show that CTPs improve by 10 40 hPa for MODIS AIRS CTPs over those from MODIS alone. The 1DVAR approach is applied to process the AIRS longwave cloudy radiance measurements; results are compared with MODIS and Geostationary Operational Environmental Satellite sounder cloud products. Data from ground-based instrumentation at the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed in Oklahoma are used for validation; results show that MODIS AIRS improves the MODIS CTP, especially in low-level clouds. The operational use of a high-spatial-resolution imager, along with information from a high-spectral-resolution sounder will be possible with instruments planned for the next-generation geostationary operational instruments.

MODIS GPP/NPP for complex land use area: a case study of comparison between MODIS GPP/NPP and ground-based measurements over Korea

NASA Astrophysics Data System (ADS)

Shim, C.

2013-12-01

The Moderate Resolution Imaging Radiometer (MODIS) Gross Primary Productivity (GPP)/Net Primary Productivity (NPP) has been widely used for the study on global terrestrial ecosystem and carbon cycle. The current MODIS product with ~ 1 km spatial resolution, however, has limitation on the information on local scale environment (< 1km), particularly on the regions with complex land-use types. Here we try to test the performance of MODIS annual GPP/NPP for a case of Korea, where the vegetation types are mostly heterogeneous within a size of MODIS products (~1km). We selected the sites where the ground/tower flux measurements and MODIS retrievals were simultaneously available and the land classification of sites agreed the forest type map (~71m) (1 site over Gwangneung flux tower (GDK) for 2006-2008 and 2 sites of ground measurements over Cheongju (CJ1 and CJ2) for 2011). The MODIS GPP are comparable to that of GDK (largely deciduous forest) within -6.3 ~ +2.3% of bias (-104.5 - 37.9 gCm-2yr-1). While the MODIS NPP of CJ1 at Cheongju (largely Larix leptolepis) underestimated NPP by 34% (-224.5 gCm-2yr-1), the MODIS NPP of CJ2 (largely Pinus densiflora) agreed well with -0.2% of bias (1.6 gCm-2yr-1). The fairly comparable values of the MODIS here however, cannot assure the quality of the MOD17 over the complex vegetation area of Korea since the ground measurements except the eddy covariance tower flux measurements are highly inconsistent. Therefore, the comprehensive experiments to represents GPP/NPP over diverse vegetation types for a comparable scale of MODIS with a consistent measurement technique are necessary in order to evaluate the MODIS vegetation productivity data over Korea, which contains a large portion of highly heterogeneous vegetation area.

A web-based subsetting service for regional scale MODIS land products

DOE Office of Scientific and Technical Information (OSTI.GOV)

SanthanaVannan, Suresh K; Cook, Robert B; Holladay, Susan K

2009-12-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor has provided valuable information on various aspects of the Earth System since March 2000. The spectral, spatial, and temporal characteristics of MODIS products have made them an important data source for analyzing key science questions relating to Earth System processes at regional, continental, and global scales. The size of the MODIS product and native HDF-EOS format are not optimal for use in field investigations at individual sites (100 - 100 km or smaller). In order to make MODIS data readily accessible for field investigations, the NASA-funded Distributed Active Archive Center (DAAC) for Biogeochemicalmore » Dynamics at Oak Ridge National Laboratory (ORNL) has developed an online system that provides MODIS land products in an easy-to-use format and in file sizes more appropriate to field research. This system provides MODIS land products data in a nonproprietary comma delimited ASCII format and in GIS compatible formats (GeoTIFF and ASCII grid). Web-based visualization tools are also available as part of this system and these tools provide a quick snapshot of the data. Quality control tools and a multitude of data delivery options are available to meet the demands of various user communities. This paper describes the important features and design goals for the system, particularly in the context of data archive and distribution for regional scale analysis. The paper also discusses the ways in which data from this system can be used for validation, data intercomparison, and modeling efforts.« less

If the MODIS Aerosol Product is so Infested with Cloud Contamination, Why Does Everybody Use the Product?

NASA Technical Reports Server (NTRS)

Remeer, Lorraine A.

2011-01-01

The MODIS aerosol cloud mask is based on a spatial variability test, using the assumption that aerosols are more homogeneous than clouds. On top of this first line of defense are a series of additional tests based on threshold values and ratios of various MODIS channels. The goal is to eliminate clouds and keep the aerosol. How well have we succeeded? There have been several studies showing cloud contamination in the MODIS aerosol product and several alternative cloud masks proposed. There are even "competing" MODIS aerosol products that offer an alternative "cloud free" world. Are these alternative products an improvement to the old standard product? We find there is a trade-off between retrieval availability and cloud contamination, and for many applications it is better to have a little bit of cloud in the product than to not have enough product. I will review the decisions that led us to the present MODIS cloud mask, and show how it is simultaneously too liberal and too conservative, some ideas on how to make it better and why in the end it doesn't matter. I hope to inspire a spirited discussion and will be very willing to take your complaints and suggestions.

Tropical Atlantic Dust and Smoke Aerosol Variabilities Related to the Madden-Julian Oscillation in MODIS and MISR Observations

NASA Technical Reports Server (NTRS)

Guo, Yanjuan; Tian, Baijun; Kahn, Ralph A.; Kalashnikova, Olga; Wong, Sun; Waliser, Duane E.

2012-01-01

In this study, MODIS fine mode fraction and MISR non-spherical fraction are 2used to derive dust and smoke AOT components (tau(sub dust) and tau(sub smoke)) over the tropical Atlantic, and their variabilities related to the Madden-Julian Oscillation (MJO) are then investigated. Both MODIS and MISR show a very similar dust and smoke winter climatology. tau(sub dust) is found to be the dominant aerosol component over the tropical Atlantic while tau(sub smoke) is significantly smaller than tau(sub dust). The daily MODIS and MISR tau(sub dust) are overall highly correlated, with the correlation coefficients typically about 0.7 over the North Atlantic. The consistency between the MODIS and MISR dust and smoke aerosol climatology and daily variations give us confidence to use these two data sets to investigate their relative contributions to the total AOT variation associated with the MJO. However, unlike the MISR dust discrimination, which is based on particle shape retrievals, the smoke discrimination is less certain, based on assumed partitioning of maritime aerosol for both MISR and MODIS. The temporal evolution and spatial patterns of the tau(sub dust) anomalies associated with the MJO are consistent between MODIS and MISR. The tau(sub dust) anomalies are very similar to those of tau anomalies, and are of comparable magnitude. In contrast, the MJO-related tau(sub smoke) anomalies are rather small, and the tau(sub mar) anomalies are negligible. The consistency between the MODIS and MISR results suggests that dust aerosol is the dominant component on the intra-seasonal time scale over the tropical Atlantic Ocean.

Sprite Climatology in the Eastern Mediterranean Region

NASA Astrophysics Data System (ADS)

Yair, Yoav; Price, Colin; Katzenelson, Dor; Rosenthal, Neta; Rubanenko, Lior; Ben-Ami, Yuval; Arnone, Enrico

2015-04-01

We present statistical analysis of 436 sprites observed in 7 winter campaigns from 2006/7-2012/13. Results show a clear peak in the frequency of sprite detections, with maximum values (< 40% of events) between 00:30-02:15 LST (22:30-00:15 UT; LST=UT+2). The detection times of sprites are well-correlated with a relative increase in the fraction of +CG strokes, which exhibit maxima between 00:00-02:00 LST. The morphological distribution of 339 sprites, that we were able to clearly identify, is dominated by column sprites (49.3%), with angels (33.0%) and carrots (25.7%) being less frequent. This is similar to reports of winter sprites over the Sea of Japan and summer ones in central Europe. Other shapes such as trees, wishbones, etc. appear quite rarely. Single element events constitute 16.5% of observations, with 83.5% containing 2 elements or more. Clusters of homogeneous types are slightly more frequent than mixed ones (55%). Our observations suggest winter East Mediterranean thunderstorms to have a vertical structure that is an intermediate type between high tropical convective systems and the lower cloud-top cells in winter thunderstorms over the Sea of Japan. The climatology shows that the Eastern Mediterranean is a major sprite producer during Northern Hemisphere winter, and thus the existing and future optical observation infrastructure in Israel offers ground-based coverage for upcoming space missions that aim to map global sprite activity.

Inter-Comparison of S-NPP VIIRS and Aqua MODIS Thermal Emissive Bands Using Hyperspectral Infrared Sounder Measurements as a Transfer Reference

NASA Technical Reports Server (NTRS)

Li, Yonghong; Wu, Aisheng; Xiong, Xiaoxiong

2016-01-01

This paper compares the calibration consistency of the spectrally-matched thermal emissive bands (TEB) between the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), using observations from their simultaneous nadir overpasses (SNO). Nearly-simultaneous hyperspectral measurements from the Aqua Atmospheric Infrared Sounder(AIRS) and the S-NPP Cross-track Infrared Sounder (CrIS) are used to account for existing spectral response differences between MODIS and VIIRS TEB. The comparison uses VIIRS Sensor Data Records (SDR) in MODIS five-minute granule format provided by the NASA Land Product and Evaluation and Test Element (PEATE) and Aqua MODIS Collection 6 Level 1 B (L1B) products. Each AIRS footprint of 13.5 km (or CrIS field of view of 14 km) is co-located with multiple MODIS (or VIIRS) pixels. The corresponding AIRS- and CrIS-simulated MODIS and VIIRS radiances are derived by convolutions based on sensor-dependent relative spectral response (RSR) functions. The VIIRS and MODIS TEB calibration consistency is evaluated and the two sensors agreed within 0.2 K in brightness temperature.Additional factors affecting the comparison such as geolocation and atmospheric water vapor content are also discussed in this paper.

Comparative Analysis of Aerosol Retrievals from MODIS, OMI and MISR Over Sahara Region

NASA Technical Reports Server (NTRS)

Lyapustin, A.; Wang, Y.; Hsu, C.; Terres, O.; Leptoukh, G.; Kalashnikova, O.; Korkin, S.

2011-01-01

MODIS is a wide field-of-view sensor providing daily global observations of the Earth. Currently, global MODIS aerosol retrievals over land are performed with the main Dark Target algorithm complimented with the Deep Blue (DB) Algorithm over bright deserts. The Dark Target algorithm relies on surface parameterization which relates reflectance in MODIS visible bands with the 2.1 micrometer region, whereas the Deep Blue algorithm uses an ancillary angular distribution model of surface reflectance developed from the time series of clear-sky MODIS observations. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over both dark vegetative surfaces and bright deserts and performs retrievals at 1 km resolution. In this work, we will provide a comparative analysis of DB, MAIAC, MISR and OMI aerosol products over bright deserts of northern Africa.

Impact of Sensor Degradation on the MODIS NDVI Time Series

NASA Technical Reports Server (NTRS)

Wang, Dongdong; Morton, Douglas Christopher; Masek, Jeffrey; Wu, Aisheng; Nagol, Jyoteshwar; Xiong, Xiaoxiong; Levy, Robert; Vermote, Eric; Wolfe, Robert

2012-01-01

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, the impact of sensor degradation on trend detection was evaluated using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470 nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004 yr-1 decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends in MODIS NDVI over North America were consistentwith simulated results,with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in detection of NDVI trends.

Impact of Sensor Degradation on the MODIS NDVI Time Series

NASA Technical Reports Server (NTRS)

Wang, Dongdong; Morton, Douglas; Masek, Jeffrey; Wu, Aisheng; Nagol, Jyoteshwar; Xiong, Xiaoxiong; Levy, Robert; Vermote, Eric; Wolfe, Robert

2011-01-01

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, we evaluated the impact of sensor degradation on trend detection using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004/yr decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends MODIS NDVI over North America were consistent with simulated results, with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in NDVI trends over vegetation.