A COMPARISON OF THE SALINITY REGIME ALONG THE TEXAS COAST WITH TERRESTRIAL VEGETATION GREENNESS AND WATER USE IN THE GALVESTON BAY WATERSHED USING REMOTING SENSING

EPA Science Inventory

Variability in vegetation greenness was determined for the Galveston Bay watershed using biweekly Normalized Difference Vegetation Index (NDVI) data derived from the Advanced Very High Resolution Radiometer (A VHRR) flown on NOAA satellites. NDVI variability was compared with reg...

TERRESTRIAL VEGETATION GREENNESS OF THE LOWER GALVESTON BAY WATERSHED FROM SATELLITE REMOTE SENSING AND ITS RELATION TO WATER AND THE SALINITY REGIME OF THE GALVESTON BAY ESTUARY

EPA Science Inventory

Spatial and temporal variability of vegetation greenness have been determined for coastal Texas using biweekly Normalized Difference Vegetation Index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR). Results are presented on relationships between grou...

Exact Delaunay normalization of the perturbed Keplerian Hamiltonian with tesseral harmonics

NASA Astrophysics Data System (ADS)

Mahajan, Bharat; Vadali, Srinivas R.; Alfriend, Kyle T.

2018-03-01

A novel approach for the exact Delaunay normalization of the perturbed Keplerian Hamiltonian with tesseral and sectorial spherical harmonics is presented in this work. It is shown that the exact solution for the Delaunay normalization can be reduced to quadratures by the application of Deprit's Lie-transform-based perturbation method. Two different series representations of the quadratures, one in powers of the eccentricity and the other in powers of the ratio of the Earth's angular velocity to the satellite's mean motion, are derived. The latter series representation produces expressions for the short-period variations that are similar to those obtained from the conventional method of relegation. Alternatively, the quadratures can be evaluated numerically, resulting in more compact expressions for the short-period variations that are valid for an elliptic orbit with an arbitrary value of the eccentricity. Using the proposed methodology for the Delaunay normalization, generalized expressions for the short-period variations of the equinoctial orbital elements, valid for an arbitrary tesseral or sectorial harmonic, are derived. The result is a compact unified artificial satellite theory for the sub-synchronous and super-synchronous orbit regimes, which is nonsingular for the resonant orbits, and is closed-form in the eccentricity as well. The accuracy of the proposed theory is validated by comparison with numerical orbit propagations.

A new comprehensive index for drought monitoring with TM data

NASA Astrophysics Data System (ADS)

Wang, Yuanyuan

2017-10-01

Drought is one of the most important and frequent natural hazards to agriculture production in North China Plain. To improve agriculture water management, accurate drought monitoring information is needed. This study proposed a method for comprehensive drought monitoring by combining a meteorological index and three satellite drought indices of TM data together. SPI (Standard Precipitation Index), the meteorological drought index, is used to measure precipitation deficiency. Three satellite drought indices (Temperature Vegetation Drought Index, Land Surface Water Index, Modified Perpendicular Drought Index) are used to evaluate agricultural drought risk by exploring data from various channels (VIS, NIR, SWIR, TIR). Considering disparities in data ranges of different drought indices, normalization is implemented before combination. First, SPI is normalized to 0 — 100 given that its normal range is -4 - +4. Then, the three satellite drought indices are normalized to 0 - 100 according to the maximum and minimum values in the image, and aggregated using weighted average method (the result is denoted as ADI, Aggregated drought index). Finally, weighed geometric mean of SPI and ADI are calculated (the result is denoted as DIcombined). A case study in North China plain using three TM images acquired during April-May 2007 show that the method proposed in this study is effective. In spatial domain, DIcombined demonstrates dramatically more details than SPI; in temporal domain, DIcombined shows more reasonable drought development trajectory than satellite indices that are derived from independent TM images.

Vegetation, topography and daily weather influenced burn severity in central Idaho and western Montana forests

Treesearch

Donovan S. Birch; Penelope Morgan; Crystal A. Kolden; John T. Abatzoglou; Gregory K. Dillon; Andrew T. Hudak; Alistair M. S. Smith

2015-01-01

Burn severity as inferred from satellite-derived differenced Normalized Burn Ratio (dNBR) is useful for evaluating fire impacts on ecosystems but the environmental controls on burn severity across large forest fires are both poorly understood and likely to be different than those influencing fire extent. We related dNBR to environmental variables including vegetation,...

Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture.

PubMed

Hassan-Esfahani, Leila; Ebtehaj, Ardeshir M; Torres-Rua, Alfonso; McKee, Mac

2017-09-14

Applications of satellite-borne observations in precision agriculture (PA) are often limited due to the coarse spatial resolution of satellite imagery. This paper uses high-resolution airborne observations to increase the spatial resolution of satellite data for related applications in PA. A new variational downscaling scheme is presented that uses coincident aerial imagery products from "AggieAir", an unmanned aerial system, to increase the spatial resolution of Landsat satellite data. This approach is primarily tested for downscaling individual band Landsat images that can be used to derive normalized difference vegetation index (NDVI) and surface soil moisture (SSM). Quantitative and qualitative results demonstrate promising capabilities of the downscaling approach enabling effective increase of the spatial resolution of Landsat imageries by orders of 2 to 4. Specifically, the downscaling scheme retrieved the missing high-resolution feature of the imageries and reduced the root mean squared error by 15, 11, and 10 percent in visual, near infrared, and thermal infrared bands, respectively. This metric is reduced by 9% in the derived NDVI and remains negligibly for the soil moisture products.

Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture

PubMed Central

Hassan-Esfahani, Leila; Ebtehaj, Ardeshir M.; McKee, Mac

2017-01-01

Applications of satellite-borne observations in precision agriculture (PA) are often limited due to the coarse spatial resolution of satellite imagery. This paper uses high-resolution airborne observations to increase the spatial resolution of satellite data for related applications in PA. A new variational downscaling scheme is presented that uses coincident aerial imagery products from “AggieAir”, an unmanned aerial system, to increase the spatial resolution of Landsat satellite data. This approach is primarily tested for downscaling individual band Landsat images that can be used to derive normalized difference vegetation index (NDVI) and surface soil moisture (SSM). Quantitative and qualitative results demonstrate promising capabilities of the downscaling approach enabling effective increase of the spatial resolution of Landsat imageries by orders of 2 to 4. Specifically, the downscaling scheme retrieved the missing high-resolution feature of the imageries and reduced the root mean squared error by 15, 11, and 10 percent in visual, near infrared, and thermal infrared bands, respectively. This metric is reduced by 9% in the derived NDVI and remains negligibly for the soil moisture products. PMID:28906428

Human Artificial Chromosomes with Alpha Satellite-Based De Novo Centromeres Show Increased Frequency of Nondisjunction and Anaphase Lag

PubMed Central

Rudd, M. Katharine; Mays, Robert W.; Schwartz, Stuart; Willard, Huntington F.

2003-01-01

Human artificial chromosomes have been used to model requirements for human chromosome segregation and to explore the nature of sequences competent for centromere function. Normal human centromeres require specialized chromatin that consists of alpha satellite DNA complexed with epigenetically modified histones and centromere-specific proteins. While several types of alpha satellite DNA have been used to assemble de novo centromeres in artificial chromosome assays, the extent to which they fully recapitulate normal centromere function has not been explored. Here, we have used two kinds of alpha satellite DNA, DXZ1 (from the X chromosome) and D17Z1 (from chromosome 17), to generate human artificial chromosomes. Although artificial chromosomes are mitotically stable over many months in culture, when we examined their segregation in individual cell divisions using an anaphase assay, artificial chromosomes exhibited more segregation errors than natural human chromosomes (P < 0.001). Naturally occurring, but abnormal small ring chromosomes derived from chromosome 17 and the X chromosome also missegregate more than normal chromosomes, implicating overall chromosome size and/or structure in the fidelity of chromosome segregation. As different artificial chromosomes missegregate over a fivefold range, the data suggest that variable centromeric DNA content and/or epigenetic assembly can influence the mitotic behavior of artificial chromosomes. PMID:14560014

Human artificial chromosomes with alpha satellite-based de novo centromeres show increased frequency of nondisjunction and anaphase lag.

PubMed

Rudd, M Katharine; Mays, Robert W; Schwartz, Stuart; Willard, Huntington F

2003-11-01

Human artificial chromosomes have been used to model requirements for human chromosome segregation and to explore the nature of sequences competent for centromere function. Normal human centromeres require specialized chromatin that consists of alpha satellite DNA complexed with epigenetically modified histones and centromere-specific proteins. While several types of alpha satellite DNA have been used to assemble de novo centromeres in artificial chromosome assays, the extent to which they fully recapitulate normal centromere function has not been explored. Here, we have used two kinds of alpha satellite DNA, DXZ1 (from the X chromosome) and D17Z1 (from chromosome 17), to generate human artificial chromosomes. Although artificial chromosomes are mitotically stable over many months in culture, when we examined their segregation in individual cell divisions using an anaphase assay, artificial chromosomes exhibited more segregation errors than natural human chromosomes (P < 0.001). Naturally occurring, but abnormal small ring chromosomes derived from chromosome 17 and the X chromosome also missegregate more than normal chromosomes, implicating overall chromosome size and/or structure in the fidelity of chromosome segregation. As different artificial chromosomes missegregate over a fivefold range, the data suggest that variable centromeric DNA content and/or epigenetic assembly can influence the mitotic behavior of artificial chromosomes.

Determination of wind from NIMBUS 6 satellite sounding data

NASA Technical Reports Server (NTRS)

Carle, W. E.; Scoggins, J. R.

1981-01-01

Objective methods of computing upper level and surface wind fields from NIMBUS 6 satellite sounding data are developed. These methods are evaluated by comparing satellite derived and rawinsonde wind fields on gridded constant pressure charts in four geographical regions. Satellite-derived and hourly observed surface wind fields are compared. Results indicate that the best satellite-derived wind on constant pressure charts is a geostrophic wind derived from highly smoothed fields of geopotential height. Satellite-derived winds computed in this manner and rawinsonde winds show similar circulation patterns except in areas of small height gradients. Magnitudes of the standard deviation of the differences between satellite derived and rawinsonde wind speeds range from approximately 3 to 12 m/sec on constant pressure charts and peak at the jet stream level. Fields of satellite-derived surface wind computed with the logarithmic wind law agree well with fields of observed surface wind in most regions. Magnitudes of the standard deviation of the differences in surface wind speed range from approximately 2 to 4 m/sec, and satellite derived surface winds are able to depict flow across a cold front and around a low pressure center.

Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors.

PubMed

Lange, Maximilian; Dechant, Benjamin; Rebmann, Corinna; Vohland, Michael; Cuntz, Matthias; Doktor, Daniel

2017-08-11

Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure.

Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors

PubMed Central

Lange, Maximilian; Rebmann, Corinna; Cuntz, Matthias; Doktor, Daniel

2017-01-01

Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure. PMID:28800065

Remotely-sensed detection of effects of extreme droughts on gross primary production.

PubMed

Vicca, Sara; Balzarolo, Manuela; Filella, Iolanda; Granier, André; Herbst, Mathias; Knohl, Alexander; Longdoz, Bernard; Mund, Martina; Nagy, Zoltan; Pintér, Krisztina; Rambal, Serge; Verbesselt, Jan; Verger, Aleixandre; Zeileis, Achim; Zhang, Chao; Peñuelas, Josep

2016-06-15

Severe droughts strongly impact photosynthesis (GPP), and satellite imagery has yet to demonstrate its ability to detect drought effects. Especially changes in vegetation functioning when vegetation state remains unaltered (no browning or defoliation) pose a challenge to satellite-derived indicators. We evaluated the performance of different satellite indicators to detect strong drought effects on GPP in a beech forest in France (Hesse), where vegetation state remained largely unaffected while GPP decreased substantially. We compared the results with three additional sites: a Mediterranean holm oak forest (Puéchabon), a temperate beech forest (Hainich), and a semi-arid grassland (Bugacpuszta). In Hesse, a three-year reduction in GPP following drought was detected only by the Enhanced Vegetation Index (EVI). The Photochemical Reflectance Index (PRI) also detected this drought effect, but only after normalization for absorbed light. In Puéchabon normalized PRI outperformed the other indicators, while the short-term drought effect in Hainich was not detected by any tested indicator. In contrast, most indicators, but not PRI, captured the drought effects in Bugacpuszta. Hence, PRI improved detection of drought effects on GPP in forests and we propose that PRI normalized for absorbed light is considered in future algorithms to estimate GPP from space.

The use of NOAA AVHRR data for assessment of the urban heat sland effect

USGS Publications Warehouse

Gallo, K.P.; McNab, A. L.; Karl, Thomas R.; Brown, Jesslyn F.; Hood, J. J.; Tarpley, J.D.

1993-01-01

A vegetation index and a radiative surface temperature were derived from satellite data acquired at approximately 1330 LST for each of 37 cities and for their respective nearby rural regions from 28 June through 8 August 1991. Urban–rural differences for the vegetation index and the surface temperatures were computed and then compared to observed urban–rural differences in minimum air temperatures. The purpose of these comparisons was to evaluate the use of satellite data to assess the influence of the urban environment on observed minimum air temperatures (the urban heat island effect). The temporal consistency of the data, from daily data to weekly, biweekly, and monthly intervals, was also evaluated. The satellite-derived normalized difference (ND) vegetation-index data, sampled over urban and rural regions composed of a variety of land surface environments, were linearly related to the difference in observed urban and rural minimum temperatures. The relationship between the ND index and observed differences in minimum temperature was improved when analyses were restricted by elevation differences between the sample locations and when biweekly or monthly intervals were utilized. The difference in the ND index between urban and rural regions appears to be an indicator of the difference in surface properties (evaporation and heat storage capacity) between the two environments that are responsible for differences in urban and rural minimum temperatures. The urban and rural differences in the ND index explain a greater amount of the variation observed in minimum temperature differences than past analyses that utilized urban population data. The use of satellite data may contribute to a globally consistent method for analysis of urban heat island bias.

Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

NASA Astrophysics Data System (ADS)

Moody, Daniela I.; Wilson, Cathy J.; Rowland, Joel C.; Altmann, Garrett L.

2015-06-01

Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.

Response of spectral vegetation indices to soil moisture in grasslands and shrublands

USGS Publications Warehouse

Zhang, Li; Ji, Lei; Wylie, Bruce K.

2011-01-01

The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture.

The impact of climatic and non-climatic factors on land surface temperature in southwestern Romania

NASA Astrophysics Data System (ADS)

Roşca, Cristina Florina; Harpa, Gabriela Victoria; Croitoru, Adina-Eliza; Herbel, Ioana; Imbroane, Alexandru Mircea; Burada, Doina Cristina

2017-11-01

Land surface temperature is one of the most important parameters related to global warming. It depends mainly on soil type, discontinuous vegetation cover, or lack of precipitation. The main purpose of this paper is to investigate the relationship between high LST, synoptic conditions and air masses trajectories, vegetation cover, and soil type in one of the driest region in Romania. In order to calculate the land surface temperature and normalized difference vegetation index, five satellite images of LANDSAT missions 5 and 7, covering a period of 26 years (1986-2011), were selected, all of them collected in the month of June. The areas with low vegetation density were derived from normalized difference vegetation index, while soil types have been extracted from Corine Land Cover database. HYSPLIT application was employed to identify the air masses origin based on their backward trajectories for each of the five study cases. Pearson, logarithmic, and quadratic correlations were used to detect the relationships between land surface temperature and observed ground temperatures, as well as between land surface temperature and normalized difference vegetation index. The most important findings are: strong correlation between land surface temperature derived from satellite images and maximum ground temperature recorded in a weather station located in the area, as well as between areas with land surface temperature equal to or higher than 40.0 °C and those with lack of vegetation; the sandy soils are the most prone to high land surface temperature and lack of vegetation, followed by the chernozems and brown soils; extremely severe drought events may occur in the region.

Probabilistic drought intensification forecasts using temporal patterns of satellite-derived drought indicators

NASA Astrophysics Data System (ADS)

Park, Sumin; Im, Jungho; Park, Seonyeong

2016-04-01

A drought occurs when the condition of below-average precipitation in a region continues, resulting in prolonged water deficiency. A drought can last for weeks, months or even years, so can have a great influence on various ecosystems including human society. In order to effectively reduce agricultural and economic damage caused by droughts, drought monitoring and forecasts are crucial. Drought forecast research is typically conducted using in situ observations (or derived indices such as Standardized Precipitation Index (SPI)) and physical models. Recently, satellite remote sensing has been used for short term drought forecasts in combination with physical models. In this research, drought intensification was predicted using satellite-derived drought indices such as Normalized Difference Drought Index (NDDI), Normalized Multi-band Drought Index (NMDI), and Scaled Drought Condition Index (SDCI) generated from Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) products over the Korean Peninsula. Time series of each drought index at the 8 day interval was investigated to identify drought intensification patterns. Drought condition at the previous time step (i.e., 8 days before) and change in drought conditions between two previous time steps (e.g., between 16 days and 8 days before the time step to forecast) Results show that among three drought indices, SDCI provided the best performance to predict drought intensification compared to NDDI and NMDI through qualitative assessment. When quantitatively compared with SPI, SDCI showed a potential to be used for forecasting short term drought intensification. Finally this research provided a SDCI-based equation to predict short term drought intensification optimized over the Korean Peninsula.

Normalized difference vegetation index as an estimator for abundance and quality of avian herbivore forage in arctic Alaska

USGS Publications Warehouse

Hogrefe, Kyle R.; Patil, Vijay; Ruthrauff, Daniel R.; Meixell, Brandt W.; Budde, Michael E.; Hupp, Jerry W.; Ward, David H.

2017-01-01

Tools that can monitor biomass and nutritional quality of forage plants are needed to understand how arctic herbivores may respond to the rapidly changing environment at high latitudes. The Normalized Difference Vegetation Index (NDVI) has been widely used to assess changes in abundance and distribution of terrestrial vegetative communities. However, the efficacy of NDVI to measure seasonal changes in biomass and nutritional quality of forage plants in the Arctic remains largely un-evaluated at landscape and fine-scale levels. We modeled the relationships between NDVI and seasonal changes in aboveground biomass and nitrogen concentration in halophytic graminoids, a key food source for arctic-nesting geese. The model was calibrated based on data collected at one site and validated using data from another site. Effects of spatial scale on model accuracy were determined by comparing model predictions between NDVI derived from moderate resolution (250 × 250 m pixels) satellite data and high resolution (20 cm diameter area) handheld spectrometer data. NDVI derived from the handheld spectrometer was a superior estimator (R2 ≥ 0.67) of seasonal changes in aboveground biomass compared to satellite-derived NDVI (R2 ≤ 0.40). The addition of temperature and precipitation variables to the model for biomass improved fit, but provided minor gains in predictive power beyond that of the NDVI-only model. This model, however, was only a moderately accurate estimator of biomass in an ecologically-similar halophytic graminoid wetland located 100 km away, indicating the necessity for site-specific validation. In contrast to assessments of biomass, satellite-derived NDVI was a better estimator for the timing of peak percent of nitrogen than NDVI derived from the handheld spectrometer. We confirmed that the date when NDVI reached 50% of its seasonal maximum was a reasonable approximation of the period of peak spring vegetative green-up and peak percent nitrogen. This study demonstrates the importance of matching the scale of NDVI measurements to the vegetation properties of biomass and nitrogen phenology.

Boosted Regression Trees Outperforms Support Vector Machines in Predicting (Regional) Yields of Winter Wheat from Single and Cumulated Dekadal Spot-VGT Derived Normalized Difference Vegetation Indices

NASA Astrophysics Data System (ADS)

Stas, Michiel; Dong, Qinghan; Heremans, Stien; Zhang, Beier; Van Orshoven, Jos

2016-08-01

This paper compares two machine learning techniques to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION satellite imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Although the exact selections differed between the prefectures, certain periods with high influence scores for multiple prefectures could be identified. The same period of high influence stretching from March to June was detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for actual yield forecasting. Whereas both machine learning methods returned very low prediction errors, BRT seems to slightly but consistently outperform SVM.

Combining ground-based measurements and satellite-based spectral vegetation indices to track biomass accumulation in post-fire chaparral

NASA Astrophysics Data System (ADS)

Uyeda, K. A.; Stow, D. A.; Roberts, D. A.; Riggan, P. J.

2015-12-01

Multi-temporal satellite imagery can provide valuable information on patterns of vegetation growth over large spatial extents and long time periods, but corresponding ground-referenced biomass information is often difficult to acquire, especially at an annual scale. In this study, I test the relationship between annual biomass estimated using shrub growth rings and metrics of seasonal growth derived from Moderate Resolution Imaging Spectroradiometer (MODIS) spectral vegetation indices (SVIs) for a small area of southern California chaparral to evaluate the potential for mapping biomass at larger spatial extents. The site had most recently burned in 2002, and annual biomass accumulation measurements were available from years 5 - 11 post-fire. I tested metrics of seasonal growth using six SVIs (Normalized Difference Vegetation Index, Enhanced Vegetation Index, Soil Adjusted Vegetation Index, Normalized Difference Water Index, Normalized Difference Infrared Index 6, and Vegetation Atmospherically Resistant Index). While additional research would be required to determine which of these metrics and SVIs are most promising over larger spatial extents, several of the seasonal growth metrics/ SVI combinations have a very strong relationship with annual biomass, and all SVIs have a strong relationship with annual biomass for at least one of the seasonal growth metrics.

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronand; Russell, Jeff; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the foundation of any interoperability or change detection technique. Satellite intercomparisons and accurate vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), require the generation of accurate reflectance maps (NDVI is used to describe or infer a wide variety of biophysical parameters and is defined in terms of near-infrared (NIR) and red band reflectances). Accurate reflectance-map generation from satellite imagery relies on the removal of solar and satellite geometry and of atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance has been widely applied to a few systems only. The ability to obtain atmospherically corrected imagery and products from various satellites is essential to enable widescale use of remotely sensed, multitemporal imagery for a variety of applications. An atmospheric correction approach derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that can be applied to high-spatial-resolution satellite imagery under many conditions was evaluated to demonstrate a reliable, effective reflectance map generation method. Additional information is included in the original extended abstract.

Observations of Typhoon Center by Using Satellite-derived Normalized Difference Convection Index

NASA Astrophysics Data System (ADS)

Liu, Chung-Chih; Chen, Chun-Hsu

2015-04-01

A technique involving differencing water vapor and infrared window channel brightness temperature values to identify and quantify intense convection in tropical cyclones using bispectral geostationary satellite imagery was proposed by Olander and Velden (2009). Rouse et al. (1974) calculated a normalized ratio of the near infrared and red bands and proposed an index called the normalized difference vegetation index. It was then used in many fields such as estimations of vegetation biomass, leaf area, the proportion of absorbed photosynthetically active radiation, etc. The present study used the spectral features of the IR1 and WV channels of the satellite to define a new index, the brightness temperature of the infrared window channel minus the brightness temperature of the water vapor channel divided by the brightness temperature of the infrared window channel plus the brightness temperature of the water vapor channel. The values obtained by this formula are called the Normalized Difference Convection Index (NDCI) values. The NDCI value is between -1 and 1. The NDCI value at WV = 0K is the highest, 1; while that at IR1 = 0K is the lowest, -1. In cases of a clear sky or atmosphere with thin cloud and dry air, NDCI values should be larger than 0. In cases of a convective cloud system, NDCI values should be lower than 0. In addition, the newly defined NDCI does show significant difference from simple difference of IR1-WV. For example, the NDCI value is -0.0017 at IR1=299K and WV=300K, while the NDCI value is -0.0033 at IR1=149K and WV=150K. The two times difference of NDCI values shows the features of clouds with NDCI value -0.0017 are quite different from those with NDCI value -0.0033. The former may be low level clouds, but the latter may be deep convections. However, the simple difference of IR1-WV cannot be used to distinguish the difference. The NDCI was applied to determine the centers of Typhoon Longwang (2005). The results showed that the two-dimensional NDCI analysis helped to identify positions of overshooting areas. In addition, because the NDCI values near a typhoon eye were rather significant, if a typhoon eye was formed, the NDCI cross-section analysis could help to confirm its position. When the center of a typhoon was covered by the high Anvils and Cirrus Layers, it could still be found qualitatively through the two-dimensional analysis. Keywords:Typhoon, Satellite imagery, Normalized Difference Convection Index

Monitoring global vegetation using Nimbus-7 37 GHz data - Some empirical relations

NASA Technical Reports Server (NTRS)

Choudhury, B. J.; Tucker, C. J.

1987-01-01

The difference of the vertically and horizontally polarized brightness temperatures observed by the 37 GHz channel of the SMMR on board the Nimbus-7 satellite are correlated temporally with three indicators of vegetation density, namely the temporal variation of the atmospheric CO2 concentration at Mauna Loa (Hawaii), rainfall over the Sahel and the normalized difference vegetation index derived from the AVHRR on board the NOAA-7 satellite. SMMR 37 GHz and AVHRR provide complementary data sets for monitoring global vegetation, the 37 GHz data being more suitable for arid and semiarid regions as these data are more sensitive to changes in sparse vegetation. The 37-GHz data might be useful for understanding desertification and indexing Co2 exchange between the biosphere and the atmosphere.

Remotely-sensed detection of effects of extreme droughts on gross primary production

PubMed Central

Vicca, Sara; Balzarolo, Manuela; Filella, Iolanda; Granier, André; Herbst, Mathias; Knohl, Alexander; Longdoz, Bernard; Mund, Martina; Nagy, Zoltan; Pintér, Krisztina; Rambal, Serge; Verbesselt, Jan; Verger, Aleixandre; Zeileis, Achim; Zhang, Chao; Peñuelas, Josep

2016-01-01

Severe droughts strongly impact photosynthesis (GPP), and satellite imagery has yet to demonstrate its ability to detect drought effects. Especially changes in vegetation functioning when vegetation state remains unaltered (no browning or defoliation) pose a challenge to satellite-derived indicators. We evaluated the performance of different satellite indicators to detect strong drought effects on GPP in a beech forest in France (Hesse), where vegetation state remained largely unaffected while GPP decreased substantially. We compared the results with three additional sites: a Mediterranean holm oak forest (Puéchabon), a temperate beech forest (Hainich), and a semi-arid grassland (Bugacpuszta). In Hesse, a three-year reduction in GPP following drought was detected only by the Enhanced Vegetation Index (EVI). The Photochemical Reflectance Index (PRI) also detected this drought effect, but only after normalization for absorbed light. In Puéchabon normalized PRI outperformed the other indicators, while the short-term drought effect in Hainich was not detected by any tested indicator. In contrast, most indicators, but not PRI, captured the drought effects in Bugacpuszta. Hence, PRI improved detection of drought effects on GPP in forests and we propose that PRI normalized for absorbed light is considered in future algorithms to estimate GPP from space. PMID:27301671

The Application of Jason-Measurements to Estimate the Global Near Surface Ocean Circulation for Climate Research

NASA Technical Reports Server (NTRS)

Niiler, Pearn P.

2004-01-01

The scientific objective of this research program were to utilize drifter and satellite sea level data for the determination of time mean and time variable surface currents of the global ocean. To accomplish these tasks has required the processing of drifter data to include a wide variety of different configurations of drifters into a uniform format and to process the along track satellite altimeter data for computing the geostrophic current components normal to the track. These tasks were accomplished, which resulted in an increase of drifter data by about 40% and the development of new algorithms for obtaining satellite derived geostrophic velocity data that was consistent with the drifter observations of geostrophic time-variable currents. The methodologies and the research results using these methodologies were reported in the publications listed in this paper.

(How) Can We Use Satellite Data to Estimate Effects of Extreme Drought on Photosynthesis?

NASA Astrophysics Data System (ADS)

Vicca, S.; Balzarolo, M.; Filella, I.; Granier, A.; Herbst, M.; Knohl, A.; Longdoz, B.; Mund, M.; Nagy, Z.; Pintér, K.; Rambal, S.; Verbesselt, J.; Verger, A.; Zeileis, A.; Zhang, C.; Penuelas, J.

2017-12-01

Severe droughts can strongly impact photosynthesis (GPP), but the tool best suited for large-scale and long-term monitoring, satellite imagery, has yet to prove its ability to detect drought effects on GPP. Especially changes in vegetation functioning when vegetation state remains unaltered (no browning or defoliation) pose a challenge to satellite-derived indicators. We evaluated the performance of different satellite indicators to detect effect of a strong drought (that started during the European heatwave of 2003) on GPP in a beech forest in France (Hesse). While vegetation state remained largely unaffected by the drought, Eddy Covariance data revealed a substantial decrease in GPP and GPP recovered only after about three years. This three-year reduction in GPP was, however not detected by severaly commonly used reflectance indices (like NDVI and FAPAR) or by MODIS GPP product. Only he Enhanced Vegetation Index (EVI) and the Photochemical Reflectance Index (PRI) detected the drought effect, but the PRI only after normalization for absorbed light. These results were compared to a two other forests where a severe drought event had affected GPP and these data confirmed that especially the PRI normalized for absorbed light provides useful information about vegetation functioning that is not captured by other remote sensing indicators under test.

Statistical Evaluation of VIIRS Ocean Color Products

NASA Astrophysics Data System (ADS)

Mikelsons, K.; Wang, M.; Jiang, L.

2016-02-01

Evaluation and validation of satellite-derived ocean color products is a complicated task, which often relies on precise in-situ measurements for satellite data quality assessment. However, in-situ measurements are only available in comparatively few locations, expensive, and not for all times. In the open ocean, the variability in spatial and temporal scales is longer, and the water conditions are generally more stable. We use this fact to perform extensive statistical evaluations of consistency for ocean color retrievals based on comparison of retrieved data at different times, and corresponding to various retrieval parameters. We have used the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system for ocean color product data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS). We show the results for statistical dependence of normalized water-leaving radiance spectra with respect to various parameters of retrieval geometry, such as solar- and sensor-zenith angles, as well as physical variables, such as wind speed, air pressure, ozone amount, water vapor, etc. In most cases, the results show consistent retrievals within the relevant range of retrieval parameters, showing a good performance with the MSL12 in the open ocean. The results also yield the upper bounds of solar- and sensor-zenith angles for reliable ocean color retrievals, and also show a slight increase of VIIRS-derived normalized water-leaving radiances with wind speed and water vapor concentration.

The celestial mechanics approach: application to data of the GRACE mission

NASA Astrophysics Data System (ADS)

Beutler, Gerhard; Jäggi, Adrian; Mervart, Leoš; Meyer, Ulrich

2010-11-01

The celestial mechanics approach (CMA) has its roots in the Bernese GPS software and was extensively used for determining the orbits of high-orbiting satellites. The CMA was extended to determine the orbits of Low Earth Orbiting satellites (LEOs) equipped with GPS receivers and of constellations of LEOs equipped in addition with inter-satellite links. In recent years the CMA was further developed and used for gravity field determination. The CMA was developed by the Astronomical Institute of the University of Bern (AIUB). The CMA is presented from the theoretical perspective in (Beutler et al. 2010). The key elements of the CMA are illustrated here using data from 50 days of GPS, K-Band, and accelerometer observations gathered by the Gravity Recovery And Climate Experiment (GRACE) mission in 2007. We study in particular the impact of (1) analyzing different observables [Global Positioning System (GPS) observations only, inter-satellite measurements only], (2) analyzing a combination of observations of different types on the level of the normal equation systems (NEQs), (3) using accelerometer data, (4) different orbit parametrizations (short-arc, reduced-dynamic) by imposing different constraints on the stochastic orbit parameters, and (5) using either the inter-satellite ranges or their time derivatives. The so-called GRACE baseline, i.e., the achievable accuracy of the GRACE gravity field for a particular solution strategy, is established for the CMA.

Satellite Ocean-Color Validation Using Ships of Opportunity. Chapter 5

NASA Technical Reports Server (NTRS)

Frouin, Robert; Cutchin, David L.; Gross-Colzy, Lydwine; Poteau, Antoine; Deschamps, Pierre-Yves

2003-01-01

The investigation s main objective is to collect from platforms of opportunity (merchant ships, research vessels) concomitant normalized water-leaving radiance and aerosol optical thickness data over the world s oceans. A global, long-term data set of these variables is needed to verify whether satellite retrievals of normalized water-leaving radiance are within acceptable error limits and, eventually, to adjust atmospheric correction schemes. To achieve this objective, volunteer officers, technicians, and scientists onboard the selected ships collect data from portable SIMBAD and Advanced SIMBAD (SIMBADA) radiometers. These instruments are specifically designed for evaluation of satellite-derived ocean color. They measure radiance in spectral bands typical of ocean-color sensors. The SIMBAD version measures in 5 spectral bands centered at 443, 490, 560, 670, and 870 nm, and the Advanced SIMBAD version in 11 spectral bands centered at 350, 380, 412, 443, 490, 510, 565, 620, 670, 750, and 870 nm. Aerosol optical thickness is obtained by viewing the sun disk like a classic sun photometer. Normalized water-leaving radiance, or marine reflectance, is obtained by viewing the ocean surface through a vertical polarizer in a specific geometry (nadir angle of 45o and relative azimuth angle of 135deg) to minimize direct sun glint and reflected sky radiation. The SIMBAD and SIMBADA data, after proper quality control and processing, are delivered to the SIMBIOS project office for inclusion in the SeaBASS archive. They complement data collected in a similar way by the Laboratoire d'Optique Atmospherique of the University of Lille, France. The SIMBAD and SIMBADA data are used to check the radiometric calibration of satellite ocean-color sensors after launch and to evaluate derived ocean-color variables (i.e., normalized water-leaving radiance, aerosol optical thickness, and aerosol type). Analysis of the SIMBAD and SIMBADA data provides information on the accuracy of satellite retrievals of normalized water-leaving radiance, an understanding of the discrepancies between satellite and in situ data, and algorithms that reduce the discrepancies, contributing to more accurate and consistent global ocean color data sets.

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa

PubMed Central

2012-01-01

Introduction The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping. Methods A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST). Results The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points. Conclusions Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming. PMID:22443452

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa.

PubMed

Dambach, Peter; Machault, Vanessa; Lacaux, Jean-Pierre; Vignolles, Cécile; Sié, Ali; Sauerborn, Rainer

2012-03-23

The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping. A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST). The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points. Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming. © 2012 Dambach et al; licensee BioMed Central Ltd.

NOAA AVHRR and its uses for rainfall and evapotranspiration monitoring

NASA Technical Reports Server (NTRS)

Kerr, Yann H.; Imbernon, J.; Dedieu, G.; Hautecoeur, O.; Lagouarde, J. P.

1989-01-01

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) Global Vegetation Indices (GVI) were used during the 1986 rainy season (June-September) over Senegal to monitor rainfall. The satellite data were used in conjunction with ground-based measurements so as to derive empirical relationships between rainfall and GVI. The regression obtained was then used to map the total rainfall corresponding to the growing season, yielding good results. Normalized Difference Vegetation Indices (NDVI) derived from High Resolution Picture Transmission (HRPT) data were also compared with actual evapotranspiration (ET) data and proved to be closely correlated with it with a time lapse of 20 days.

Identifying Hail Signatures in Satellite Imagery from the 9-10 August 2011 Severe Weather Event

NASA Technical Reports Server (NTRS)

Dryden, Rachel L.; Molthan, Andrew L.; Cole, Tony A.; Bell, Jordan

2014-01-01

Severe thunderstorms can produce large hail that causes property damage, livestock fatalities, and crop failure. However, detailed storm surveys of hail damage conducted by the National Weather Service (NWS) are not required. Current gaps also exist between Storm Prediction Center (SPC) hail damage estimates and crop-insurance payouts. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites can be used to support NWS damage assessments, particularly to crops during the growing season. The two-day severe weather event across western Nebraska and central Kansas during 9-10 August 2011 offers a case study for investigating hail damage signatures by examining changes in Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery. By analyzing hail damage swaths in satellite imagery, potential economic losses due to crop damage can be quantified and further improve the estimation of weather impacts on agriculture without significantly increasing manpower requirements.

Digital image classification approach for estimating forest clearing and regrowth rates and trends

NASA Technical Reports Server (NTRS)

Sader, Steven A.

1987-01-01

A technique is presented to monitor vegetation changes for a selected study area in Costa Rica. A normalized difference vegetation index was computed for three dates of Landsat satellite data and a modified parallelipiped classifier was employed to generate a multitemporal greenness image representing all three dates. A second-generation image was created by partitioning the intensity levels at each date into high, medium, and low and thereby reducing the number of classes to 21. A sampling technique was applied to describe forest and other land cover change occurring between time periods based on interpretation of aerial photography that closely matched the dates of satellite acquisition. Comparison of the Landsat-derived classes with the photo-interpreted sample areas can provide a basis for evaluating the satellite monitoring technique and the accuracy of estimating forest clearing and regrowth rates and trends.

Validation of satellite-retrieved MBL cloud properties using DOE ARM AMF measurements at the Azores

NASA Astrophysics Data System (ADS)

Xi, B.; Dong, X.; Minnis, P.; Sun-Mack, S.

2013-05-01

Marine Boundary Layer (MBL) cloud properties derived for the Clouds and the Earth's Radiant Energy System (CERES) Project using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with observations taken at the Atmospheric Radiation Measurement (ARM) AMF AZORES site from June 2009 through December 2010. Retrievals from ARM surface-based data were averaged over a 1-hour interval centered at the time of each satellite overpass, and the CERES-MODIS Ed4 cloud properties were averaged within a 30-km x 30-km box centered on the ARM AZORES site. Two datasets were analyzed: all of the single-layered unbroken decks (SL) and those cases without temperature inversions. The CERES-MODIS cloud top/base heights were determined from cloud top/base temperature by using a lapse rate method normalized to the 24-h mean surface air temperature. The preliminary results show: for all SL MBL at daytime, they are, on average, 0.148 km (cloud top) and 0.087 km (cloud base) higher than the ARM radar-lidar observed cloud top and base, respectively. At nighttime, they are 0.446 km (cloud top) and 0.334 km (cloud base). For those cases without temperature inversions, the comparisons are close to their SL counterparts. For cloud temperatures, the MODIS-derived cloud-top and -base temperatures are 1.6 K lower and 0.4 K higher than the surface values with correlations of 0.92 during daytime. At nighttime, the differences are slightly larger and correlations are lower than daytime comparisons. Variations in the height difference are mainly caused by uncertainties in the surface air temperatures and lapse rates. Based on a total of 61 daytime and 87 nighttime samples (ALL SL cases), the temperature inversion layers occur about 72% during daytime and 83% during nighttime. The difference of surface-observed lapse rate and the satellite derived lapse rate can be 1.6 K/km for daytime and 3.3K/km for nighttime. From these lapse rates, we can further analyze the surface air temperature difference that used to calculate these lapse rate, which are ~3K difference between surface-observed and the satellite derived during the daytime and 5.1 K during nighttime. Further studies of the cause of the temperature inversions that may help the cloud heights retrievals by satellite. The preliminary comparisons in MBL microphysical properties have shown that the averaged CERES-MODIS derived MBL cloud-droplet effective radius is only 1.5 μm larger than ARM retrieval (13.2 μm), and LWP values are also very close to each other (112 vs. 124 gm-2) with a relative large difference in optical depth (10.6 vs. 14.4).

3D-information fusion from very high resolution satellite sensors

NASA Astrophysics Data System (ADS)

Krauss, T.; d'Angelo, P.; Kuschk, G.; Tian, J.; Partovi, T.

2015-04-01

In this paper we show the pre-processing and potential for environmental applications of very high resolution (VHR) satellite stereo imagery like these from WorldView-2 or Pl'eiades with ground sampling distances (GSD) of half a metre to a metre. To process such data first a dense digital surface model (DSM) has to be generated. Afterwards from this a digital terrain model (DTM) representing the ground and a so called normalized digital elevation model (nDEM) representing off-ground objects are derived. Combining these elevation based data with a spectral classification allows detection and extraction of objects from the satellite scenes. Beside the object extraction also the DSM and DTM can directly be used for simulation and monitoring of environmental issues. Examples are the simulation of floodings, building-volume and people estimation, simulation of noise from roads, wave-propagation for cellphones, wind and light for estimating renewable energy sources, 3D change detection, earthquake preparedness and crisis relief, urban development and sprawl of informal settlements and much more. Also outside of urban areas volume information brings literally a new dimension to earth oberservation tasks like the volume estimations of forests and illegal logging, volume of (illegal) open pit mining activities, estimation of flooding or tsunami risks, dike planning, etc. In this paper we present the preprocessing from the original level-1 satellite data to digital surface models (DSMs), corresponding VHR ortho images and derived digital terrain models (DTMs). From these components we present how a monitoring and decision fusion based 3D change detection can be realized by using different acquisitions. The results are analyzed and assessed to derive quality parameters for the presented method. Finally the usability of 3D information fusion from VHR satellite imagery is discussed and evaluated.

Satellite-derived land covers for runoff estimation using SCS-CN method in Chen-You-Lan Watershed, Taiwan

NASA Astrophysics Data System (ADS)

Zhang, Wen-Yan; Lin, Chao-Yuan

2017-04-01

The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.

The relationship between satellite-derived indices and species diversity across African savanna ecosystems

NASA Astrophysics Data System (ADS)

Mapfumo, Ratidzo B.; Murwira, Amon; Masocha, Mhosisi; Andriani, R.

2016-10-01

The ability to use remotely sensed diversity is important for the management of ecosystems at large spatial extents. However, to achieve this, there is still need to develop robust methods and approaches that enable large-scale mapping of species diversity. In this study, we tested the relationship between species diversity measured in situ with the Normalized Difference Vegetation Index (NDVI) and the Coefficient of Variation in the NDVI (CVNDVI) derived from high and medium spatial resolution satellite data at dry, wet and coastal savanna woodlands. We further tested the effect of logging on NDVI along the transects and between transects as disturbance may be a mechanism driving the patterns observed. Overall, the results of this study suggest that high tree species diversity is associated with low and high NDVI and at intermediate levels is associated with low tree species diversity and NDVI. High tree species diversity is associated with high CVNDVI and vice versa and at intermediate levels is associated with high tree species diversity and CVNDVI.

Estimation of the Earth's gravity field by combining normal equation matrices from GRACE and SLR

NASA Astrophysics Data System (ADS)

Haberkorn, Christoph; Bloßfeld, Mathis; Bouman, Johannes

2014-05-01

Since 2002, GRACE observes the Earth's gravity field with a spatial resolution up to 150 km. The main goal of this mission is the determination of temporal variations in the Earth's gravity field to detect mass displacements. The GRACE mission consists of two identical satellites, which observe the range along the line of sight of both satellites. GRACE observations can be linked with the Earth's gravitational potential, which is expressed in terms of spherical harmonics for global solutions. However, the estimation of low degree coefficients is difficult with GRACE. In contrast to gravity field missions, which observe the gravity field with high spectral resolution, SLR data allow to estimate the lower degree coefficients. Therefore, the coefficient C20 is often replaced by a value derived from Satellite Laser Ranging (SLR). Instead of replacing C20, it can be determined consistently by a combined estimation using GRACE and SLR data. We compute monthly normal equation (NEQ) matrices for GRACE and SLR. Coefficients from monthly GRACE gravity field models of different institutions (Center for Space Research (CSR), USA, Geoforschungszentrum Potsdam (GFZ), Germany and Jet Propulsion Laboratory (JPL), USA) and coefficients from monthly gravity field models of our SLR processing are then combined using the NEQ matrices from both techniques. We will evaluate several test scenarios with gravity field models from different institutions and with different set ups for the SLR NEQ matrices. The effect of the combination on the estimated gravity field will be analysed and presented.

Model improvements and validation of TerraSAR-X precise orbit determination

NASA Astrophysics Data System (ADS)

Hackel, S.; Montenbruck, O.; Steigenberger, P.; Balss, U.; Gisinger, C.; Eineder, M.

2017-05-01

The radar imaging satellite mission TerraSAR-X requires precisely determined satellite orbits for validating geodetic remote sensing techniques. Since the achieved quality of the operationally derived, reduced-dynamic (RD) orbit solutions limits the capabilities of the synthetic aperture radar (SAR) validation, an effort is made to improve the estimated orbit solutions. This paper discusses the benefits of refined dynamical models on orbit accuracy as well as estimated empirical accelerations and compares different dynamic models in a RD orbit determination. Modeling aspects discussed in the paper include the use of a macro-model for drag and radiation pressure computation, the use of high-quality atmospheric density and wind models as well as the benefit of high-fidelity gravity and ocean tide models. The Sun-synchronous dusk-dawn orbit geometry of TerraSAR-X results in a particular high correlation of solar radiation pressure modeling and estimated normal-direction positions. Furthermore, this mission offers a unique suite of independent sensors for orbit validation. Several parameters serve as quality indicators for the estimated satellite orbit solutions. These include the magnitude of the estimated empirical accelerations, satellite laser ranging (SLR) residuals, and SLR-based orbit corrections. Moreover, the radargrammetric distance measurements of the SAR instrument are selected for assessing the quality of the orbit solutions and compared to the SLR analysis. The use of high-fidelity satellite dynamics models in the RD approach is shown to clearly improve the orbit quality compared to simplified models and loosely constrained empirical accelerations. The estimated empirical accelerations are substantially reduced by 30% in tangential direction when working with the refined dynamical models. Likewise the SLR residuals are reduced from -3 ± 17 to 2 ± 13 mm, and the SLR-derived normal-direction position corrections are reduced from 15 to 6 mm, obtained from the 2012-2014 period. The radar range bias is reduced from -10.3 to -6.1 mm with the updated orbit solutions, which coincides with the reduced standard deviation of the SLR residuals. The improvements are mainly driven by the satellite macro-model for the purpose of solar radiation pressure modeling, improved atmospheric density models, and the use of state-of-the-art gravity field models.

Investigation the Behavior of Modis Ocean Color Products Under the 2008 Red Tide in the Eastern Persian Gulf

NASA Astrophysics Data System (ADS)

Ghanea, M.; Moradi, M.; Kabiri, K.

2015-12-01

Biophysical properties of water undergo serious variations under red tide (RT) outbreak. During RT conditions, algal blooms spread out in the estuarine, marine and fresh waters due to different triggering factors such as nutrient loading, marine currents, and monsoonal winds. The Persian Gulf (PG) was a talent region subjected to different RTs in recent decade. A massive RT started from the Strait of Hormuz in October 2008 and extended towards the northern parts of the PG covering more than 1200 km of coastlines. The bloom of microorganism C. Polykrikoides was the main specie that generated large fish mortalities, and hampered marine industries, and water desalination appliances. Ocean color satellite data have many advantages to monitor and alarm RT occurrences, such as wide and continuous extent, short time of imagery, high accessibility, and appropriate estimation of ocean color parameters. Since 1999, MODerate Resolution Imaging Spectroradiometer (MODIS) satellite sensor has estimated satellite derived chlorophyll-a (Chl-a), normalized fluorescence line height (nFLH), and diffuse attenuation coefficient at 490nm (kd490). It provides a capability to study the behavior of these parameters during RT and normal conditions. This study monitors variations in satellite derived Chl-a, nFLH, and kd490 under both RT and normal conditions of the PG between 2002 and 2008. Up to now, daily and monthly variations in these products were no synchronously investigated under RT conditions in the PG. In doing so, the MODIS L1B products were provided from NASA data archive. They were corrected for Rayleigh scattering and gaseous absorption, and atmospheric interference in turbid coastal waters, and then converted to level 2 data. In addition, Enhanced Red Green Blue (ERGB) image was used to illustrate better water variations. ERGB image was built with three normalized leaving water radiance between 443 to 560nm. All the above data processes were applied by SeaDAS 7 software package. The Strait of Hormuz was selected as the study area in the eastern part of the PG. Images including high cloud coverage (>50%) over the study area were filtered out. The classification maps of the above products were shown during RT and normal periods. Monthly variations of mentioned products were calculated for the dates before, during, and after RT appearance. The results were demonstrated as time-series diagrams. All the above calculations and presentations were performed in Matlab 7 software package. The results show that MODIS Chl-a, nFLH, and kd490 increased during the 2008 RT. Based on the feedback of these parameters under RT conditions, hybrid ocean color index (HOCI) is defined. HOCI is able to display better water variations during RT outbreak. High values of HOCI show RT affected areas.

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.

Spectroradiometric calibration of the Thematic Mapper and Multispectral Scanner system. [White Sands, New Mexico

NASA Technical Reports Server (NTRS)

Palmer, J. M. (Principal Investigator); Slater, P. N.

1984-01-01

The newly built Caste spectropolarimeters gave satisfactory performance during tests in the solar radiometer and helicopter modes. A bandwidth normalization technique based on analysis of the moments of the spectral responsivity curves was used to analyze the spectral bands of the MSS and TM subsystems of LANDSAT 4 and 5 satellites. Results include the effective wavelength, the bandpass, the wavelength limits, and the normalized responsivity for each spectral channel. Temperature coefficients for TM PF channel 6 were also derived. The moments normalization method used yields sensor parameters whose derivation is independent of source characteristics (i.e., incident solar spectral irradiance, atmospheric transmittance, or ground reflectance). The errors expected using these parameters are lower than those expected using other normalization methods.

Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra

PubMed Central

Huang, Weijiao; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Shi, Jingjing

2013-01-01

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from −12.67% to 36.30% for the red reflectance, −8.52% to −0.23% for the NIR reflectance, and −9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors. PMID:24287529

Comparability of red/near-infrared reflectance and NDVI based on the spectral response function between MODIS and 30 other satellite sensors using rice canopy spectra.

PubMed

Huang, Weijiao; Huang, Jingfeng; Wang, Xiuzhen; Wang, Fumin; Shi, Jingjing

2013-11-26

Long-term monitoring of regional and global environment changes often depends on the combined use of multi-source sensor data. The most widely used vegetation index is the normalized difference vegetation index (NDVI), which is a function of the red and near-infrared (NIR) spectral bands. The reflectance and NDVI data sets derived from different satellite sensor systems will not be directly comparable due to different spectral response functions (SRF), which has been recognized as one of the most important sources of uncertainty in the multi-sensor data analysis. This study quantified the influence of SRFs on the red and NIR reflectances and NDVI derived from 31 Earth observation satellite sensors. For this purpose, spectroradiometric measurements were performed for paddy rice grown under varied nitrogen levels and at different growth stages. The rice canopy reflectances were convoluted with the spectral response functions of various satellite instruments to simulate sensor-specific reflectances in the red and NIR channels. NDVI values were then calculated using the simulated red and NIR reflectances. The results showed that as compared to the Terra MODIS, the mean relative percentage difference (RPD) ranged from -12.67% to 36.30% for the red reflectance, -8.52% to -0.23% for the NIR reflectance, and -9.32% to 3.10% for the NDVI. The mean absolute percentage difference (APD) compared to the Terra MODIS ranged from 1.28% to 36.30% for the red reflectance, 0.84% to 8.71% for the NIR reflectance, and 0.59% to 9.32% for the NDVI. The lowest APD between MODIS and the other 30 satellite sensors was observed for Landsat5 TM for the red reflectance, CBERS02B CCD for the NIR reflectance and Landsat4 TM for the NDVI. In addition, the largest APD between MODIS and the other 30 satellite sensors was observed for IKONOS for the red reflectance, AVHRR1 onboard NOAA8 for the NIR reflectance and IKONOS for the NDVI. The results also indicated that AVHRRs onboard NOAA7-17 showed higher differences than did the other sensors with respect to MODIS. A series of optimum models were presented for remote sensing data assimilation between MODIS and other sensors.

Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors

NASA Astrophysics Data System (ADS)

Muench, R.; Jones, M.; Herndon, K. E.; Bell, J. R.; Anderson, E. R.; Markert, K. N.; Molthan, A.; Adams, E. C.; Shultz, L.; Cherrington, E. A.; Flores, A.; Lucey, R.; Munroe, T.; Layne, G.; Pulla, S. T.; Weigel, A. M.; Tondapu, G.

2017-12-01

On August 25, 2017, Hurricane Harvey made landfall between Port Aransas and Port O'Connor, Texas, bringing with it unprecedented amounts of rainfall and flooding. In times of natural disasters of this nature, emergency responders require timely and accurate information about the hazard in order to assess and plan for disaster response. Due to the extreme flooding impacts associated with Hurricane Harvey, delineations of water extent were crucial to inform resource deployment. Through the USGS's Hazards Data Distribution System, government and commercial vendors were able to acquire and distribute various satellite imagery to analysts to create value-added products that can be used by these emergency responders. Rapid-response water extent maps were created through a collaborative multi-organization and multi-sensor approach. One team of researchers created Synthetic Aperture Radar (SAR) water extent maps using modified Copernicus Sentinel data (2017), processed by ESA. This group used backscatter images, pre-processed by the Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (HyP3), to identify and apply a threshold to identify water in the image. Quality control was conducted by manually examining the image and correcting for potential errors. Another group of researchers and graduate student volunteers derived water masks from high resolution DigitalGlobe and SPOT images. Through a system of standardized image processing, quality control measures, and communication channels the team provided timely and fairly accurate water extent maps to support a larger NASA Disasters Program response. The optical imagery was processed through a combination of various band thresholds by using Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and cloud masking. Several aspects of the pre-processing and image access were run on internal servers to expedite the provision of images to analysts who could focus on manipulating thresholds and quality control checks for maximum accuracy within the time constraints. The combined results of the radar- and optical-derived value-added products through the coordination of multiple organizations provided timely information for emergency response and recovery efforts

Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors

NASA Technical Reports Server (NTRS)

Muench, Rebekke; Jones, Madeline; Herndon, Kelsey; Schultz, Lori; Bell, Jordan; Anderson, Eric; Markert, Kel; Molthan, Andrew; Adams, Emily; Cherrington, Emil;

Multi-satellites normalization of the FengYun-2s visible detectors by the MVP method

NASA Astrophysics Data System (ADS)

Li, Yuan; Rong, Zhi-guo; Zhang, Li-jun; Sun, Ling; Xu, Na

2013-08-01

After January 13, 2012, FY-2F had successfully launched, the total number of the in orbit operating FengYun-2 geostationary meteorological satellites reached three. For accurate and efficient application of multi-satellite observation data, the study of the multi-satellites normalization of the visible detector was urgent. The method required to be non-rely on the in orbit calibration. So as to validate the calibration results before and after the launch; calculate day updating surface bidirectional reflectance distribution function (BRDF); at the same time track the long-term decay phenomenon of the detector's linearity and responsivity. By research of the typical BRDF model, the normalization method was designed. Which could effectively solute the interference of surface directional reflectance characteristics, non-rely on visible detector in orbit calibration. That was the Median Vertical Plane (MVP) method. The MVP method was based on the symmetry of principal plane, which were the directional reflective properties of the general surface targets. Two geostationary satellites were taken as the endpoint of a segment, targets on the intersecting line of the segment's MVP and the earth surface could be used as a normalization reference target (NRT). Observation on the NRT by two satellites at the moment the sun passing through the MVP brought the same observation zenith, solar zenith, and opposite relative direction angle. At that time, the linear regression coefficients of the satellite output data were the required normalization coefficients. The normalization coefficients between FY-2D, FY-2E and FY-2F were calculated, and the self-test method of the normalized results was designed and realized. The results showed the differences of the responsivity between satellites could up to 10.1%(FY-2E to FY-2F); the differences of the output reflectance calculated by the broadcast calibration look-up table could up to 21.1%(FY-2D to FY-2F); the differences of the output reflectance from FY-2D and FY-2E calculated by the site experiment results reduced to 2.9%(13.6% when using the broadcast table). The normalized relative error was also calculated by the self-test method, which was less than 0.2%.

Reconciling Satellite-Derived Atmospheric Properties with Fine-Resolution Land Imagery: Insights for Atmospheric Correction

NASA Technical Reports Server (NTRS)

Zelazowski, Przemyslaw; Sayer, Andrew M.; Thomas, Gareth E; Grainger, Roy G.

2011-01-01

This paper investigates to what extent satellite measurements of atmospheric properties can be reconciled with fine-resolution land imagery, in order to improve the estimates of surface reflectance through physically based atmospheric correction. The analysis deals with mountainous area (Landsat scene of Peruvian Amazon/Andes, 72 E and 13 S), where the atmosphere is highly variable. Data from satellite sensors were used for characterization of the key atmospheric constituents: total water vapor (TWV), aerosol optical depth (AOD), and total ozone. Constituent time series revealed the season-dependent mean state of the atmosphere and its variability. Discrepancies between AOD from the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS) highlighted substantial uncertainty of atmospheric aerosol properties. The distribution of TWV and AOD over a Landsat scene was found to be exponentially related to ground elevation (mean R(sup 2) of 0.82 and 0.29, respectively). In consequence, the atmosphere-induced and seasonally varying bias of the top-of-atmosphere signal was also elevation dependent (e.g., mean Normalized Difference Vegetation Index bias at 500 m was 0.06 and at 4000 m was 0.01). We demonstrate that satellite measurements of key atmospheric constituents can be downscaled and gap filled with the proposed "background + anomalies" approach, to allow for a better compatibility with fine-resolution land surface imagery. Older images (i.e., predating the MODIS/ATSR era), without coincident atmospheric data, can be corrected using climatologies derived from time series of satellite retrievals. Averaging such climatologies over space compromises the quality of correction result to a much greater degree than averaging them over time. We conclude that the quality of both recent and older fine-resolution land surface imagery can be improved with satellite-based atmospheric data acquired to date.

Relations between soil moisture and satellite vegetation indices in the U.S. Corn Belt

USGS Publications Warehouse

Adegoke, Jimmy O.; Carleton, A.M.

2002-01-01

Satellite-derived vegetation indices extracted over locations representative of midwestern U.S. cropland and forest for the period 1990–94 are analyzed to determine the sensitivity of the indices to neutron probe soil moisture measurements of the Illinois Climate Network (ICN). The deseasoned (i.e., departures from multiyear mean annual cycle) soil moisture measurements are shown to be weakly correlated with the deseasoned full resolution (1 km × 1 km) normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) data over both land cover types. The association, measured by the Pearson-moment-correlation coefficient, is stronger over forest than over cropland during the growing season (April–September). The correlations improve successively when the NDVI and FVC pixel data are aggregated to 3 km × 3 km, 5 km × 5 km, and 7 km × 7 km areas. The improved correlations are partly explained by the reduction in satellite navigation errors as spatial aggregation occurs, as well as the apparent scale dependence of the NDVI–soil moisture association. Similarly, stronger relations are obtained with soil moisture data that are lagged by up to 8 weeks with respect to the vegetation indices, implying that soil moisture may be a useful predictor of warm season satellite-derived vegetation conditions. This study suggests that a “long-term” memory of several weeks is present in the near-surface hydrological characteristics, especially soil water content, of the Midwest Corn Belt. The memory is integrated into the satellite vegetation indices and may be useful for predicting crop yield estimates and surface temperature anomalies.

Efficient Maize and Sunflower Multi-year Mapping with NDVI Time Series of HJ-1A/1B in Hetao Irrigation District of Inner Mongolia, China

NASA Astrophysics Data System (ADS)

Yu, B.; Shang, S.

2016-12-01

Food shortage is one of the major challenges that human beings are facing. It is urgent to improve the monitoring of the plantation and distribution of the main crops to solve the following economic and social issues. Recently, with the extensive use of remote sensing satellite data, it has provided favorable conditions for crop identification in large irrigation district with complex planting structure. Difference of different crop phenology is the main basis for crop identification, and the normalized difference vegetation index (NDVI) time-series could better delineate crop phenology cycle. Therefore, the key of crop identification is to obtain high quality NDVI time-series. MODIS and Landsat TM satellite images are the most frequently used, however, neither of them could guarantee high temporal and spatial resolutions at once. Accordingly, this paper makes use of NDVI time-series extracted from China Environment Satellites data, which has two-day-repeat temporal and 30m spatial resolutions. The NDVI time-series are fitted with an asymmetric logistic curve, the fitting effect is good and the correlation coefficient is greater than 0.9. The phonological parameters are derived from NDVI fitting curves, and crop identification is carried out by different relation ellipses between NDVI and its phonological parameters of different crops. This paper takes Hetao Irrigation District of Inner Mongolia as an example, to identify multi-year maize and sunflower in the district, and the identification result is good. Compared with the official statistics, the relative errors are both lower than 5%. The results show that the NDVI time-series dataset derived from HJ-1A/1B CCD could delineate the crop phenology cycle accurately and demonstrate its application in crop identification in irrigated district.

Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

USGS Publications Warehouse

Tian, Y.Q.; Yu, Q.; Zimmerman, M.J.; Flint, S.; Waldron, M.C.

2010-01-01

This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water-quality standards. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near-infrared (NIR)-Green Angle Index (NGAI) and normalized water absorption depth (DH), at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral-derived NDVI. The IKONOS-based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the DH. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High-resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. Interpretation of biophysical parameters derived from high-resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments. ?? 2010 Blackwell Publishing Ltd.

The remote sensing of ocean primary productivity - Use of a new data compilation to test satellite algorithms

NASA Technical Reports Server (NTRS)

Balch, William; Evans, Robert; Brown, Jim; Feldman, Gene; Mcclain, Charles; Esaias, Wayne

1992-01-01

Global pigment and primary productivity algorithms based on a new data compilation of over 12,000 stations occupied mostly in the Northern Hemisphere, from the late 1950s to 1988, were tested. The results showed high variability of the fraction of total pigment contributed by chlorophyll, which is required for subsequent predictions of primary productivity. Two models, which predict pigment concentration normalized to an attenuation length of euphotic depth, were checked against 2,800 vertical profiles of pigments. Phaeopigments consistently showed maxima at about one optical depth below the chlorophyll maxima. CZCS data coincident with the sea truth data were also checked. A regression of satellite-derived pigment vs ship-derived pigment had a coefficient of determination. The satellite underestimated the true pigment concentration in mesotrophic and oligotrophic waters and overestimated the pigment concentration in eutrophic waters. The error in the satellite estimate showed no trends with time between 1978 and 1986.

The relationship between wind vector and normalized radar cross section used to derive Seasat-A Satellite Scatterometer winds

NASA Technical Reports Server (NTRS)

Schroeder, L. C.; Jones, W. L.; Boggs, D. H.; Halberstam, I. M.; Dome, G.; Pierson, W. J.; Wentz, F. J.

1982-01-01

The Seasat-A Satellite Scatterometer (SASS) ocean normalized radar cross section (NRCS) dependence on the 19.5-m neutral stability wind vector may be specified as a function of radar incidence angle, the angle between wind direction and radar azimuth, and the neutral stability wind speed expressed in m/sec at a height of 19.5 m. An account is given of the development of models both expressing this relationship and providing the basis of inversion of NRCS to SASS winds, from initially aircraft scatterometer measurement-based forms to three Seasat field-validation experiments which furnish model NRCS versus surface windspeed data for comparison with SASS data.

Monitoring Cyanobacteria with Satellites Webinar

EPA Pesticide Factsheets

real-world satellite applications can quantify cyanobacterial harmful algal blooms and related water quality parameters. Provisional satellite derived cyanobacteria data and different software tools are available to state environmental and health agencies.

Scripps Ocean Modeling and Remote Sensing (SOMARS)

DTIC Science & Technology

1990-04-10

1990: Satellite derived estimates of the normal and tangential components of near-surface flow. Internat. J. Rem. Sens., submitted. Mr. Timothy ... Gallaudet - M.S,. in Oceanography/Applied Ocean Sciences (Naval student; not a terminal M.S. degree) Thesis Advisor: Topic will relate to AVIIRR analyses of

CD133+ cells derived from skeletal muscles of Duchenne muscular dystrophy patients have a compromised myogenic and muscle regenerative capability.

PubMed

Meng, Jinhong; Muntoni, Francesco; Morgan, Jennifer

2018-05-12

Cell-mediated gene therapy is a possible means to treat muscular dystrophies like Duchenne muscular dystrophy. Autologous patient stem cells can be genetically-corrected and transplanted back into the patient, without causing immunorejection problems. Regenerated muscle fibres derived from these cells will express the missing dystrophin protein, thus improving muscle function. CD133+ cells derived from normal human skeletal muscle contribute to regenerated muscle fibres and form muscle stem cells after their intra-muscular transplantation into an immunodeficient mouse model. But it is not known whether CD133+ cells derived from DMD patient muscles have compromised muscle regenerative function. To test this, we compared CD133+ cells derived from DMD and normal human muscles. DMD CD133+ cells had a reduced capacity to undergo myogenic differentiation in vitro compared with CD133+ cells derived from normal muscle. In contrast to CD133+ cells derived from normal human muscle, those derived from DMD muscle formed no satellite cells and gave rise to significantly fewer muscle fibres of donor origin, after their intra-muscular transplantation into an immunodeficient, non-dystrophic, mouse muscle. DMD CD133+ cells gave rise to more clones of smaller size and more clones that were less myogenic than did CD133+ cells derived from normal muscle. The heterogeneity of the progeny of CD133+ cells, combined with the reduced proliferation and myogenicity of DMD compared to normal CD133+ cells, may explain the reduced regenerative capacity of DMD CD133+ cells. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Integrating proximal and satellite optical data for the analysis of ecosystem carbon uptake and plant phenology at the European larch Specnet site

NASA Astrophysics Data System (ADS)

Galvagno, Marta; Gamon, John; Cremonese, Edoardo; Garrity, Steven; Huemmrich, K. Fred; Filippa, Gianluca; Morra di Cella, Umberto; Rossini, Micol

2017-04-01

Automated canopy-level optical sampling in tandem with ecosystem-atmosphere flux observations is continuously carried on at a variety of ecosystems through the Specnet network (http://specnet.info/). Specifically, 9 sites within US and Europe were selected since 2015, to investigate the use of novel NDVI and PRI low-cost sensors for the analysis of ecosystem functioning and phenology. Different plant functional types, such as grasslands, deciduous, and evergreen forests belong to the network, here we present specific data from the larch (Larix decidua Mill.) forest Italian site. Three automated NDVI and three automated PRI spectral reflectance sensors (Decagon Devices Inc.) were installed in 2015 on the top of the 20-meters eddy covariance tower, pointing toward the west, north, and east orientations. An additional system, composed by one NDVI and PRI system was installed to monitor the understory component. The objective of this analysis is the comparison between these in-situ inexpensive sensors, independent NDVI and PRI sensors (Skye Instruments) previously installed on the 20-meters tower and satellite-derived NDVI. Both MODIS and Sentinel NDVI data were used for the comparison. Moreover, the newly derived chlorophyll/carotenoid index (CCI, Gamon et al. 2016), computed as the normalized difference between the NDVI red band and PRI 532 nm band, was tested to estimate the seasonal pattern of daily Gross Primary Productivity (GPP) of the larch forest. Results showed that the seasonality of NDVI was comparable among in-situ sensors and satellite data, though orientation-specific differences were observed. Both NDVI and CCI tracked daily GPP, but with different sensitivity to its seasonality. Future analysis will be directed toward a comparison between this site-based results with the other sites within the Specnet network.

Spatial Downscaling of TRMM Precipitation using MODIS product in the Korean Peninsula

NASA Astrophysics Data System (ADS)

Cho, H.; Choi, M.

2013-12-01

Precipitation is a major driving force in the water cycle. But, it is difficult to provide spatially distributed precipitation data from isolated individual in situ. The Tropical Rainfall Monitoring Mission (TRMM) satellite can provide precipitation data with relatively coarse spatial resolution (0.25° scale) at daily basis. In order to overcome the coarse spatial resolution of TRMM precipitation products, we conducted a downscaling technique using a scaling parameter from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor. In this study, statistical relations between precipitation estimates derived from the TRMM satellite and the normalized difference vegetation index (NDVI) which is obtained from the MODIS sensor in TERRA satellite are found for different spatial scales on the Korean peninsula in northeast Asia. We obtain the downscaled precipitation mapping by regression equation between yearly TRMM precipitations values and annual average NDVI aggregating 1km to 25 degree. The downscaled precipitation is validated using time series of the ground measurements precipitation dataset provided by Korea Meteorological Organization (KMO) from 2002 to 2005. To improve the spatial downscaling of precipitation, we will conduct a study about correlation between precipitation and land surface temperature, perceptible water and other hydrological parameters.

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

USGS Publications Warehouse

Tadesse, Tsegaye; Senay, Gabriel B.; Berhan, Getachew; Regassa, Teshome; Beyene, Shimelis

2015-01-01

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia’s cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R2) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

NASA Astrophysics Data System (ADS)

Tadesse, Tsegaye; Senay, Gabriel B.; Berhan, Getachew; Regassa, Teshome; Beyene, Shimelis

2015-08-01

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia's cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation (R2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships (R2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation (R2) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.

[Research of Identify Spatial Object Using Spectrum Analysis Technique].

PubMed

Song, Wei; Feng, Shi-qi; Shi, Jing; Xu, Rong; Wang, Gong-chang; Li, Bin-yu; Liu, Yu; Li, Shuang; Cao Rui; Cai, Hong-xing; Zhang, Xi-he; Tan, Yong

2015-06-01

The high precision scattering spectrum of spatial fragment with the minimum brightness of 4.2 and the resolution of 0.5 nm has been observed using spectrum detection technology on the ground. The obvious differences for different types of objects are obtained by the normalizing and discrete rate analysis of the spectral data. Each of normalized multi-frame scattering spectral line shape for rocket debris is identical. However, that is different for lapsed satellites. The discrete rate of the single frame spectrum of normalized space debris for rocket debris ranges from 0.978% to 3.067%, and the difference of oscillation and average value is small. The discrete rate for lapsed satellites ranges from 3.118 4% to 19.472 7%, and the difference of oscillation and average value relatively large. The reason is that the composition of rocket debris is single, while that of the lapsed satellites is complex. Therefore, the spectrum detection technology on the ground can be used to the classification of the spatial fragment.

Satellite Derived Volcanic Ash Product Inter-Comparison in Support to SCOPE-Nowcasting

NASA Astrophysics Data System (ADS)

Siddans, Richard; Thomas, Gareth; Pavolonis, Mike; Bojinski, Stephan

2016-04-01

In support of aeronautical meteorological services, WMO organized a satellite-based volcanic ash retrieval algorithm inter-comparison activity, to improve the consistency of quantitative volcanic ash products from satellites, under the Sustained, Coordinated Processing of Environmental Satellite Data for Nowcasting (SCOPEe Nowcasting) initiative (http:/ jwww.wmo.int/pagesjprogjsatjscopee nowcasting_en.php). The aims of the intercomparison were as follows: 1. Select cases (Sarychev Peak 2009, Eyjafyallajökull 2010, Grimsvötn 2011, Puyehue-Cordón Caulle 2011, Kirishimayama 2011, Kelut 2014), and quantify the differences between satellite-derived volcanic ash cloud properties derived from different techniques and sensors; 2. Establish a basic validation protocol for satellite-derived volcanic ash cloud properties; 3. Document the strengths and weaknesses of different remote sensing approaches as a function of satellite sensor; 4. Standardize the units and quality flags associated with volcanic cloud geophysical parameters; 5. Provide recommendations to Volcanic Ash Advisory Centers (VAACs) and other users on how to best to utilize quantitative satellite products in operations; 6. Create a "road map" for future volcanic ash related scientific developments and inter-comparison/validation activities that can also be applied to SO2 clouds and emergent volcanic clouds. Volcanic ash satellite remote sensing experts from operational and research organizations were encouraged to participate in the inter-comparison activity, to establish the plans for the inter-comparison and to submit data sets. RAL was contracted by EUMETSAT to perform a systematic inter-comparison of all submitted datasets and results were reported at the WMO International Volcanic Ash Inter-comparison Meeting to held on 29 June - 2 July 2015 in Madison, WI, USA (http:/ /cimss.ssec.wisc.edujmeetings/vol_ash14). 26 different data sets were submitted, from a range of passive imagers and spectrometers and these were inter-compared against each other and against validation data such as CALIPSO lidar, ground-based lidar and aircraft observations. Results of the comparison exercise will be presented together with the conclusions and recommendations arising from the activity.

Prediction, Assessment of the Rift Valley Fever Activity in East and Southern Africa 2006–2008 and Possible Vector Control Strategies

PubMed Central

Anyamba, Assaf; Linthicum, Kenneth J.; Small, Jennifer; Britch, Seth C.; Pak, Edwin; de La Rocque, Stephane; Formenty, Pierre; Hightower, Allen W.; Breiman, Robert F.; Chretien, Jean-Paul; Tucker, Compton J.; Schnabel, David; Sang, Rosemary; Haagsma, Karl; Latham, Mark; Lewandowski, Henry B.; Magdi, Salih Osman; Mohamed, Mohamed Ally; Nguku, Patrick M.; Reynes, Jean-Marc; Swanepoel, Robert

2010-01-01

Historical outbreaks of Rift Valley fever (RVF) since the early 1950s have been associated with cyclical patterns of the El Niño/Southern Oscillation (ENSO) phenomenon, which results in elevated and widespread rainfall over the RVF endemic areas of Africa. Using satellite measurements of global and regional elevated sea surface temperatures, elevated rainfall, and satellite derived-normalized difference vegetation index data, we predicted with lead times of 2–4 months areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa, Sudan, and Southern Africa at different time periods from September 2006 to March 2008. Predictions were confirmed by entomological field investigations of virus activity and by reported cases of RVF in human and livestock populations. This represents the first series of prospective predictions of RVF outbreaks and provides a baseline for improved early warning, control, response planning, and mitigation into the future. PMID:20682905

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.

Geographic techniques and recent applications of remote sensing to landscape-water quality studies

USGS Publications Warehouse

Griffith, J.A.

2002-01-01

This article overviews recent advances in studies of landscape-water quality relationships using remote sensing techniques. With the increasing feasibility of using remotely-sensed data, landscape-water quality studies can now be more easily performed on regional, multi-state scales. The traditional method of relating land use and land cover to water quality has been extended to include landscape pattern and other landscape information derived from satellite data. Three items are focused on in this article: 1) the increasing recognition of the importance of larger-scale studies of regional water quality that require a landscape perspective; 2) the increasing importance of remotely sensed data, such as the imagery-derived normalized difference vegetation index (NDVI) and vegetation phenological metrics derived from time-series NDVI data; and 3) landscape pattern. In some studies, using landscape pattern metrics explained some of the variation in water quality not explained by land use/cover. However, in some other studies, the NDVI metrics were even more highly correlated to certain water quality parameters than either landscape pattern metrics or land use/cover proportions. Although studies relating landscape pattern metrics to water quality have had mixed results, this recent body of work applying these landscape measures and satellite-derived metrics to water quality analysis has demonstrated their potential usefulness in monitoring watershed conditions across large regions.

Developing satellite-derived estimates of surface moisture status

NASA Technical Reports Server (NTRS)

Nemani, Ramakhrishna; Pierce, Lars; Running, Steve; Goward, Samuel

1993-01-01

An evaluation is made of the remotely sensed surface temperature (Ts)/normalized difference vegetation index (NDVI) relationship in studies of the influence of biome type on the slope of Ts/NDVI, and of the automation of the process of defining the relationship so that the surface moisture status can be compared with Ts/NDVI at continental scales. The analysis is conducted using the NOAA AVHRR over a 300 x 300 km area in western Montana, as well as biweekly composite AVHRR data. A strong negative relationship is established between NDVI and Ts over all biome types.

Coastline detection with time series of SAR images

NASA Astrophysics Data System (ADS)

Ao, Dongyang; Dumitru, Octavian; Schwarz, Gottfried; Datcu, Mihai

2017-10-01

For maritime remote sensing, coastline detection is a vital task. With continuous coastline detection results from satellite image time series, the actual shoreline, the sea level, and environmental parameters can be observed to support coastal management and disaster warning. Established coastline detection methods are often based on SAR images and wellknown image processing approaches. These methods involve a lot of complicated data processing, which is a big challenge for remote sensing time series. Additionally, a number of SAR satellites operating with polarimetric capabilities have been launched in recent years, and many investigations of target characteristics in radar polarization have been performed. In this paper, a fast and efficient coastline detection method is proposed which comprises three steps. First, we calculate a modified correlation coefficient of two SAR images of different polarization. This coefficient differs from the traditional computation where normalization is needed. Through this modified approach, the separation between sea and land becomes more prominent. Second, we set a histogram-based threshold to distinguish between sea and land within the given image. The histogram is derived from the statistical distribution of the polarized SAR image pixel amplitudes. Third, we extract continuous coastlines using a Canny image edge detector that is rather immune to speckle noise. Finally, the individual coastlines derived from time series of .SAR images can be checked for changes.

Comparing land surface phenology derived from satellite and GPS network microwave remote sensing.

PubMed

Jones, Matthew O; Kimball, John S; Small, Eric E; Larson, Kristine M

2014-08-01

The land surface phenology (LSP) start of season (SOS) metric signals the seasonal onset of vegetation activity, including canopy growth and associated increases in land-atmosphere water, energy and carbon (CO2) exchanges influencing weather and climate variability. The vegetation optical depth (VOD) parameter determined from satellite passive microwave remote sensing provides for global LSP monitoring that is sensitive to changes in vegetation canopy water content and biomass, and insensitive to atmosphere and solar illumination constraints. Direct field measures of canopy water content and biomass changes desired for LSP validation are generally lacking due to the prohibitive costs of maintaining regional monitoring networks. Alternatively, a normalized microwave reflectance index (NMRI) derived from GPS base station measurements is sensitive to daily vegetation water content changes and may provide for effective microwave LSP validation. We compared multiyear (2007-2011) NMRI and satellite VOD records at over 300 GPS sites in North America, and their derived SOS metrics for a subset of 24 homogenous land cover sites to investigate VOD and NMRI correspondence, and potential NMRI utility for LSP validation. Significant correlations (P<0.05) were found at 276 of 305 sites (90.5 %), with generally favorable correspondence in the resulting SOS metrics (r (2)=0.73, P<0.001, RMSE=36.8 days). This study is the first attempt to compare satellite microwave LSP metrics to a GPS network derived reflectance index and highlights both the utility and limitations of the NMRI data for LSP validation, including spatial scale discrepancies between local NMRI measurements and relatively coarse satellite VOD retrievals.

Comparison between global latent heat flux computed from multisensor (SSM/I and AVHRR) and from in situ data

NASA Technical Reports Server (NTRS)

Jourdan, Didier; Gautier, Catherine

1995-01-01

Comprehensive Ocean-Atmosphere Data Set (COADS) and satellite-derived parameters are input to a similarity theory-based model and treated in completely equivalent ways to compute global latent heat flux (LHF). In order to compute LHF exclusively from satellite measurements, an empirical relationship (Q-W relationship) is used to compute the air mixing ratio from Special Sensor Microwave/Imager (SSM/I) precipitable water W and a new one is derived to compute the air temperature also from retrieved W(T-W relationship). First analyses indicate that in situ and satellite LHF computations compare within 40%, but systematic errors increase the differences up to 100% in some regions. By investigating more closely the origin of the discrepancies, the spatial sampling of ship reports has been found to be an important source of error in the observed differences. When the number of in situ data records increases (more than 20 per month), the agreement is about 50 W/sq m rms (40 W/sq m rms for multiyear averages). Limitations of both empirical relationships and W retrieval errors strongly affect the LHF computation. Systematic LHF overestimation occurs in strong subsidence regions and LHF underestimation occurs within surface convergence zones and over oceanic upwelling areas. The analysis of time series of the different parameters in these regions confirms that systematic LHF discrepancies are negatively correlated with the differences between COADS and satellite-derived values of the air mixing ratio and air temperature. To reduce the systematic differences in satellite-derived LHF, a preliminary ship-satellite blending procedure has been developed for the air mixing ratio and air temperature.

Ocean color remote sensing of turbid plumes in the southern California coastal waters during storm events

NASA Astrophysics Data System (ADS)

Lahet, Florence; Stramski, Dariusz

2007-09-01

Water-leaving radiance data obtained from MODIS-Aqua satellite images at spatial resolution of 250 m (band 1 at 645 nm) and 500 m (band 4 at 555 nm) were used to analyze the correlation between plume area and rainfall during strong storm events in coastal waters of Southern California. Our study is focused on the area between Point Loma and the US-Mexican border in San Diego, which is influenced by terrigenous input of particulate and dissolved materials from San Diego and Tijuana watersheds and non-point sources along the shore. For several events of intense rainstorms that occurred in the winter of 2004-2005, we carried out a correlational analysis between the satellite-derived plume area and rainfall parameters. We examined several rainfall parameters and methods for the estimation of plume area. We identified the optimal threshold values of satellite-derived normalized water-leaving radiances at 645 nm and 555 nm for distinguishing the plume from ambient ocean waters. The satellite-derived plume size showed high correlation with the amount of precipitated water accumulated during storm event over the San Diego and Tijuana watersheds. Our results support the potential of ocean color imagery with relatively high spatial resolution for the study of turbid plumes in the coastal ocean.

Dust storms and their impact on ocean and human health: dust in Earth's atmosphere

USGS Publications Warehouse

Griffin, Dale W.; Kellog, Christina A.

2004-01-01

Satellite imagery has greatly influenced our understanding of dust activity on a global scale. A number of different satellites such as NASA's Earth-Probe Total Ozone Mapping Spectrometer (TOMS) and Se-viewing Field-of-view Sensor (SeaWiFS) acquire daily global-scale data used to produce imagery for monitoring dust storm formation and movement. This global-scale imagery has documented the frequent transmission of dust storm-derived soils through Earth's atmosphere and the magnitude of many of these events. While various research projects have been undertaken to understand this normal planetary process, little has been done to address its impact on ocean and human health. This review will address the ability of dust storms to influence marine microbial population densities and transport of soil-associated toxins and pathogenic microorganisms to marine environments. The implications of dust on ocean and human health in this emerging scientific field will be discussed.

The First Prediction of a Rift Valley Fever Outbreak

NASA Technical Reports Server (NTRS)

Anyamba, Assaf; Chretien, Jean-Paul; Small, Jennifer; Tucker, Compton J.; Formenty, Pierre; Richardson, Jason H.; Britch, Seth C.; Schnabel, David C.; Erickson, Ralph L.; Linthicum, Kenneth J.

2009-01-01

El Nino/Southern Oscillation (ENSO) related anomalies were analyzed using a combination of satellite measurements of elevated sea surface temperatures, and subsequent elevated rainfall and satellite derived normalized difference vegetation index data. A Rift Valley fever risk mapping model using these climate data predicted areas where outbreaks of Rift Valley fever in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. This is the first prospective prediction of a Rift Valley fever outbreak.

Benefit of Modeling the Observation Error in a Data Assimilation Framework Using Vegetation Information Obtained From Passive Based Microwave Data

NASA Technical Reports Server (NTRS)

Bolten, John D.; Mladenova, Iliana E.; Crow, Wade; De Jeu, Richard

2016-01-01

A primary operational goal of the United States Department of Agriculture (USDA) is to improve foreign market access for U.S. agricultural products. A large fraction of this crop condition assessment is based on satellite imagery and ground data analysis. The baseline soil moisture estimates that are currently used for this analysis are based on output from the modified Palmer two-layer soil moisture model, updated to assimilate near-real time observations derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The current data assimilation system is based on a 1-D Ensemble Kalman Filter approach, where the observation error is modeled as a function of vegetation density. This allows for offsetting errors in the soil moisture retrievals. The observation error is currently adjusted using Normalized Difference Vegetation Index (NDVI) climatology. In this paper we explore the possibility of utilizing microwave-based vegetation optical depth instead.

Prediction of a Rift Valley fever outbreak

PubMed Central

Anyamba, Assaf; Chretien, Jean-Paul; Small, Jennifer; Tucker, Compton J.; Formenty, Pierre B.; Richardson, Jason H.; Britch, Seth C.; Schnabel, David C.; Erickson, Ralph L.; Linthicum, Kenneth J.

2009-01-01

El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of satellite measurements of elevated sea-surface temperatures and subsequent elevated rainfall and satellite-derived normalized difference vegetation index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. To our knowledge, this is the first prospective prediction of a RVF outbreak. PMID:19144928

Estimating Ground-Level Particulate Matter (PM) Concentration using Satellite-derived Aerosol Optical Depth (AOD)

NASA Astrophysics Data System (ADS)

Park, Seohui; Im, Jungho

2017-04-01

Atmospheric aerosols are strongly associated with adverse human health effects. In particular, particulate matter less than 10 micrometers and 2.5 micrometers (i.e., PM10 and PM2.5, respectively) can cause cardiovascular and lung diseases such as asthma and chronic obstructive pulmonary disease (COPD). Air quality including PM has typically been monitored using station-based in-situ measurements over the world. However, in situ measurements do not provide spatial continuity over large areas. An alternative approach is to use satellite remote sensing as it provides data over vast areas at high temporal resolution. The literature shows that PM concentrations are related with Aerosol Optical Depth (AOD) that is derived from satellite observations, but it is still difficult to identify PM concentrations directly from AOD. Some studies used statistical approaches for estimating PM concentrations from AOD while some others combined numerical models and satellite-derived AOD. In this study, satellite-derived products were used to estimate ground PM concentrations based on machine learning over South Korea. Satellite-derived products include AOD from Geostationary Ocean Color Imager (GOCI), precipitation from Tropical Rainfall Measuring Mission (TRMM), soil moisture from AMSR-2, elevation from Shuttle Radar Topography Mission (SRTM), and land cover, land surface temperature and normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS). PM concentrations data were collected from 318 stations. A statistical ordinary least squares (OLS) approach was also tested and compared with the machine learning approach (i.e., random forest). PM concentration was estimated during spring season (from March to May) in 2015 that typically shows high concentration of PM. The randomly selected 80% of data were used for model calibration and the remaining 20% were used for validation. The developed models were further tested for prediction of PM concentration. Results show that the estimation of PM10 was better than that of PM2.5 for both approaches. The performance of machine learning random forest was better (R2=0.53 and RMSE=17.74µm/m3 for PM10; R2=0.36 and RMSE=26.17 µm/m3 for PM2.5) than the statistical OLS approach (R2=0.13 and RMSE=23.66µm/m3 for PM10; R2=0.09 and RMSE=27.74 µm/m3 for PM2.5). However, both approaches did not fully model the entire dynamic range of PM concentrations, especially for very high concentrations, resulting in moderate underestimation.

Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa

PubMed Central

Sedda, Luigi; Tatem, Andrew J.; Morley, David W.; Atkinson, Peter M.; Wardrop, Nicola A.; Pezzulo, Carla; Sorichetta, Alessandro; Kuleszo, Joanna; Rogers, David J.

2015-01-01

Background Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI. Methods In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa. Results This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found. Conclusions These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality. PMID:25733559

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronald; Russell, Jeffrey A.; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the basis for many inter-sensor interoperability or change detection techniques. Satellite inter-comparisons and accurate vegetation indices such as the Normalized Difference Vegetation Index, which is used to describe or to imply a wide variety of biophysical parameters and is defined in terms of near-infrared and redband reflectance, require the generation of accurate reflectance maps. This generation relies upon the removal of solar illumination, satellite geometry, and atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance, however, has been widely applied to only a few systems. In this study, we atmospherically corrected commercially available, high spatial resolution IKONOS and QuickBird imagery using several methods to determine the accuracy of the resulting reflectance maps. We used extensive ground measurement datasets for nine IKONOS and QuickBird scenes acquired over a two-year period to establish reflectance map accuracies. A correction approach using atmospheric products derived from Moderate Resolution Imaging Spectrometer data created excellent reflectance maps and demonstrated a reliable, effective method for reflectance map generation.

A Non-Stationary 1981-2012 AVHRR NDVI(sub 3g) Time Series

NASA Technical Reports Server (NTRS)

Pinzon, Jorge E.; Tucker, Compton J.

2014-01-01

The NDVI(sub 3g) time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of plus or minus 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set.

Kriging and local polynomial methods for blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems

USGS Publications Warehouse

Verdin, Andrew; Funk, Christopher C.; Rajagopalan, Balaji; Kleiber, William

2016-01-01

Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.

Evaluation and cross-comparison of vegetation indices for crop monitoring from sentinel-2 and worldview-2 images

NASA Astrophysics Data System (ADS)

Psomiadis, Emmanouil; Dercas, Nicholas; Dalezios, Nicolas R.; Spyropoulos, Nikolaos V.

2017-10-01

Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop vigor problems. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation. However, due to the various sensor characteristics, there are differences among VIs derived from multiple sensors for the same target. Therefore, multi-sensor VI capability and effectiveness are critical but complicated issues in the application of multi-sensor vegetation observations. Various factors such as the atmospheric conditions during acquisition, sensor and geometric characteristics, such as viewing angle, field of view, and sun elevation influence direct comparability of vegetation indicators among different sensors. In the present study, two experimental areas were used which are located near the villages Nea Lefki and Melia of Larissa Prefecture in Thessaly Plain area, containing a wheat and a cotton crop, respectively. Two satellite systems with different spatial resolution, WorldView-2 (W2) and Sentinel-2 (S2) with 2 and 10 meters pixel size, were used. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were calculated and a statistical comparison of the VIs was made to designate their correlation and dependency. Finally, several other innovative indices were calculated and compared to evaluate their effectiveness in the detection of problematic plant growth areas.

Impact of the Combination of GNSS and Altimetry Data on the Derived Global Ionosphere Maps

NASA Astrophysics Data System (ADS)

Todorova, S.; Schuh, H.; Hobiger, T.; Hernandez-Pajares, M.

2007-05-01

The classical input data for development of Global Ionosphere Maps (GIM) of the Total Electron Content (TEC) is the so called "geometry free linear combination", obtained from the dual-frequency Global Navigation Satellite System (GNSS) observations. Such maps in general achieve good quality of the ionosphere representation. However, the GNSS stations are inhomogeneously distributed, with large gaps particularly over the sea surface, which lowers the precision of the GIM over these areas. On the other hand, the dual-frequency satellite altimetry missions such as Jason-1 and TOPEX/Poseidon provide information about the parameter of the ionosphere precisely above the sea surface, where the altimetry observations are preformed. Due to the limited spread of the measurements and some open issues related to systematic errors, the ionospheric data from satellite altimetry is used only for cross-validation of the GNSS GIM. It can be anticipated however, that some specifics of the ionosphere parameter derived by satellite altimetry will partly balance the inhomogeneity of the GNSS data. Such important features are complementing in the global resolution, different biasing and the absence of additional mapping, as it is the case in GNSS. In this study we create two-hourly GIM from GNSS data and additionally introduce satellite altimetry observations, which help to compensate the insufficient GNSS coverage of the oceans. The combination of the data from around 180 GNSS stations and the satellite altimetry mission Jason-1 is performed on the normal equation level. The comparison between the integrated ionosphere models and the GNSS-only maps shows a higher accuracy of the combined GIM over the seas. A further effect of the combination is that the method allows the independent estimation of daily values of the Differential Code Biases (DCB) for all GNSS satellites and receivers, and of the systematic errors affecting the altimetry measurements. Such errors should include a hardware delay similar to the GNSS DCB as well as the impact of the topside ionosphere, which is not sampled by Jason-1. At this stage, for testing purposes we estimate a constant daily value, which will be further investigated. The final aim of the study is the development of improved combined global TEC maps, which make best use of the advantages of each particular type of data and have higher accuracy and reliability than the results derived by the two methods if treated individually.

The impact of acquisition times on the accuracy of microwave soil moisture retrievals over the contiguous U.S.

USDA-ARS?s Scientific Manuscript database

Satellite-derived soil moisture products have become an important data source for the study of land surface processes and related applications. For satellites with sun-synchronous orbits, these products are typically derived separately for ascending and descending overpasses with different local acq...

Agreement evaluation of AVHRR and MODIS 16-day composite NDVI data sets

USGS Publications Warehouse

Ji, Lei; Gallo, Kevin P.; Eidenshink, Jeffery C.; Dwyer, John L.

2008-01-01

Satellite-derived normalized difference vegetation index (NDVI) data have been used extensively to detect and monitor vegetation conditions at regional and global levels. A combination of NDVI data sets derived from AVHRR and MODIS can be used to construct a long NDVI time series that may also be extended to VIIRS. Comparative analysis of NDVI data derived from AVHRR and MODIS is critical to understanding the data continuity through the time series. In this study, the AVHRR and MODIS 16-day composite NDVI products were compared using regression and agreement analysis methods. The analysis shows a high agreement between the AVHRR-NDVI and MODIS-NDVI observed from 2002 and 2003 for the conterminous United States, but the difference between the two data sets is appreciable. Twenty per cent of the total difference between the two data sets is due to systematic difference, with the remainder due to unsystematic difference. The systematic difference can be eliminated with a linear regression-based transformation between two data sets, and the unsystematic difference can be reduced partially by applying spatial filters to the data. We conclude that the continuity of NDVI time series from AVHRR to MODIS is satisfactory, but a linear transformation between the two sets is recommended.

A method for estimating direct normal solar irradiation from satellite data for a tropical environment

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

Janjai, Serm

In order to investigate a potential use of concentrating solar power technologies and select an optimum site for these technologies, it is necessary to obtain information on the geographical distribution of direct normal solar irradiation over an area of interest. In this work, we have developed a method for estimating direct normal irradiation from satellite data for a tropical environment. The method starts with the estimation of global irradiation on a horizontal surface from MTSAT-1R satellite data and other ground-based ancillary data. Then a satellite-based diffuse fraction model was developed and used to estimate the diffuse component of the satellite-derivedmore » global irradiation. Based on this estimated global and diffuse irradiation and the solar radiation incident angle, the direct normal irradiation was finally calculated. To evaluate its performance, the method was used to estimate the monthly average hourly direct normal irradiation at seven pyrheliometer stations in Thailand. It was found that values of monthly average hourly direct normal irradiation from the measurements and those estimated from the proposed method are in reasonable agreement, with a root mean square difference of 16% and a mean bias of -1.6%, with respect to mean measured values. After the validation, this method was used to estimate the monthly average hourly direct normal irradiation over Thailand by using MTSAT-1R satellite data for the period from June 2005 to December 2008. Results from the calculation were displayed as hourly and yearly irradiation maps. These maps reveal that the direct normal irradiation in Thailand was strongly affected by the tropical monsoons and local topography of the country. (author)« less

Thunderstorm Research International Program (TRIP 77) report to management

NASA Technical Reports Server (NTRS)

Taiani, A. J.

1977-01-01

A post analysis of the previous day's weather, followed by the day's forecast and an outlook on weather conditions for the following day is given. The normal NOAA weather charts were used, complemented by the latest GOES satellite pictures, the latest rawinsonde sounding, and the computer-derived thunderstorm probability forecasts associated with the sounding.

Field-scale mapping of evaporative stress indicators of crop yield: an application over Mead, Nebraska

USDA-ARS?s Scientific Manuscript database

The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At coarse, regional scales (5-10 km resolution), the ESI has demonstrated capacity to captur...

Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models

NASA Technical Reports Server (NTRS)

Gillies, Robert R.; Carlson, Toby N.

1995-01-01

This study outlines a method for the estimation of regional patterns of surface moisture availability (M(sub 0)) and fractional vegetation (Fr) in the presence of spatially variable vegetation cover. The method requires relating variations in satellite-derived (NOAA, Advanced Very High Resolution Radiometer (AVHRR)) surface radiant temperature to a vegetation index (computed from satellite visible and near-infrared data) while coupling this association to an inverse modeling scheme. More than merely furnishing surface soil moisture values, the method constitues a new conceptual and practical approach for combining thermal infrared and vegetation index measurements for incorporating the derived values of M(sub 0) into hydrologic and atmospheric prediction models. Application of the technique is demonstrated for a region in and around the city of Newcastle upon Tyne situated in the northeast of England. A regional estimate of M(sub 0) is derived and is probabbly good for fractional vegetation cover up to 80% before errors in the estimated soil water content become unacceptably large. Moreover, a normalization scheme is suggested from which a nomogram, `universal triangle,' is constructed and is seen to fit the observed data well. The universal triangle also simplifies the inclusion of remotely derived M(sub 0) in hydrology and meteorological models and is perhaps a practicable step toward integrating derived data from satellite measurements in weather forecasting.

Comparison of U.S. Forest Land AreaEstimates From Forest Inventory and Analysis, National Resources Inventory, and Four Satellite Image-Derived Land Cover Data Sets

Treesearch

Mark D. Nelson; Ronald E. McRoberts; Veronica C. Lessard

2005-01-01

Our objective was to test one application of remote sensing technology for complementing forest resource assessments by comparing a variety of existing satellite image-derived land cover maps with national inventory-derived estimates of United States forest land area. National Resources Inventory (NRI) 1997 estimates of non-Federal forest land area differed by 7.5...

Application of the Coastal and Marine Ecological Classification Standard using Satellite-derived and Modeled Data Products for Pelagic Habitats in the Northern Gulf of Mexico

EPA Science Inventory

Satellite-derived data for sea surface temperature, salinity, chlorophyll; euphotic depth; and modeled bottom to surface temperature differences (Delta t) were evaluated to assess the utility of these products as proxies for in situ measurements. The data were used to classify su...

Monitoring of the Spatial Distribution and Temporal Dynamics of the Green Vegetation Fraction of Croplands in Southwest Germany Using High-Resolution RapidEye Satellite Images

NASA Astrophysics Data System (ADS)

Imukova, Kristina; Ingwersen, Joachim; Streck, Thilo

2014-05-01

The green vegetation fraction (GVF) is a key input variable to the evapotranspiration scheme applied in the widely used NOAH land surface model (LSM). In standard applications of the NOAH LSM, the GVF is taken from a global map with a 15 km×15 km resolution. The central objective of the present study was (a) to derive gridded GVF data in a high spatial and temporal resolution from RapidEye images for a region in Southwest Germany, and (b) to improve the representation of the GVF dynamics of croplands in the NOAH LSM for a better simulation of water and energy exchange between land surface and atmosphere. For the region under study we obtained monthly RapidEye satellite images with a resolution 5 m×5 m by the German Aerospace Center (DLR). The images hold five spectral bands: blue, green, red, red-edge and near infrared (NIR). The GVF dynamics were determined based on the Normalized Difference Vegetation Index (NDVI) calculated from the red and near-infrared bands of the satellite images. The satellite GVF data were calibrated and validated against ground truth measurements. Digital colour photographs above the canopy were taken with a boom-mounted digital camera at fifteen permanently marked plots (1 m×1 m). Crops under study were winter wheat, winter rape and silage maize. The GVF was computed based on the red and the green band of the photographs according to Rundquist's method (2002). Based on the obtained calibration scheme GVF maps were derived in a monthly resolution for the region. Our results confirm a linear relationship between GVF and NDVI and demonstrate that it is possible to determine the GVF of croplands from RapidEye images based on a simple two end-member mixing model. Our data highlight the high variability of the GVF in time and space. At the field scale, the GVF was normally distributed with a coefficient of variation of about 32%. Variability was mainly caused by soil heterogeneities and management differences. At the regional scale the GVF showed a bimodal distribution, which could be related to the different cultivation schemes of crops. We suggest to divide croplands according their distinctly different temporal dynamics of the GVF into "early covering - maturing" crops (winter rape, winter wheat, spring barley) and "late covering - non-maturing" crops (sugar beet, silage maize). Based on the achieved results we recommend that simulations with LSM should take into account this differentiation of croplands since it is to be expected that these two crop groups have pronounced differences with regard to energy partitioning at the land surface.

Analysis of the role of urban vegetation in local climate of Budapest using satellite measurements

NASA Astrophysics Data System (ADS)

Pongracz, Rita; Bartholy, Judit; Dezso, Zsuzsanna; Fricke, Cathy

2016-08-01

Urban areas significantly modify the natural environment due to the concentrated presence of humans and the associated anthropogenic activities. In order to assess this effect, it is essential to evaluate the relationship between urban and vegetated surface covers. In our study we focused on the Hungarian capital, Budapest, in which about 1.7 million inhabitants are living nowadays. The entire city is divided by the river Danube into the hilly, greener Buda side on the west, and the flat, more densely built-up Pest side on the east. Most of the extended urban vegetation, i.e., forests are located in the western Buda side. The effects of the past changing of these green areas are analyzed using surface temperature data calculated from satellite measurements in the infrared channels, and NDVI (Normalized Difference Vegetation Index) derived from visible and near-infrared satellite measurements. For this purpose, data available from sensor MODIS (Moderate Resolution Imaging Spectroradiometer) of NASA satellites (i.e., Terra and Aqua) are used. First, the climatological effects of forests on the urban heat island intensity are evaluated. Then, we also aim to evaluate the relationship of surface temperature and NDVI in this urban environment with special focus on vegetation-related sections of the city where the vegetation cover either increased or decreased remarkably.

Gravity Models from CHAMP and other Satellite Data

NASA Technical Reports Server (NTRS)

Lemoime, Frank G.; Cox, C. M.; Chinn, D. S.; Zelensky, N. P.; Thompson, B. F.; Rowlands, D. D.; Luthdke, S. B.; Nerem, R. S.

2003-01-01

CHAMP spacecraft is the first of a series of new spacecraft missions that are revolutionizing our ability to model the Earth's geopotential. We report on the analysis of over 100 days of CHAMP data in 2001 and 2002, merged with tracking data of other satellites such as Jason, Topex, GFO, Starlette, Stella, Spot-2, as well as satellite altimetry. We find that the CHAMP-only component of these solutions is a significant improvement over pre-CHAMP satellite only models with respect to the high degree information expressed by the geopotential model coefficients. For example, the variance of the differences with altimeter-derived anomalies through degree 70 is 2.80 mGal(sup 2) for the CHAMP-only solution based on 87 days of data vs. 10.19 mGal(sup 2) for EGM96S. Nonetheless, in order to model properly the various resonances to which different satellites are sensitive, we must include other satellite data. We evaluate the performance of these new CHAMP derived solutions with EGM96 and the EIGEN series of solutions. We review carefully the performance of these models for altimetric satellites.

Deriving Surface NO2 Mixing Ratios from DISCOVER-AQ ACAM Observations: A Method to Assess Surface NO2 Spatial Variability

NASA Astrophysics Data System (ADS)

Silverman, M. L.; Szykman, J.; Chen, G.; Crawford, J. H.; Janz, S. J.; Kowalewski, M. G.; Lamsal, L. N.; Long, R.

2015-12-01

Studies have shown that satellite NO2 columns are closely related to ground level NO2 concentrations, particularly over polluted areas. This provides a means to assess surface level NO2 spatial variability over a broader area than what can be monitored from ground stations. The characterization of surface level NO2 variability is important to understand air quality in urban areas, emissions, health impacts, photochemistry, and to evaluate the performance of chemical transport models. Using data from the NASA DISCOVER-AQ campaign in Baltimore/Washington we calculate NO2 mixing ratios from the Airborne Compact Atmospheric Mapper (ACAM), through four different methods to derive surface concentration from column measurements. High spectral resolution lidar (HSRL) mixed layer heights, vertical P3B profiles, and CMAQ vertical profiles are used to scale ACAM vertical column densities. The derived NO2 mixing ratios are compared to EPA ground measurements taken at Padonia and Edgewood. We find similar results from scaling with HSRL mixed layer heights and normalized P3B vertical profiles. The HSRL mixed layer heights are then used to scale ACAM vertical column densities across the DISCOVER-AQ flight pattern to assess spatial variability of NO2 over the area. This work will help define the measurement requirements for future satellite instruments.

Spring green-up date derived from GIMMS3g and SPOT-VGT NDVI of winter wheat cropland in the North China Plain

NASA Astrophysics Data System (ADS)

Liu, Zhengjia; Wu, Chaoyang; Liu, Yansui; Wang, Xiaoyue; Fang, Bin; Yuan, Wenping; Ge, Quansheng

2017-08-01

Satellite temporal resolution affects the fitting accuracy of vegetation growth curves. However, there are few studies that evaluate the impact of different satellite data (including temporal resolution and time series change) on spring green-up date (GUD) extraction. In this study, four GUD algorithms and two different temporal resolution satellite data (GIMMS3g during 1982-2013 and SPOT-VGT during 1999-2013) were used to investigate winter wheat GUD in the North China Plain. Four GUD algorithms included logistic-NDVI (normalized difference vegetation index), logistic-cumNDVI (cumulative NDVI), polynomial-NDVI and polynomial-cumNDVI algorithms. All algorithms and data were first regrouped into eight controlled cases. At site scale, we evaluated the performance of each case using correlation coefficient (r), bias and root mean square error (RMSE). We further compared spatial patterns and inter-annual trends of GUD inferred from different algorithms, and then analyzed the difference between GIMMS3g-based GUD and SPOT-VGT-based GUD. Our results showed that all satellite-based GUD were correlated with observations with r ranging from 0.32 to 0.57 (p < 0.01). SPOT-VGT-based GUD generally had better correlations with observed GUD than those of GIMMS3g. Spatially, SPOT-VGT-based GUD performed more reasonable spatial distributions. Inter-annual regional averaged satellite-based GUD presented overall advanced trends during 1982-2013 (0.3-2.0 days/decade) while delayed trends were observed during 1999-2013 (1.7-7.4 days/decade for GIMMS3g and 3.8-7.4 days/decade for SPOT-VGT). However, their significance levels were highly dependent on the data and algorithms used. Our findings suggest cautions on previous results of inter-annual variability of phenology from a single data/method.

Evaluation of Existing Image Matching Methods for Deriving Glacier Surface Displacements Globally from Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Heid, T.; Kääb, A.

2011-12-01

Automatic matching of images from two different times is a method that is often used to derive glacier surface velocity. Nearly global repeat coverage of the Earth's surface by optical satellite sensors now opens the possibility for global-scale mapping and monitoring of glacier flow with a number of applications in, for example, glacier physics, glacier-related climate change and impact assessment, and glacier hazard management. The purpose of this study is to compare and evaluate different existing image matching methods for glacier flow determination over large scales. The study compares six different matching methods: normalized cross-correlation (NCC), the phase correlation algorithm used in the COSI-Corr software, and four other Fourier methods with different normalizations. We compare the methods over five regions of the world with different representative glacier characteristics: Karakoram, the European Alps, Alaska, Pine Island (Antarctica) and southwest Greenland. Landsat images are chosen for matching because they expand back to 1972, they cover large areas, and at the same time their spatial resolution is as good as 15 m for images after 1999 (ETM+ pan). Cross-correlation on orientation images (CCF-O) outperforms the three similar Fourier methods, both in areas with high and low visual contrast. NCC experiences problems in areas with low visual contrast, areas with thin clouds or changing snow conditions between the images. CCF-O has problems on narrow outlet glaciers where small window sizes (about 16 pixels by 16 pixels or smaller) are needed, and it also obtains fewer correct matches than COSI-Corr in areas with low visual contrast. COSI-Corr has problems on narrow outlet glaciers and it obtains fewer correct matches compared to CCF-O when thin clouds cover the surface, or if one of the images contains snow dunes. In total, we consider CCF-O and COSI-Corr to be the two most robust matching methods for global-scale mapping and monitoring of glacier velocities. If combining CCF-O with locally adaptive template sizes and by filtering the matching results automatically by comparing the displacement matrix to its low pass filtered version, the matching process can be automated to a large degree. This allows the derivation of glacier velocities with minimal (but not without!) user interaction and hence also opens up the possibility of global-scale mapping and monitoring of glacier flow.

Incorporating GOES Satellite Photosynthetically Active Radiation (PAR) Retrievals to Improve Biogenic Emission Estimates in Texas

NASA Astrophysics Data System (ADS)

Zhang, Rui; White, Andrew T.; Pour Biazar, Arastoo; McNider, Richard T.; Cohan, Daniel S.

2018-01-01

This study examines the influence of insolation and cloud retrieval products from the Geostationary Operational Environmental Satellite (GOES) system on biogenic emission estimates and ozone simulations in Texas. Compared to surface pyranometer observations, satellite-retrieved insolation and photosynthetically active radiation (PAR) values tend to systematically correct the overestimation of downwelling shortwave radiation in the Weather Research and Forecasting (WRF) model. The correlation coefficient increases from 0.93 to 0.97, and the normalized mean error decreases from 36% to 21%. The isoprene and monoterpene emissions estimated by the Model of Emissions of Gases and Aerosols from Nature are on average 20% and 5% less, respectively, when PAR from the direct satellite retrieval is used rather than the control WRF run. The reduction in biogenic emission rates using satellite PAR reduced the predicted maximum daily 8 h ozone concentration by up to 5.3 ppbV over the Dallas-Fort Worth (DFW) region on some days. However, episode average ozone response is less sensitive, with a 0.6 ppbV decrease near DFW and 0.3 ppbV increase over East Texas. The systematic overestimation of isoprene concentrations in a WRF control case is partially corrected by using satellite PAR, which observes more clouds than are simulated by WRF. Further, assimilation of GOES-derived cloud fields in WRF improved CAMx model performance for ground-level ozone over Texas. Additionally, it was found that using satellite PAR improved the model's ability to replicate the spatial pattern of satellite-derived formaldehyde columns and aircraft-observed vertical profiles of isoprene.

Satellite-observed changes in vegetation sensitivities to surface soil moisture and total water storage variations since the 2011 Texas drought

NASA Astrophysics Data System (ADS)

A, Geruo; Velicogna, Isabella; Kimball, John S.; Du, Jinyang; Kim, Youngwook; Colliander, Andreas; Njoku, Eni

2017-05-01

We combine soil moisture (SM) data from AMSR-E and AMSR-2, and changes in terrestrial water storage (TWS) from time-variable gravity data from GRACE to delineate and characterize the evolution of drought and its impact on vegetation growth. GRACE-derived TWS provides spatially continuous observations of changes in overall water supply and regional drought extent, persistence and severity, while satellite-derived SM provides enhanced delineation of shallow-depth soil water supply. Together these data provide complementary metrics quantifying available plant water supply. We use these data to investigate the supply changes from water components at different depths in relation to satellite-based enhanced vegetation index (EVI) and gross primary productivity (GPP) from MODIS and solar-induced fluorescence (SIF) from GOME-2, during and following major drought events observed in the state of Texas, USA and its surrounding semiarid area for the past decade. We find that in normal years the spatial pattern of the vegetation-moisture relationship follows the gradient in mean annual precipitation. However since the 2011 hydrological drought, vegetation growth shows enhanced sensitivity to surface SM variations in the grassland area located in central Texas, implying that the grassland, although susceptible to drought, has the capacity for a speedy recovery. Vegetation dependency on TWS weakens in the shrub-dominated west and strengthens in the grassland and forest area spanning from central to eastern Texas, consistent with changes in water supply pattern. We find that in normal years GRACE TWS shows strong coupling and similar characteristic time scale to surface SM, while in drier years GRACE TWS manifests stronger persistence, implying longer recovery time and prolonged water supply constraint on vegetation growth. The synergistic combination of GRACE TWS and surface SM, along with remote-sensing vegetation observations provides new insights into drought impact on vegetation-moisture relationship, and unique information regarding vegetation resilience and the recovery of hydrological drought.

The Effects of Climate Variability on Phytoplankton Composition in the Equatorial Pacific Ocean using a Model and a Satellite-Derived Approach

NASA Technical Reports Server (NTRS)

Rousseaux, C. S.; Gregg, W. W.

2012-01-01

Compared the interannual variation in diatoms, cyanobacteria, coccolithophores and chlorophytes from the NASA Ocean Biogeochemical Model with those derived from satellite data (Hirata et al. 2011) between 1998 and 2006 in the Equatorial Pacific. Using NOBM, La Ni a events were characterized by an increase in diatoms (correlation with MEI, r=-0.81, P<0.05), while cyanobacteria concentrations decreased significantly (r=0.61; P<0.05). El Nino produced the reverse pattern, with cyanobacteria populations increasing while diatoms plummeted. This represented a radical shift in the phytoplankton community in response to climate variability. However, satellite-derived phytoplankton groups were all negatively correlated with climate variability (r ranged from -0.39 for diatoms to -0.64 for coccolithophores, P<0.05). Spatially, the satellite-derived approach was closer to an independent in situ dataset for all phytoplankton groups except diatoms than NOBM. However, the different responses of phytoplankton to intense interannual events in the Equatorial Pacific raises questions about the representation of phytoplankton dynamics in models and algorithms: is a phytoplankton community shift as in the model or an across-the-board change in abundances of all phytoplankton as in the satellite-derived approach.

Comparison of satellite-derived dynamical quantities for the stratosphere of the Southern Hemisphere

NASA Technical Reports Server (NTRS)

Miles, Thomas (Editor); Oneill, Alan (Editor)

1989-01-01

As part of the international Middle Atmosphere Program (MAP), a project was instituted to study the dynamics of the Middle Atmosphere in the Southern Hemisphere (MASH). A pre-MASH workshop was held with two aims: comparison of Southern Hemisphere dynamical quantities derived from various archives of satellite data; and assessing the impact of different base-level height information on such derived quantities. The dynamical quantities examined included geopotential height, zonal wind, potential vorticity, eddy heat and momentum fluxes, and Eliassen-Palm fluxes. It was found that while there was usually qualitative agreement between the different sets of fields, substantial quantitative differences were evident, particularly in high latitudes. The fidelity of the base-level analysis was found to be of prime importance in calculating derived quantities - especially the Eliassen-Palm flux divergence and potential vorticity. Improvements in base-level analyses are recommended. In particular, quality controls should be introduced to remove spurious localized features from analyses, and information from all Antarctic radiosondes should be utilized where possible. Caution in drawing quantitative inferences from satellite data for the middle atmosphere of the Southern Hemisphere is advised.

Modelling of charged satellite motion in Earth's gravitational and magnetic fields

NASA Astrophysics Data System (ADS)

Abd El-Bar, S. E.; Abd El-Salam, F. A.

2018-05-01

In this work Lagrange's planetary equations for a charged satellite subjected to the Earth's gravitational and magnetic force fields are solved. The Earth's gravity, and magnetic and electric force components are obtained and expressed in terms of orbital elements. The variational equations of orbit with the considered model in Keplerian elements are derived. The solution of the problem in a fully analytical way is obtained. The temporal rate of changes of the orbital elements of the spacecraft are integrated via Lagrange's planetary equations and integrals of the normalized Keplerian motion obtained by Ahmed (Astron. J. 107(5):1900, 1994).

Atmospheric correction using near-infrared bands for satellite ocean color data processing in the turbid western Pacific region.

PubMed

Wang, Menghua; Shi, Wei; Jiang, Lide

2012-01-16

A regional near-infrared (NIR) ocean normalized water-leaving radiance (nL(w)(λ)) model is proposed for atmospheric correction for ocean color data processing in the western Pacific region, including the Bohai Sea, Yellow Sea, and East China Sea. Our motivation for this work is to derive ocean color products in the highly turbid western Pacific region using the Geostationary Ocean Color Imager (GOCI) onboard South Korean Communication, Ocean, and Meteorological Satellite (COMS). GOCI has eight spectral bands from 412 to 865 nm but does not have shortwave infrared (SWIR) bands that are needed for satellite ocean color remote sensing in the turbid ocean region. Based on a regional empirical relationship between the NIR nL(w)(λ) and diffuse attenuation coefficient at 490 nm (K(d)(490)), which is derived from the long-term measurements with the Moderate-resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, an iterative scheme with the NIR-based atmospheric correction algorithm has been developed. Results from MODIS-Aqua measurements show that ocean color products in the region derived from the new proposed NIR-corrected atmospheric correction algorithm match well with those from the SWIR atmospheric correction algorithm. Thus, the proposed new atmospheric correction method provides an alternative for ocean color data processing for GOCI (and other ocean color satellite sensors without SWIR bands) in the turbid ocean regions of the Bohai Sea, Yellow Sea, and East China Sea, although the SWIR-based atmospheric correction approach is still much preferred. The proposed atmospheric correction methodology can also be applied to other turbid coastal regions.

Analysis of ground-measured and passive-microwave-derived snow depth variations in midwinter across the Northern Great Plains

USGS Publications Warehouse

Chang, A.T.C.; Kelly, R.E.J.; Josberger, E.G.; Armstrong, R.L.; Foster, J.L.; Mognard, N.M.

2005-01-01

Accurate estimation of snow mass is important for the characterization of the hydrological cycle at different space and time scales. For effective water resources management, accurate estimation of snow storage is needed. Conventionally, snow depth is measured at a point, and in order to monitor snow depth in a temporally and spatially comprehensive manner, optimum interpolation of the points is undertaken. Yet the spatial representation of point measurements at a basin or on a larger distance scale is uncertain. Spaceborne scanning sensors, which cover a wide swath and can provide rapid repeat global coverage, are ideally suited to augment the global snow information. Satellite-borne passive microwave sensors have been used to derive snow depth (SD) with some success. The uncertainties in point SD and areal SD of natural snowpacks need to be understood if comparisons are to be made between a point SD measurement and satellite SD. In this paper three issues are addressed relating satellite derivation of SD and ground measurements of SD in the northern Great Plains of the United States from 1988 to 1997. First, it is shown that in comparing samples of ground-measured point SD data with satellite-derived 25 ?? 25 km2 pixels of SD from the Defense Meteorological Satellite Program Special Sensor Microwave Imager, there are significant differences in yearly SD values even though the accumulated datasets showed similarities. Second, from variogram analysis, the spatial variability of SD from each dataset was comparable. Third, for a sampling grid cell domain of 1?? ?? 1?? in the study terrain, 10 distributed snow depth measurements per cell are required to produce a sampling error of 5 cm or better. This study has important implications for validating SD derivations from satellite microwave observations. ?? 2005 American Meteorological Society.

Applying the concept of ecohydrological equilibrium to predict steady-state leaf area index for Australian ecosystems

NASA Astrophysics Data System (ADS)

Yang, J.; Medlyn, B.; De Kauwe, M. G.; Duursma, R.

2017-12-01

Leaf Area Index (LAI) is a key variable in modelling terrestrial vegetation, because it has a major impact on carbon, water and energy fluxes. However, LAI is difficult to predict: several recent intercomparisons have shown that modelled LAI differs significantly among models, and between models and satellite-derived estimates. Empirical studies show that long-term mean LAI is strongly related to mean annual precipitation. This observation is predicted by the theory of ecohydrological equilibrium, which provides a promising alternative means to predict steady-state LAI. We implemented this theory in a simple optimisation model. We hypothesized that, when water availability is limited, plants should adjust long-term LAI and stomatal behavior (g1) to maximize net canopy carbon export, under the constraint that canopy transpiration is a fixed fraction of total precipitation. We evaluated the predicted LAI (Lopt) for Australia against ground-based observations of LAI at 135 sites, and continental-scale satellite-derived estimates. For the site-level data, the RMSE of predicted Lopt was 0.14 m2 m-2, which was similar to the RMSE of a comparison of the data against nine-year mean satellite-derived LAI at those sites. Continentally, Lopt had a R2 of over 70% when compared to satellite-derived LAI, which is comparable to the R2 obtained when different satellite products are compared against each other. The predicted response of Lopt to the increase in atmospheric CO2 over the last 30 years also agreed with the estimate based on satellite-derivatives. Our results indicate that long-term equilibrium LAI can be successfully predicted from a simple application of ecohydrological theory. We suggest that this theory could be usefully incorporated into terrestrial vegetation models to improve their predictions of LAI.

Satellite remote sensing assessment of climate impact on forest vegetation dynamics

NASA Astrophysics Data System (ADS)

Zoran, M.

2009-04-01

Forest vegetation phenology constitutes an efficient bio-indicator of impacts of climate and anthropogenic changes and a key parameter for understanding and modelling vegetation-climate interactions. Climate variability represents the ensemble of net radiation, precipitation, wind and temperature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of Normalized Difference Vegetation Index (NDVIs), which requires NDVI time-series with good time resolution, over homogeneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). Spatio-temporal vegetation dynamics have been quantified as the total amount of vegetation (mean NDVI) and the seasonal difference (annual NDVI amplitude) by a time series analysis of NDVI satellite images with the Harmonic ANalysis of Time Series algorithm. A climate indicator (CI) was created from meteorological data (precipitation over net radiation). The relationships between the vegetation dynamics and the CI have been determined spatially and temporally. The driest test regions prove to be the most sensitive to climate impact. The spatial and temporal patterns of the mean NDVI are the same, while they are partially different for the seasonal difference. The aim of this paper was to quantify this impact over a forest ecosystem placed in the North-Eastern part of Bucharest town, Romania, with Normalized Difference Vegetation Index (NDVI) parameter extracted from IKONOS and LANDSAT TM and ETM satellite images and meteorological data over l995-2007 period. For investigated test area, considerable NDVI decline was observed between 1995 and 2007 due to the drought events during 2003 and 2007 years. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics. Specific aim of this paper was to assess, forecast, and mitigate the risks of climatic changes on forest systems and its biodiversity as well as on adjacent environment areas and to provide early warning strategies on the basis of spectral information derived from satellite data regarding atmospheric effects of forest biome degradation . The paper aims to describe observed trends and potential impacts based on scenarios from simulations with regional climate models and other downscaling procedures.

Northern hemisphere total ozone values from 1989-1993 determined with the NOAA-11 Solar Backscatter Ultraviolet (SBUV/2) instrument

NASA Technical Reports Server (NTRS)

Planet, W. G.; Lienesch, J. H.; Miller, A. J.; Nagatani, R.; Mcpeters, R. D.; Hilsenrath, E.; Cebula, R. P.; Deland, M. T.; Wellemeyer, C. G.; Horvath, K.

1994-01-01

Determinations of global total ozone amounts have been made from recently reprocessed measurements with the SBUV/2 on the NOAA-11 environmental satellite since January 1989. This data set employs a new algorithm and an updated calibration. Comparisons with total ozone amounts derived from a significant subset of the global network of Dobson spectrophotometers shows a 0.3% bias between the satellite and ground measurements for the period January 1989-May 1993. Comparisons with the data from individual stations exhibit differing degrees of agreement which could be due to the matchup procedures and also to the uncertainties in the Dobson data. The SBUV/2 data set discussed here traces the Northern Hemisphere total ozone from 1989 to the present, showing a marked decrease from the average of those years starting in the summer of 1992 and continuing into 1993, with an apparent returning to more normal levels in late 1993.

Comparison of Shortwave Cloud Radiative Forcing Derived from ARM SGP Surface and GOES-8 Satellite Measurements During ARESE I and ARESE II

NASA Technical Reports Server (NTRS)

Rapp, A. D.; Doelling, D. R.; Khaiyer, M. M.; Minnis, P.; Smith, W. L., Jr.; Nguyen, L.; Haeffelin, M. P.; Valero, F. P. J.; Asano, S.

2001-01-01

One of the objectives of the ARM Enhanced Shortwave Experiment (ARESE) is to investigate the absorption of solar radiation by clouds over the ARM Southern Great Plains central facility. A variety of techniques employing various combinations Of Surface, aircraft, and satellite data have been used to estimate the absorption empirically. During ARESE-I conducted during fall 1995, conflicting results were produced from different analyses of the combined datasets leading to the need for a more controlled experiment. ARESE-II was conducted during spring 2000. Improved calibrations, different sampling strategies, and broadband satellite data were all available to minimize some of the sources of uncertainty in the data. In this paper, cloud absorption or its parametric surrogates (e.g., Cess et al. 1995) are derived from collocated and coincident surface and satellite radiometer data from both ARESE-I and ARESE-II using the latest satellite and surface instrument calibrations.

Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat: Tucson

USGS Publications Warehouse

Wallace, Cynthia S.A.; Advised by Marsh, Stuart E.

2002-01-01

The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat. By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data, effective models of habitat preference were created.Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition.Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data. Such relationships, once verified and established, can be used to help indirectly monitor the population.Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat. Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations.The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape.

Comparison of Ground-Based and Satellite-Derived Solar UV Index Levels at Six South African Sites.

PubMed

Cadet, Jean-Maurice; Bencherif, Hassan; Portafaix, Thierry; Lamy, Kévin; Ncongwane, Katlego; Coetzee, Gerrie J R; Wright, Caradee Y

2017-11-14

South Africa has been measuring the ground-based solar UV index for more than two decades at six sites to raise awareness about the impacts of the solar UV index on human health. This paper is an exploratory study based on comparison with satellite UV index measurements from the OMI/AURA experiment. Relative UV index differences between ground-based and satellite-derived data ranged from 0 to 45% depending on the site and year. Most of time, these differences appear in winter. Some ground-based stations' data had closer agreement with satellite-derived data. While the ground-based instruments are not intended for long-term trend analysis, they provide UV index information for public awareness instead, with some weak signs suggesting such long-term trends may exist in the ground-based data. The annual cycle, altitude, and latitude effects clearly appear in the UV index data measured in South Africa. This variability must be taken into account for the development of an excess solar UV exposure prevention strategy.

Comparison of Ground-Based and Satellite-Derived Solar UV Index Levels at Six South African Sites

PubMed Central

Cadet, Jean-Maurice; Bencherif, Hassan; Portafaix, Thierry; Lamy, Kévin; Ncongwane, Katlego; Coetzee, Gerrie J. R.; Wright, Caradee Y.

2017-01-01

South Africa has been measuring the ground-based solar UV index for more than two decades at six sites to raise awareness about the impacts of the solar UV index on human health. This paper is an exploratory study based on comparison with satellite UV index measurements from the OMI/AURA experiment. Relative UV index differences between ground-based and satellite-derived data ranged from 0 to 45% depending on the site and year. Most of time, these differences appear in winter. Some ground-based stations’ data had closer agreement with satellite-derived data. While the ground-based instruments are not intended for long-term trend analysis, they provide UV index information for public awareness instead, with some weak signs suggesting such long-term trends may exist in the ground-based data. The annual cycle, altitude, and latitude effects clearly appear in the UV index data measured in South Africa. This variability must be taken into account for the development of an excess solar UV exposure prevention strategy. PMID:29135965

Evaluating the potential of Landsat TM/ETM+ imagery for assessing fire severity in Alaskan black spruce forests

Treesearch

Elizabeth E. Hoy; Nancy H.F. French; Merritt R. Turetsky; Simon N. Trigg; Eric S. Kasischke

2008-01-01

Satellite remotely sensed data of fire disturbance offers important information; however, current methods to study fire severity may need modifications for boreal regions. We assessed the potential of the differenced Normalized Burn Ratio (dNBR) and other spectroscopic indices and image transforms derived from Landsat TM/ETM+ data for mapping fire severity in Alaskan...

Satellited 4q identified in amniotic fluid cells

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

Miller, I.; Hsieh, C.L.; Songster, G.

Extra material was identified on the distal long arm of a chromosome 4 in an amniotic fluid specimen sampled at 16.6 weeks of gestational age. There was no visible loss of material from chromosome 4, and no evidence for a balanced rearrangement. The primary counseling issue in this case was advanced maternal age. Ultrasound findings were normal, and family history was unremarkable. The identical 4qs chromosome was observed in cells from a paternal peripheral blood specimen and appeared to be an unbalanced rearrangement. This extra material was NOR positive in lymphocytes from the father, but was negative in the fetalmore » amniocytes. Father`s relatives were studied to verify the familial origin of this anomaly. In situ hybridization with both exon and intron sequences of ribosomal DNA demonstrated that ribosomal DNA is present at the terminus of the 4qs chromosome in the fetus, father, and paternal grandmother. This satellited 4q might have been derived from a translocation event that resulted in very little or no loss from the 4q and no specific phenotype. This derivative chromosome 4 has been inherited through at least 3 generations of phenotypically normal individuals. 8 refs., 3 figs.« less

Interannual growth dynamics of vegetation in the Kuparuk River watershed, Alaska based on the Normalized Difference Vegetation Index

USGS Publications Warehouse

Hope, A.S.; Boynton, W.L.; Stow, D.A.; Douglas, David C.

2003-01-01

Interannual above-ground production patterns are characterized for three tundra ecosystems in the Kuparuk River watershed of Alaska using NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. NDVI values integrated over each growing season (SINDVI) were used to represent seasonal production patterns between 1989 and 1996. Spatial differences in ecosystem production were expected to follow north-south climatic and soil gradients, while interannual differences in production were expected to vary with variations in seasonal precipitation and temperature. It was hypothesized that the increased vegetation growth in high latitudes between 1981 and 1991 previously reported would continue through the period of investigation for the study watershed. Zonal differences in vegetation production were confirmed but interannual variations did not covary with seasonal precipitation or temperature totals. A sharp reduction in the SINDVI in 1992 followed by a consistent increase up to 1996 led to a further hypothesis that the interannual variations in SINDVI were associated with variations in stratospheric optical depth. Using published stratospheric optical depth values derived from the SAGE and SAGE-II satellites, it is demonstrated that variations in these depths are likely the primary cause of SINDVI interannual variability.

Using satellite data on meteorological and vegetation characteristics and soil surface humidity in the Land Surface Model for the vast territory of agricultural destination

NASA Astrophysics Data System (ADS)

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

2017-04-01

The model of water and heat exchange between vegetation covered territory and atmosphere (LSM, Land Surface Model) for vegetation season has been developed to calculate soil water content, evapotranspiration, infiltration of water into the soil, vertical latent and sensible heat fluxes and other water and heat balances components as well as soil surface and vegetation cover temperatures and depth distributions of moisture and temperature. The LSM is suited for utilizing satellite-derived estimates of precipitation, land surface temperature and vegetation characteristics and soil surface humidity for each pixel. Vegetation and meteorological characteristics being the model parameters and input variables, correspondingly, have been estimated by ground observations and thematic processing measurement data of scanning radiometers AVHRR/NOAA, SEVIRI/Meteosat-9, -10 (MSG-2, -3) and MSU-MR/Meteor-M № 2. Values of soil surface humidity has been calculated from remote sensing data of scatterometers ASCAT/MetOp-A, -B. The case study has been carried out for the territory of part of the agricultural Central Black Earth Region of European Russia with area of 227300 km2 located in the forest-steppe zone for years 2012-2015 vegetation seasons. The main objectives of the study have been: - to built estimates of precipitation, land surface temperatures (LST) and vegetation characteristics from MSU-MR measurement data using the refined technologies (including algorithms and programs) of thematic processing satellite information matured on AVHRR and SEVIRI data. All technologies have been adapted to the area of interest; - to investigate the possibility of utilizing satellite-derived estimates of values above in the LSM including verification of obtained estimates and development of procedure of their inputting into the model. From the AVHRR data there have been built the estimates of precipitation, three types of LST: land skin temperature Tsg, air temperature at a level of vegetation cover (taken for vegetation temperature) Ta and efficient radiation temperature Ts.eff, as well as land surface emissivity E, normalized difference vegetation index NDVI, vegetation cover fraction B, and leaf area index LAI. The SEVIRI-based retrievals have included precipitation, LST Tls and Ta, E at daylight and nighttime, LAI (daily), and B. From the MSU-MR data there have been retrieved values of all the same characteristics as from the AVHRR data. The MSU-MR-based daily and monthly sums of precipitation have been calculated using the developed earlier and modified Multi Threshold Method (MTM) intended for the cloud detection and identification of its types around the clock as well as allocation of precipitation zones and determination of instantaneous maximum rainfall intensities for each pixel at that the transition from assessing rainfall intensity to estimating their daily values is a key element of the MTM. Measurement data from 3 IR MSU-MR channels (3.8, 11 i 12 μm) as well as their differences have been used in the MTM as predictors. Controlling the correctness of the MSU-MR-derived rainfall estimates has been carried out when comparing with analogous AVHRR- and SEVIRI-based retrievals and with precipitation amounts measured at the agricultural meteorological station of the study region. Probability of rainfall zones determination from the MSU-MR data, to match against the actual ones, has been 75-85% as well as for the AVHRR and SEVIRI data. The time behaviors of satellite-derived and ground-measured daily and monthly precipitation sums for vegetation season and yeaŗ correspondingly, have been in good agreement with each other although the first ones have been smoother than the latter. Discrepancies have existed for a number of local maxima for which satellite-derived precipitation estimates have been less than ground-measured values. It may be due to the different spatial scales of areal satellite-derived and point ground-based estimates. Some spatial displacement of the satellite-determined rainfall maxima and minima regarding to ground-based data can be explained by the discrepancy between the cloud location on satellite images and in reality at high angles of the satellite sightings and considerable altitudes of the cloud tops. Reliability of MSU-MR-derived rainfall estimates at each time step obtained using the MTM has been verified by comparing their values determined from the MSU-MR, AVHRR and SEVIRI measurements and distributed over the study area with similar estimates obtained by interpolation of ground observation data. The MSU-MR-derived estimates of temperatures Tsg, Ts.eff, and Ta have been obtained using computational algorithm developed on the base of the MTM and matured on AVHRR and SEVIRI data for the region under investigation. Since the apparatus MSU-MR is similar to radiometer AVHRR, the developed methods of satellite estimating Tsg, Ts.eff, and Ta from AVHRR data could be easily transferred to the MSU-MR data. Comparison of the ground-measured and MSU-MR-, AVHRR- and SEVIRI-derived LSTs has shown that the differences between all the estimates for the vast majority of observation terms have not exceed the RMSE of these quantities built from the AVHRR data. The similar conclusion has been also made from the results of building the time behavior of the MSU-MR-derived value of LAI for vegetation season. Satellite-based estimates of precipitation, LST, LAI and B have been utilized in the model with the help of specially developed procedures of replacing these values determined from observations at agricultural meteorological stations by their satellite-derived values taking into account spatial heterogeneity of their fields. Adequacy of such replacement has been confirmed by the results of comparing modeled and ground-measured values of soil moisture content W and evapotranspiration Ev. Discrepancies between the modeled and ground-measured values of W and Ev have been in the range of 10-15 and 20-25 %, correspondingly. It may be considered as acceptable result. Resulted products of the model calculations using satellite data have been spatial fields of W, Ev, vertical sensible and latent heat fluxes and other water and heat regime characteristics for the region of interest over the year 2012-2015 vegetation seasons. Thus, there has been shown the possibility of utilizing MSU-MR/Meteor-M №2 data jointly with those of other satellites in the LSM to calculate characteristics of water and heat regimes for the area under consideration. Besides the first trial estimations of the soil surface moisture from ASCAT scatterometers data for the study region have been obtained for the years 2014-2015 vegetation seasons, their comparison has been performed with the results of modeling for several agricultural meteorological stations of the region that has been carried out utilizing ground-based and satellite data, specific requirements for the obtained information have been formulated. To date, estimates of surface moisture built from ASCAT data can be used for the selection of the model soil parameter values and the initial soil moisture conditions for the vegetation season.

Comparing near-earth and satellite remote sensing based phenophase estimates: an analysis using multiple webcams and MODIS (Invited)

NASA Astrophysics Data System (ADS)

Hufkens, K.; Richardson, A. D.; Migliavacca, M.; Frolking, S. E.; Braswell, B. H.; Milliman, T.; Friedl, M. A.

2010-12-01

In recent years several studies have used digital cameras and webcams to monitor green leaf phenology. Such "near-surface" remote sensing has been shown to be a cost effective means of accurately capturing phenology. Specifically, it allows for accurate tracking of intra- and inter-annual phenological dynamics at high temporal frequency and over broad spatial scales compared to visual observations or tower-based fAPAR and broadband NDVI measurements. Near surface remote sensing measurements therefore show promise for bridging the gap between traditional in-situ measurements of phenology and satellite remote sensing data. For this work, we examined the relationship between phenophase estimates derived from satellite remote sensing (MODIS) and near-earth remote sensing derived from webcams for a select set of sites with high-quality webcam data. A logistic model was used to characterize phenophases for both the webcam and MODIS data. We documented model fit accuracy, phenophase estimates, and model biases for both data sources. Our results show that different vegetation indices (VI's) derived from MODIS produce significantly different phenophase estimates compared to corresponding estimates derived from webcam data. Different VI's showed markedly different radiometric properties, and as a result, influenced phenophase estimates. The study shows that phenophase estimates are not only highly dependent on the algorithm used but also depend on the VI used by the phenology retrieval algorithm. These results highlight the need for a better understanding of how near-earth and satellite remote data relate to eco-physiological and canopy changes during different parts of the growing season.

Temporal Stability of the NDVI-LAI Relationship in a Napa Valley Vineyard

NASA Technical Reports Server (NTRS)

Johnson, L. F.

2003-01-01

Remotely sensed normalized difference vegetation index (NDVI) values, derived from high-resolution satellite images, were compared with ground measurements of vineyard leaf area index (LAI) periodically during the 2001 growing season. The two variables were strongly related at six ground calibration sites on each of four occasions (r squared = 0.91 to 0.98). Linear regression equations relating the two variables did not significantly differ by observation date, and a single equation accounted for 92 percent of the variance in the combined dataset. Temporal stability of the relationship opens the possibility of transforming NDVI maps to LAI in the absence of repeated ground calibration fieldwork. In order to take advantage of this circumstance, however, steps should be taken to assure temporal consistency in spectral data values comprising the NDVI.

Modelling high arctic percent vegetation cover using field digital images and high resolution satellite data

NASA Astrophysics Data System (ADS)

Liu, Nanfeng; Treitz, Paul

2016-10-01

In this study, digital images collected at a study site in the Canadian High Arctic were processed and classified to examine the spatial-temporal patterns of percent vegetation cover (PVC). To obtain the PVC of different plant functional groups (i.e., forbs, graminoids/sedges and mosses), field near infrared-green-blue (NGB) digital images were classified using an object-based image analysis (OBIA) approach. The PVC analyses comparing different vegetation types confirmed: (i) the polar semi-desert exhibited the lowest PVC with a large proportion of bare soil/rock cover; (ii) the mesic tundra cover consisted of approximately 60% mosses; and (iii) the wet sedge consisted almost exclusively of graminoids and sedges. As expected, the PVC and green normalized difference vegetation index (GNDVI; (RNIR - RGreen)/(RNIR + RGreen)), derived from field NGB digital images, increased during the summer growing season for each vegetation type: i.e., ∼5% (0.01) for polar semi-desert; ∼10% (0.04) for mesic tundra; and ∼12% (0.03) for wet sedge respectively. PVC derived from field images was found to be strongly correlated with WorldView-2 derived normalized difference spectral indices (NDSI; (Rx - Ry)/(Rx + Ry)), where Rx is the reflectance of the red edge (724.1 nm) or near infrared (832.9 nm and 949.3 nm) bands; Ry is the reflectance of the yellow (607.7 nm) or red (658.8 nm) bands with R2's ranging from 0.74 to 0.81. NDSIs that incorporated the yellow band (607.7 nm) performed slightly better than the NDSIs without, indicating that this band may be more useful for investigating Arctic vegetation that often includes large proportions of senescent vegetation throughout the growing season.

Use of a Closed-Loop Tracking Algorithm for Orientation Bias Determination of an S-Band Ground Station

NASA Technical Reports Server (NTRS)

Welch, Bryan W.; Piasecki, Marie T.; Schrage, Dean S.

2015-01-01

The Space Communications and Navigation (SCaN) Testbed project completed installation and checkout testing of a new S-Band ground station at the NASA Glenn Research Center in Cleveland, Ohio in 2015. As with all ground stations, a key alignment process must be conducted to obtain offset angles in azimuth (AZ) and elevation (EL). In telescopes with AZ-EL gimbals, this is normally done with a two-star alignment process, where telescope-based pointing vectors are derived from catalogued locations with the AZ-EL bias angles derived from the pointing vector difference. For an antenna, the process is complicated without an optical asset. For the present study, the solution was to utilize the gimbal control algorithms closed-loop tracking capability to acquire the peak received power signal automatically from two distinct NASA Tracking and Data Relay Satellite (TDRS) spacecraft, without a human making the pointing adjustments. Briefly, the TDRS satellite acts as a simulated optical source and the alignment process proceeds exactly the same way as a one-star alignment. The data reduction process, which will be discussed in the paper, results in two bias angles which are retained for future pointing determination. Finally, the paper compares the test results and provides lessons learned from the activity.

Study of the Effect of Temporal Sampling Frequency on DSCOVR Observations Using the GEOS-5 Nature Run Results (Part I): Earths Radiation Budget

NASA Technical Reports Server (NTRS)

Holdaway, Daniel; Yang, Yuekui

2016-01-01

Satellites always sample the Earth-atmosphere system in a finite temporal resolution. This study investigates the effect of sampling frequency on the satellite-derived Earth radiation budget, with the Deep Space Climate Observatory (DSCOVR) as an example. The output from NASA's Goddard Earth Observing System Version 5 (GEOS-5) Nature Run is used as the truth. The Nature Run is a high spatial and temporal resolution atmospheric simulation spanning a two-year period. The effect of temporal resolution on potential DSCOVR observations is assessed by sampling the full Nature Run data with 1-h to 24-h frequencies. The uncertainty associated with a given sampling frequency is measured by computing means over daily, monthly, seasonal and annual intervals and determining the spread across different possible starting points. The skill with which a particular sampling frequency captures the structure of the full time series is measured using correlations and normalized errors. Results show that higher sampling frequency gives more information and less uncertainty in the derived radiation budget. A sampling frequency coarser than every 4 h results in significant error. Correlations between true and sampled time series also decrease more rapidly for a sampling frequency less than 4 h.

Global Weather States and Their Properties from Passive and Active Satellite Cloud Retrievals

NASA Technical Reports Server (NTRS)

Tselioudis, George; Rossow, William; Zhang, Yuanchong; Konsta, Dimitra

2013-01-01

In this study, the authors apply a clustering algorithm to International Satellite Cloud Climatology Project (ISCCP) cloud optical thickness-cloud top pressure histograms in order to derive weather states (WSs) for the global domain. The cloud property distribution within each WS is examined and the geographical variability of each WS is mapped. Once the global WSs are derived, a combination of CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical cloud structure retrievals is used to derive the vertical distribution of the cloud field within each WS. Finally, the dynamic environment and the radiative signature of the WSs are derived and their variability is examined. The cluster analysis produces a comprehensive description of global atmospheric conditions through the derivation of 11 WSs, each representing a distinct cloud structure characterized by the horizontal distribution of cloud optical depth and cloud top pressure. Matching those distinct WSs with cloud vertical profiles derived from CloudSat and CALIPSO retrievals shows that the ISCCP WSs exhibit unique distributions of vertical layering that correspond well to the horizontal structure of cloud properties. Matching the derived WSs with vertical velocity measurements shows a normal progression in dynamic regime when moving from the most convective to the least convective WS. Time trend analysis of the WSs shows a sharp increase of the fair-weather WS in the 1990s and a flattening of that increase in the 2000s. The fact that the fair-weather WS is the one with the lowest cloud radiative cooling capability implies that this behavior has contributed excess radiative warming to the global radiative budget during the 1990s.

Using NDVI to estimate carbon fluxes from small rotationally grazed pastures

USDA-ARS?s Scientific Manuscript database

Satellite-based Normalized Difference Vegetation Index (NDVI) data have been extensively used for estimating gross primary productivity (GPP) and yield of grazing lands throughout the world. However, the usefulness of satellite-based images for monitoring rotationally-grazed pastures in the northea...

Arctic sea ice albedo - A comparison of two satellite-derived data sets

NASA Technical Reports Server (NTRS)

Schweiger, Axel J.; Serreze, Mark C.; Key, Jeffrey R.

1993-01-01

Spatial patterns of mean monthly surface albedo for May, June, and July, derived from DMSP Operational Line Scan (OLS) satellite imagery are compared with surface albedos derived from the International Satellite Cloud Climatology Program (ISCCP) monthly data set. Spatial patterns obtained by the two techniques are in general agreement, especially for June and July. Nevertheless, systematic differences in albedo of 0.05 - 0.10 are noted which are most likely related to uncertainties in the simple parameterizations used in the DMSP analyses, problems in the ISCCP cloud-clearing algorithm and other modeling simplifications. However, with respect to the eventual goal of developing a reliable automated retrieval algorithm for compiling a long-term albedo data base, these initial comparisons are very encouraging.

Standardized Photometric Calibrations for Panchromatic SSA Sensors

NASA Astrophysics Data System (ADS)

Castro, P.; Payne, T.; Battle, A.; Cole, Z.; Moody, J.; Gregory, S.; Dao, P.

2016-09-01

Panchromatic sensors used for Space Situational Awareness (SSA) have no standardized method for transforming the net flux detected by a CCD without a spectral filter into an exo-atmospheric magnitude in a standard magnitude system. Each SSA data provider appears to have their own method for computing the visual magnitude based on panchromatic brightness making cross-comparisons impossible. We provide a procedure in order to standardize the calibration of panchromatic sensors for the purposes of SSA. A technique based on theoretical modeling is presented that derives standard panchromatic magnitudes from the Johnson-Cousins photometric system defined by Arlo Landolt. We verify this technique using observations of Landolt standard stars and a Vega-like star to determine empirical panchromatic magnitudes and compare these to synthetically derived panchromatic magnitudes. We also investigate color terms caused by differences in the quantum efficiency (QE) between the Landolt standard system and panchromatic systems. We evaluate calibrated panchromatic satellite photometry by observing several GEO satellites and standard stars using three different sensors. We explore the effect of satellite color terms by comparing the satellite signatures. In order to remove other variables affecting the satellite photometry, two of the sensors are at the same site using different CCDs. The third sensor is geographically separate from the first two allowing for a definitive test of calibrated panchromatic satellite photometry.

Green leaf phenology at Landsat resolution: scaling from the plot to satellite

NASA Astrophysics Data System (ADS)

Fisher, J. I.; Mustard, J. F.; Vadeboncour, M.

2005-12-01

Despite the large number of in situ, plot-level phenological measurements and satellite-derived phenological studies, there has been little success to date in merging these records temporally or spatially. In particular, while most phenological patterns and trends derived from satellites appear realistic and coherent, they may not reflect spatial and temporal patterns at the plot level. An obvious explanation is the drastic scale difference from plot-level to most satellite observations. In this research, we bridge this scale gap through higher resolution satellite records (Landsat) and quantify the accuracy of satellite-derived metrics with direct field measurements. We compiled fifty-seven Landsat scenes from southern New England (P12 R51) from 1984 to 2002. Green vegetation areal abundance for each scene was derived from spectral mixture analysis and a single set of endmembers. The leaf area signal was fit with a logistic-growth simulating sigmoid curve to derive phenological markers (half-maximum leaf-onset and offset). Spring leaf-onset dates in homogenous stands of deciduous forests displayed significant and persistent local variability. The local variability was validated with multiple springtime ground observations (r2 = 0.91). The highest degree of verified small-scale variation occurred where contiguous forests displayed leaf-onset gradients of 10-14 days over short distances (<500 m). These dramatic gradients, of a similar magnitude to differences in leaf-onset from MD to MA, occur in of low-relief (<40 m) upland regions. The patterns suggest that microclimates resulting from springtime cold-air drainage may be influential in governing the start of leaf growth. These microclimates may be of crucial importance in interpreting in situ records and interpolating phenology from satellite data. Regional patterns from the Landsat analyses suggest topographic, coastal, and land-use controls on phenology. For example, our results indicate that deciduous forests in the Providence, RI metropolitan area leaf out 5-7 days earlier than comparable rural areas. In preliminary work, we validated the Landsat-derived metrics with similar analyses of MODIS and AVHRR, and demonstrate that aggregating diverse local phenologies into coarse grids may convolute interpretations. Despite these complications, the platform-independent curve-fit methodology may be extended across platforms and field data. The methodologically consistent approach, in tandem with Landsat data, allows us to effectively scale between plot and satellite phenological observations.

Earth System Data Records of Mass Transport from Time-Variable Gravity Data

NASA Astrophysics Data System (ADS)

Zlotnicki, V.; Talpe, M.; Nerem, R. S.; Landerer, F. W.; Watkins, M. M.

2014-12-01

Satellite measurements of time variable gravity have revolutionized the study of Earth, by measuring the ice losses of Greenland, Antarctica and land glaciers, changes in groundwater including unsustainable losses due to extraction of groundwater, the mass and currents of the oceans and their redistribution during El Niño events, among other findings. Satellite measurements of gravity have been made primarily by four techniques: satellite tracking from land stations using either lasers or Doppler radio systems, satellite positioning by GNSS/GPS, satellite to satellite tracking over distances of a few hundred km using microwaves, and through a gravity gradiometer (radar altimeters also measure the gravity field, but over the oceans only). We discuss the challenges in the measurement of gravity by different instruments, especially time-variable gravity. A special concern is how to bridge a possible gap in time between the end of life of the current GRACE satellite pair, launched in 2002, and a future GRACE Follow-On pair to be launched in 2017. One challenge in combining data from different measurement systems consists of their different spatial and temporal resolutions and the different ways in which they alias short time scale signals. Typically satellite measurements of gravity are expressed in spherical harmonic coefficients (although expansions in terms of 'mascons', the masses of small spherical caps, has certain advantages). Taking advantage of correlations among spherical harmonic coefficients described by empirical orthogonal functions and derived from GRACE data it is possible to localize the otherwise coarse spatial resolution of the laser and Doppler derived gravity models. This presentation discusses the issues facing a climate data record of time variable mass flux using these different data sources, including its validation.

Vegetation Cover Change in Yosemite National Park (California) Detected using Landsat Satellite Image Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher

2015-01-01

Landsat image analysis over the past 20+ years showed that consistent increases in the satellite normalized difference vegetation index (NDVI) during relatively dry years were confined to large wildfire areas that burned in the late 1980s and 1990s.

Ocean color products from the Korean Geostationary Ocean Color Imager (GOCI).

PubMed

Wang, Menghua; Ahn, Jae-Hyun; Jiang, Lide; Shi, Wei; Son, SeungHyun; Park, Young-Je; Ryu, Joo-Hyung

2013-02-11

The first geostationary ocean color satellite sensor, Geostationary Ocean Color Imager (GOCI), which is onboard South Korean Communication, Ocean, and Meteorological Satellite (COMS), was successfully launched in June of 2010. GOCI has a local area coverage of the western Pacific region centered at around 36°N and 130°E and covers ~2500 × 2500 km(2). GOCI has eight spectral bands from 412 to 865 nm with an hourly measurement during daytime from 9:00 to 16:00 local time, i.e., eight images per day. In a collaboration between NOAA Center for Satellite Applications and Research (STAR) and Korea Institute of Ocean Science and Technology (KIOST), we have been working on deriving and improving GOCI ocean color products, e.g., normalized water-leaving radiance spectra (nLw(λ)), chlorophyll-a concentration, diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), etc. The GOCI-covered ocean region includes one of the world's most turbid and optically complex waters. To improve the GOCI-derived nLw(λ) spectra, a new atmospheric correction algorithm was developed and implemented in the GOCI ocean color data processing. The new algorithm was developed specifically for GOCI-like ocean color data processing for this highly turbid western Pacific region. In this paper, we show GOCI ocean color results from our collaboration effort. From in situ validation analyses, ocean color products derived from the new GOCI ocean color data processing have been significantly improved. Generally, the new GOCI ocean color products have a comparable data quality as those from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua. We show that GOCI-derived ocean color data can provide an effective tool to monitor ocean phenomenon in the region such as tide-induced re-suspension of sediments, diurnal variation of ocean optical and biogeochemical properties, and horizontal advection of river discharge. In particular, we show some examples of ocean diurnal variations in the region, which can be provided effectively from satellite geostationary measurements.

Measuring phenological variability from satellite imagery

USGS Publications Warehouse

Reed, Bradley C.; Brown, Jesslyn F.; Vanderzee, D.; Loveland, Thomas R.; Merchant, James W.; Ohlen, Donald O.

1994-01-01

Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large- area land cover mapping and monitoring. The utility of re- motely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.

Barx2 is Expressed in Satellite Cells and is Required for Normal Muscle Growth and Regeneration

PubMed Central

Meech, Robyn; Gonzalez, Katie N.; Barro, Marietta; Gromova, Anastasia; Zhuang, Lizhe; Hulin, Julie-Ann; Makarenkova, Helen P.

2015-01-01

Muscle growth and regeneration are regulated through a series of spatiotemporally dependent signaling and transcriptional cascades. Although the transcriptional program controlling myogenesis has been extensively investigated, the full repertoire of transcriptional regulators involved in this process is far from defined. Various homeodomain transcription factors have been shown to play important roles in both muscle development and muscle satellite cell-dependent repair. Here, we show that the homeodomain factor Barx2 is a new marker for embryonic and adult myoblasts and is required for normal postnatal muscle growth and repair. Barx2 is coexpressed with Pax7, which is the canonical marker of satellite cells, and is upregulated in satellite cells after muscle injury. Mice lacking the Barx2 gene show reduced postnatal muscle growth, muscle atrophy, and defective muscle repair. Moreover, loss of Barx2 delays the expression of genes that control proliferation and differentiation in regenerating muscle. Consistent with the in vivo observations, satellite cell-derived myoblasts cultured from Barx2−/− mice show decreased proliferation and ability to differentiate relative to those from wild-type or Barx2+/− mice. Barx2−/− myoblasts show reduced expression of the differentiation-associated factor myogenin as well as cell adhesion and matrix molecules. Finally, we find that mice lacking both Barx2 and dystrophin gene expression have severe early onset myopathy. Together, these data indicate that Barx2 is an important regulator of muscle growth and repair that acts via the control of satellite cell proliferation and differentiation. PMID:22076929

Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index Composites

USGS Publications Warehouse

,

2005-01-01

The Advanced Very High Resolution Radiometer (AVHRR) is a broad-band scanner with four to six bands, depending on the model. The AVHRR senses in the visible, near-, middle-, and thermal- infrared portions of the electromagnetic spectrum. This sensor is carried on a series of National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POES), beginning with the Television InfraRed Observation Satellite (TIROS-N) in 1978. Since 1989, the United States Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) has been mapping the vegetation condition of the United States and Alaska using satellite information from the AVHRR sensor. The vegetation condition composites, more commonly called greenness maps, are produced every week using the latest information on the growth and condition of the vegetation. One of the most important aspects of USGS greenness mapping is the historical archive of information dating back to 1989. This historical stretch of information has allowed the USGS to determine a 'normal' vegetation condition. As a result, it is possible to compare the current week's vegetation condition with normal vegetation conditions. An above normal condition could indicate wetter or warmer than normal conditions, while a below normal condition could indicate colder or dryer than normal conditions. The interpretation of departure from normal will depend on the season and geography of a region.

Using Small Drone (UAS) Imagery to Bridge the Gap Between Field- and Satellite-Based Measurements of Vegetation Structure and Change

NASA Astrophysics Data System (ADS)

Mayes, M. T.; Estes, L. D.; Gago, X.; Debats, S. R.; Caylor, K. K.; Manfreda, S.; Oudemans, P.; Ciraolo, G.; Maltese, A.; Nadal, M.; Estrany, J.

2016-12-01

Leaf area is an important ecosystem variable that relates to vegetation biomass, productivity, water and nutrient use in natural and agricultural systems globally. Since the 1980s, optical satellite image-based estimates of leaf area based on indices such as Normalized Difference Vegetation Index (NDVI) have greatly improved understanding of vegetation structure, function, and responses to disturbance at landscape (10^3 km2) to continental (10^6 km2) spatial scales. However, at landscape scales, satellites have failed to capture many leaf area patterns indicative of vegetation succession, crop types, stress and other conditions important for ecological processes. Small drones (UAS - unmanned aerial systems) offer new means for assessing leaf area and vegetation structure at higher spatial resolutions (<1 m) and land cover features such as substrate exposure that may affect estimates of vegetation structure in satellite data. Yet it is unclear how differences in spatial and spectral resolution between UAS and satellite data affect their relationships to each other, and to common field measurements of leaf area (e.g. LiCOR photosensors) and land cover. Constraining these relationships is important for leveraging UAS data to improve scaling of field data on leaf area and biomass to satellite data from Landsat, Sentinel-2, and increasing numbers of commercial sensors. Here, we quantify relationships among field, UAS and satellite estimates of vegetation leaf area and biomass in three case study landscapes spanning semi-arid Mediterranean (Matera, Southern Italy and Mallorca, Spain) and North American temperate ecosystems (New Jersey, USA). We assess how land cover and sensor spectral characteristics affect UAS and satellite-derived NDVI, leaf-area and biomass estimates. Then, we assess the fidelity of UAS, WorldView-2, and Landsat leaf-area and biomass estimates to field-measured landscape changes and variability, including vegetation recovery from fire (Mallorca), and leaf-area and biomass variability due to orchard type and agro-ecosystem management (Matera, New Jersey). Finally, we highlight promising ways forward for improving field data collection and the use of UAS observations to monitor vegetation leaf-area and biomass change at landscape scales in natural and agricultural systems.

Hydrological differentiation and spatial distribution of high altitude wetlands in a semi-arid Andean region derived from satellite data

NASA Astrophysics Data System (ADS)

Otto, M.; Scherer, D.; Richters, J.

2011-05-01

High Altitude Wetlands of the Andes (HAWA) belong to a unique type of wetland within the semi-arid high Andean region. Knowledge about HAWA has been derived mainly from studies at single sites within different parts of the Andes at only small time scales. On the one hand, HAWA depend on water provided by glacier streams, snow melt or precipitation. On the other hand, they are suspected to influence hydrology through water retention and vegetation growth altering stream flow velocity. We derived HAWA land cover from satellite data at regional scale and analysed changes in connection with precipitation over the last decade. Perennial and temporal HAWA subtypes can be distinguished by seasonal changes of photosynthetically active vegetation (PAV) indicating the perennial or temporal availability of water during the year. HAWA have been delineated within a region of 12 800 km2 situated in the Northwest of Lake Titicaca. The multi-temporal classification method used Normalized Differenced Vegetation Index (NDVI) and Normalized Differenced Infrared Index (NDII) data derived from two Landsat ETM+ scenes at the end of austral winter (September 2000) and at the end of austral summer (May 2001). The mapping result indicates an unexpected high abundance of HAWA covering about 800 km2 of the study region (6 %). Annual HAWA mapping was computed using NDVI 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS). Analyses on the relation between HAWA and precipitation was based on monthly precipitation data of the Tropical Rain Measurement Mission (TRMM 3B43) and MODIS Eight Day Maximum Snow Extent data (MOD10A2) from 2000 to 2010. We found HAWA subtype specific dependencies on precipitation conditions. A strong relation exists between perennial HAWA and snow fall (r2: 0.82) in dry austral winter months (June to August) and between temporal HAWA and precipitation (r2: 0.75) during austral summer (March to May). Annual changes in spatial extend of perennial HAWA indicate alterations in annual water supply generated from snow melt.

Hydrological differentiation and spatial distribution of high altitude wetlands in a semi-arid Andean region derived from satellite data

NASA Astrophysics Data System (ADS)

Otto, M.; Scherer, D.; Richters, J.

2011-01-01

High Altitude Wetlands of the Andes (HAWA) are unique types of wetlands within the semi-arid high Andean region. Knowledge about HAWA has been derived mainly from studies at single sites within different parts of the Andes at only small time scales. On the one hand HAWA depend on water provided by glacier streams, snow melt or precipitation. On the other hand, they are suspected to influence hydrology through water retention and vegetation growth altering stream flow velocity. We derived HAWA land cover from satellite data at regional scale and analysed changes in connection with precipitation over the last decade. Perennial and temporal HAWA subtypes can be distinguished by seasonal changes of photosynthetically active vegetation (PAV) indicating the perennial or temporal availability of water during the year. HAWA have been delineated within a region of 11 000 km2 situated in the Northwest of Lake Titicaca. The multi temporal classification method used Normalized Differenced Vegetation Index (NDVI) and Normalized Differenced Infrared Index (NDII) data derived from two Landsat ETM+ scenes at the end of austral winter (September 2000) and at the end of austral summer (May 2001). The mapping result indicates an unexpected high abundance of HAWA covering about 800 km2 of the study region (6%). Annual HAWA mapping was computed using NDVI 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS). Analyses on the reletation between HAWA and precipitation was based on monthly precipitation data of the Tropical Rain Measurement Mission (TRMM 3B43) and MODIS Eight Day Maximum Snow Extent data (MOD10A2) from 2000 to 2010. We found HAWA subtype specific dependencies to precipitation conditions. Strong relation exists between perennial HAWA and snow fall (r2: 0.82) in dry austral winter months (June to August) and between temporal HAWA and precipitation (r2: 0.75) during austral summer (March to May). Annual spatial patterns of perennial HAWA indicated spatial alteration of water supply for PAV up to several hundred metres at a single HAWA site.

Vegetation Cover Change in Yellowstone National Park Detected Using Landsat Satellite Image Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher S.

2015-01-01

Results from Landsat satellite image analysis since 1987 in all unburned areas (since the 1880s) of Yellowstone National Park (YNP) showed that consistent decreases in the normalized difference vegetation index (NDVI) have been strongly dependent on periodic variations in peak annual snow water equivalents (SWE).

Thirty Years of Vegetation Change in the Coastal Santa Cruz Mountains of Northern California Detected Using Landsat Satellite Image Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher

2015-01-01

Results from Landsat satellite image times series analysis since 1983 of this study area showed gradual, statistically significant increases in the normalized difference vegetation index (NDVI) in more than 90% of the (predominantly second-growth) evergreen forest locations sampled.

Harmonizing estimates of forest land area from national-level forest inventory and satellite imagery

Treesearch

Bonnie Ruefenacht; Mark D. Nelson; Mark Finco

2009-01-01

Estimates of forest land area are derived both from national-level forest inventories and satellite image-based map products. These estimates can differ substantially within subregional extents (e.g., states or provinces) primarily due to differences in definitions of forest land between inventory- and image-based approaches. We present a geospatial modeling approach...

Assessment of spatially distributed values of Kc using vegetation indices derived from medium resolution satellite data

NASA Astrophysics Data System (ADS)

Greco, M.; Simoniello, T.; Lanfredi, M.; Russo, A. L.

2010-09-01

In the last years, the theme of suitable assessment of irrigation water supply has been raised relevant interest for both general principles of sustainable development and optimization of water resources techniques and management. About 99% of the water used in agriculture is lost by crops as evapotranspiration (ET). Thus, it becomes crucial to drive direct or indirect measurement in order to perform a suitable evaluation of water loss by evapotranspiration (i.e. actual evapotranspiration) as well as crop water status and its effect on the production. The main methods used to measure evapotranspiration are available only at field scale (Bowen ratio, eddy correlation system, soil water balance) confined to a small pilot area, generally due to expense and logistical constraints. This led over the last 50 years to the development of a large number of empirical methods to estimate evapotranspiration through different climatic and meteorological variables as well as combining models, based on aerodynamic theory and energy balance, taking into account both canopy properties and meteorological conditions. Among these, the Penman-Monteith equation seems to give the best results providing a robust and consistent method world wide accepted. Such conventional methods only provide accurate evapotranspiration assessment for a homogeneous region nearby the meteorological gauge station and cannot be extrapolated to other different sites; whereas remote sensing techniques allow for filling up such a gap. Some of these satellite techniques are based on the use of thermal band signals as inputs for energy balance equations. Another common approach is mainly based on the FAO method for estimating crop evapotranspiration, in which evapotranspiration data are multiplied by crop coefficients, Kc, derived from satellite multispectral vegetation indices obtained. The rationale behind such a link considers that Kc and vegetation indices are sensitive to both leaf area index and fractional ground cover. Thermal-based energy balance models are more suitable than the FAO-Kc model for estimating crop ET, especially under moisture stress conditions, but they require many inputs and detailed theoretical background knowledge; so they can be only used in regions where high quality, hourly agricultural weather data are readily available providing instantaneous values of heat fluxes corresponding to the time of the satellite overpass. Thus, FAO-Kc approach is widely used in research activities and real-time irrigation scheduling for several water applications since it does not require temporal upscaling for obtaining daily values and satellite imagery in the reflective bands used for vegetation index computation are more readily available at higher spatial resolution than thermal band data. There is no simple way to compute crop coefficients because they depend on climate, soil type, crop and its varieties, irrigation method, soil water, nutrient content and plant phenology. Consequently, specific calibrations of crop coefficient are required in various climatic regions. Many authors suggested a linear relationship between Kc and vegetation indices, but non-linear relationships have been proposed too. However, according to the radiative transfer theory, the nature of such relationships depends on the crop architecture and the definition of the adopted vegetation index, but the linear assumption can be adopted as first. Such studies, mainly investigated the possibility to use high resolution satellite data, such as Quickbird, Ikonos, TM, which are not suitable for operational purposes since in spite of the high spatial sampling they have an inadequate revisiting time over a given area. To obtain adequate temporal sampling, some authors proposed the use of a virtual constellation made by all currently available high-resolution satellites (e.g., DEMETER project). However the joint use of data from different satellites requires a carefully inter-satellite cross-calibration and co-registration. In order to avoid such problems and to generate spatially distributed values of Kc capturing field-specific crop development, the employment of vegetation indices derived from medium resolution MODIS data having a higher temporal sampling has been investigated. The spatial and temporal correlation between NDVI (Normalized Difference Vegetation Index) and crop coefficients for different herbaceous and arboreal cultivations has been investigated to define their relationships. Through this approach site-specific crop coefficients were derived taking into account the effective ground coverage and status. The analysis has been applied on the 2005-2008 time series for the Basilicata region, Southern Italy.

Distribution of the GNSS-LEO occultation events over Egypt

NASA Astrophysics Data System (ADS)

Ghoniem, Ibrahim; Mousa, Ashraf El-Kutb; El-Fiky, Gamal

2017-06-01

The space-based GNSS RO technique is a promising tool for monitoring the Earth's atmosphere and ionosphere (Mousa et al., 2006). The current paper presents the distribution of the occultation events over Egypt using the operating LEO satellites and GNSS by its two operating systems. By the present research, Egypt could raise NWP Models efficiency by improving meteorological data quality. Twenty operating LEO missions (e.g. Argentinean SAC-C, European MetOp-A, German TerraSAR-X, Indian OceanSat-2, etc.) sent by different countries all over the world were used to derive the occultation events position through Egypt borders by receiving signal from the American global positioning system (GPS) and the Russian global navigation satellite system (GLONASS). Approximately 20,000 km Altitude satellites are transmitting enormous number of rays by the day to approximately 800 km satellites passing by the Earth atmosphere. Our mission is to derive all of these rays position (start and end) by calculating satellites position by the time, determine the rays in the occultation case and derive the atmosphere tangent point position for all occultating rays on the Earth surface (Occultation Events).

Use of MODIS-Derived Fire Radiative Energy to Estimate Smoke Aerosol Emissions over Different Ecosystems

NASA Technical Reports Server (NTRS)

Ichoku, Charles; Kaufman, Yoram J.

2003-01-01

Biomass burning is the main source of smoke aerosols and certain trace gases in the atmosphere. However, estimates of the rates of biomass consumption and emission of aerosols and trace gases from fires have not attained adequate reliability thus far. Traditional methods for deriving emission rates employ the use of emission factors e(sub x), (in g of species x per kg of biomass burned), which are difficult to measure from satellites. In this era of environmental monitoring from space, fire characterization was not a major consideration in the design of the early satellite-borne remote sensing instruments, such as AVHRR. Therefore, although they are able to provide fire location information, they were not adequately sensitive to variations in fire strength or size, because their thermal bands used for fire detection saturated at the lower end of fire radiative temperature range. As such, hitherto, satellite-based emission estimates employ proxy techniques using satellite derived fire pixel counts (which do not express the fire strength or rate of biomass consumption) or burned areas (which can only be obtained after the fire is over). The MODIS sensor, recently launched into orbit aboard EOS Terra (1999) and Aqua (2002) satellites, have a much higher saturation level and can, not only detect the fire locations 4 times daily, but also measures the at-satellite fire radiative energy (which is a measure of the fire strength) based on its 4 micron channel temperature. Also, MODIS measures the optical thickness of smoke and other aerosols. Preliminary analysis shows appreciable correlation between the MODIS-derived rates of emission of fire radiative energy and smoke over different regions across the globe. These relationships hold great promise for deriving emission coefficients, which can be used for estimating smoke aerosol emissions from MODIS active fire products. This procedure has the potential to provide more accurate emission estimates in near real-time, providing opportunities for various disaster management applications such as alerts, evacuation and, smoke dispersion forecasting.

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.

New Microwave-Based Missions Applications for Rainfed Crops Characterization

NASA Astrophysics Data System (ADS)

Sánchez, N.; Lopez-Sanchez, J. M.; Arias-Pérez, B.; Valcarce-Diñeiro, R.; Martínez-Fernández, J.; Calvo-Heras, J. M.; Camps, A.; González-Zamora, A.; Vicente-Guijalba, F.

2016-06-01

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.

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.

Monitoring Lake and Reservoir Level: Satellite Observations, Modeling and Prediction

NASA Astrophysics Data System (ADS)

Ricko, M.; Birkett, C. M.; Adler, R. F.; Carton, J.

2013-12-01

Satellite measurements of lake and reservoir water levels complement in situ observations by providing stage information for un-gauged basins and by filling data gaps in gauge records. However, different satellite radar altimeter-derived continental water level products may differ significantly owing to choice of satellites and data processing methods. To explore the impacts of these differences, a direct comparison between three different altimeter-based surface water level estimates (USDA/NASA GRLM, LEGOS and ESA-DMU) will be presented and products validated with lake level gauge time series for lakes and reservoirs of a variety of sizes and conditions. The availability of satellite-based rainfall (i.e., TRMM and GPCP) and satellite-based lake/reservoir levels offers exciting opportunities to estimate and monitor the hydrologic properties of the lake systems. Here, a simple water balance model is utilized to relate net freshwater flux on a catchment basin to lake/reservoir level. Focused on tropical lakes and reservoirs it allows a comparison of the flux to altimetric lake level estimates. The combined use of model, satellite-based rainfall, evaporation information and reanalysis products, can be used to output water-level hindcasts and seasonal future forecasts. Such a tool is fundamental for understanding present-day and future variations in lake/reservoir levels and enabling a better understand of climatic variations on inter-annual to inter-decadal time-scales. New model-derived water level estimates of lakes and reservoirs, on regional to global scales, would assist communities with interests in climate studies focusing on extreme events, such as floods and droughts, and be important for water resources management.

Evaluation of voice codecs for the Australian mobile satellite system

NASA Technical Reports Server (NTRS)

Bundrock, Tony; Wilkinson, Mal

1990-01-01

The evaluation procedure to choose a low bit rate voice coding algorithm is described for the Australian land mobile satellite system. The procedure is designed to assess both the inherent quality of the codec under 'normal' conditions and its robustness under 'severe' conditions. For the assessment, normal conditions were chosen to be random bit error rate with added background acoustic noise and the severe condition is designed to represent burst error conditions when mobile satellite channel suffers from signal fading due to roadside vegetation. The assessment is divided into two phases. First, a reduced set of conditions is used to determine a short list of candidate codecs for more extensive testing in the second phase. The first phase conditions include quality and robustness and codecs are ranked with a 60:40 weighting on the two. Second, the short listed codecs are assessed over a range of input voice levels, BERs, background noise conditions, and burst error distributions. Assessment is by subjective rating on a five level opinion scale and all results are then used to derive a weighted Mean Opinion Score using appropriate weights for each of the test conditions.

A supplemental catalog of atmospheric densities from satellite-drag analysis

NASA Technical Reports Server (NTRS)

Jacchia, L. G.; Slowey, J. W.

1972-01-01

The catalog of densities derived from satellite-drag analysis extends and supplements similar earlier publications. The densities were computed from nine artificial satellites for effective heights ranging from 300 to 1130 km, and are presented together with pertinent data that permit the location in time and space of the point to which they refer. The intervals covered vary between 1 and 6 years for the different satellites and average about 3.5 years for the nine satellites. The cutoff date for the data included is, in most cases, January 14, 1970.

INTEGRAL hard X-ray spectra of the cosmic X-ray background and Galactic ridge emission

NASA Astrophysics Data System (ADS)

Türler, M.; Chernyakova, M.; Courvoisier, T. J.-L.; Lubiński, P.; Neronov, A.; Produit, N.; Walter, R.

2010-03-01

Aims: We derive the spectra of the cosmic X-ray background (CXB) and of the Galactic ridge X-ray emission (GRXE) in the ~20-200 keV range from the data of the IBIS instrument aboard the INTEGRAL satellite obtained during the four dedicated Earth-occultation observations in early 2006. Methods: We analyze the modulation of the IBIS/ISGRI detector counts induced by the passage of the Earth through the field of view of the instrument. Unlike previous studies, we do not fix the spectral shape of the various contributions, but model instead their spatial distribution and derive for each of them the expected modulation of the detector counts. The spectra of the diffuse emission components are obtained by fitting the normalizations of the model lightcurves to the observed modulation in different energy bins. Because of degeneracy, we guide the fits with a realistic choice of the input parameters and a constraint for spectral smoothness. Results: The obtained CXB spectrum is consistent with the historic HEAO-1 results and falls slightly below the spectrum derived with Swift/BAT. A 10% higher normalization of the CXB cannot be completely excluded, but it would imply an unrealistically high albedo of the Earth. The derived spectrum of the GRXE confirms the presence of a minimum around 80 keV with improved statistics and yields an estimate of ~0.6 M⊙ for the average mass of white dwarfs in the Galaxy. The analysis also provides updated normalizations for the spectra of the Earth's albedo and the cosmic-ray induced atmospheric emission. Conclusions: This study demonstrates the potential of INTEGRAL Earth-occultation observations to derive the hard X-ray spectra of three fundamental components: the CXB, the GRXE and the Earth emission. Further observations would be extremely valuable to confirm our results with improved statistics.

Representation of vegetation by continental data sets derived from NOAA-AVHRR data

NASA Technical Reports Server (NTRS)

Justice, C. O.; Townshend, J. R. G.; Kalb, V. L.

1991-01-01

Images of the normalized difference vegetation index (NDVI) are examined with specific attention given to the effect of spatial scales on the understanding of surface phenomena. A scale variance analysis is conducted on NDVI annual and seasonal images of Africa taken from 1987 NOAA-AVHRR data at spatial scales ranging from 8-512 km. The scales at which spatial variation takes place are determined and the relative magnitude of the variations are considered. Substantial differences are demonstrated, notably an increase in spatial variation with coarsening spatial resolution. Different responses in scale variance as a function of spatial resolution are noted in an analysis of maximum value composites for February and September; the difference is most marked in areas with very seasonal vegetation. The spatial variation at different scales is attributed to different factors, and methods involving the averaging of areas of transition and surface heterogeneity can oversimplify surface conditions. The spatial characteristics and the temporal variability of areas should be considered to accurately apply satellite data to global models.

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.

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

NASA Astrophysics Data System (ADS)

Ruiz-Pérez, Guiomar; Koch, Julian; Manfreda, Salvatore; Caylor, Kelly; Francés, Félix

2017-12-01

Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment - the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors

NASA Technical Reports Server (NTRS)

Holben, Brent N.; Shimabukuro, Yosio E.

1993-01-01

A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55-3.95 micron channel was used with the two reflective channels 0.58-0.68 micron and 0.725-1.1 micron to run a constrained least squares model to generate fraction images for an area in the west central region of Brazil. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse spatial resolution data for global studies.

Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

NASA Technical Reports Server (NTRS)

Ulsig, Laura; Nichol, Caroline J.; Huemmrich, Karl F.; Landis, David R.; Middleton, Elizabeth M.; Lyapustin, Alexei I.; Mammarella, Ivan; Levula, Janne; Porcar-Castell, Albert

2017-01-01

Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R (sup 2) equals 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R (sup 2) is greater than 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

Monitoring vegetation greenness with satellite data

Treesearch

Robert E. Burgan; Roberta A. Hartford

1993-01-01

Vegetation greenness can be monitored at 1-km resolution for the conterminous United States through data obtained from the Advanced Very High Resolution Radiometer on the NOAA-11 weather satellites. The data are used to calculate biweekly composites of the Normalized Difference Vegetation Index. The resulting composite images are updated weekly and made available to...

Assessment of Climate Impact Changes on Forest Vegetation Dynamics by Satellite Remote Sensing

NASA Astrophysics Data System (ADS)

Zoran, Maria

Climate variability represents the ensemble of net radiation, precipitation, wind and temper-ature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Forest vegetation phenology constitutes an efficient bio-indicator of climate and anthropogenic changes impacts and a key parameter for understanding and modelling vegetation-climate in-teractions. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of Normalized Difference Vege-tation Index (NDVIs), which requires NDVI time-series with good time resolution, over homo-geneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). Spatio-temporal vegetation dynamics have been quantified as the total amount of vegetation (mean NDVI) and the seasonal difference (annual NDVI amplitude) by a time series analysis of NDVI satellite images with the Harmonic ANalysis of Time Series algorithm. A climate indicator (CI) was created from meteorological data (precipitation over net radiation). The relationships between the vegetation dynamics and the CI have been determined spatially and temporally. The driest test regions prove to be the most sensitive to climate impact. The spatial and temporal patterns of the mean NDVI are the same, while they are partially different for the seasonal difference. The aim of this paper was to quantify this impact over a forest ecosystem placed in the North-Eastern part of Bucharest town, Romania, with Normalized Difference Vegetation Index (NDVI) parameter extracted from IKONOS and LANDSAT TM and ETM satellite images and meteorological data over l995-2007 period. For investigated test area, considerable NDVI decline was observed between 1995 and 2008 due to the drought events during 2003 and 2007 years. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and to-pography are not correlated with NDVI dynamics. Specific aim of this paper was to assess, forecast, and mitigate the risks of climatic changes on forest systems and its biodiversity as well as on adjacent environment areas and to provide early warning strategies on the basis of spectral information derived from satellite data regarding atmospheric effects of forest biome degradation . The paper aims to describe observed trends and potential impacts based on scenarios from simulations with regional climate models and other downscaling procedures.

Angular radiation models for Earth-atmosphere system. Volume 1: Shortwave radiation

NASA Technical Reports Server (NTRS)

Suttles, J. T.; Green, R. N.; Minnis, P.; Smith, G. L.; Staylor, W. F.; Wielicki, B. A.; Walker, I. J.; Young, D. F.; Taylor, V. R.; Stowe, L. L.

1988-01-01

Presented are shortwave angular radiation models which are required for analysis of satellite measurements of Earth radiation, such as those fro the Earth Radiation Budget Experiment (ERBE). The models consist of both bidirectional and directional parameters. The bidirectional parameters are anisotropic function, standard deviation of mean radiance, and shortwave-longwave radiance correlation coefficient. The directional parameters are mean albedo as a function of Sun zenith angle and mean albedo normalized to overhead Sun. Derivation of these models from the Nimbus 7 ERB (Earth Radiation Budget) and Geostationary Operational Environmental Satellite (GOES) data sets is described. Tabulated values and computer-generated plots are included for the bidirectional and directional modes.

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

Relationship of multispectral satellite data to land surface evaporation from the Australian continent

NASA Technical Reports Server (NTRS)

Smith, R. C. G.; Choudhury, B. J.

1990-01-01

Based on NOAA-9 AVHRR and Nimbus-7 SMMR satellite data, satellite indices of vegetation from the Australian continent are calculated for the period of May 1986 to April 1987. Visible (VIS) and near infrared (NIR) reflectances and the normalized difference (ND) vegetation index are calculated from the AVHRR sensor. The microwave polarization difference (PD) is also calculated as the difference between the vertically and horizontally polarized brightness temperatures at 37 GHz. ND, PD, VIS, and NIR indices were plotted against rainfall and water balance estimates of evaporation. It is concluded that direct satellite monitoring of annual evaporation across the Australian continent using PD or VIS satellite indices of vegetation biomass appears possible for areas with evaporation less than 600 mm/y and that use of the ND relationship at continental scale may underpredict monthly evaporation of forests relative to agriculture.

Vegetation Coverage Mapping and Soil Effect Correction in Estimating Vegetation Water Content and Dry Biomass from Satellites

NASA Astrophysics Data System (ADS)

Huang, J.; Chen, D.

2005-12-01

Vegetation water content (VWC) attracts great research interests in hydrology research in recent years. As an important parameter describing the horizontal expansion of vegetation, vegetation coverage is essential to implement soil effect correction for partially vegetated fields to estimate VWC accurately. Ground measurements of corn and soybeans in SMEX02 resulted in an identical expolinear relationship between vegetation coverage and leaf area index (LAI), which is used for vegetation coverage mapping. Results illustrated two parts of LAI growth quantitatively: the horizontal expansion of leaf coverage and the vertical accumulation of leaf layers. It is believed that the former part contributes significantly to LAI growth at initial vegetation growth stage and the latter is more dominant after vegetation coverage reaches a certain level. The Normalized Difference Water Index (NDWI) using short-wave infrared bands is convinced for its late saturation at high LAI values, in contrast to the Normalized Difference Vegetation Index (NDVI). NDWI is then utilized to estimate LAI, via another expolinear relationship, which is evidenced having vegetation species independency in study of corn and soybeans in SMEX02 sites. It is believed that the surface reflectance measured at satellites spectral bands are the mixed results of signals reflected from vegetation and bare soil, especially at partially vegetated fields. A simple linear mixture model utilizing vegetation coverage information is proposed to correct soil effect in such cases. Surface reflectance fractions for -rpure- vegetation are derived from the model. Comparing with ground measurements, empirical models using soil effect corrected vegetation indices to estimate VWC and dry biomass (DB) are generated. The study enhanced the in-depth understanding of the mechanisms how vegetation growth takes effect on satellites spectral reflectance with and without soil effect, which are particularly useful for modeling in hydrology, agriculture, forestry and meteorology etc.

A Combined Soil Moisture Product of the Tibetan Plateau using Different Sensors Simultaneously

NASA Astrophysics Data System (ADS)

Zeng, Y.; Dente, L.; Su, B.; Wang, L.

2012-12-01

It is always challenging to find a single satellite-derived soil moisture product that has complete coverage of the Tibetan Plateau for a long time period and is suitable for climate change studies at sub-continental scale. Meanwhile, having a number of independent satellite-derived soil moisture data sets does not mean that it is straightforward to create long-term consistent time series, due to the differences among the data sets related to the different retrieval approaches. Therefore, this study is focused on the development and validation of a simple Bayesian based method to merge/blend different satellite-derived soil moisture data. The merging method was firstly tested over the Maqu region (north-eastern fringe of the Tibetan Plateau), where in situ soil moisture data were collected, for the period from May 2008 to December 2010. The in situ data provided by the 20 monitoring stations in the Maqu region were compared to the AMSR-E soil moisture products by VUA-NASA and the ASCAT soil moisture products by TU Wien, in order to determine bias and standard deviation. It was found that the bias between the satellite and the in situ data varies with seasons. The satellite-derived products were first corrected for the bias and then merged. This is generally caused by notable differences in the represented depth, spatial extent and so on. The systematic bias is affected by the spatial variability and the temporal stability (Dente et al. 2012). The dependence of the bias on season was investigated and identified as the monsoon season only (May-September), in winter only (December - February), and in the period between the monsoon season and winter (March-April, October-November, called the transition season) (Dente et al. 2012, Su et al. 2011). After the date merging procedure, the standard deviations between the satellite and the in situ data reduced from 0.0839 to 0.0622 for ASCAT data, and from 0.0682 to 0.0593 for AMSR-E data. The developed merging method is therefore suitable to provide a more accurate soil moisture product than the AMSR-E and ASCAT products. As the merging method was shown to be promising over the Maqu region, it will be extended to the entire Tibetan Plateau. Then, the combined soil moisture product will be validated over the monitored sites located in Ngari and Naqu regions. References: Dente, L.; Vekerdy, Z.; Wen, J.; Su, Z., (2012) Maqu network for validation of satellite-derived soil moisture products. International Journal of Applied Earth Observation and Geoinformation, 17, 55-65. Su, Z., Wen, J., Dente, L., van der Velde, R. and ... [et al.] (2011) The Tibetan plateau observatory of plateau scale soil moisture and soil temperature, Tibet - Obs, for quantifying uncertainties in coarse resolution satellite and model products. In: Hydrology and earth system sciences (HESS): open access, 15 (2011)7 pp. 2303-2016.

Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets

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

Polo, J.; Wilbert, S.; Ruiz-Arias, J. A.

2016-07-01

At any site, the bankability of a projected solar power plant largely depends on the accuracy and general quality of the solar radiation data generated during the solar resource assessment phase. The term 'site adaptation' has recently started to be used in the framework of solar energy projects to refer to the improvement that can be achieved in satellite-derived solar irradiance and model data when short-term local ground measurements are used to correct systematic errors and bias in the original dataset. This contribution presents a preliminary survey of different possible techniques that can improve long-term satellite-derived and model-derived solar radiationmore » data through the use of short-term on-site ground measurements. The possible approaches that are reported here may be applied in different ways, depending on the origin and characteristics of the uncertainties in the modeled data. This work, which is the first step of a forthcoming in-depth assessment of methodologies for site adaptation, has been done within the framework of the International Energy Agency Solar Heating and Cooling Programme Task 46 'Solar Resource Assessment and Forecasting.'« less

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

Lopez, Anthony

Presentation at ASHRAE about the spatial and temporal variability of gridded TMYs, discussing advanced GIS and Web services that allow for direct access to data, surface-based observations for thousands of stations, climate reanalysis data, and products derived from satellite data; new developments in NREL's solar databases based on both observed data and satellite-derived gridded data, status of TMY3 weather files, and NREL's plans for the next-generation TMY weather files; and also covers what is new and different in the Climatic Design Conditions Table in the 2013 ASHRAE Handbook of Fundamentals.

Mapping landscape phenology preference of yellow-billed cuckoo with AVHRR data

USGS Publications Warehouse

Wallace, Cynthia S.A.; Villarreal, Miguel; van Riper, Charles

2013-01-01

We mapped habitat for threatened Yellow-billed Cuckoo (Coccycus americanus occidentalis) in the State of Arizona using the temporal greenness dynamics of the landscape, or the landscape phenology. Landscape phenometrics were derived from Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data for 1998 and 1999 by using Fourier harmonic analysis to analyze the waveform of the annual NDVI profile at each pixel. We modeled the spatial distribution of Yellow-billed Cuckoo habitat by coupling the field data of Cuckoo presence or absence and point-based samples of riparian and cottonwood-willow vegetation types with satellite phenometrics for 1998. Models were validated using field and satellite data collected in 1999. The results indicate that Yellow-billed Cuckoo occupy locations within their preferred habitat that exhibit peak greenness after the start of the summer monsoon and are greener and more dynamic than “average” habitat. Identification of preferred phenotypes within recognized habitat areas can be used to refine habitat models, inform predictions of habitat response to climate change, and suggest adaptation strategies.

Assessing satellite-derived start-of-season measures in the conterminous USA

USGS Publications Warehouse

Schwartz, Mark D.; Reed, Bradley C.; White, Michael A.

2002-01-01

National Oceanic and Atmospheric Administration (NOAA)-series satellites, carrying advanced very high-resolution radiometer (AVHRR) sensors, have allowed moderate resolution (1 km) measurements of the normalized difference vegetation index (NDVI) to be collected from the Earth's land surfaces for over 20 years. Across the conterminous USA, a readily accessible and decade-long data set is now available to study many aspects of vegetation activity in this region. One feature, the onset of deciduous plant growth at the start of the spring season (SOS) is of special interest, as it appears to be crucial for accurate computation of several important biospheric processes, and a sensitive measure of the impacts of global change. In this study, satellite-derived SOS dates produced by the delayed moving average (DMA) and seasonal midpoint NDVI (SMN) methods, and modelled surface phenology (spring indices, SI) were compared at widespread deciduous forest and mixed woodland sites during 1990–93 and 1995–99, and these three measures were also matched to native species bud-break data collected at the Harvard Forest (Massachusetts) over the same time period. The results show that both SOS methods are doing a modestly accurate job of tracking the general pattern of surface phenology, but highlight the temporal limitations of biweekly satellite data. Specifically, at deciduous forest sites: (1) SMN SOS dates are close in time to SI first bloom dates (average bias of +0.74 days), whereas DMA SOS dates are considerably earlier (average bias of −41.24 days) and also systematically earlier in late spring than in early spring; (2) SMN SOS tracks overall yearly trends in deciduous forests somewhat better than DMA SOS, but with larger average error (MAEs 8.64 days and 7.37 days respectively); and (3) error in both SOS techniques varies considerably by year. Copyright © 2002 Royal Meteorological Society.

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.

Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

NASA Astrophysics Data System (ADS)

Alonso, Carmelo; Tarquis, Ana M.; Zúñiga, Ignacio; Benito, Rosa M.

2017-03-01

Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.

Bias Reduction in Short Records of Satellite Soil Moisture

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Koster, Randal D.

2004-01-01

Although surface soil moisture data from different sources (satellite retrievals, ground measurements, and land model integrations of observed meteorological forcing data) have been shown to contain consistent and useful information in their seasonal cycle and anomaly signals, they typically exhibit very different mean values and variability. These biases pose a severe obstacle to exploiting the useful information contained in satellite retrievals through data assimilation. A simple method of bias removal is to match the cumulative distribution functions (cdf) of the satellite and model data. However, accurate cdf estimation typically requires a long record of satellite data. We demonstrate here that by wing spatial sampling with a 2 degree moving window we can obtain local statistics based on a one-year satellite record that are a good approximation to those that would be derived from a much longer time series. This result should increase the usefulness of relatively short satellite data records.

Urban Land Cover/use Change Detection Using High Resolution SPOT 5 and SPOT 6 Images and Urban Atlas Nomenclature

NASA Astrophysics Data System (ADS)

Akay, S. S.; Sertel, E.

2016-06-01

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time. This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was conducted by creating a confusion matrix to illustrate the thematic accuracy of each class.

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.

Angular aberration in the problem of power beaming to geostationary satellites through the atmosphere.

PubMed

Baryshnikov, F F

1995-10-20

The influence of angular aberration of radiation as a result of the difference in speed of a geostationary satellite and the speed of the Earth's surface on laser power beaming to satellites is considered. Angular aberration makes it impossible to direct the energy to the satellite, and additional beam rotation is necessary. Because the Earth's rotation may cause bad phase restoration, we face a serious problem: how to transfer incoherent radiation to remote satellites. In the framework of the Kolmogorov turbulence model simple conditions of energy transfer are derived and discussed.

Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach

NASA Astrophysics Data System (ADS)

Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

2016-06-01

The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.

Assessment of climate variability of the Greenland Ice Sheet: Integration of in situ and satellite data

NASA Technical Reports Server (NTRS)

Steffen, K.; Abdalati, W.; Stroeve, J.; Key, J.

1994-01-01

The proposed research involves the application of multispectral satellite data in combination with ground truth measurements to monitor surface properties of the Greenland Ice Sheet which are essential for describing the energy and mass of the ice sheet. Several key components of the energy balance are parameterized using satellite data and in situ measurements. The analysis will be done for a ten year time period in order to get statistics on the seasonal and interannual variations of the surface processes and the climatology. Our goal is to investigate to what accuracy and over what geographic areas large scale snow properties and radiative fluxes can be derived based upon a combination of available remote sensing and meteorological data sets. Operational satellite sensors are calibrated based on ground measurements and atmospheric modeling prior to large scale analysis to ensure the quality of the satellite data. Further, several satellite sensors of different spatial and spectral resolution are intercompared to access the parameter accuracy. Proposed parameterization schemes to derive key component of the energy balance from satellite data are validated. For the understanding of the surface processes a field program was designed to collect information on spectral albedo, specular reflectance, soot content, grain size and the physical properties of different snow types. Further, the radiative and turbulent fluxes at the ice/snow surface are monitored for the parameterization and interpretation of the satellite data. The expected results include several baseline data sets of albedo, surface temperature, radiative fluxes, and different snow types of the entire Greenland Ice Sheet. These climatological data sets will be of potential use for climate sensitivity studies in the context of future climate change.

Comparison of CERES Cloud Properties Derived from Aqua and Terra MODIS Data and TRMM VIRS Radiances

NASA Astrophysics Data System (ADS)

Minnis, P.; Young, D. F.; Sun-Mack, S.; Trepte, Q. Z.; Chen, Y.; Heck, P. W.; Wielicki, B. A.

2003-12-01

The Clouds and Earth's Radiant Energy System (CERES) Project is obtaining Earth radiation budget measurements of unprecedented accuracy as a result of improved instruments and an analysis system that combines simultaneous, high-resolution cloud property retrievals with the broadband radiance data. The cloud properties are derived from three different satellite imagers: the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission (TRMM) and the Moderate Resolution Imaging Spectroradiometers (MODIS) on the Aqua and Terra satellites. A single set of consistent algorithms using the 0.65, 1.6 or 2.1, 3.7, 10.8, and 12.0-æm channels are applied to all three imagers. The cloud properties include, cloud coverage, height, thickness, temperature, optical depth, phase, effective particle size, and liquid or ice water path. Because each satellite is in a different orbit, the results provide information on the diurnal cycle of cloud properties. Initial intercalibrations show excellent consistency between the three images except for some differences of ~ 1K between the 3.7-æm channel on Terra and those on VIRS and Aqua. The derived cloud properties are consistent with the known diurnal characteristics of clouds in different areas. These datasets should be valuable for exploring the role of clouds in the radiation budget and hydrological cycle.

BRE facilitates skeletal muscle regeneration by promoting satellite cell motility and differentiation

PubMed Central

Xiao, Lihai; Lee, Kenneth Ka Ho

2016-01-01

ABSTRACT The function of the Bre gene in satellite cells was investigated during skeletal muscle regeneration. The tibialis anterior leg muscle was experimentally injured in Bre knockout mutant (BRE-KO) mice. It was established that the accompanying muscle regeneration was impaired as compared with their normal wild-type counterparts (BRE-WT). There were significantly fewer pax7+ satellite cells and smaller newly formed myofibers present in the injury sites of BRE-KO mice. Bre was required for satellite cell fusion and myofiber formation. The cell fusion index and average length of newly-formed BRE-KO myofibers were found to be significantly reduced as compared with BRE-WT myofibers. It is well established that satellite cells are highly invasive which confers on them the homing ability to reach the muscle injury sites. Hence, we tracked the migratory behavior of these cells using time-lapse microscopy. Image analysis revealed no difference in directionality of movement between BRE-KO and BRE-WT satellite cells but there was a significant decrease in the velocity of BRE-KO cell movement. Moreover, chemotactic migration assays indicated that BRE-KO satellite cells were significantly less responsive to chemoattractant SDF-1α than BRE-WT satellite cells. We also established that BRE normally protects CXCR4 from SDF-1α-induced degradation. In sum, BRE facilitates skeletal muscle regeneration by enhancing satellite cell motility, homing and fusion. PMID:26740569

Thermospheric density variations: Observability using precision satellite orbits and effects on orbit propagation

NASA Astrophysics Data System (ADS)

Lechtenberg, Travis; McLaughlin, Craig A.; Locke, Travis; Krishna, Dhaval Mysore

2013-01-01

paper examines atmospheric density estimated using precision orbit ephemerides (POE) from the CHAMP and GRACE satellites during short periods of greater atmospheric density variability. The results of the calibration of CHAMP densities derived using POEs with those derived using accelerometers are examined for three different types of density perturbations, [traveling atmospheric disturbances (TADs), geomagnetic cusp phenomena, and midnight density maxima] in order to determine the temporal resolution of POE solutions. In addition, the densities are compared to High-Accuracy Satellite Drag Model (HASDM) densities to compare temporal resolution for both types of corrections. The resolution for these models of thermospheric density was found to be inadequate to sufficiently characterize the short-term density variations examined here. Also examined in this paper is the effect of differing density estimation schemes by propagating an initial orbit state forward in time and examining induced errors. The propagated POE-derived densities incurred errors of a smaller magnitude than the empirical models and errors on the same scale or better than those incurred using the HASDM model.

Uncertainties and applications of satellite-derived coastal water quality products

NASA Astrophysics Data System (ADS)

Zheng, Guangming; DiGiacomo, Paul M.

2017-12-01

Recent and forthcoming launches of a plethora of ocean color radiometry sensors, coupled with increasingly adopted free and open data policies are expected to boost usage of satellite ocean color data and drive the demand to use these data in a quantitative and routine manner. Here we review factors that introduce uncertainties to various satellite-derived water quality products and recommend approaches to minimize the uncertainty of a specific product. We show that the regression relationships between remote-sensing reflectance and water turbidity (in terms of nephelometric units) established for different regions tend to converge and therefore it is plausible to develop a global satellite water turbidity product derived using a single algorithm. In contrast, solutions to derive suspended particulate matter concentration are much less generalizable; in one case it might be more accurate to estimate this parameter based on satellite-derived particulate backscattering coefficient, whereas in another the nonagal particulate absorption coefficient might be a better proxy. Regarding satellite-derived chlorophyll concentration, known to be subject to large uncertainties in coastal waters, studies summarized here clearly indicate that the accuracy of classical reflectance band-ratio algorithms depends largely on the contribution of phytoplankton to total light absorption coefficient as well as the degree of correlation between phytoplankton and the dominant nonalgal contributions. Our review also indicates that currently available satellite-derived water quality products are restricted to optically significant materials, whereas many users are interested in toxins, nutrients, pollutants, and pathogens. Presently, proxies or indicators for these constituents are inconsistently (and often incorrectly) developed and applied. Progress in this general direction will remain slow unless, (i) optical oceanographers and environmental scientists start collaborating more closely and make optical and environmental measurements in parallel, (ii) more efforts are devoted to identifying optical, ecological, and environmental forerunners of autochthonous water quality issues (e.g., onsite growth of pathogens), and, (iii) environmental processes associated with the source, transport, and transformation of allochthonous issues (e.g., transport of nutrients) are better understood. Accompanying these challenges, the need still exists to conduct fundamental research in satellite ocean color radiometry, including development of more robust atmospheric correction methods as well as inverse models for coastal regions where optical properties of both aerosols and hydrosols are complex.

The Swarm Initial Field Model for the 2014 Geomagnetic Field

NASA Technical Reports Server (NTRS)

Olsen, Nils; Hulot, Gauthier; Lesur, Vincent; Finlay, Christopher C.; Beggan, Ciaran; Chulliat, Arnaud; Sabaka, Terence J.; Floberghagen, Rune; Friis-Christensen, Eigil; Haagmans, Roger

2015-01-01

Data from the first year of ESA's Swarm constellation mission are used to derive the Swarm Initial Field Model (SIFM), a new model of the Earth's magnetic field and its time variation. In addition to the conventional magnetic field observations provided by each of the three Swarm satellites, explicit advantage is taken of the constellation aspect by including east-west magnetic intensity gradient information from the lower satellite pair. Along-track differences in magnetic intensity provide further information concerning the north-south gradient. The SIFM static field shows excellent agreement (up to at least degree 60) with recent field models derived from CHAMP data, providing an initial validation of the quality of the Swarm magnetic measurements. Use of gradient data improves the determination of both the static field and its secular variation, with the mean misfit for east-west intensity differences between the lower satellite pair being only 0.12 nT.

Using the Multiplicative Schwarz Alternating Algorithm (MSAA) for Solving the Large Linear System of Equations Related to Global Gravity Field Recovery up to Degree and Order 120

NASA Astrophysics Data System (ADS)

Safari, A.; Sharifi, M. A.; Amjadiparvar, B.

2010-05-01

The GRACE mission has substantiated the low-low satellite-to-satellite tracking (LL-SST) concept. The LL-SST configuration can be combined with the previously realized high-low SST concept in the CHAMP mission to provide a much higher accuracy. The line of sight (LOS) acceleration difference between the GRACE satellite pair is the mostly used observable for mapping the global gravity field of the Earth in terms of spherical harmonic coefficients. In this paper, mathematical formulae for LOS acceleration difference observations have been derived and the corresponding linear system of equations has been set up for spherical harmonic up to degree and order 120. The total number of unknowns is 14641. Such a linear equation system can be solved with iterative solvers or direct solvers. However, the runtime of direct methods or that of iterative solvers without a suitable preconditioner increases tremendously. This is the reason why we need a more sophisticated method to solve the linear system of problems with a large number of unknowns. Multiplicative variant of the Schwarz alternating algorithm is a domain decomposition method, which allows it to split the normal matrix of the system into several smaller overlaped submatrices. In each iteration step the multiplicative variant of the Schwarz alternating algorithm solves linear systems with the matrices obtained from the splitting successively. It reduces both runtime and memory requirements drastically. In this paper we propose the Multiplicative Schwarz Alternating Algorithm (MSAA) for solving the large linear system of gravity field recovery. The proposed algorithm has been tested on the International Association of Geodesy (IAG)-simulated data of the GRACE mission. The achieved results indicate the validity and efficiency of the proposed algorithm in solving the linear system of equations from accuracy and runtime points of view. Keywords: Gravity field recovery, Multiplicative Schwarz Alternating Algorithm, Low-Low Satellite-to-Satellite Tracking

Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome.

PubMed

Yuan, Wenping; Liu, Shuguang; Dong, Wenjie; Liang, Shunlin; Zhao, Shuqing; Chen, Jingming; Xu, Wenfang; Li, Xianglan; Barr, Alan; Andrew Black, T; Yan, Wende; Goulden, Mike L; Kulmala, Liisa; Lindroth, Anders; Margolis, Hank A; Matsuura, Yojiro; Moors, Eddy; van der Molen, Michiel; Ohta, Takeshi; Pilegaard, Kim; Varlagin, Andrej; Vesala, Timo

2014-06-26

The satellite-derived normalized difference vegetation index (NDVI), which is used for estimating gross primary production (GPP), often includes contributions from both mosses and vascular plants in boreal ecosystems. For the same NDVI, moss can generate only about one-third of the GPP that vascular plants can because of its much lower photosynthetic capacity. Here, based on eddy covariance measurements, we show that the difference in photosynthetic capacity between these two plant functional types has never been explicitly included when estimating regional GPP in the boreal region, resulting in a substantial overestimation. The magnitude of this overestimation could have important implications regarding a change from a current carbon sink to a carbon source in the boreal region. Moss abundance, associated with ecosystem disturbances, needs to be mapped and incorporated into GPP estimates in order to adequately assess the role of the boreal region in the global carbon cycle.

Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome

USGS Publications Warehouse

Yuan, Wenping; Liu, Shuguang; Dong, Wenjie; Liang, Shunlin; Zhao, Shuqing; Chen, Jingming; Xu, Wenfang; Li, Xianglan; Barr, Alan; Black, T. Andrew; Yan, Wende; Goulden, Michael; Kulmala, Liisa; Lindroth, Anders; Margolis, Hank A.; Matsuura, Yojiro; Moors, Eddy; van der Molen, Michiel; Ohta, Takeshi; Pilegaard, Kim; Varlagin, Andrej; Vesala, Timo

2014-01-01

The satellite-derived normalized difference vegetation index (NDVI), which is used for estimating gross primary production (GPP), often includes contributions from both mosses and vascular plants in boreal ecosystems. For the same NDVI, moss can generate only about one-third of the GPP that vascular plants can because of its much lower photosynthetic capacity. Here, based on eddy covariance measurements, we show that the difference in photosynthetic capacity between these two plant functional types has never been explicitly included when estimating regional GPP in the boreal region, resulting in a substantial overestimation. The magnitude of this overestimation could have important implications regarding a change from a current carbon sink to a carbon source in the boreal region. Moss abundance, associated with ecosystem disturbances, needs to be mapped and incorporated into GPP estimates in order to adequately assess the role of the boreal region in the global carbon cycle.

A new CM SAF Solar Surface Radiation Climate Data Set derived from Meteosat Satellite Observations

NASA Astrophysics Data System (ADS)

Trentmann, J.; Mueller, R. W.; Pfeifroth, U.; Träger-Chatterjee, C.; Cremer, R.

2014-12-01

The incoming surface solar radiation has been defined as an essential climate variable by GCOS. It is mandatory to monitor this part of the earth's energy balance, and thus gain insights on the state and variability of the climate system. In addition, data sets of the surface solar radiation have received increased attention over the recent years as an important source of information for the planning of solar energy applications. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) is deriving surface solar radiation from geostationary and polar-orbiting satellite instruments. While CM SAF is focusing on the generation of high-quality long-term climate data records, also operationally data is provided in short time latency within 8 weeks. Here we present SARAH (Solar Surface Radiation Dataset - Heliosat), i.e. the new CM SAF Solar Surface Radiation data set based on Meteosat satellite observations. SARAH provides instantaneous, daily- and monthly-averaged data of the effective cloud albedo (CAL), the direct normalized solar radiation (DNI) and the solar irradiance (SIS) from 1983 to 2013 for the full view of the Meteosat satellite (i.e, Europe, Africa, parts of South America, and the Atlantic ocean). The data sets are generated with a high spatial resolution of 0.05 deg allowing for detailed regional studies, and are available in netcdf-format at no cost without restrictions at www.cmsaf.eu. We provide an overview of the data sets, including a validation against reference measurements from the BSRN and GEBA surface station networks.

PEDF-derived peptide promotes skeletal muscle regeneration through its mitogenic effect on muscle progenitor cells.

PubMed

Ho, Tsung-Chuan; Chiang, Yi-Pin; Chuang, Chih-Kuang; Chen, Show-Li; Hsieh, Jui-Wen; Lan, Yu-Wen; Tsao, Yeou-Ping

2015-08-01

In response injury, intrinsic repair mechanisms are activated in skeletal muscle to replace the damaged muscle fibers with new muscle fibers. The regeneration process starts with the proliferation of satellite cells to give rise to myoblasts, which subsequently differentiate terminally into myofibers. Here, we investigated the promotion effect of pigment epithelial-derived factor (PEDF) on muscle regeneration. We report that PEDF and a synthetic PEDF-derived short peptide (PSP; residues Ser(93)-Leu(112)) induce satellite cell proliferation in vitro and promote muscle regeneration in vivo. Extensively, soleus muscle necrosis was induced in rats by bupivacaine, and an injectable alginate gel was used to release the PSP in the injured muscle. PSP delivery was found to stimulate satellite cell proliferation in damaged muscle and enhance the growth of regenerating myofibers, with complete regeneration of normal muscle mass by 2 wk. In cell culture, PEDF/PSP stimulated C2C12 myoblast proliferation, together with a rise in cyclin D1 expression. PEDF induced the phosphorylation of ERK1/2, Akt, and STAT3 in C2C12 myoblasts. Blocking the activity of ERK, Akt, or STAT3 with pharmacological inhibitors attenuated the effects of PEDF/PSP on the induction of C2C12 cell proliferation and cyclin D1 expression. Moreover, 5-bromo-2'-deoxyuridine pulse-labeling demonstrated that PEDF/PSP stimulated primary rat satellite cell proliferation in myofibers in vitro. In summary, we report for the first time that PSP is capable of promoting the regeneration of skeletal muscle. The signaling mechanism involves the ERK, AKT, and STAT3 pathways. These results show the potential utility of this PEDF peptide for muscle regeneration. Copyright © 2015 the American Physiological Society.

The VLBI time delay function for synchronous orbits

NASA Technical Reports Server (NTRS)

Rosenbaum, B.

1972-01-01

The VLBI is a satellite tracking technique that to date was applied largely to the tracking of synchronous orbits. These orbits are favorable for VLBI in that the remote satellite range allows continuous viewing from widely separated stations. The primary observable, geometric time delay is the time difference for signal propagation between satellite and baseline terminals. Extraordinary accuracy in angular position data on the satellite can be obtained by observation from baselines of continental dimensions. In satellite tracking though the common objective is to derive orbital elements. A question arises as to how the baseline vector bears on the accuracy of determining the elements. Our approach to this question is to derive an analytic expression for the time delay function in terms of Kepler elements and station coordinates. The analysis, which is for simplicity based on elliptic motion, shows that the resolution for the inclination of the orbital plane depends on the magnitude of the baseline polar component and the resolution for in-plane elements depends on the magnitude of a projected equatorial baseline component.

Climate Change Studies over Bangalore using Multi-source Remote Sensing Data and GIS

NASA Astrophysics Data System (ADS)

B, S.; Gouda, K. C.; Laxmikantha, B. P.; Bhat, N.

2014-12-01

Urbanization is a form of metropolitan growth that is a response to often bewildering sets of economic, social, and political forces and to the physical geography of an area. Some of the causes of the sprawl include - population growth, economy, patterns of infrastructure initiatives like the construction of roads and the provision of infrastructure using public money encouraging development. The direct implication of such urban sprawl is the change in land use and land cover of the region. In this study the long term climate data from multiple sources like NCEP reanalysis, IMD observations and various satellite derived products from MAIRS, IMD, ERSL and TRMM are considered and analyzed using the developed algorithms for the better understanding of the variability in the climate parameters over Bangalore. These products are further mathematically analyzed to arrive at desired results by extracting land surface temperature (LST), Potential evapo-transmission (PET), Rainfall, Humidity etc. Various satellites products are derived from NASA (National Aeronautics Space Agency), Indian meteorological satellites and global satellites are helpful in massive study of urban issues at global and regional scale. Climate change analysis is well studied by using either single source data such as Temperature or Rainfall from IMD (Indian Meteorological Department) or combined data products available as in case of MAIRS (Monsoon Asia Integrated Regional Scale) program to get rainfall at regional scale. Finally all the above said parameters are normalized and analyzed with the help of various open source available software's for pre and post processing our requirements to obtain desired results. A sample of analysis i.e. the Inter annual variability of annual averaged Temperature over Bangalore is presented in figure 1, which clearly shows the rising trend of the temperature (0.06oC/year). Also the Land use and land cover (LULC) analysis over Bangalore, Day light hours from satellite derived products are analyzed and the correlation of climate parameters with LULC are presented.

An assessment of the differences between spatial resolution and grid size for the SMAP enhanced soil moisture product over homogeneous sites

USDA-ARS?s Scientific Manuscript database

Satellite-based passive microwave remote sensing typically involves a scanning antenna that makes measurements at irregularly spaced locations. These locations can change on a day to day basis. Soil moisture products derived from satellite-based passive microwave remote sensing are usually resampled...

Using spectrotemporal indices to improve the fruit-tree crop classification accuracy

NASA Astrophysics Data System (ADS)

Peña, M. A.; Liao, R.; Brenning, A.

2017-06-01

This study assesses the potential of spectrotemporal indices derived from satellite image time series (SITS) to improve the classification accuracy of fruit-tree crops. Six major fruit-tree crop types in the Aconcagua Valley, Chile, were classified by applying various linear discriminant analysis (LDA) techniques on a Landsat-8 time series of nine images corresponding to the 2014-15 growing season. As features we not only used the complete spectral resolution of the SITS, but also all possible normalized difference indices (NDIs) that can be constructed from any two bands of the time series, a novel approach to derive features from SITS. Due to the high dimensionality of this "enhanced" feature set we used the lasso and ridge penalized variants of LDA (PLDA). Although classification accuracies yielded by the standard LDA applied on the full-band SITS were good (misclassification error rate, MER = 0.13), they were further improved by 23% (MER = 0.10) with ridge PLDA using the enhanced feature set. The most important bands to discriminate the crops of interest were mainly concentrated on the first two image dates of the time series, corresponding to the crops' greenup stage. Despite the high predictor weights provided by the red and near infrared bands, typically used to construct greenness spectral indices, other spectral regions were also found important for the discrimination, such as the shortwave infrared band at 2.11-2.19 μm, sensitive to foliar water changes. These findings support the usefulness of spectrotemporal indices in the context of SITS-based crop type classifications, which until now have been mainly constructed by the arithmetic combination of two bands of the same image date in order to derive greenness temporal profiles like those from the normalized difference vegetation index.

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

NASA Technical Reports Server (NTRS)

Mueller, Thomas; Tucker, Compton J.; Dressler, Gunnar; Pinzon, Jorge E.; Leimgruber, Peter; Dubayah, Ralph O.; Hurtt, George C.; Boehning-Gaese, Katrin; Fagan, William F.

2014-01-01

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth's human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects.

Spatial structure, sampling design and scale in remotely-sensed imagery of a California savanna woodland

NASA Technical Reports Server (NTRS)

Mcgwire, K.; Friedl, M.; Estes, J. E.

1993-01-01

This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.

Using the satellite-derived NDVI to assess ecological responses to environmental change.

PubMed

Pettorelli, Nathalie; Vik, Jon Olav; Mysterud, Atle; Gaillard, Jean-Michel; Tucker, Compton J; Stenseth, Nils Chr

2005-09-01

Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation. The ability to predict ecological responses has often been hampered by our rather limited understanding of trophic interactions. Indeed, it has proven difficult to discern direct and indirect effects of environmental change on animal populations owing to limited information about vegetation at large temporal and spatial scales. The rapidly increasing use of the Normalized Difference Vegetation Index (NDVI) in ecological studies has recently changed this situation. Here, we review the use of the NDVI in recent ecological studies and outline its possible key role in future research of environmental change in an ecosystem context.

Sprouty1 Regulates Reversible Quiescence of a Self-Renewing Adult Muscle Stem Cell Pool during Regeneration

PubMed Central

Shea, Kelly L.; Xiang, Wanyi; LaPorta, Vincent S.; Licht, Jonathan D.; Keller, Charles; Basson, M. Albert; Brack, Andrew S.

2010-01-01

Summary Satellite cells are a heterogeneous population of skeletal muscle specific stem cells capable of self-renewal and differentiation after transplantation. Whether quiescent satellite cells can self-renew and contribute to muscle fiber repair in their endogenous environment in normal regenerating muscle has remained unknown. The transcription factor Pax7 is expressed in satellite cells and is critical for establishing the adult satellite cell pool. Using a temporally-inducible genetic lineage tracing approach (Pax7-CreERtm; R26R-lacZ) to fate-map adult satellite cells, we show that in response to injury quiescent adult Pax7+ cells enter the cell cycle; a subpopulation return to quiescence to fully replenish the satellite cell compartment and the others contribute to de novo muscle fiber formation. We demonstrate that Sprouty1 (Spry1), an inhibitor of receptor tyrosine kinase signaling, is robustly expressed in quiescent Pax7+ satellite cells in uninjured adult muscle, down-regulated in proliferating myogenic cells in injured muscles, and re-induced as Pax7+ cells return to quiescence in regenerated muscles. We show through deletion of Spry1 specifically in cycling adult Pax7+ satellite cells, that Spry1 is required for the return to quiescence and homeostasis of the self-renewing Pax7+ satellite cell pool during repair. Satellite cells unable to return to quiescence succumb to apoptosis leading to a diminished self-renewing Pax7-derived satellite cell pool. Our results define a novel role for Spry1 in adult stem cell biology and tissue repair. PMID:20144785

Normal and Tangential Momentum Accommodation for Earth Satellite Conditions

NASA Technical Reports Server (NTRS)

Knechtel, Earl D.; Pitts, William C.

1973-01-01

Momentum accommodation was determined experimentally for gas-surface interactions simulating in a practical way those of near-earth satellites. Throughout the ranges of gas energies and incidence angles of interest for earth-conditions, two components of force were measured by means of a vacuum microbalance to determine the normal and tangential momentum-accommodation coefficients for nitrogen ions on technical-quality aluminum surfaces. For these experimental conditions, the electrodynamics of ion neutralization near the surface indicate that results for nitrogen ions should differ relatively little from those for nitrogen molecules, which comprise the largest component of momentum flux for near-earth satellites. The experimental results indicated that both normal and tangential momentum-accommodation coefficients varied widely with energy, tending to be relatively well accommodated at the higher energies, but becoming progressively less accommodated as the energy was reduced to and below that for earth-satellite speeds. Both coefficients also varied greatly with incidence angle, the normal momentum becoming less accommodated as the incidence angle became more glancing, whereas the tangential momentum generally became more fully accommodated. For each momentum coefficient, an empirical correlation function was obtained which closely approximated the experimental results over the ranges of energy and incidence angle. Most of the observed variations of momentum accommodation with energy and incidence angle were qualitatively indicated by a calculation using a three-dimensional model that simulated the target surface by a one-dimensional attractive potential and hard sphere reflectors.

On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales

NASA Astrophysics Data System (ADS)

Härer, Stefan; Bernhardt, Matthias; Siebers, Matthias; Schulz, Karsten

2018-05-01

Knowledge of current snow cover extent is essential for characterizing energy and moisture fluxes at the Earth's surface. The snow-covered area (SCA) is often estimated by using optical satellite information in combination with the normalized-difference snow index (NDSI). The NDSI thereby uses a threshold for the definition if a satellite pixel is assumed to be snow covered or snow free. The spatiotemporal representativeness of the standard threshold of 0.4 is however questionable at the local scale. Here, we use local snow cover maps derived from ground-based photography to continuously calibrate the NDSI threshold values (NDSIthr) of Landsat satellite images at two European mountain sites of the period from 2010 to 2015. The Research Catchment Zugspitzplatt (RCZ, Germany) and Vernagtferner area (VF, Austria) are both located within a single Landsat scene. Nevertheless, the long-term analysis of the NDSIthr demonstrated that the NDSIthr at these sites are not correlated (r = 0.17) and different than the standard threshold of 0.4. For further comparison, a dynamic and locally optimized NDSI threshold was used as well as another locally optimized literature threshold value (0.7). It was shown that large uncertainties in the prediction of the SCA of up to 24.1 % exist in satellite snow cover maps in cases where the standard threshold of 0.4 is used, but a newly developed calibrated quadratic polynomial model which accounts for seasonal threshold dynamics can reduce this error. The model minimizes the SCA uncertainties at the calibration site VF by 50 % in the evaluation period and was also able to improve the results at RCZ in a significant way. Additionally, a scaling experiment shows that the positive effect of a locally adapted threshold diminishes using a pixel size of 500 m or larger, underlining the general applicability of the standard threshold at larger scales.

Satellite precipitation estimation over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Porcu, F.; Gjoka, U.

2012-04-01

Precipitation characteristics over the Tibetan Plateau are very little known, given the scarcity of reliable and widely distributed ground observation, thus the satellite approach is a valuable choice for large scale precipitation analysis and hydrological cycle studies. However,the satellite perspective undergoes various shortcomings at the different wavelengths used in atmospheric remote sensing. In the microwave spectrum often the high soil emissivity masks or hides the atmospheric signal upwelling from light-moderate precipitation layers, while low and relatively thin precipitating clouds are not well detected in the visible-infrared, because of their low contrast with cold and bright (if snow covered) background. In this work an IR-based, statistical rainfall estimation technique is trained and applied over the Tibetan Plateau hydrological basin to retrive precipitation intensity at different spatial and temporal scales. The technique is based on a simple artificial neural network scheme trained with two supervised training sets assembled for monsoon season and for the rest of the year. For the monsoon season (estimated from June to September), the ground radar precipitation data for few case studies are used to build the training set: four days in summer 2009 are considered. For the rest of the year, CloudSat-CPR derived snowfall rate has been used as reference precipitation data, following the Kulie and Bennartz (2009) algorithm. METEOSAT-7 infrared channels radiance (at 6.7 and 11 micometers) and derived local variability features (such as local standard deviation and local average) are used as input and the actual rainrate is obtained as output for each satellite slot, every 30 minutes on the satellite grid. The satellite rainrate maps for three years (2008-2010) are computed and compared with available global precipitation products (such as C-MORPH and TMPA products) and with other techniques applied to the Plateau area: similarities and differences are discussed. Relevant characteristics of precipitation fields are derived and analyzed, such as diurnal cycle, precipitation frequency, maximum rainrate distribution and dry areas detection. Interannual variability of precipitation pattern and intensity is also discussed.

An assessment of satellite temperature distributions used to derive the net diabatic transport for zonally averaged models of the middle atmosphere

NASA Technical Reports Server (NTRS)

Remsberg, Ellis E.; Bhatt, Praful P.; Miles, Thomas

1994-01-01

Determinations of the zonally averaged and diabatically derived residual mean circulation (RMC) are particularly sensitive to the assumed zonal mean temperature distribution used as input. Several different middle atmosphere satellite temperature distributions have been employed in models and are compared here: a 4-year (late 1978 to early 1982) National Meteorological Center (NMC) climatology, the Barnett and Corney (or BC) climatology, and the 7 months of Nimbus 7 limb infrared monitor of the stratosphere (LIMS) temperatures. All three climatologies are generally accurate below the 10 hPa level, but there are systematic differences between them of up to +/-5 K in the upper stratosphere and lower mesosphere. The NMC/LIMS differences are evaluated using time series of rocketsonde and reconstructed satellite temperatures at station locations. Much of those biases can be explained by the differing vertical resolutions for the satellite-derived temperatures; the time series of reconstructed LIMS profiles have higher resolution and are more accurate. Because the LIMS temperatures are limited to just two full seasons, one cannot obtain monthly RMCs from them for an annual model calculation. Two alternate monthly climatologies are examined briefly: the 4-year Nimbus 7 stratospheric and mesospheric sounder (SAMS) temperatures and for the mesosphere the distribution from the Solar Mesosphere Explorer (SME), both of which are limb viewers of medium vertical resolution. There are also differences of the order of +/-5 K for those data sets. It is concluded that a major source of error in the determination of diabatic RMCs is a persistent pattern of temperature bias whose characteristics vary according to the vertical resolution of each individual climatology.

Fusing Satellite-Derived Irradiance and Point Measurements through Optimal Interpolation

NASA Astrophysics Data System (ADS)

Lorenzo, A.; Morzfeld, M.; Holmgren, W.; Cronin, A.

2016-12-01

Satellite-derived irradiance is widely used throughout the design and operation of a solar power plant. While satellite-derived estimates cover a large area, they also have large errors compared to point measurements from sensors on the ground. We describe an optimal interpolation routine that fuses the broad spatial coverage of satellite-derived irradiance with the high accuracy of point measurements. The routine can be applied to any satellite-derived irradiance and point measurement datasets. Unique aspects of this work include the fact that information is spread using cloud location and thickness and that a number of point measurements are collected from rooftop PV systems. The routine is sensitive to errors in the satellite image geolocation, so care must be taken to adjust the cloud locations based on the solar and satellite geometries. Analysis of the optimal interpolation routine over Tucson, AZ, with 20 point measurements shows a significant improvement in the irradiance estimate for two distinct satellite image to irradiance algorithms. Improved irradiance estimates can be used for resource assessment, distributed generation production estimates, and irradiance forecasts.

Detection of Rift Valley fever viral activity in Kenya by satellite remote sensing imagery

NASA Technical Reports Server (NTRS)

Linthicum, Kenneth J.; Bailey, Charles L.; Davies, F. Glyn; Tucker, Compton J.

1987-01-01

Data from the advanced very high resolution radiometer on board the National Oceanic and Atmospheric Administration's polar-orbiting meteorological satellites have been used to infer ecological parameters associated with Rift Valley fever (RVF) viral activity in Kenya. An indicator of potential viral activity was produced from satellite data for two different ecological regions in Kenya, where RVF is enzootic. The correlation between the satellite-derived green vegetation index and the ecological parameters associated with RVF virus suggested that satellite data may become a forecasting tool for RVF in Kenya and, perhaps, in other areas of sub-Saharan Africa.

Snowmelt on the Greenland Ice Sheet as Derived From Passive Microwave Satellite Data

NASA Technical Reports Server (NTRS)

Abdalati, Waleed; Steffen, Konrad

1997-01-01

The melt extent of the snow on the Greenland ice sheet is of considerable importance to the ice sheet's mass and energy balance, as well as Arctic and global climates. By comparing passive microwave satellite data to field observations, variations in melt extent have been detected by establishing melt thresholds in the cross-polarized gradient ratio (XPGR). The XPGR, defined as the normalized difference between the 19-GHz horizontal channel and the 37-GHz vertical channel of the Special Sensor Microwave/Imager (SSM/I), exploits the different effects of snow wetness on different frequencies and polarizations and establishes a distinct melt signal. Using this XPGR melt signal, seasonal and interannual variations in snowmelt extent of the ice sheet are studied. The melt is found to be most extensive on the western side of the ice sheet and peaks in late July. Moreover, there is a notable increasing trend in melt area between the years 1979 and 1991 of 4.4% per year, which came to an abrupt halt in 1992 after the eruption of Mt. Pinatubo. A similar trend is observed in the temperatures at six coastal stations. The relationship between the warming trend and increasing melt trend between 1979 and 1991 suggests that a 1 C temperature rise corresponds to an increase in melt area of 73000 sq km, which in general exceeds one standard deviation of the natural melt area variability.

Sensitivity of Satellite Altimetry Data Assimilation on a Naval Anti-Submarine Warfare Weapon System

DTIC Science & Technology

2004-09-01

representing the actual ocean structure than static climatology databases (Fox et. al., 2002; Chu et. al., 2004). It is expected that this 2...pressure amplitude function and ( , )P P r z= is the phase function, or eikonal . Doing this and collecting real and imaginary terms yields an equation... eikonal equation, [ ]2 2P k∇ = , (13) from which differential equations for rays can be derived (Etter, 1991). The rays are the normals to surfaces

Assessment of Developing Intensity Duration Frequency Curves using Satellite Observations (Case Study)

NASA Astrophysics Data System (ADS)

Ombadi, Mohammed; Nguyen, Phu; Sorooshian, Soroosh

2017-12-01

Intensity Duration Frequency (IDF) curves are essential for the resilient design of infrastructures. Since their earlier development, IDF relationships have been derived using precipitation records from rainfall gauge stations. However, with the recent advancement in satellite observation of precipitation which provides near global coverage and high spatiotemporal resolution, it is worthy of attention to investigate the validity of utilizing the relatively short record length of satellite rainfall to generate robust IDF relationships. These satellite-based IDF can address the paucity of such information in the developing countries. Few studies have used satellite precipitation data in IDF development but mainly focused on merging satellite and gauge precipitation. In this study, however, IDF have been derived solely from satellite observations using PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record). The unique PERSIANN-CDR attributes of high spatial resolution (0.25°×0.25°), daily temporal resolution and a record dating back to 1983 allow for the investigation at fine resolution. The results are compared over most of the contiguous United States against NOAA Atlas 14. The impact of using different methods of sampling, distribution estimators and regionalization in the resulting relationships is investigated. Main challenges to estimate robust and accurate IDF from satellite observations are also highlighted.

Deriving a Core Magnetic Field Model from Swarm Satellite Data

NASA Astrophysics Data System (ADS)

Lesur, V.; Rother, M.; Wardinski, I.

2014-12-01

A model of the Earth's core magnetic field has been built using Swarm satellite mission data and observatory quasi-definitive data. The satellite data processing scheme, which was used to derive previous satellite field models (i.e. GRIMM series), has been modified to handle discrepancies between the satellite total intensity data derived from the vector fluxgate magnetometer and the absolute scalar instrument. Further, the Euler angles, i.e. the angles between the vector magnetometer and the satellite reference frame, have been recalculated on a series of 30-day windows to obtain an accurate model of the core field for 2014. Preliminary derivations of core magnetic field and SV models for 2014 present the same characteristics as during the CHAMP era. The acceleration (i.e. the field second time derivative) has shown a rapid evolution over the last few years, and is present in the current model, which confirms previous observations.

Satellite imagery in the fight against Malaria, the case for Genetic Programming

NASA Astrophysics Data System (ADS)

Ssentongo, J. S.; Hines, E. L.

The analysis of multi-temporal data is a critical issue in the field of remote sensing and presents a constant challenge The approach used here relies primarily on utilising a method commonly used in statistics and signal processing Empirical Orthogonal Function EOF analysis Normalized Difference Vegetation Index NDVI and Rainfall Estimate RFE satellite images pertaining to the Sub-Saharan Africa region were obtained The images are derived from the Advanced Very High Resolution Radiometer AVHRR on the United States National Oceanic and Atmospheric Administration NOAA polar orbiting satellites spanning from January 2000 to December 2002 The region of interest was narrowed down to the Limpopo Province Northern Province of South Africa EOF analyses of the space-time-intensity series of dekadal mean NDVI values was been performed They reveal that NDVI can be accurately approximated by its principal component time series and contains a near sinusoidal oscillation pattern Peak greenness essentially what NDVI measures seasons last approximately 8 weeks This oscillation period is very similar to that of Malaria cases reported in the same period but lags behind by 4 dekads about 40 days Singular Value Decomposition SVD of Coupled Fields is performed on the spacetime-intensity series of dekadal mean NDVI and RFE values Correlation analyses indicate that both Malaria and greenness appear to be dependant on rainfall the onset of their seasonal highs always following an arrival of rain There is a greater

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.

Associations of Residential Long-Term Air Pollution Exposures and Satellite-Derived Greenness with Insulin Resistance in German Adolescents

PubMed Central

Thiering, Elisabeth; Markevych, Iana; Brüske, Irene; Fuertes, Elaine; Kratzsch, Jürgen; Sugiri, Dorothea; Hoffmann, Barbara; von Berg, Andrea; Bauer, Carl-Peter; Koletzko, Sibylle; Berdel, Dietrich; Heinrich, Joachim

2016-01-01

Background: Epidemiological studies have identified associations between air pollution and green space access with type 2 diabetes in adults. However, it remains unclear to what extent associations with greenness are attributable to air pollution exposure. Objectives: We aimed to investigate associations between long-term exposure to air pollution and satellite-derived greenness with insulin resistance in adolescents. Methods: A total of 837 participants of two German birth cohorts (LISAplus and GINIplus) were included in the analysis. Generalized additive models were used to determine the association of individual satellite-derived greenness defined by the Normalized Difference Vegetation Index (NDVI), long-term air pollution exposure estimated by land-use regression (LUR) models with insulin resistance (HOMA-IR) in 15-year-old adolescents. Models were adjusted for study area, cohort, socioeconomic, and individual characteristics such as body mass index, physical activity, and smoking. Results: Increases of 2 SDs in nitrogen dioxide (NO2; 8.9 μg/m3) and particulate matter ≤ 10 μm in diameter (PM10; 6.7 μg/m3) were significantly associated with 11.4% (95% CI: 4.4, 18.9) and 11.4% (95% CI: 0.4, 23.7) higher HOMA-IR. A 2-SD increase in NDVI in a 1,000-m buffer (0.2 units) was significantly associated with a lower HOMA-IR (–7.4%; 95% CI: –13.3, –1.1). Associations tended to be stronger in adolescents who spent more time outside and in those with lower socioeconomic status. In combined models including both air pollution and greenness, only NO2 remained significantly associated with HOMA-IR, whereas effect estimates for all other exposures attenuated after adjustment for NO2. Conclusions: NO2, often considered as a marker of traffic, was independently associated with insulin resistance. The observed association between higher greenness exposure and lower HOMA-IR in adolescents might thus be attributable mainly to the lower co-exposure to traffic-related air pollution. Citation: Thiering E, Markevych I, Brüske I, Fuertes E, Kratzsch J, Sugiri D, Hoffmann B, von Berg A, Bauer CP, Koletzko S, Berdel D, Heinrich J. 2016. Associations of residential long-term air pollution exposures and satellite-derived greenness with insulin resistance in German adolescents. Environ Health Perspect 124:1291–1298; http://dx.doi.org/10.1289/ehp.1509967 PMID:26863688

Quantitative assessment of AOD from 17 CMIP5 models based on satellite-derived AOD over India

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

Misra, Amit; Kanawade, Vijay P.; Tripathi, Sachchida Nand

Aerosol optical depth (AOD) values from 17 CMIP5 models are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) derived AODs over India. The objective is to identify the cases of successful AOD simulation by CMIP5 models, considering satellite-derived AOD as a benchmark. Six years of AOD data (2000–2005) from MISR and MODIS are processed to create quality-assured gridded AOD maps over India, which are compared with corresponding maps of 17 CMIP5 models at the same grid resolution. Intercomparison of model and satellite data shows that model-AOD is better correlated with MISR-derived AOD than MODIS. The correlation between model-AOD andmore » MISR-AOD is used to segregate the models into three categories identifying their performance in simulating the AOD over India. Maps of correlation between model-AOD and MISR-/MODIS-AOD are generated to provide quantitative information about the intercomparison. The two sets of data are examined for different seasons and years to examine the seasonal and interannual variation in the correlation coefficients. In conclusion, latitudinal and longitudinal variations in AOD as simulated by models are also examined and compared with corresponding variations observed by satellites.« less

Quantitative assessment of AOD from 17 CMIP5 models based on satellite-derived AOD over India

DOE PAGES

Misra, Amit; Kanawade, Vijay P.; Tripathi, Sachchida Nand

2016-08-03

Aerosol optical depth (AOD) values from 17 CMIP5 models are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) derived AODs over India. The objective is to identify the cases of successful AOD simulation by CMIP5 models, considering satellite-derived AOD as a benchmark. Six years of AOD data (2000–2005) from MISR and MODIS are processed to create quality-assured gridded AOD maps over India, which are compared with corresponding maps of 17 CMIP5 models at the same grid resolution. Intercomparison of model and satellite data shows that model-AOD is better correlated with MISR-derived AOD than MODIS. The correlation between model-AOD andmore » MISR-AOD is used to segregate the models into three categories identifying their performance in simulating the AOD over India. Maps of correlation between model-AOD and MISR-/MODIS-AOD are generated to provide quantitative information about the intercomparison. The two sets of data are examined for different seasons and years to examine the seasonal and interannual variation in the correlation coefficients. In conclusion, latitudinal and longitudinal variations in AOD as simulated by models are also examined and compared with corresponding variations observed by satellites.« less

Does model structure limit the use of satellite data as hydrologic forcing for distributed operational models?

NASA Astrophysics Data System (ADS)

Bowman, A. L.; Franz, K.; Hogue, T. S.

2015-12-01

We are investigating the implications for use of satellite data in operational streamflow prediction. Specifically, the consequence of potential hydrologic model structure deficiencies on the ability to achieve improved forecast accuracy through the use of satellite data. We want to understand why advanced data do not lead to improved streamflow simulations by exploring how various fluxes and states differ among models of increasing complexity. In a series of prior studies, we investigated the use of a daily satellite-derived potential evapotranspiration (PET) estimate as input to the National Weather Service (NWS) streamflow forecast models for watersheds in the Upper Mississippi and Red river basins. Although the spatial PET product appears to represent the day-to-day variability in PET more realistically than current climatological methods used by the NWS, the impact of the satellite data on streamflow simulations results in slightly poorer model efficiency overall. Analysis of the model states indicates the model progresses differently between simulations with baseline PET and the satellite-derived PET input, though variation in streamflow simulations overall is negligible. For instance, the upper zone states, responsible for the high flows of a hydrograph, show a profound difference, while simulation of the peak flows tend to show little variation in the timing and magnitude. Using the spatial PET input, the lower zone states show improvement with simulating the recession limb and baseflow portion of the hydrograph. We anticipate that through a better understanding of the relationship between model structure, model states, and simulated streamflow we will be able to diagnose why simulations of discharge from the forecast model have failed to improve when provided seemingly more representative input data. Identifying model limitations are critical to demonstrating the full benefit of a satellite data for operational use.

Geographical representation of radial orbit perturbations due to ocean tides: Implications for satellite altimetry

NASA Technical Reports Server (NTRS)

Bettadpur, Srinivas V.; Eanes, Richard J.

1994-01-01

In analogy to the geographical representation of the zeroth-order radial orbit perturbations due to the static geopotential, similar relationships have been derived for radial orbit perturbations due to the ocean tides. At each location these perturbations are seen to be coherent with the tide height variations. The study of this singularity is of obvious importance to the estimation of ocean tides from satellite altimeter data. We derive analytical expressions for the sensitivity of altimeter derived ocean tide models to the ocean tide force model induced errors in the orbits of the altimeter satellite. In particular, we focus on characterizing and quantifying the nonresonant tidal orbit perturbations, which cannot be adjusted into the empirical accelerations or radial perturbation adjustments commonly used during orbit determination and in altimeter data processing. As an illustration of the utility of this technique, we study the differences between a TOPEX/POSEIDON-derived ocean tide model and the Cartwright and Ray 1991 Geosat model. This analysis shows that nearly 60% of the variance of this difference for M(sub 2) can be explained by the Geosat radial orbit eror due to the omission of coefficients from the GEM-T2 background ocean tide model. For O(sub 1), K(sub 1), S(sub 2), and K(sub 2) the orbital effects account for approximately 10 to 40% of the variances of these differences. The utility of this technique to assessment of the ocean tide induced errors in the TOPEX/POSEIDON-derived tide models is also discussed.

Total ozone column derived from GOME and SCIAMACHY using KNMI retrieval algorithms: Validation against Brewer measurements at the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Antón, M.; Kroon, M.; López, M.; Vilaplana, J. M.; Bañón, M.; van der A, R.; Veefkind, J. P.; Stammes, P.; Alados-Arboledas, L.

2011-11-01

This article focuses on the validation of the total ozone column (TOC) data set acquired by the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite remote sensing instruments using the Total Ozone Retrieval Scheme for the GOME Instrument Based on the Ozone Monitoring Instrument (TOGOMI) and Total Ozone Retrieval Scheme for the SCIAMACHY Instrument Based on the Ozone Monitoring Instrument (TOSOMI) retrieval algorithms developed by the Royal Netherlands Meteorological Institute. In this analysis, spatially colocated, daily averaged ground-based observations performed by five well-calibrated Brewer spectrophotometers at the Iberian Peninsula are used. The period of study runs from January 2004 to December 2009. The agreement between satellite and ground-based TOC data is excellent (R2 higher than 0.94). Nevertheless, the TOC data derived from both satellite instruments underestimate the ground-based data. On average, this underestimation is 1.1% for GOME and 1.3% for SCIAMACHY. The SCIAMACHY-Brewer TOC differences show a significant solar zenith angle (SZA) dependence which causes a systematic seasonal dependence. By contrast, GOME-Brewer TOC differences show no significant SZA dependence and hence no seasonality although processed with exactly the same algorithm. The satellite-Brewer TOC differences for the two satellite instruments show a clear and similar dependence on the viewing zenith angle under cloudy conditions. In addition, both the GOME-Brewer and SCIAMACHY-Brewer TOC differences reveal a very similar behavior with respect to the satellite cloud properties, being cloud fraction and cloud top pressure, which originate from the same cloud algorithm (Fast Retrieval Scheme for Clouds from the Oxygen A-Band (FRESCO+)) in both the TOSOMI and TOGOMI retrieval algorithms.

Assessment of Two Types of Observations (SATWND and GPSRO) for the Operational Global 4DVAR System

NASA Astrophysics Data System (ADS)

Leng, H.

2017-12-01

The performance of a data assimilation system is significantly dependent on the quality and quantity of observations assimilated. In these years, more and more satellite observations have been applied in many operational assimilation systems. In this paper, the assessment of satellite-derived winds (SATWND) and GPS radio occultation (GPSRO) bending angles has been performed using a range of diagnostics. The main positive impacts are made when satellite-derived cloud data (GOES cloud data and MODIS cloud data) is assimilated, but benefit is hardly obtained from GPSRO data in the Operational Global 4DVAR System. In a full system configuration, the assimilation of satellite-derived observations is globally beneficial on the analysis, and the benefit can be well propagated into the forecast. The assimilation of the GPSRO observations has a slightly positive impact in the Tropics, but is neutral in the Northern Hemisphere and in the Southern Hemisphere. To assess the synergies of satellite-derived observations with other types of observation, experiments assimilating satellite-derived data and AMSU-A and AMSU-B observations were run. The results show that the analysis increments structure is not modified when AMSU-A and AMSU-B observations are also assimilated. This suggests that the impact of satellite-derived observations is not limited by the large impact of satellite radiance observations.

FORMOSAT-3/COSMIC POD Data Processing and Initial Results

NASA Astrophysics Data System (ADS)

Tang, C.

2006-12-01

The six satellites of the collaborative Taiwan-U.S. FORMOSAT-3/COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) space program were successfully launched from Vandenberg, U.S.A. on April 15, 2006. As of September 7, 2006, one satellite (FM5) has already been transferred to the 800-km final orbit, while the other five satellites (FM1-4 and FM6) are currently waiting in the ~520-km parking orbit for subsequent orbit raising deployment. There are two GPS antennas with different orientation onboard each satellite whose measurements are used specifically for precise orbit determination (POD). The received GPS signals by the POD antennas were rather sparse and unstable in the initial 5 weeks. Since then, the available GPS measurements have gradually increased from 10-20% in the early stage to almost 90% in 11 weeks after the launch. For the two POD antennas (POD+X and POD-X), one antenna can perform normally and record observations from up to 9 GPS satellites in view; however, the other antenna is programmed to track up to 4 GPS satellites due to onboard memory limitation. For this reason, we first performed orbit computation using zero-difference GPS phases collected by the normal antenna. For each day's orbit computation, we designed a 6-hr (25%) overlap for inner orbital accuracy assessment, and overlap analysis shows that the achievable 3D RMS was around 19 cm, or 11 cm per axis. In a separate effort, orbit computation based on the lesser antenna was also performed. The orbital difference between the results obtained from the two antennas was significant, with a 3D RMS value of 64 cm. The early results indicate that more work is needed in order to incorporate GPS data from both antennas into a unified solution.

Value of Available Global Soil Moisture Products for Agricultural Monitoring

NASA Astrophysics Data System (ADS)

Mladenova, Iliana; Bolten, John; Crow, Wade; de Jeu, Richard

2016-04-01

The first operationally derived and publicly distributed global soil moil moisture product was initiated with the launch of the Advanced Scanning Microwave Mission on the NASA's Earth Observing System Aqua satellite (AMSR-E). AMSR-E failed in late 2011, but its legacy is continued by AMSR2, launched in 2012 on the JAXA Global Change Observation Mission-Water (GCOM-W) mission. AMSR is a multi-frequency dual-polarization instrument, where the lowest two frequencies (C- and X-band) were used for soil moisture retrieval. Theoretical research and small-/field-scale airborne campaigns, however, have demonstrated that soil moisture would be best monitored using L-band-based observations. This consequently led to the development and launch of the first L-band-based mission-the ESA's Soil Moisture Ocean Salinity (SMOS) mission (2009). In early 2015 NASA launched the second L-band-based mission, the Soil Moisture Active Passive (SMAP). These satellite-based soil moisture products have been demonstrated to be invaluable sources of information for mapping water stress areas, crop monitoring and yield forecasting. Thus, a number of agricultural agencies routinely utilize and rely on global soil moisture products for improving their decision making activities, determining global crop production and crop prices, identifying food restricted areas, etc. The basic premise of applying soil moisture observations for vegetation monitoring is that the change in soil moisture conditions will precede the change in vegetation status, suggesting that soil moisture can be used as an early indicator of expected crop condition change. Here this relationship was evaluated across multiple microwave frequencies by examining the lag rank cross-correlation coefficient between the soil moisture observations and the Normalized Difference Vegetation Index (NDVI). A main goal of our analysis is to evaluate and inter-compare the value of the different soil moisture products derived using L-band (SMOS) versus C-/X-band (AMSR2) observations. The soil moisture products analyzed here were derived using the Land Parameter Retrieval Model.

Comparison of several satellite-derived Sun-Induced Fluorescence products

NASA Astrophysics Data System (ADS)

Bacour, C.; Maignan, F.; MacBean, N.; Köhler, P.; Vountas, M.; Khosravi, N.; Guanter, L.; Joiner, J.; Frankenberg, C.; Somkuti, P.; Peylin, P.

2017-12-01

Large uncertainties remain in our representation of the global carbon budget, in particular regarding the spatial and temporal dynamics of the net land surface CO2 fluxes along with its two constitutive components, photosynthesis and respiration. Bolstered by the evidenced linear relationship between remotely sensed sun-induced fluorescence (SIF) and plant gross carbon uptake (GPP - gross primary productivity) at broad spatial and temporal scales, satellite SIF products are foreseen to provide significant constraint on one of the key component of the terrestrial carbon cycle, and ultimately to help reducing the uncertainties in the projections of the fate of carbon sinks and sources under a changing climate.Global SIF estimates are now "routinely" produced from observations of space-borne spectrometers having sufficient spectral resolution/sampling in solar Fraunhofer lines or atmospheric absorption bands in the visible - near-infrared domain. Differences between SIF products derived from different instruments are expected depending on evaluated wavelengths (SIF has a spectral signature with maxima around 685 and 740 nm) and their own observation characteristics (time of satellite overpass, spatial resolution, revisit frequency, spectral resolution, etc.). For instance, SIF products estimated at 760 nm (GOSAT, OCO-2) are about 1.5 times lower than estimates at 740 nm (GOME-2, SCIAMACHY). However, as highlighted by Köhler et al. (2015), strong discrepancies in SIF absolute values may arise for products derived from the same set of observations (GOME-2) but using different estimation algorithms. In the view of using satellite SIF products to calibrate terrestrial biosphere models (e.g. through data assimilation), this is highly problematic, especially for evergreen ecosystems where SIF magnitude is the only observational constraint that can be made use of.In this study, we compare several gridded satellite SIF products and quantify their similarities/discrepancies with respect to both their absolute value and seasonality (plant phenology): GOME-2, OCO2, GOSAT, and SCIAMACHY. Our main objective is to assess the potential impacts of their differences in a data assimilation perspective.

Remote Sensing of Aerosol and their Radiative Properties from the MODIS Instrument on EOS-Terra Satellite: First Results and Evaluation

NASA Technical Reports Server (NTRS)

Kaufman, Yoram; Tanre, Didier; Remer, Lorraine; Holben, Brent; Lau, William K.-M. (Technical Monitor)

2001-01-01

The MODIS instrument was launched on the NASA Terra satellite in Dec. 1999. Since last Oct., the sensor and the aerosol algorithm reached maturity and provide global daily retrievals of aerosol optical thickness and properties. MODIS has 36 spectral channels in the visible to IR with resolution down to 250 m. This allows accurate cloud screening and multi-spectral aerosol retrievals. We derive the aerosol optical thickness over the ocean and most of the land areas, distinguishing between fine (mainly man-made aerosol) and coarse aerosol particles. The information is more precise over the ocean where we derive also the effective radius and scattering asymmetry parameter of the aerosol. New methods to derive the aerosol single scattering albedo are also being developed. These measurements are use to track different aerosol sources, transport and the radiative forcing at the top and bottom of the atmosphere. The AErosol RObotic NETwork of ground based radiometers is used for global validation of the satellite derived optical thickness, size parameters and single scattering albedo and measure additional aerosol parameters that cannot be derived from space.

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

NASA Technical Reports Server (NTRS)

Zeng, Fanwei; Collatz, George James; Pinzon, Jorge E.; Ivanoff, Alvaro

2013-01-01

Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

The Satellite Cell in Male and Female, Developing and Adult Mouse Muscle: Distinct Stem Cells for Growth and Regeneration

PubMed Central

Neal, Alice; Boldrin, Luisa; Morgan, Jennifer Elizabeth

2012-01-01

Satellite cells are myogenic cells found between the basal lamina and the sarcolemma of the muscle fibre. Satellite cells are the source of new myofibres; as such, satellite cell transplantation holds promise as a treatment for muscular dystrophies. We have investigated age and sex differences between mouse satellite cells in vitro and assessed the importance of these factors as mediators of donor cell engraftment in an in vivo model of satellite cell transplantation. We found that satellite cell numbers are increased in growing compared to adult and in male compared to female adult mice. We saw no difference in the expression of the myogenic regulatory factors between male and female mice, but distinct profiles were observed according to developmental stage. We show that, in contrast to adult mice, the majority of satellite cells from two week old mice are proliferating to facilitate myofibre growth; however a small proportion of these cells are quiescent and not contributing to this growth programme. Despite observed changes in satellite cell populations, there is no difference in engraftment efficiency either between satellite cells derived from adult or pre-weaned donor mice, male or female donor cells, or between male and female host muscle environments. We suggest there exist two distinct satellite cell populations: one for muscle growth and maintenance and one for muscle regeneration. PMID:22662253

A Five-Year Analysis of MODIS NDVI and NDWI for Rangeland Drought Assessment: Preliminary Results

NASA Astrophysics Data System (ADS)

Gu, Y.; Brown, J. F.; Verdin, J. P.; Wardlow, B.

2006-12-01

Drought is one of the most costly natural disasters in the United States. Traditionally, drought monitoring has been based on weather station observations, which lack the continuous spatial coverage needed to adequately characterize and monitor detailed spatial patterns of drought conditions. Satellite remote sensing observations can provide a synoptic view of the land and provide a spatial context for measuring drought. A common satellite-based index, the normalized difference vegetation index (NDVI) has a 30-year history of use for vegetation condition monitoring. NDVI is calculated from the visible red and near infrared channels and measures the changes in chlorophyll absorption and reflection in the spongy mesophyll of the vegetation canopy that are reflected in these respective bands. The normalized difference water index (NDWI) is another index, derived from the near-infrared and short wave infrared channels, and reflects changes in both the water content and spongy mesophyll in the vegetation canopy. As a result, the NDWI is influenced by both desiccation and wilting in the vegetation canopy and may be a more sensitive indicator than the NDVI for large- area drought monitoring. The objective of this study was to process and evaluate a 5-year history of 500-meter NDVI and NDWI data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument and to investigate methods for measuring and monitoring drought in rangeland over the southern plains of the United States. This initial study included: (1) the development of a climatological database for MODIS NDVI and NDWI, (2) a study of the relationship between the NDVI, NDWI, and drought condition over rangeland, (3) the development of a method to provide threshold NDVI/NDWI values under drought conditions based on the 5-year NDVI/NDWI/drought condition analysis, and (4) the investigation of additional vegetation drought information provided by the NDWI versus the NDVI in a 5-year comparison of the two indices. The MODIS data were obtained from the Land Processes Distributed Active Archive System. Results show strong relationships among NDVI, NDWI, and drought analyzed over grasslands in the Flint Hills region of Kansas and Oklahoma. During the summer months, the average NDVI and NDWI values were consistently lower (NDVI<0.5 and NDWI<0.3) for the tallgrass prairie under drought conditions than under normal climate conditions (NDVI>0.6 and NDWI>0.4). The distinctions between drought conditions and normal climate conditions are based on the historic U.S. Drought Monitor maps and the historic Palmer index data. To take advantage of information contained in both indices, we calculated the difference between NDVI and NDWI (NDVI-NDWI). The difference between NDVI and NDWI slightly increases during the summer drought condition. Based on these analyses, the NDWI appears to be more sensitive than NDVI to drought conditions. The results of statistical analysis of the relationships among these indices will be presented in the poster.

Estimating Net Primary Productivity Using Satellite and Ancillary Data

NASA Technical Reports Server (NTRS)

Choudhury, B. J.; Houser, Paul (Technical Monitor)

2001-01-01

The net primary productivity (C) or annual rate of carbon accumulation per unit ground area by terrestrial plant communities is the difference of the rate of gross photosynthesis (A(sub g)) and autotrophic respiration (R) per unit ground area. Although available observations show that R is a large and variable fraction of A(sub g), viz., 0.3 to 0.7, it is generally recognized that much uncertainties exist in this fraction due to difficulties associated with the needed measurements. Additional uncertainties arise when these measurements are extrapolated to regional or global land surface using empirical equations, for example, using regression equations relating C to mean annual precipitation and air temperature. Here, a process-based approach has been taken to calculate A(sub g) and R using satellite and ancillary data. A(sub g) has been expressed as a product of radiation use efficiency, magnitude of intercepted photosynthetically active radiation (PAR), and normalized by stresses due to soil water shortage and air temperature away from the optimum range. A biophysical model has been used to determine the radiation use efficiency from the maximum rate of carbon assimilation by a leaf, foliage temperature, and the fraction of diffuse PAR incident on a canopy. All meteorological data (PAR, air temperature, precipitation, etc.) needed for the calculation are derived from satellite observations, while a land use, land cover data (based on satellite and ground measurements) have been used to assess the maximum rate of carbon assimilation by a leaf of varied cover type based on field measurements. R has been calculated as the sum of maintenance and growth components. The maintenance respiration of foliage and live fine roots at a standard temperature of different land cover has been determined from their nitrogen content using field and satellite measurements, while that of living fraction of woody stem (viz., sapwood) from the seasonal maximum leaf area index as determined from satellite observations. These maintenance respiration values were then adjusted to that corresponding to air temperature according to a prescribed non-linear variation of respiration with temperature. The growth respiration has been calculated from the difference of Ag and maintenance respiration, according to the two-compartment model. The results of calculations will be reported for 36 consecutive months (1987-1989) over large contiguous areas (ca. 10(exp 5) sq km) Of agricultural land and tropical humid evergreen forests, and compared with available field data.

The 2010 Russian Drought Impact on Satellite Measurements of Solar-Induced Chlorophyll Fluorescence: Insights from Modeling and Comparisons with the Normalized Differential Vegetation Index (NDVI)

NASA Technical Reports Server (NTRS)

Yoshida, Y.; Joiner, J.; Tucker, C.; Berry, J.; Lee, J. -E.; Walker, G.; Reichle, R.; Koster, R.; Lyapustin, A.; Wang, Y.

2015-01-01

We examine satellite-based measurements of chlorophyll solar-induced fluorescence (SIF) over the region impacted by the Russian drought and heat wave of 2010. Like the popular Normalized Difference Vegetation Index (NDVI) that has been used for decades to measure photosynthetic capacity, SIF measurements are sensitive to the fraction of absorbed photosynthetically-active radiation (fPAR). However, in addition, SIF is sensitive to the fluorescence yield that is related to the photosynthetic yield. Both SIF and NDVI from satellite data show drought-related declines early in the growing season in 2010 as compared to other years between 2007 and 2013 for areas dominated by crops and grasslands. This suggests an early manifestation of the dry conditions on fPAR. We also simulated SIF using a global land surface model driven by observation-based meteorological fields. The model provides a reasonable simulation of the drought and heat impacts on SIF in terms of the timing and spatial extents of anomalies, but there are some differences between modeled and observed SIF. The model may potentially be improved through data assimilation or parameter estimation using satellite observations of SIF (as well as NDVI). The model simulations also offer the opportunity to examine separately the different components of the SIF signal and relationships with Gross Primary Productivity (GPP).

Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

NASA Astrophysics Data System (ADS)

Shang, Jiali; Liu, Jiangui; Huffman, Ted; Qian, Budong; Pattey, Elizabeth; Wang, Jinfei; Zhao, Ting; Geng, Xiaoyuan; Kroetsch, David; Dong, Taifeng; Lantz, Nicholas

2014-01-01

This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination (R2) of 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops.

Applications of cluster analysis to satellite soundings

NASA Technical Reports Server (NTRS)

Munteanu, M. J.; Jakubowicz, O.; Kalnay, E.; Piraino, P.

1984-01-01

The advantages of the use of cluster analysis in the improvement of satellite temperature retrievals were evaluated since the use of natural clusters, which are associated with atmospheric temperature soundings characteristic of different types of air masses, has the potential for improving stratified regression schemes in comparison with currently used methods which stratify soundings based on latitude, season, and land/ocean. The method of discriminatory analysis was used. The correct cluster of temperature profiles from satellite measurements was located in 85% of the cases. Considerable improvement was observed at all mandatory levels using regression retrievals derived in the clusters of temperature (weighted and nonweighted) in comparison with the control experiment and with the regression retrievals derived in the clusters of brightness temperatures of 3 MSU and 5 IR channels.

A near real time regional JPSS and GOES-R data assimilation system for high impact weather research and applications

NASA Astrophysics Data System (ADS)

Li, J.; Wang, P.; Han, H.; Schmit, T. J.

2014-12-01

JPSS and GOES-R observations play important role in numerical weather prediction (NWP). However, how to best represent the information from satellite observations and how to get value added information from these satellite data into regional NWP models, including both radiance and derived products, still need investigations. In order to enhance the applications of JPSS and GOES-R data in regional NWP for high impact weather forecasts, scientists from Cooperative Institute of Meteorological Satellite Studies (CIMSS) at University of Wisconsin-Madison have recently developed a near realtime regional Satellite Data Assimilation system for Tropical storm forecasts (SDAT) (http://cimss.ssec.wisc.edu/sdat). The system consists of the community Gridpoint Statistical Interpolation (GSI) assimilation system and the advanced Weather Research Forecast (WRF) model. In addition to assimilate GOES, AMSUA/AMSUB, HIRS, MHS, ATMS (Suomi-NPP), AIRS and IASI radiances, the SDAT is also able to assimilate satellite-derived products such as hyperspectral IR retrieved temperature and moisture profiles, total precipitable water (TPW), GOES Sounder (and future GOES-R) layer precipitable water (LPW) and GOES Imager atmospheric motion vector (AMV) products into the system. Real time forecasted GOES infrared (IR) images simulated from SDAT output have also been part of the SDAT system for applications and forecast evaluations. To set up the system parameters, a series of experiments have been carried out to test the impacts of different initialization schemes, including different background error matrix, different NCEP global model date sets, and different WRF model horizontal resolutions. Using SDAT as a research testbed, researches have been conducted for different satellite data impacts study, as well as different techniques for handling clouds in radiance assimilation. Since the fall of 2013, the SDAT system has been running in near real time. The results from historical cases and 2014 hurricane season cases will be compared with the operational GFS and HWRF, and presented at the meeting.

Comparison between MODIS-derived day and night cloud cover and surface observations over the North China Plain

NASA Astrophysics Data System (ADS)

Zhang, Xiao; Tan, Saichun; Shi, Guangyu

2018-02-01

Satellite and human visual observation are two of the most important observation approaches for cloud cover. In this study, the total cloud cover (TCC) observed by MODIS onboard the Terra and Aqua satellites was compared with Synop meteorological station observations over the North China Plain and its surrounding regions for 11 years during daytime and 7 years during nighttime. The Synop data were recorded eight times a day at 3-h intervals. Linear interpolation was used to interpolate the Synop data to the MODIS overpass time in order to reduce the temporal deviation between the satellite and Synop observations. Results showed that MODIS-derived TCC had good consistency with the Synop observations; the correlation coefficients ranged from 0.56 in winter to 0.73 in summer for Terra MODIS, and from 0.55 in winter to 0.71 in summer for Aqua MODIS. However, they also had certain differences. On average, the MODIS-derived TCC was 15.16% higher than the Synop data, and this value was higher at nighttime (15.58%-16.64%) than daytime (12.74%-14.14%). The deviation between the MODIS and Synop TCC had large seasonal variation, being largest in winter (29.53%-31.07%) and smallest in summer (4.46%-6.07%). Analysis indicated that cloud with low cloud-top height and small cloud optical thickness was more likely to cause observation bias. Besides, an increase in the satellite view zenith angle, aerosol optical depth, or snow cover could lead to positively biased MODIS results, and this affect differed among different cloud types.

A comparative theoretical study on core-hole excitation spectra of azafullerene and its derivatives

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

Deng, Yunfeng; Department of Physics, Guizhou University, Guiyang 550025; Gao, Bin, E-mail: bin.gao@uit.no

2014-03-28

The core-hole excitation spectra—near-edge x-ray absorption spectroscopy (NEXAFS), x-ray emission spectroscopy (XES), and x-ray photoelectron spectroscopy (XPS) shake-up satellites have been simulated at the level of density functional theory for the azafullerene C{sub 59}N and its derivatives (C{sub 59}N){sup +}, C{sub 59}HN, (C{sub 59}N){sub 2}, and C{sub 59}N–C{sub 60}, in which the XPS shake-up satellites were simulated using our developed equivalent core hole Kohn-Sham (ECH-KS) density functional theory approach [B. Gao, Z. Wu, and Y. Luo, J. Chem. Phys. 128, 234704 (2008)] which aims for the study of XPS shake-up satellites of large-scale molecules. Our calculated spectra are generally inmore » good agreement with available experimental results that validates the use of the ECH-KS method in the present work. The nitrogen K-edge NEXAFS, XES, and XPS shake-up satellites spectra in general can be used as fingerprints to distinguish the azafullerene C{sub 59}N and its different derivatives. Meanwhile, different carbon K-edge spectra could also provide detailed information of (local) electronic structures of different molecules. In particular, a peak (at around 284.5 eV) in the carbon K-edge NEXAFS spectrum of the heterodimer C{sub 59}N–C{sub 60} is confirmed to be related to the electron transfer from the C{sub 59}N part to the C{sub 60} part in this charge-transfer complex.« less

Error Estimation of Pathfinder Version 5.3 SST Level 3C Using Three-way Error Analysis

NASA Astrophysics Data System (ADS)

Saha, K.; Dash, P.; Zhao, X.; Zhang, H. M.

2017-12-01

One of the essential climate variables for monitoring as well as detecting and attributing climate change, is Sea Surface Temperature (SST). A long-term record of global SSTs are available with observations obtained from ships in the early days to the more modern observation based on in-situ as well as space-based sensors (satellite/aircraft). There are inaccuracies associated with satellite derived SSTs which can be attributed to the errors associated with spacecraft navigation, sensor calibrations, sensor noise, retrieval algorithms, and leakages due to residual clouds. Thus it is important to estimate accurate errors in satellite derived SST products to have desired results in its applications.Generally for validation purposes satellite derived SST products are compared against the in-situ SSTs which have inaccuracies due to spatio/temporal inhomogeneity between in-situ and satellite measurements. A standard deviation in their difference fields usually have contributions from both satellite as well as the in-situ measurements. A real validation of any geophysical variable must require the knowledge of the "true" value of the said variable. Therefore a one-to-one comparison of satellite based SST with in-situ data does not truly provide us the real error in the satellite SST and there will be ambiguity due to errors in the in-situ measurements and their collocation differences. A Triple collocation (TC) or three-way error analysis using 3 mutually independent error-prone measurements, can be used to estimate root-mean square error (RMSE) associated with each of the measurements with high level of accuracy without treating any one system a perfectly-observed "truth". In this study we are estimating the absolute random errors associated with Pathfinder Version 5.3 Level-3C SST product Climate Data record. Along with the in-situ SST data, the third source of dataset used for this analysis is the AATSR reprocessing of climate (ARC) dataset for the corresponding period. All three SST observations are collocated, and statistics of difference between each pair is estimated. Instead of using a traditional TC analysis we have implemented the Extended Triple Collocation (ETC) approach to estimate the correlation coefficient of each measurement system w.r.t. the unknown target variable along with their RMSE.

Phenomapping of rangelands in South Africa using time series of RapidEye data

NASA Astrophysics Data System (ADS)

Parplies, André; Dubovyk, Olena; Tewes, Andreas; Mund, Jan-Peter; Schellberg, Jürgen

2016-12-01

Phenomapping is an approach which allows the derivation of spatial patterns of vegetation phenology and rangeland productivity based on time series of vegetation indices. In our study, we propose a new spatial mapping approach which combines phenometrics derived from high resolution (HR) satellite time series with spatial logistic regression modeling to discriminate land management systems in rangelands. From the RapidEye time series for selected rangelands in South Africa, we calculated bi-weekly noise reduced Normalized Difference Vegetation Index (NDVI) images. For the growing season of 2011⿿2012, we further derived principal phenology metrics such as start, end and length of growing season and related phenological variables such as amplitude, left derivative and small integral of the NDVI curve. We then mapped these phenometrics across two different tenure systems, communal and commercial, at the very detailed spatial resolution of 5 m. The result of a binary logistic regression (BLR) has shown that the amplitude and the left derivative of the NDVI curve were statistically significant. These indicators are useful to discriminate commercial from communal rangeland systems. We conclude that phenomapping combined with spatial modeling is a powerful tool that allows efficient aggregation of phenology and productivity metrics for spatially explicit analysis of the relationships of crop phenology with site conditions and management. This approach has particular potential for disaggregated and patchy environments such as in farming systems in semi-arid South Africa, where phenology varies considerably among and within years. Further, we see a strong perspective for phenomapping to support spatially explicit modelling of vegetation.

Distribution and Variability of Satellite-Derived Signals of Isolated Convection Initiation Events Over Central Eastern China

NASA Astrophysics Data System (ADS)

Huang, Yipeng; Meng, Zhiyong; Li, Jing; Li, Wanbiao; Bai, Lanqiang; Zhang, Murong; Wang, Xi

2017-11-01

This study combined measurements from the Chinese operational geostationary satellite Fengyun-2E (FY-2E) and ground-based weather radars to conduct a statistical survey of isolated convection initiation (CI) over central eastern China (CEC). The convective environment in CEC is modulated by the complex topography and monsoon climate. From May to August 2010, a total of 1,630 isolated CI signals were derived from FY-2E using a semiautomated method. The formation of these satellite-derived CI signals peaks in the early afternoon and occurs with high frequency in areas with remarkable terrain inhomogeneity (e.g., mountain, water, and mountain-water areas). The high signal frequency areas shift from northwest CEC (dry, high altitude) in early summer to southeast CEC (humid, low altitude) in midsummer along with an increasing monthly mean frequency. The satellite-derived CI signals tend to have longer lead times (the time difference between satellite-derived signal formation and radar-based CI) in the late morning and afternoon than in the early morning and night. During the early morning and night, the distinction between cloud top signatures and background terrestrial radiation becomes less apparent, resulting in delayed identification of the signals and thus short and even negative lead times. A decline in the lead time is observed from May to August, likely due to the increasing cloud growth rate and warm-rain processes. Results show increasing lead times with increasing landscape elevation, likely due to more warm-rain processes over the coastal sea and plain, along with a decreasing cloud growth rate from hill and mountain to the plateau.

Vegetation Earth System Data Record from DSCOVR EPIC Observations

NASA Astrophysics Data System (ADS)

Knyazikhin, Y.; Song, W.; Yang, B.; Mottus, M.; Rautiainen, M.; Stenberg, P.

2017-12-01

The NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR) mission was launched on February 11, 2015 to the Sun-Earth Lagrangian L1 point where it began to collect radiance data of the entire sunlit Earth every 65 to 110 min in June 2015. It provides imageries in near backscattering directions with the scattering angle between 168° and 176° at ten ultraviolet to near infrared (NIR) narrow spectral bands centered at 317.5 (band width 1.0) nm, 325.0 (2.0) nm, 340.0 (3.0) nm, 388.0 (3.0) nm, 433.0 (3.0) nm, 551.0 (3.0) nm, 680.0 (3.0) nm, 687.8 (0.8) nm, 764.0 (1.0) nm and 779.5 (2.0) nm. This poster presents current status of the Vegetation Earth System Data Record of global Leaf Area Index (LAI), solar zenith angle dependent Sunlit Leaf Area Index (SLAI), Fraction vegetation absorbed Photosynthetically Active Radiation (FPAR) and Normalized Difference Vegetation Index (NDVI) derived from the DSCOVR EPIC observations. Whereas LAI is a standard product of many satellite missions, the SLAI is a new satellite-derived parameter. Sunlit and shaded leaves exhibit different radiative response to incident Photosynthetically Active Radiation (400-700 nm), which in turn triggers various physiological and physical processes required for the functioning of plants. FPAR, LAI and SLAI are key state parameters in most ecosystem productivity models and carbon/nitrogen cycle. The product at 10 km sinusoidal grid and 65 to 110 min temporal frequency as well as accompanying Quality Assessment (QA) variables will be publicly available from the NASA Langley Atmospheric Science Data Center. The Algorithm Theoretical Basis (ATBD) and product validation strategy are also discussed in this poster.

Changes in Cirrus Cloudiness and their Relationship to Contrails

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Ayers, J. Kirk; Palikonda, Rabindra; Doelling, David R.; Schumann, Ulrich; Gierens, Klaus

2001-01-01

Condensation trails, or contrails, formed in the wake of high-altitude aircraft have long been suspected of causing the formation of additional cirrus cloud cover. More cirrus is possible because 10 - 20% of the atmosphere at typical commercial flight altitudes is clear but ice-saturated. Since they can affect the radiation budget like natural cirrus clouds of equivalent optical depth and microphysical properties, contrail -generated cirrus clouds are another potential source of anthropogenic influence on climate. Initial estimates of contrail radiative forcing (CRF) were based on linear contrail coverage and optical depths derived from a limited number of satellite observations. Assuming that such estimates are accurate, they can be considered as the minimum possible CRF because contrails often develop into cirrus clouds unrecognizable as contrails. These anthropogenic cirrus are not likely to be identified as contrails from satellites and would, therefore, not contribute to estimates of contrail coverage. The mean lifetime and coverage of spreading contrails relative to linear contrails are needed to fully assess the climatic effect of contrails, but are difficult to measure directly. However, the maximum possible impact can be estimated using the relative trends in cirrus coverage over regions with and without air traffic. In this paper, the upper bound of CRF is derived by first computing the change in cirrus coverage over areas with heavy air traffic relative to that over the remainder of the globe assuming that the difference between the two trends is due solely to contrails. This difference is normalized to the corresponding linear contrail coverage for the same regions to obtain an average spreading factor. The maximum contrail-cirrus coverage, estimated as the product of the spreading factor and the linear contrail coverage, is then used in the radiative model to estimate the maximum potential CRF for current air traffic.

Estimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.; Howard, Daniel M.

2015-01-01

Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass over large areas and across different geographic locations. The main goal of this study is to develop a data-driven multiple regression switchgrass productivity model and identify the optimal climate and environment conditions for the highly productive switchgrass in the Great Plains (GP). Environmental and climate variables used in the study include elevation, soil organic carbon, available water capacity, climate, and seasonal weather. Satellite-derived growing season averaged Normalized Difference Vegetation Index (GSN) was used as a proxy for switchgrass productivity. Multiple regression analyses indicate that there are strong correlations between site environmental variables and switchgrass productivity (r = 0.95). Sufficient precipitation and suitable temperature during the growing season (i.e., not too hot or too cold) are favorable for switchgrass growth. Elevation and soil characteristics (e.g., soil available water capacity) are also an important factor impacting switchgrass productivity. An anticipated switchgrass biomass productivity map for the entire GP based on site environmental and climate conditions and switchgrass productivity model was generated. Highly productive switchgrass areas are mainly located in the eastern part of the GP. Results from this study can help land managers and biofuel plant investors better understand the general environmental and climate conditions influencing switchgrass growth and make optimal land use decisions regarding switchgrass development in the GP.

Estimating Soil Moisture from Satellite Microwave Observations

NASA Technical Reports Server (NTRS)

Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.

1998-01-01

Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of soil moisture. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive soil moisture measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of soil moisture from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in soil moisture estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data and used to derive the one-way canopy transmissivity. Using a simple radiative transfer model, this information was combined with horizontally polarized 6.6 GHz SMMR observations to derive a 9-year time series of soil moisture for all of Spain at a one quarter degree spatial scale. Both day and night SMMR observations were used independently, in order to check the consistency of the results. A first order Fourier Transform was performed on the mean monthly soil moisture values to identify major characteristics of time series such as trend, amplitude, and phase shift.

Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations.

PubMed

Krasnopolsky, Vladimir; Nadiga, Sudhir; Mehra, Avichal; Bayler, Eric; Behringer, David

2016-01-01

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived "ocean color" (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA's operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed--signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN's generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.

Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

PubMed Central

Nadiga, Sudhir; Mehra, Avichal; Bayler, Eric; Behringer, David

2016-01-01

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived “ocean color” (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA's operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed—signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN's generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series. PMID:26819586

Analysis of satellite altimeter signal characteristics and investigation of sea-truth data requirements

NASA Technical Reports Server (NTRS)

1972-01-01

Results are presented of analysis of satellite signal characteristics as influenced by ocean surface roughness and an investigation of sea truth data requirements. The first subject treated is that of postflight waveform reconstruction for the Skylab S-193 radar altimeter. Sea state estimation accuracies are derived based on analytical and hybrid computer simulation techniques. An analysis of near-normal incidence, microwave backscattering from the ocean's surface is accomplished in order to obtain the minimum sea truth data necessary for good agreement between theoretical and experimental scattering results. Sea state bias is examined from the point of view of designing an experiment which will lead to a resolution of the problem. A discussion is given of some deficiencies which were found in the theory underlying the Stilwell technique for spectral measurements.

Geopotential Error Analysis from Satellite Gradiometer and Global Positioning System Observables on Parallel Architecture

NASA Technical Reports Server (NTRS)

Schutz, Bob E.; Baker, Gregory A.

1997-01-01

The recovery of a high resolution geopotential from satellite gradiometer observations motivates the examination of high performance computational techniques. The primary subject matter addresses specifically the use of satellite gradiometer and GPS observations to form and invert the normal matrix associated with a large degree and order geopotential solution. Memory resident and out-of-core parallel linear algebra techniques along with data parallel batch algorithms form the foundation of the least squares application structure. A secondary topic includes the adoption of object oriented programming techniques to enhance modularity and reusability of code. Applications implementing the parallel and object oriented methods successfully calculate the degree variance for a degree and order 110 geopotential solution on 32 processors of the Cray T3E. The memory resident gradiometer application exhibits an overall application performance of 5.4 Gflops, and the out-of-core linear solver exhibits an overall performance of 2.4 Gflops. The combination solution derived from a sun synchronous gradiometer orbit produce average geoid height variances of 17 millimeters.

Geopotential error analysis from satellite gradiometer and global positioning system observables on parallel architectures

NASA Astrophysics Data System (ADS)

Baker, Gregory Allen

The recovery of a high resolution geopotential from satellite gradiometer observations motivates the examination of high performance computational techniques. The primary subject matter addresses specifically the use of satellite gradiometer and GPS observations to form and invert the normal matrix associated with a large degree and order geopotential solution. Memory resident and out-of-core parallel linear algebra techniques along with data parallel batch algorithms form the foundation of the least squares application structure. A secondary topic includes the adoption of object oriented programming techniques to enhance modularity and reusability of code. Applications implementing the parallel and object oriented methods successfully calculate the degree variance for a degree and order 110 geopotential solution on 32 processors of the Cray T3E. The memory resident gradiometer application exhibits an overall application performance of 5.4 Gflops, and the out-of-core linear solver exhibits an overall performance of 2.4 Gflops. The combination solution derived from a sun synchronous gradiometer orbit produce average geoid height variances of 17 millimeters.

Evaluating MODIS satellite versus terrestrial data driven productivity estimates in Austria

NASA Astrophysics Data System (ADS)

Petritsch, R.; Boisvenue, C.; Pietsch, S. A.; Hasenauer, H.; Running, S. W.

2009-04-01

Sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite, are developed for monitoring global and/or regional ecosystem fluxes like net primary production (NPP). Although these systems should allow us to assess carbon sequestration issues, forest management impacts, etc., relatively little is known about the consistency and accuracy in the resulting satellite driven estimates versus production estimates driven from ground data. In this study we compare the following NPP estimation methods: (i) NPP estimates as derived from MODIS and available on the internet; (ii) estimates resulting from the off-line version of the MODIS algorithm; (iii) estimates using regional meteorological data within the offline algorithm; (iv) NPP estimates from a species specific biogeochemical ecosystem model adopted for Alpine conditions; and (v) NPP estimates calculated from individual tree measurements. Single tree measurements were available from 624 forested sites across Austria but only the data from 165 sample plots included all the necessary information for performing the comparison on plot level. To ensure independence of satellite-driven and ground-based predictions, only latitude and longitude for each site were used to obtain MODIS estimates. Along with the comparison of the different methods, we discuss problems like the differing dates of field campaigns (<1999) and acquisition of satellite images (2000-2005) or incompatible productivity definitions within the methods and come up with a framework for combining terrestrial and satellite data based productivity estimates. On average MODIS estimates agreed well with the output of the models self-initialization (spin-up) and biomass increment calculated from tree measurements is not significantly different from model results; however, correlation between satellite-derived versus terrestrial estimates are relatively poor. Considering the different scales as they are 9km² from MODIS and 1000m² from the sample plots together with the heterogeneous landscape may qualify the low correlation, particularly as the correlation increases when strongly fragmented sites are left out.

Using normalized difference vegetation index to estimate carbon fluxes from small rotationally grazed pastures

USGS Publications Warehouse

Skinner, R.H.; Wylie, B.K.; Gilmanov, T.G.

2011-01-01

Satellite-based normalized difference vegetation index (NDVI) data have been extensively used for estimating gross primary productivity (GPP) and yield of grazing lands throughout the world. However, the usefulness of satellite-based images for monitoring rotationally-grazed pastures in the northeastern United States might be limited because paddock size is often smaller than the resolution limits of the satellite image. This research compared NDVI data from satellites with data obtained using a ground-based system capable of fine-scale (submeter) NDVI measurements. Gross primary productivity was measured by eddy covariance on two pastures in central Pennsylvania from 2003 to 2008. Weekly 250-m resolution satellite NDVI estimates were also obtained for each pasture from the moderate resolution imaging spectroradiometer (MODIS) sensor. Ground-based NDVI data were periodically collected in 2006, 2007, and 2008 from one of the two pastures. Multiple-regression and regression-tree estimates of GPP, based primarily on MODIS 7-d NDVI and on-site measurements of photosynthetically active radiation (PAR), were generally able to predict growing-season GPP to within an average of 3% of measured values. The exception was drought years when estimated and measured GPP differed from each other by 11 to 13%. Ground-based measurements improved the ability of vegetation indices to capture short-term grazing management effects on GPP. However, the eMODIS product appeared to be adequate for regional GPP estimates where total growing-season GPP across a wide area would be of greater interest than short-term management-induced changes in GPP at individual sites.

Drier summers cancel out the CO2 uptake enhancement induced by warmer springs.

PubMed

Angert, A; Biraud, S; Bonfils, C; Henning, C C; Buermann, W; Pinzon, J; Tucker, C J; Fung, I

2005-08-02

An increase in photosynthetic activity of the northern hemisphere terrestrial vegetation, as derived from satellite observations, has been reported in previous studies. The amplitude of the seasonal cycle of the annually detrended atmospheric CO(2) in the northern hemisphere (an indicator of biospheric activity) also increased during that period. We found, by analyzing the annually detrended CO(2) record by season, that early summer (June) CO(2) concentrations indeed decreased from 1985 to 1991, and they have continued to decrease from 1994 up to 2002. This decrease indicates accelerating springtime net CO(2) uptake. However, the CO(2) minimum concentration in late summer (an indicator of net growing-season uptake) showed no positive trend since 1994, indicating that lower net CO(2) uptake during summer cancelled out the enhanced uptake during spring. Using a recent satellite normalized difference vegetation index data set and climate data, we show that this lower summer uptake is probably the result of hotter and drier summers in both mid and high latitudes, demonstrating that a warming climate does not necessarily lead to higher CO(2) growing-season uptake, even in high-latitude ecosystems that are considered to be temperature limited.

Petrologic model of the northern Mississippi Embayment based on satellite magnetic and ground-based geophysical data

NASA Technical Reports Server (NTRS)

Thomas, H. H.

1984-01-01

A petrologic model of the northern Mississippi Embayment, derived from gravity, seismic and rift data, is evaluated by converting the model to a magnetization model which is compared with satellite magnetic anomaly models. A magnetization contrast of approximately -0.54 A/m, determined from the petrologic model of the embayment compares favorably to values of -0.62 A/m and -0.45 A/m from a Magsat United States Apparent Magnetization Contrast Map and a published POGO magnetization contrast model, respectively. The petrologic model suggests that the magnetic anomaly low associated with the Mississippi Embayment may be largely due to the intrusion under non-oxidizing conditions of low Curie temperature gabbroic material at the base of the crust of the embayment. Near-surface mafic plutons, bordering the Mississippi Valley Graben, appear from aeromagnetic data to have higher magnetizations than the deeper gabbroic material; however, it is impossible to ascertain if this is due to compositional differences or similar material at shallower (lower temperature) depths. These results indicate that variations in the Curie temperatures of intrusions accompanying rifting may account for a large part of the wide range of magnetic anomalies associated with presently inactive rifts with normal heat flow.

Interactive visualization of vegetation dynamics

USGS Publications Warehouse

Reed, B.C.; Swets, D.; Bard, L.; Brown, J.; Rowland, James

2001-01-01

Satellite imagery provides a mechanism for observing seasonal dynamics of the landscape that have implications for near real-time monitoring of agriculture, forest, and range resources. This study illustrates a technique for visualizing timely information on key events during the growing season (e.g., onset, peak, duration, and end of growing season), as well as the status of the current growing season with respect to the recent historical average. Using time-series analysis of normalized difference vegetation index (NDVI) data from the advanced very high resolution radiometer (AVHRR) satellite sensor, seasonal dynamics can be derived. We have developed a set of Java-based visualization and analysis tools to make comparisons between the seasonal dynamics of the current year with those from the past twelve years. In addition, the visualization tools allow the user to query underlying databases such as land cover or administrative boundaries to analyze the seasonal dynamics of areas of their own interest. The Java-based tools (data exploration and visualization analysis or DEVA) use a Web-based client-server model for processing the data. The resulting visualization and analysis, available via the Internet, is of value to those responsible for land management decisions, resource allocation, and at-risk population targeting.

UARS in-flight jitter study for EOS

NASA Technical Reports Server (NTRS)

Molnar, John; Garnek, Mike

1993-01-01

Response data collected from gyroscopes on board the Upper Atmosphere Research Satellite (UARS) provided a unique opportunity to analyze actual flight pointing jitter data. Flight modal frequencies and damping values are derived from the measured data using an Eigensystem Realization Algorithm (ERA). Flight frequencies at various solar array positions are compared to analytical predictions obtained with a Finite Element Model. The solar array modal frequencies change with position due to the modes acting about different spacecraft inertial axes. Higher order modes were difficult to identify due to the limited instrumentation. Future flight jitter studies on other spacecraft would be significantly aided by additional instrumentation. Spacecraft jitter due to continuous disturbance sources such as the 1.6 meter scanning microwave antenna, the solar array drive, and reaction wheels is presented. The solar array drive disturbance dominates the spacecraft response during normal operation.

The Caspian Sea water dynamics based on satellite imagery and altimetry

NASA Astrophysics Data System (ADS)

Kostianoy, Andrey G.; Lebedev, Sergey

The Caspian Sea water dynamics is poorly known due to a lack of special hydrographic measurements. The known schemes of general circulation of the sea proposed by N.M. Knipovich in 1914-1915 and 1921, A.I. Mikhalevskiy (1931), G.N. Zaitsev (1935) and V.N. Zenin (1942) represent the basin-scale cyclonic gyres in the Middle and Southern Caspian, and no clear scheme for the shallow Northern Caspian. Later numerical models could move forward from these simple schemes of circulation to the more detailed seasonal or climatic schemes of currents, but different approaches and models give different results which significantly differ from each other (Trukhchev et al., 1995; Ibrayev et al., 2003, 2010; Popov, 2004, 2009; Knysh et al., 2008). Satellite monitoring of the Caspian Sea, we perform since 2000, is a useful tool for investigation of water dynamics in the Caspian Sea. To determine mesoscale water structure and dynamics, we used different kind of physical (SST and ice), chemical (suspended matter and water turbidity) and biological (chlorophyll concentration and algal bloom) tracers on satellite imagery. Satellite altimetry (sea level anomalies in combination with the mean dynamic level derived from numerical modeling) provides fields of currents in the whole Caspian Sea on a regular basis (every 10 days). Seasonal fields of currents derived from satellite altimetry also differ from those obtained in numerical models. Finally, we show the results of the first drifter experiment performed in the Caspian Sea in 2006-2008 in the framework of the MACE Project. Special attention is paid to the seasonal upwelling along the eastern coast of the sea, coastal currents, and a giant intrusion of warm water from the Southern to the Middle Caspian Sea.

An Evolving Model for Capacity Building with Earth Observation Imagery

NASA Astrophysics Data System (ADS)

Sylak-Glassman, E. J.

2015-12-01

For the first forty years of Earth observation satellite imagery, all imagery was collected by civilian or military governmental satellites. Over this timeframe, countries without observation satellite capabilities had very limited access to Earth observation data or imagery. In response to the limited access to Earth observation systems, capacity building efforts were focused on satellite manufacturing. Wood and Weigel (2012) describe the evolution of satellite programs in developing countries with a technology ladder. A country moves up the ladder as they move from producing satellites with training services to building satellites locally. While the ladder model may be appropriate if the goal is to develop autonomous satellite manufacturing capability, in the realm of Earth observation, the goal is generally to derive societal benefit from the use of Earth observation-derived information. In this case, the model for developing Earth observation capacity is more appropriately described by a hub-and-spoke model in which the use of Earth observation imagery is the "hub," and the "spokes" describe the various paths to achieving that imagery: the building of a satellite (either independently or with assistance), the purchase of a satellite, participation in a constellation of satellites, and the use of freely available or purchased satellite imagery. We discuss the different capacity-building activities that are conducted in each of these pathways, such as the "Know-How Transfer and Training" program developed by Surrey Satellite Technology Ltd. , Earth observation imagery training courses run by SERVIR in developing countries, and the use of national or regional remote sensing centers (such as those in Morocco, Malaysia, and Kenya) to disseminate imagery and training. In addition, we explore the factors that determine through which "spoke" a country arrives at the ability to use Earth observation imagery, and discuss best practices for achieving the capability to use imagery.

Analysis and geological interpretation of gravity data from GEOS-3 altimeter

NASA Technical Reports Server (NTRS)

Talwani, M.; Watts, A. B.; Chapman, M. E.

1978-01-01

A number of detailed gravimetric geoids of portions of the world's oceans from marine gravity measurements were constructed. The geoids were constructed by computing 1 x 1 deg or 10 x 10 deg averages of free-air anomaly data and subtracting these values from currently used satellite derived Earth models. The resulting difference gravity anomalies are then integrated over a sphere using a simplified form of Stoke's equation to obtain a difference geoid. This difference geoid is added to the satellite derived model to obtain a 1 x 1 deg or 10 x 10 deg total gravimetric geoid. The geoid undulations are studied by comparison of the altimeter measurements with the morphology of the ocean floor. Utilizing a combination of altimetry data, gravity and seismic reflection data, geophysical models of the earth can be constructed.

Requirement of MEF2A, C, and D for skeletal muscle regeneration

PubMed Central

Liu, Ning; Nelson, Benjamin R.; Bezprozvannaya, Svetlana; Shelton, John M.; Richardson, James A.; Bassel-Duby, Rhonda; Olson, Eric N.

2014-01-01

Regeneration of adult skeletal muscle following injury occurs through the activation of satellite cells, an injury-sensitive muscle stem cell population that proliferates, differentiates, and fuses with injured myofibers. Members of the myocyte enhancer factor 2 (MEF2) family of transcription factors play essential roles in muscle differentiation during embryogenesis, but their potential contributions to adult muscle regeneration have not been systematically explored. To investigate the potential involvement of MEF2 factors in muscle regeneration, we conditionally deleted the Mef2a, c, and d genes, singly and in combination, within satellite cells in mice, using tamoxifen-inducible Cre recombinase under control of the satellite cell-specific Pax7 promoter. We show that deletion of individual Mef2 genes has no effect on muscle regeneration in response to cardiotoxin injury. However, combined deletion of the Mef2a, c, and d genes results in a blockade to regeneration. Satellite cell-derived myoblasts lacking MEF2A, C, and D proliferate normally in culture, but cannot differentiate. The absence of MEF2A, C, and D in satellite cells is associated with aberrant expression of a broad collection of known and unique protein-coding and long noncoding RNA genes. These findings reveal essential and redundant roles of MEF2A, C, and D in satellite cell differentiation and identify a MEF2-dependent transcriptome associated with skeletal muscle regeneration. PMID:24591619

Station-Keeping Strategies for Lead-Trail Formation Flying

NASA Astrophysics Data System (ADS)

Martinot, V.; Rozanes, P.

Numerous projects in the Science and Observation domains involve the use of formation flying to ensure the mission performance. The formation flying configurations proposed in some of them are quite complex with several satellites in different planes generating relative differential motions between the satellites like in case of circular projected formation-flying. However, more simple designs consisting of two satellites in a lead-trail formation appear to be sufficient for a wide range of applications (interferometry, geodesy,...). This article concentrates on the station- keeping phase of such formations in Low-Earth Orbits The station-keeping criterion for such formations can be expressed for example in terms of difference in argument of latitude between both satellites and at the altitudes considered, it evolves mainly under the differential effect of the atmospheric drag between the trailing and leading satellites. In the present paper, this differential effect is supposed to originate from the difference in the area-to-mass ratio between the satellites due to their different designs. A preliminary estimation of the navigation performance is first given assuming that on-board GPS receiver are mounted on each satellite of the formation to acquire pseudo-range measurements between the LEO satellites and the MEO GPS constellation. The distance between both satellites of the formation is derived from independent orbit restitution performed for each LEO satellite in a ground master control station processing the GPS measurements. A strategy for controlling the satellite formation disturbed by the differential effect of the drag is then proposed. Simulations are performed to assess the feasibility of the station-keeping with different types of engines. As by-products, the propellant budget and the frequency of the station-keeping manoeuvres are also given. A case study inspired from the ESA project Acechem/Metop is used for the simulations.

Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland.

PubMed

Ren, Shilong; Chen, Xiaoqiu; An, Shuai

2017-04-01

Plant phenology is a key link for controlling interactions between climate change and biogeochemical cycles. Satellite-derived normalized difference vegetation index (NDVI) has been extensively used to detect plant phenology at regional scales. Here, we introduced a new vegetation index, plant senescence reflectance index (PSRI), and determined PSRI-derived start (SOS) and end (EOS) dates of the growing season using Moderate Resolution Imaging Spectroradiometer data from 2000 to 2011 in the Inner Mongolian Grassland. Then, we validated the reliability of PSRI-derived SOS and EOS dates using NDVI-derived SOS and EOS dates. Moreover, we conducted temporal and spatial correlation analyses between PSRI-derived SOS/EOS date and climatic factors and revealed spatiotemporal patterns of PSRI-derived SOS and EOS dates across the entire research region at pixel scales. Results show that PSRI has similar performance with NDVI in extracting SOS and EOS dates in the Inner Mongolian Grassland. Precipitation regime is the key climate driver of interannual variation of grassland phenology, while temperature and precipitation regimes are the crucial controlling factors of spatial differentiation of grassland phenology. Thus, PSRI-derived vegetation phenology can effectively reflect land surface vegetation dynamics and its response to climate change. Moreover, a significant linear trend of PSRI-derived SOS and EOS dates was detected only at small portions of pixels, which is consistent with that of greenup and brownoff dates of herbaceous plant species in the Inner Mongolian Grassland. Overall, PSRI is a useful and robust metric in addition to NDVI for monitoring land surface grassland phenology.

Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland

NASA Astrophysics Data System (ADS)

Ren, Shilong; Chen, Xiaoqiu; An, Shuai

2017-04-01

Plant phenology is a key link for controlling interactions between climate change and biogeochemical cycles. Satellite-derived normalized difference vegetation index (NDVI) has been extensively used to detect plant phenology at regional scales. Here, we introduced a new vegetation index, plant senescence reflectance index (PSRI), and determined PSRI-derived start (SOS) and end (EOS) dates of the growing season using Moderate Resolution Imaging Spectroradiometer data from 2000 to 2011 in the Inner Mongolian Grassland. Then, we validated the reliability of PSRI-derived SOS and EOS dates using NDVI-derived SOS and EOS dates. Moreover, we conducted temporal and spatial correlation analyses between PSRI-derived SOS/EOS date and climatic factors and revealed spatiotemporal patterns of PSRI-derived SOS and EOS dates across the entire research region at pixel scales. Results show that PSRI has similar performance with NDVI in extracting SOS and EOS dates in the Inner Mongolian Grassland. Precipitation regime is the key climate driver of interannual variation of grassland phenology, while temperature and precipitation regimes are the crucial controlling factors of spatial differentiation of grassland phenology. Thus, PSRI-derived vegetation phenology can effectively reflect land surface vegetation dynamics and its response to climate change. Moreover, a significant linear trend of PSRI-derived SOS and EOS dates was detected only at small portions of pixels, which is consistent with that of greenup and brownoff dates of herbaceous plant species in the Inner Mongolian Grassland. Overall, PSRI is a useful and robust metric in addition to NDVI for monitoring land surface grassland phenology.

Comparison between AVHRR surface temperature data and in-situ weather station temperatures over the Greenland Ice Sheet

NASA Astrophysics Data System (ADS)

Rezvanbehbahani, S.; Csatho, B. M.; Comiso, J. C.; Babonis, G. S.

2011-12-01

Advanced Very-High Resolution Radiometer (AVHRR) images have been exhaustively used to measure surface temperature time series of the Greenland Ice sheet. The purpose of this study is to assess the accuracy of monthly average ice sheet surface temperatures, derived from thermal infrared AVHRR satellite imagery on a 6.25 km grid. In-situ temperature data sets are from the Greenland Collection Network (GC-Net). GC-Net stations comprise sensors monitoring air temperature at 1 and 2 meter above the snow surface, gathered at every 60 seconds and monthly averaged to match the AVHRR temporal resolution. Our preliminary results confirm the good agreement between satellite and in-situ temperature measurements reported by previous studies. However, some large discrepancies still exist. While AVHRR provides ice surface temperature, in-situ stations measure air temperatures at different elevations above the snow surface. Since most in-situ data on ice sheets are collected by Automatic Weather Station (AWS) instruments, it is important to characterize the difference between surface and air temperatures. Therefore, we compared and analyzed average monthly AVHRR ice surface temperatures using data collected in 2002. Differences between these temperatures correlate with in-situ temperatures and GC-Net station elevations, with increasing differences at lower elevations and higher temperatures. The Summit Station (3199 m above sea level) and the Swiss Camp (1176 m above sea level) results were compared as high altitude and low altitude stations for 2002, respectively. Our results show that AVHRR derived temperatures were 0.5°K warmer than AWS temperature at the Summit Station, while this difference was 2.8°K in the opposite direction for the Swiss Camp with surface temperatures being lower than air temperatures. The positive bias of 0.5°K at the high altitude Summit Station (surface warmer than air) is within the retrieval error of AVHRR temperatures and might be in part due to atmospheric inversion. The large negative bias of 2.8°K at the low altitude Swiss Camp (surface colder than the air) could be caused by a combination of different factors including local effects such as more windy circumstances above the snow surface and biases introduced by the cloud-masking applied on the AVHRR images. Usually only satellite images acquired in clear-sky conditions are used for deriving monthly AVHRR average temperatures. Since cloud-free days are usually warmer, satellite derived temperatures tend to underestimate the real average temperatures, especially regions with frequent cloud cover, such as Swiss Camp. Therefore, cautions must be exercised while using ice surface temperatures derived from satellite imagery for glaciological applications. Eliminating the cloudy day's' temperature from the in-situ data prior to the comparison with AVHRR derived temperatures will provide a better assessment of AVHRR surface temperature measurement accuracy.

Temporal spectral response of a corn canopy

NASA Technical Reports Server (NTRS)

Markham, B. L.; Kimes, D. S.; Tucker, C. J.; Mcmurtrey, J. E., III

1981-01-01

Techniques developed for the prediction of winter wheat yields from remotely sensed data indicating crop status over the growing season are tested for their applicability to corn. Ground-based spectral measurements in the Landsat Thematic Mapper bands 3 (0.62-0.69 microns), 4 (0.76-0.90 microns) and 5 (1.55-1.75 microns) were performed at one-week intervals throughout the growing season for 24 plots of corn, and analyzed to derive spectral ratios and normalized spectral differences of the IR and shortwave IR bands with the red. The ratios of the near IR and shortwave IR bands are found to provide the highest and most consistent correlations with corn yield and dry matter accumulation, however the value of band 5 could not be tested due to the absence of water stress conditions. Integration of spectral ratios over several dates improved the correlations over those of any single date by achieving a seasonal, rather than instantaneous, estimate of crop status. Results point to the desirability of further tests under other growth conditions to determine whether satellite-derived data will be useful in providing corn yield information.

Impact of changes in GRACE derived terrestrial water storage on vegetation growth in Eurasia

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Kim, Y.

2015-12-01

We use GRACE-derived terrestrial water storage (TWS) and ERA-interim air temperature, as proxy for available water and temperature constraints on vegetation productivity, inferred from MODIS satellite normalized difference vegetation index (NDVI), in Northern Eurasia during 2002-2011. We investigate how changes in TWS affect the correlation between NDVI and temperature during the non-frozen season. We find that vegetation growth exhibits significant spatial and temporal variability associated with varying trend in TWS and temperature. The largest NDVI gains occur over boreal forests associated with warming and wetting. The largest NDVI losses occur over grasslands in the Southwestern Ob associated with regional drying and cooling, with dominant constraint from TWS. Over grasslands and temperate forests in the Southeast Ob and South Yenisei, wetting and cooling lead to a dominant temperature constraint due to the relaxation of TWS constraints. Overall, we find significant monthly correlation of NDVI with TWS and temperature over 35% and 50% of the domain, respectively. These results indicate that water availability (TWS) plays a major role in modulating Eurasia vegetation response to temperature changes.

Epigenetic analysis of bovine parthenogenetic embryonic fibroblasts.

PubMed

Kaneda, Masahiro; Takahashi, Masashi; Yamanaka, Ken-Ichi; Saito, Koji; Taniguchi, Masanori; Akagi, Satoshi; Watanabe, Shinya; Nagai, Takashi

2017-08-19

Although more than 100 imprinted genes have already been identified in the mouse and human genomes, little is known about genomic imprinting in cattle. For a better understanding of these genes in cattle, parthenogenetically activated bovine blastocysts were transferred to recipient cows to obtain parthenotes, and fibroblasts derived from a Day 40 (Day 0 being the day of parthenogenetic activation) parthenogenetic embryo (BpEFs) were successfully obtained. Bovine embryonic fibroblasts (BEFs) were also isolated from a normal fertilized embryo obtained from an artificially inseminated cow. The expression of imprinted genes was analyzed by RT-PCR. Paternally expressed genes (PEGs) in mouse (viz., IGF2, PEG3, ZAC1, NDN, DLK1, SGCE, and PEG10) were expressed in BEFs, but not in BpEFs, suggesting that these genes are also imprinted in cattle. However, other PEGs in mouse (viz., IMPACT, MAGEL2, SNRPN, and PEG1/MEST) were expressed in both BEFs and BpEFs. These genes may not be imprinted in BEFs. The expression of seven maternally expressed genes in mouse was also analyzed, and only CDKN1C was not expressed in BpEFs. The DNA methylation patterns of repetitive elements (Satellite I, Satellite II, alpha-satellite, and Art2) were not different between the BEFs and BpEFs; however, the differentially methylated region (DMR) of paternally methylated H19 was hypomethylated, whereas those of maternally methylated PEG3 and PEG10 were hypermethylated in BpEFs, as expected. The methylation of the SNRPN DMR was not different between the BEFs and BpEFs, in accordance with the SNRPN expression levels in both cell types. The XIST gene, which is essential for X chromosome inactivation in females, was expressed in BpEFs, whereas its DMR was half-methylated, suggesting that X chromosome inactivation is normal in these cells. Microarray analysis was also applied to identify novel PEGs that should be expressed only in BEFs but not in BpEFs. More than 300 PEG candidate genes, including IGF2, PEG3, and PEG10, were obtained. These results illustrate the epigenetic characteristic of bovine parthenogenetic embryos and contribute to the identification of novel imprinted genes in cattle.

Initial results of centralized autonomous orbit determination of the new-generation BDS satellites with inter-satellite link measurements

NASA Astrophysics Data System (ADS)

Tang, Chengpan; Hu, Xiaogong; Zhou, Shanshi; Liu, Li; Pan, Junyang; Chen, Liucheng; Guo, Rui; Zhu, Lingfeng; Hu, Guangming; Li, Xiaojie; He, Feng; Chang, Zhiqiao

2018-01-01

Autonomous orbit determination is the ability of navigation satellites to estimate the orbit parameters on-board using inter-satellite link (ISL) measurements. This study mainly focuses on data processing of the ISL measurements as a new measurement type and its application on the centralized autonomous orbit determination of the new-generation Beidou navigation satellite system satellites for the first time. The ISL measurements are dual one-way measurements that follow a time division multiple access (TDMA) structure. The ranging error of the ISL measurements is less than 0.25 ns. This paper proposes a derivation approach to the satellite clock offsets and the geometric distances from TDMA dual one-way measurements without a loss of accuracy. The derived clock offsets are used for time synchronization, and the derived geometry distances are used for autonomous orbit determination. The clock offsets from the ISL measurements are consistent with the L-band two-way satellite, and time-frequency transfer clock measurements and the detrended residuals vary within 0.5 ns. The centralized autonomous orbit determination is conducted in a batch mode on a ground-capable server for the feasibility study. Constant hardware delays are present in the geometric distances and become the largest source of error in the autonomous orbit determination. Therefore, the hardware delays are estimated simultaneously with the satellite orbits. To avoid uncertainties in the constellation orientation, a ground anchor station that "observes" the satellites with on-board ISL payloads is introduced into the orbit determination. The root-mean-square values of orbit determination residuals are within 10.0 cm, and the standard deviation of the estimated ISL hardware delays is within 0.2 ns. The accuracy of the autonomous orbits is evaluated by analysis of overlap comparison and the satellite laser ranging (SLR) residuals and is compared with the accuracy of the L-band orbits. The results indicate that the radial overlap differences between the autonomous orbits are less than 15.0 cm for the inclined geosynchronous orbit (IGSO) satellites and less than 10.0 cm for the MEO satellites. The SLR residuals are approximately 15.0 cm for the IGSO satellites and approximately 10.0 cm for the MEO satellites, representing an improvement over the L-band orbits.

Comparison of cloud top heights derived from FY-2 meteorological satellites with heights derived from ground-based millimeter wavelength cloud radar

NASA Astrophysics Data System (ADS)

Wang, Zhe; Wang, Zhenhui; Cao, Xiaozhong; Tao, Fa

2018-01-01

Clouds are currently observed by both ground-based and satellite remote sensing techniques. Each technique has its own strengths and weaknesses depending on the observation method, instrument performance and the methods used for retrieval. It is important to study synergistic cloud measurements to improve the reliability of the observations and to verify the different techniques. The FY-2 geostationary orbiting meteorological satellites continuously observe the sky over China. Their cloud top temperature product can be processed to retrieve the cloud top height (CTH). The ground-based millimeter wavelength cloud radar can acquire information about the vertical structure of clouds-such as the cloud base height (CBH), CTH and the cloud thickness-and can continuously monitor changes in the vertical profiles of clouds. The CTHs were retrieved using both cloud top temperature data from the FY-2 satellites and the cloud radar reflectivity data for the same time period (June 2015 to May 2016) and the resulting datasets were compared in order to evaluate the accuracy of CTH retrievals using FY-2 satellites. The results show that the concordance rate of cloud detection between the two datasets was 78.1%. Higher consistencies were obtained for thicker clouds with larger echo intensity and for more continuous clouds. The average difference in the CTH between the two techniques was 1.46 km. The difference in CTH between low- and mid-level clouds was less than that for high-level clouds. An attenuation threshold of the cloud radar for rainfall was 0.2 mm/min; a rainfall intensity below this threshold had no effect on the CTH. The satellite CTH can be used to compensate for the attenuation error in the cloud radar data.

APOD Mission Status and Observations by VLBI

NASA Astrophysics Data System (ADS)

Tang, Geshi; Sun, Jing; Li, Xie; Liu, Shushi; Chen, Guangming; Ren, Tianpeng; Wang, Guangli

2016-12-01

On September 20, 2015, 20 satellites were successfully launched from the TaiYuan Satellite Launch Center by a Chinese CZ-6 test rocket and are, since then, operated in a circular, near-polar orbit at an altitude of 520 km. Among these satellites, a set of four CubSats, named APOD (Atmospheric density detection and Precise Orbit Determination), are intended for atmospheric density in-situ detection and derivation via precise orbit. The APOD satellites, manufactured by DFH Co., carry a number of instruments including a density detector, a dual-frequency GNSS (GPS/BD) receiver, an SLR reflector, and a VLBI S/X beacon. The APOD mission aims at detecting the atmospheric density below 520 km. The ground segment is controlled by BACC (Beijing Aerospace Control Center) including payload operation as well as science data receiving, processing, archiving, and distribution. Currently, the in-orbit test of the nano-satellites and their payloads are completed, and preliminary results show that the precision of the orbit determination is about 10 cm derived from both an overlap comparison and an SLR observation validation. The in-situ detected density calibrated by orbit-derived density demonstrates that the accuracy of atmospheric mass density is approximately 4.191×10^{-14} kgm^{-3}, about 5.5% of the measurement value. Since three space-geodetic techniques (i.e., GNSS, SLR, and VLBI) are co-located on the APOD nano-satellites, the observations can be used for combination and validation in order to detect systematic differences. Furthermore, the observations of the APOD satellites by VLBI radio telescopes can be used in an ideal fashion to link the dynamical reference frames of the satellite with the terrestrial and, most importantly, with the celestial reference frame as defined by the positions of quasars. The possibility of observing the APOD satellites by IVS VLBI radio telescopes will be analyzed, considering continental-size VLBI observing networks and the small telescopes with sufficient speed.

Measuring surface energy and evapotranspiration across Caribbean mangrove forests

NASA Astrophysics Data System (ADS)

Lagomasino, D.; Fatoyinbo, T. E.; Price, R.

2014-12-01

Coastal mangroves lose large amounts of water through evapotranspiration (ET) that can be equivalent to the amount of annual rainfall in certain years. Satellite remote sensing has been used to estimate surface energy and ET variability in many forested ecosystems, yet has been widely overlooked in mangrove forests. Using a combination of long-term datasets (30-year) acquired from the NASA Landsat 5 and 7 satellite databases, the present study investigated ET and surface energy balance variability between two mangrove forest sites in the Caribbean: 1) Everglades National Park (ENP; Florida, USA) and 2) Sian Ka'an Biosphere Reserve (SKBR; Quintana Roo, Mexico). A satellite-derived surface energy balance model was used to estimate ET in tall and scrub mangroves environments at ENP and SKBR. Results identified significant differences in soil heat flux measurements and ET between the tall and scrub mangrove environments. Scrub mangroves exhibited the highest soil heat flux coincident with the lowest biophysical indices (i.e., Fractional Vegetation Cover, Normalized Difference Vegetation Index, and Soil-Adjusted Vegetation Index) and ET rates. Mangrove damage and mortality was observed on the satellite images following strong tropical storms and associated with anthropogenic modifications and resulted in low values in spectral vegetation indices, higher soil heat flux, and higher ET. Recovery of the spectral characteristics, soil heat flux and ET was within 1-2 years following hurricane disturbance while, degradation caused by human disturbance persisted for many years. Remotely sensed ET of mangrove forests can provide estimates over a few decades and provide us with some understanding of how these environments respond to disturbances to the landscape in periods where no ground data exists or in locations that are difficult to access. Moreover, relationships between energy and water balance components developed for the coastal mangroves of Florida and Mexico could be extrapolated to other mangroves systems in the Caribbean to measure changes caused by natural events and human modifications.

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 2: Estimation of the urban bias

NASA Technical Reports Server (NTRS)

Epperson, David L.; Davis, Jerry M.; Bloomfield, Peter; Karl, Thomas R.; Mcnab, Alan L.; Gallo, Kevin P.

1995-01-01

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data -- the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data -- to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km x 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1 deg x 1 deg latitude-longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative (COOP) Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network (GHCN). Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40 C for monthly minimum temperatures, near 0.25 C for monthly mean temperatures, and near 0.10 C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near -1.1 C for rural stations to 2.4 C for stations from the largest urban areas. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available.

Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought

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

Wagle, Pradeep; Xiao, Xiangming; Torn, Margaret S.

2014-09-01

Drought affects vegetation photosynthesis and growth.Many studies have used the normalized difference vegetation index (NDVI), which is calculated as the normalized ratio between near infrared and red spectral bands in satellite images, to evaluate the response of vegetation to drought. In this study, we examined the impacts of drought on three vegetation indices (NDVI, enhanced vegetation index, EVI, and land surface water index, LSWI) and CO2 flux from three tallgrass prairie eddy flux tower sites in the U.S. Gross primary production (GPP) was also modeled using a satellite-based Vegetation Photosynthesis Model (VPM), and the modeled GPP (GPPVPM) was compared withmore » the GPP (GPPEC) derived from eddy covariance measurements. Precipitation at two sites in Oklahoma was 30% below the historical mean in both years of the study period (2005–2006), while the site in Illinois did not experience drought in the 2005–2007 study period. The EVI explained the seasonal dynamics of GPP better than did NDVI. The LSWI dropped below zero during severe droughts in the growing season, showing its potential to track drought. The result shows that GPP was more sensitive to drought than were vegetation indices, and EVI and LSWI were more sensitive than NDVI. We developed a modified function (Wscalar), calculated as a function of LSWI, to account for the effect of severe droughts on GPP in VPM. The GPPVPM from the modified VPM accounted for the rapid reduction in GPP during severe droughts and the seasonal dynamics of GPPVPM agreed reasonably well with GPPEC. Our analysis shows that 8-day averaged values (temperature, vapor-pressure deficit) do not reflect the short-term extreme climate events well, suggesting that satellite based models may need to be run at daily or hourly scales, especially under unfavorable climatic conditions.« less

Crustal Magnetization Model of Maud Rise in the Southwest Indian Ocean

NASA Technical Reports Server (NTRS)

Kim, Hyung Rae; vanFrese, Ralph R. B.; Golynsky, Alexander V.; Taylor, Patrick T.; Kim, Jeong Woo

2004-01-01

We modeled the crustal magnetization for the Maud Rise in the south-west Indian Ocean off the coast of East Antarctica using magnetic observations from the Oersted satellite and near-surface surveys complied by the Antarctic Digital Magnetic Anomaly Project (ADMAP). A new inversion modeling scheme of the multi-altitude anomaly fields suggests that the magnetic effects due to crustal thickness variations and remanence involving the normal polarity Cretaceous Quiet Zone (KQZ) become increasingly dominant with altitude. The magnetic crustal thickness effects were modeled in the Oersted data using crustal thickness variations derived from satellite altitude gravity data. Remanent magnetization modeling of the residual Oersted and near-surface magnetic anomalies supports extending the KQZ eastwards to the Astrid Ridge. The remaining near-surface anomalies involve crustal features with relatively high frequency effects that are strongly attenuated at satellite altitudes. The crustal modeling can be extended by the satellite magnetic anomalies across the Indian Ocean Ridge for insight on the crustal properties of the conjugate Agulhas Plateau. The modeling supports the Jurassic reconstruction of Gondwana when the African Limpopo-Zambezi and East Antarctic Princess Astrid coasts were connected as part of a relatively demagnetized crustal block.

Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia.

PubMed

Dorji, Passang; Fearns, Peter

2017-01-01

The impact of anthropogenic activities on coastal waters is a cause of concern because such activities add to the total suspended sediment (TSS) budget of the coastal waters, which have negative impacts on the coastal ecosystem. Satellite remote sensing provides a powerful tool in monitoring TSS concentration at high spatiotemporal resolution, but coastal managers should be mindful that the satellite-derived TSS concentrations are dependent on the satellite sensor's radiometric properties, atmospheric correction approaches, the spatial resolution and the limitations of specific TSS algorithms. In this study, we investigated the impact of different spatial resolutions of satellite sensor on the quantification of TSS concentration in coastal waters of northern Western Australia. We quantified the TSS product derived from MODerate resolution Imaging Spectroradiometer (MODIS)-Aqua, Landsat-8 Operational Land Image (OLI), and WorldView-2 (WV2) at native spatial resolutions of 250 m, 30 m and 2 m respectively and coarser spatial resolution (resampled up to 5 km) to quantify the impact of spatial resolution on the derived TSS product in different turbidity conditions. The results from the study show that in the waters of high turbidity and high spatial variability, the high spatial resolution WV2 sensor reported TSS concentration as high as 160 mg L-1 while the low spatial resolution MODIS-Aqua reported a maximum TSS concentration of 23.6 mg L-1. Degrading the spatial resolution of each satellite sensor for highly spatially variable turbid waters led to variability in the TSS concentrations of 114.46%, 304.68% and 38.2% for WV2, Landsat-8 OLI and MODIS-Aqua respectively. The implications of this work are particularly relevant in the situation of compliance monitoring where operations may be required to restrict TSS concentrations to a pre-defined limit.

Long-Term Trends Worldwide in Ambient NO2 Concentrations Inferred from Satellite Observations.

PubMed

Geddes, Jeffrey A; Martin, Randall V; Boys, Brian L; van Donkelaar, Aaron

2016-03-01

Air pollution is associated with morbidity and premature mortality. Satellite remote sensing provides globally consistent decadal-scale observations of ambient nitrogen dioxide (NO2) pollution. We determined global population-weighted annual mean NO2 concentrations from 1996 through 2012. We used observations of NO2 tropospheric column densities from three satellite instruments in combination with chemical transport modeling to produce a global 17-year record of ground-level NO2 at 0.1° × 0.1° resolution. We calculated linear trends in population-weighted annual mean NO2 (PWMNO2) concentrations in different regions around the world. We found that PWMNO2 in high-income North America (Canada and the United States) decreased more steeply than in any other region, having declined at a rate of -4.7%/year [95% confidence interval (CI): -5.3, -4.1]. PWMNO2 decreased in western Europe at a rate of -2.5%/year (95% CI: -3.0, -2.1). The highest PWMNO2 occurred in high-income Asia Pacific (predominantly Japan and South Korea) in 1996, with a subsequent decrease of -2.1%/year (95% CI: -2.7, -1.5). In contrast, PWMNO2 almost tripled in East Asia (China, North Korea, and Taiwan) at a rate of 6.7%/year (95% CI: 6.0, 7.3). The satellite-derived estimates of trends in ground-level NO2 were consistent with regional trends inferred from data obtained from ground-station monitoring networks in North America (within 0.7%/year) and Europe (within 0.3%/year). Our rankings of regional average NO2 and long-term trends differed from the satellite-derived estimates of fine particulate matter reported elsewhere, demonstrating the utility of both indicators to describe changing pollutant mixtures. Long-term trends in satellite-derived ambient NO2 provide new information about changing global exposure to ambient air pollution. Our estimates are publicly available at http://fizz.phys.dal.ca/~atmos/martin/?page_id=232.

Assessment of Satellite-Derived Surface Reflectances by NASA's CAR Airborne Radiometer over Railroad Valley, Nevada

NASA Technical Reports Server (NTRS)

Kharbouche, Said; Muller, Jan-Peter; Gatebe, Charles K.; Scanlon, Tracy; Banks, Andrew C.

2017-01-01

CAR (Cloud Absorption Radiometer) is a multi-angular and multi-spectral airborne radiometer instrument, whose radiometric and geometric characteristics are well calibrated and adjusted before and after each flight campaign. CAR was built by NASA (National Aeronautics and Space Administration) in 1984. On 16 May 2008, a CAR flight campaign took place over the well-known calibration and validation site of Railroad Valley in Nevada (38.504 deg N, 115.692 deg W).The campaign coincided with the overpasses of several key EO (Earth Observation) satellites such as Landsat-7, Envisat and Terra. Thus, there are nearly simultaneous measurements from these satellites and the CAR airborne sensor over the same calibration site. The CAR spectral bands are close to those of most EO satellites. CAR has the ability to cover the whole range of azimuth view angles and a variety of zenith angles depending on altitude and, as a consequence, the biases seen between satellite and CAR measurements due to both unmatched spectral bands and unmatched angles can be significantly reduced. A comparison is presented here between CARs land surface reflectance (BRF or Bidirectional Reflectance Factor) with those derived from Terra/MODIS (MOD09 and MAIAC), Terra/MISR, Envisat/MERIS and Landsat-7. In this study, we utilized CAR data from low altitude flights (approx. 180 m above the surface) in order to minimize the effects of the atmosphere on these measurements and then obtain a valuable ground-truth data set of surface reflectance. Furthermore, this study shows that differences between measurements caused by surface heterogeneity can be tolerated, thanks to the high homogeneity of the study site on the one hand, and on the other hand, to the spatial sampling and the large number of CAR samples. These results demonstrate that satellite BRF measurements over this site are in good agreement with CAR with variable biases across different spectral bands. This is most likely due to residual aerosol effects in the EO derived reflectances.

Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia

PubMed Central

Fearns, Peter

2017-01-01

The impact of anthropogenic activities on coastal waters is a cause of concern because such activities add to the total suspended sediment (TSS) budget of the coastal waters, which have negative impacts on the coastal ecosystem. Satellite remote sensing provides a powerful tool in monitoring TSS concentration at high spatiotemporal resolution, but coastal managers should be mindful that the satellite-derived TSS concentrations are dependent on the satellite sensor’s radiometric properties, atmospheric correction approaches, the spatial resolution and the limitations of specific TSS algorithms. In this study, we investigated the impact of different spatial resolutions of satellite sensor on the quantification of TSS concentration in coastal waters of northern Western Australia. We quantified the TSS product derived from MODerate resolution Imaging Spectroradiometer (MODIS)-Aqua, Landsat-8 Operational Land Image (OLI), and WorldView-2 (WV2) at native spatial resolutions of 250 m, 30 m and 2 m respectively and coarser spatial resolution (resampled up to 5 km) to quantify the impact of spatial resolution on the derived TSS product in different turbidity conditions. The results from the study show that in the waters of high turbidity and high spatial variability, the high spatial resolution WV2 sensor reported TSS concentration as high as 160 mg L-1 while the low spatial resolution MODIS-Aqua reported a maximum TSS concentration of 23.6 mg L-1. Degrading the spatial resolution of each satellite sensor for highly spatially variable turbid waters led to variability in the TSS concentrations of 114.46%, 304.68% and 38.2% for WV2, Landsat-8 OLI and MODIS-Aqua respectively. The implications of this work are particularly relevant in the situation of compliance monitoring where operations may be required to restrict TSS concentrations to a pre-defined limit. PMID:28380059

Recovery of atmospheric refractivity profiles from simulated satellite-to-satellite tracking data

NASA Technical Reports Server (NTRS)

Murray, C. W., Jr.; Rangaswamy, S.

1975-01-01

Techniques for recovering atmospheric refractivity profiles from simulated satellite-to-satellite tracking data are documented. Examples are given using the geometric configuration of the ATS-6/NIMBUS-6 Tracking Experiment. The underlying refractivity model for the lower atmosphere has the spherically symmetric form N = exp P(s) where P(s) is a polynomial in the normalized height s. For the simulation used, the Herglotz-Wiechert technique recovered values which were 0.4% and 40% different from the input values at the surface and at a height of 33 kilometers, respectively. Using the same input data, the model fitting technique recovered refractivity values 0.05% and 1% different from the input values at the surface and at a height of 50 kilometers, respectively. It is also shown that if ionospheric and water vapor effects can be properly modelled or effectively removed from the data, pressure and temperature distributions can be obtained.

New-Generation BeiDou (BDS-3) Experimental Satellite Precise Orbit Determination with an Improved Cycle-Slip Detection and Repair Algorithm

PubMed Central

Hu, Chao; Wang, Qianxin; Wang, Zhongyuan; Hernández Moraleda, Alberto

2018-01-01

Currently, five new-generation BeiDou (BDS-3) experimental satellites are working in orbit and broadcast B1I, B3I, and other new signals. Precise satellite orbit determination of the BDS-3 is essential for the future global services of the BeiDou system. However, BDS-3 experimental satellites are mainly tracked by the international GNSS Monitoring and Assessment Service (iGMAS) network. Under the current constraints of the limited data sources and poor data quality of iGMAS, this study proposes an improved cycle-slip detection and repair algorithm, which is based on a polynomial prediction of ionospheric delays. The improved algorithm takes the correlation of ionospheric delays into consideration to accurately estimate and repair cycle slips in the iGMAS data. Moreover, two methods of BDS-3 experimental satellite orbit determination, namely, normal equation stacking (NES) and step-by-step (SS), are designed to strengthen orbit estimations and to make full use of the BeiDou observations in different tracking networks. In addition, a method to improve computational efficiency based on a matrix eigenvalue decomposition algorithm is derived in the NES. Then, one-year of BDS-3 experimental satellite precise orbit determinations were conducted based on iGMAS and Multi-GNSS Experiment (MGEX) networks. Furthermore, the orbit accuracies were analyzed from the discrepancy of overlapping arcs and satellite laser range (SLR) residuals. The results showed that the average three-dimensional root-mean-square error (3D RMS) of one-day overlapping arcs for BDS-3 experimental satellites (C31, C32, C33, and C34) acquired by NES and SS are 31.0, 36.0, 40.3, and 50.1 cm, and 34.6, 39.4, 43.4, and 55.5 cm, respectively; the RMS of SLR residuals are 55.1, 49.6, 61.5, and 70.9 cm and 60.5, 53.6, 65.8, and 73.9 cm, respectively. Finally, one month of observations were used in four schemes of BDS-3 experimental satellite orbit determination to further investigate the reliability and advantages of the improved methods. It was suggested that the scheme with improved cycle-slip detection and repair algorithm based on NES was optimal, which improved the accuracy of BDS-3 experimental satellite orbits by 34.07%, 41.05%, 72.29%, and 74.33%, respectively, compared with the widely-used strategy. Therefore, improved methods for the BDS-3 experimental satellites proposed in this study are very beneficial for the determination of new-generation BeiDou satellite precise orbits. PMID:29724062

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.

Latitudinal distributions of particulate carbon export across the North Western Atlantic Ocean

NASA Astrophysics Data System (ADS)

Puigcorbé, Viena; Roca-Martí, Montserrat; Masqué, Pere; Benitez-Nelson, Claudia; Rutgers van der Loeff, Michiel; Bracher, Astrid; Moreau, Sebastien

2017-11-01

234Th-derived carbon export fluxes were measured in the Atlantic Ocean under the GEOTRACES framework to evaluate basin-scale export variability. Here, we present the results from the northern half of the GA02 transect, spanning from the equator to 64°N. As a result of limited site-specific C/234Th ratio measurements, we further combined our data with previous work to develop a basin wide C/234Th ratio depth curve. While the magnitude of organic carbon fluxes varied depending on the C/234Th ratio used, latitudinal trends were similar, with sizeable and variable organic carbon export fluxes occurring at high latitudes and low to negligible fluxes occurring in oligotrophic waters. Our results agree with previous studies, except at the boundaries between domains, where fluxes were relatively enhanced. Three different models were used to obtain satellite-derived net primary production (NPP). In general, NPP estimates had similar trends along the transect, but there were significant differences in the absolute magnitude depending on the model used. Nevertheless, organic carbon export efficiencies were generally < 25%, with the exception of a few stations located in the transition area between the riverine and the oligotrophic domains and between the oligotrophic and the temperate domains. Satellite-derived organic carbon export models from Dunne et al. (2005) (D05), Laws et al. (2011) (L11) and Henson et al. (2011) (H11) were also compared to our 234Th-derived carbon exports fluxes. D05 and L11 provided estimates closest to values obtained with the 234Th approach (within a 3-fold difference), but with no clear trends. The H11 model, on the other hand, consistently provided lower export estimates. The large increase in export data in the Atlantic Ocean derived from the GEOTRACES Program, combined with satellite observations and modeling efforts continue to improve the estimates of carbon export in this ocean basin and therefore reduce uncertainty in the global carbon budget. However, our results also suggest that tuning export models and including biological parameters at a regional scale is necessary for improving satellite-modeling efforts and providing export estimates that are more representative of in situ observations.

Comparison of satellite derived dynamical quantities in the stratosphere of the Southern Hemisphere

NASA Technical Reports Server (NTRS)

Miles, Thomas (Editor); Oneill, Alan (Editor)

1989-01-01

The proceedings are summarized from a pre-MASH planning workshop on the intercomparison of Southern Hemisphere observations, analyses and derived dynamical quantities held in Williamsburg, Virginia during April 1986. The aims of this workshop were primarily twofold: (1) comparison of Southern Hemisphere dynamical quantities derived from various satellite data archives (e.g., from limb scanners and nadir sounders); and (2) assessing the impact of different base-level height information on such derived quantities. These tasks are viewed as especially important in the Southern Hemisphere because of the paucity of conventional measurements. A further strong impetus for the MASH program comes from the recent discovery of the springtime ozone hold over Antarctica. Insight gained from validation studies such as the one reported here will contribute to an improved understanding of the role of meteorology in the development and evolution of the hold, in its interannual variability, and in its interhemispheric differences. The dynamical quantities examined in this workshop included geopotential height, zonal wind, potential vorticity, eddy heat and momentum fluxes, and Eliassen-Palm fluxes. The time periods and data sources constituting the MASH comparisons are summarized.

Predicting the Magnetic Field of Earth-Impacting CMEs

NASA Technical Reports Server (NTRS)

Kay, C.; Gopalswamy, N.; Reinard, A.; Opher, M.

2017-01-01

Predicting the impact of coronal mass ejections (CMEs) and the southward component of their magnetic field is one of the key goals of space weather forecasting. We present a new model, the ForeCAT In situ Data Observer (FIDO), for predicting the in situ magnetic field of CMEs. We first simulate a CME using ForeCAT, a model for CME deflection and rotation resulting from the background solar magnetic forces. Using the CME position and orientation from ForeCAT, we then determine the passage of the CME over a simulated spacecraft. We model the CME's magnetic field using a force-free flux rope and we determine the in situ magnetic profile at the synthetic spacecraft. We show that FIDO can reproduce the general behavior of four observed CMEs. FIDO results are very sensitive to the CME's position and orientation, and we show that the uncertainty in a CME's position and orientation from coronagraph images corresponds to a wide range of in situ magnitudes and even polarities. This small range of positions and orientations also includes CMEs that entirely miss the satellite. We show that two derived parameters (the normalized angular distance between the CME nose and satellite position and the angular difference between the CME tilt and the position angle of the satellite with respect to the CME nose) can be used to reliably determine whether an impact or miss occurs. We find that the same criteria separate the impacts and misses for cases representing all four observed CMEs.

A 16-year time series of 1 km AVHRR satellite data of the conterminous United States and Alaska

USGS Publications Warehouse

Eidenshink, Jeff

2006-01-01

The U.S. Geological Survey (USGS) has developed a 16-year time series of vegetation condition information for the conterminous United States and Alaska using 1 km Advanced Very High Resolution Radiometer (AVHRR) data. The AVHRR data have been processed using consistent methods that account for radiometric variability due to calibration uncertainty, the effects of the atmosphere on surface radiometric measurements obtained from wide field-of-view observations, and the geometric registration accuracy. The conterminous United States and Alaska data sets have an atmospheric correction for water vapor, ozone, and Rayleigh scattering and include a cloud mask derived using the Clouds from AVHRR (CLAVR) algorithm. In comparison with other AVHRR time series data sets, the conterminous United States and Alaska data are processed using similar techniques. The primary difference is that the conterminous United States and Alaska data are at 1 km resolution, while others are at 8 km resolution. The time series consists of weekly and biweekly maximum normalized difference vegetation index (NDVI) composites.

Recent Surface Reflectance Measurement Campaigns with Emphasis on Best Practices, SI Traceability and Uncertainty Estimation

NASA Technical Reports Server (NTRS)

Helder, Dennis; Thome, Kurtis John; Aaron, Dave; Leigh, Larry; Czapla-Myers, Jeff; Leisso, Nathan; Biggar, Stuart; Anderson, Nik

2012-01-01

A significant problem facing the optical satellite calibration community is limited knowledge of the uncertainties associated with fundamental measurements, such as surface reflectance, used to derive satellite radiometric calibration estimates. In addition, it is difficult to compare the capabilities of calibration teams around the globe, which leads to differences in the estimated calibration of optical satellite sensors. This paper reports on two recent field campaigns that were designed to isolate common uncertainties within and across calibration groups, particularly with respect to ground-based surface reflectance measurements. Initial results from these efforts suggest the uncertainties can be as low as 1.5% to 2.5%. In addition, methods for improving the cross-comparison of calibration teams are suggested that can potentially reduce the differences in the calibration estimates of optical satellite sensors.

Spring Hydrology Determines Summer Net Carbon Uptake in Northern Ecosystems

NASA Technical Reports Server (NTRS)

Yi, Yonghong; Kimball, John; Reichle, Rolf H.

2014-01-01

Increased photosynthetic activity and enhanced seasonal CO2 exchange of northern ecosystems have been observed from a variety of sources including satellite vegetation indices (such as the Normalized Difference Vegetation Index; NDVI) and atmospheric CO2 measurements. Most of these changes have been attributed to strong warming trends in the northern high latitudes (greater than or equal to 50N). Here we analyze the interannual variation of summer net carbon uptake derived from atmospheric CO2 measurements and satellite NDVI in relation to surface meteorology from regional observational records. We find that increases in spring precipitation and snow pack promote summer net carbon uptake of northern ecosystems independent of air temperature effects. However, satellite NDVI measurements still show an overall benefit of summer photosynthetic activity from regional warming and limited impact of spring precipitation. This discrepancy is attributed to a similar response of photosynthesis and respiration to warming and thus reduced sensitivity of net ecosystem carbon uptake to temperature. Further analysis of boreal tower eddy covariance CO2 flux measurements indicates that summer net carbon uptake is positively correlated with early growing-season surface soil moisture, which is also strongly affected by spring precipitation and snow pack based on analysis of satellite soil moisture retrievals. This is attributed to strong regulation of spring hydrology on soil respiration in relatively wet boreal and arctic ecosystems. These results document the important role of spring hydrology in determining summer net carbon uptake and contrast with prevailing assumptions of dominant cold temperature limitations to high-latitude ecosystems. Our results indicate potentially stronger coupling of boreal/arctic water and carbon cycles with continued regional warming trends.

Separation of atmospheric, oceanic and hydrological polar motion excitation mechanisms based on a combination of geometric and gravimetric space observations

NASA Astrophysics Data System (ADS)

Göttl, F.; Schmidt, M.; Seitz, F.; Bloßfeld, M.

2015-04-01

The goal of our study is to determine accurate time series of geophysical Earth rotation excitations to learn more about global dynamic processes in the Earth system. For this purpose, we developed an adjustment model which allows to combine precise observations from space geodetic observation systems, such as Satellite Laser Ranging (SLR), Global Navigation Satellite Systems, Very Long Baseline Interferometry, Doppler Orbit determination and Radiopositioning Integrated on Satellite, satellite altimetry and satellite gravimetry in order to separate geophysical excitation mechanisms of Earth rotation. Three polar motion time series are applied to derive the polar motion excitation functions (integral effect). Furthermore we use five time variable gravity field solutions from Gravity Recovery and Climate Experiment to determine not only the integral mass effect but also the oceanic and hydrological mass effects by applying suitable filter techniques and a land-ocean mask. For comparison the integral mass effect is also derived from degree 2 potential coefficients that are estimated from SLR observations. The oceanic mass effect is also determined from sea level anomalies observed by satellite altimetry by reducing the steric sea level anomalies derived from temperature and salinity fields of the oceans. Due to the combination of all geodetic estimated excitations the weaknesses of the individual processing strategies can be reduced and the technique-specific strengths can be accounted for. The formal errors of the adjusted geodetic solutions are smaller than the RMS differences of the geophysical model solutions. The improved excitation time series can be used to improve the geophysical modeling.

Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data

USGS Publications Warehouse

Belchansky, Gennady I.; Douglas, David C.

2002-01-01

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.<9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap concentrations were no more than 3.1% (S.D.<5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total ice concentrations than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total ice concentrations than the NASA Team algorithm. Total ice concentrations derived from OKEAN-01 and SSM/I satellite imagery were highly correlated during winter, spring, and fall, with mean differences of less than 8.1% (S.D.<15%) for the NASA Team algorithm, and less than 2.8% (S.D.<13.8%) for the Bootstrap algorithm. Respective differences between SSM/I NASA Team and SSM/I Bootstrap total concentrations were less than 5.3% (S.D.<6.9%). Monthly mean differences between SSM/I and OKEAN differed annually by less than 6%, with smaller differences primarily in winter. The NASA Team and Bootstrap algorithms underestimated the total sea ice concentrations relative to the RADARSAT ScanSAR no more than 3.0% (S.D.<9%) and 1.2% (S.D.<7.5%) during cold months, and no more than 12% and 7% during summer, respectively. ScanSAR tended to estimate higher ice concentrations for ice concentrations greater than 50%, when compared to SSM/I during all months. ScanSAR underestimated total sea ice concentration by 2% compared to the OKEAN-01 algorithm during cold months, and gave an overestimation by 2% during spring and summer months. Total NASA Team and Bootstrap sea ice concentration estimates derived from coincident SSM/I and OKEAN-01 data demonstrated mean differences of no more than 5.3% (S.D.<7%), 3.1% (S.D.<5.5%), 2.0% (S.D.<5.5%), and 7.3% (S.D.<10%) for fall, winter, spring, and summer periods, respectively. Large disagreements were observed between the OKEAN and NASA Team results in spring and summer for estimates of the first-year (FY) and multiyear (MY) age classes. The OKEAN-01 algorithm and data tended to estimate, on average, lower concentrations of young or FY ice and higher concentrations of total and MY ice for all months and seasons. Our results contribute to the growing body of documentation about the levels of disparity obtained when seasonal sea ice concentrations are estimated using various types of satellite data and algorithms.

Analysis of global land surface albedo climatology and spatial-temporal variation during 1981-2010 from multiple satellite products

NASA Astrophysics Data System (ADS)

He, Tao; Liang, Shunlin; Song, Dan-Xia

2014-09-01

For several decades, long-term time series data sets of multiple global land surface albedo products have been generated from satellite observations. These data sets have been used as one of the key variables in climate change studies. This study aims to assess the surface albedo climatology and to analyze long-term albedo changes, from nine satellite-based data sets for the period 1981-2010, on a global basis. Results show that climatological surface albedo data sets derived from satellite observations can be used to validate, calibrate, and further improve surface albedo simulations and parameterizations in current climate models. However, the albedo products derived from the International Satellite Cloud Climatology Project and the Global Energy and Water Exchanges Project have large seasonal biases. At latitudes higher than 50°, the maximal difference in winter zonal albedo ranges from 0.1 to 0.4 among the nine satellite data sets. Satellite-based albedo data sets agree relatively well during the summer at high latitudes, with a standard deviation of 0.04 for the 70°-80° zone in both hemispheres. The fine-resolution (0.05°) data sets agree well with each other for all the land cover types in middle to low latitudes; however, large spread was identified for their albedos at middle to high latitudes over land covers with mixed snow and sparse vegetation. By analyzing the time series of satellite-based albedo products over the past three decades, albedo of the Northern Hemisphere was found to be decreasing in July, likely due to the shrinking snow cover. Meanwhile, albedo in January was found to be increasing, likely because of the expansion of snow cover in northern winter. However, to improve the albedo estimation at high latitudes, and ultimately the climate models used for long-term climate change studies, a still better understanding of differences between satellite-based albedo data sets is required.

Cross Correlation versus Normalized Mutual Information on Image Registration

NASA Technical Reports Server (NTRS)

Tan, Bin; Tilton, James C.; Lin, Guoqing

2016-01-01

This is the first study to quantitatively assess and compare cross correlation and normalized mutual information methods used to register images in subpixel scale. The study shows that the normalized mutual information method is less sensitive to unaligned edges due to the spectral response differences than is cross correlation. This characteristic makes the normalized image resolution a better candidate for band to band registration. Improved band-to-band registration in the data from satellite-borne instruments will result in improved retrievals of key science measurements such as cloud properties, vegetation, snow and fire.

Cross Calibration of TOMS, SBUV/2 and Sciamachy Radiances from Ground Observations

NASA Technical Reports Server (NTRS)

Hillsenrath, Ernest; Ahmad, Ziauddin; Bhartia, Pawan K. (Technical Monitor)

2001-01-01

Verification of a stratospheric ozone recovery remains a high priority for environmental research and policy definition. Models predict an ozone recovery at a much lower rate than the measured depletion rate observed to date. Therefore improved precision of the satellite and ground ozone observing systems are required over the long term to verify recovery. We have shown that validation of radiances is the most effective means for correcting absolute accuracy and long term drifts of backscatter type satellite measurements. This method by-passes the algorithms used for both satellite and ground based measurements which are normally used to validate and correct the satellite data. Validation of radiances will also improve all higher level data products derived from the satellite observations. Backscatter algorithms suffer from several errors such as unrepresentative a-priori data and air mass factor corrections. Radiance comparisons employ forward models but are inherently more accurate and than inverse (retrieval) algorithms. A new method for satellite validation is planned which will compliment measurements from the existing ground-based networks. This method will employ very accurate comparisons between ground based zenith sky radiances and satellite nadir radiances. These comparisons will rely heavily on the experience derived from the Shuttle SBUV (SSBUV) program which provided a reference standard of radiance measurements for SBUV/2, TOMS, and GOME. This new measurement program, called "Skyrad", employs two well established capabilities at the Goddard Space Flight Center, 1) the SSBUV calibration facilities and 2) the radiative transfer codes used for the TOMS and SBUV/2 algorithms and their subsequent refinements. Radiative transfer calculations show that ground based zenith sky and satellite nadir backscatter ultraviolet comparisons can be made very accurately under certain viewing conditions. The Skyrad instruments (SSBUV, Brewer spectrophotometers, and possibly others) will be calibrated and maintained to a precision of a few tenths of a percent. Skyrad data will then enable long term calibration of upcoming satellite instruments such as QuickTOMS. SBUV/2s and SCIAMACHY with a high degree of precision. This technique can be further employed to monitor the performance of future instruments such as GOME-2, OMI, and OMPS. Initial ground observations taken from Goddard Space Flight Center compared with radiative transfer calculations has indicated the feasibility of this method.

Yield variability prediction by remote sensing sensors with different spatial resolution

NASA Astrophysics Data System (ADS)

Kumhálová, Jitka; Matějková, Štěpánka

2017-04-01

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

Contribution of Starlette, Stella, and AJISAI to the SLR-derived global reference frame

NASA Astrophysics Data System (ADS)

Sośnica, Krzysztof; Jäggi, Adrian; Thaller, Daniela; Beutler, Gerhard; Dach, Rolf

2014-08-01

The contribution of Starlette, Stella, and AJISAI is currently neglected when defining the International Terrestrial Reference Frame, despite a long time series of precise SLR observations and a huge amount of available data. The inferior accuracy of the orbits of low orbiting geodetic satellites is the main reason for this neglect. The Analysis Centers of the International Laser Ranging Service (ILRS ACs) do, however, consider including low orbiting geodetic satellites for deriving the standard ILRS products based on LAGEOS and Etalon satellites, instead of the sparsely observed, and thus, virtually negligible Etalons. We process ten years of SLR observations to Starlette, Stella, AJISAI, and LAGEOS and we assess the impact of these Low Earth Orbiting (LEO) SLR satellites on the SLR-derived parameters. We study different orbit parameterizations, in particular different arc lengths and the impact of pseudo-stochastic pulses and dynamical orbit parameters on the quality of the solutions. We found that the repeatability of the East and North components of station coordinates, the quality of polar coordinates, and the scale estimates of the reference are improved when combining LAGEOS with low orbiting SLR satellites. In the multi-SLR solutions, the scale and the component of geocenter coordinates are less affected by deficiencies in solar radiation pressure modeling than in the LAGEOS-1/2 solutions, due to substantially reduced correlations between the geocenter coordinate and empirical orbit parameters. Eventually, we found that the standard values of Center-of-mass corrections (CoM) for geodetic LEO satellites are not valid for the currently operating SLR systems. The variations of station-dependent differential range biases reach 52 and 25 mm for AJISAI and Starlette/Stella, respectively, which is why estimating station-dependent range biases or using station-dependent CoM, instead of one value for all SLR stations, is strongly recommended. This clearly indicates that the ILRS effort to produce CoM corrections for each satellite, which are site-specific and depend on the system characteristics at the time of tracking, is very important and needs to be implemented in the SLR data analysis.

A COMPARISON OF AEROSOL OPTICAL DEPTH SIMULATED USING CMAQ WITH SATELLITE ESTIMATES

EPA Science Inventory

Satellite data provide new opportunities to study the regional distribution of particulate matter. The aerosol optical depth (AOD) - a derived estimate from the satellite measured irradiance, can be compared against model derived estimate to provide an evaluation of the columnar ...

Monitoring arid lands using AVHRR-observed visible reflectance and SMMR37-GHz polarization difference

NASA Technical Reports Server (NTRS)

Choudhury, B. J.

1990-01-01

Visible reflectance along a transect through the Sahel and Sudan zones of Africa has been derived from observations by the AVHRR on the NOAA-7 and NOAA-9 satellites and compared with concurrent observations of the 37-GHz polarization difference by the SMMR on the Nimbus-7 satellite. The study period was January 1982 to December 1986, which included an unprecedented drought during 1984 over the Sahel zone. While spatial and temporal patterns of these two data sets are found to be highly correlated, there are also quantitative differences which need to be understood.

Characterization of precipitation features over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2013-12-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, surface observations, and models to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets of TMPA 3B42, CMORPH, and PERSIANN. The satellite based QPEs are compared over the concurrent period with the NCEP Stage IV product, which is a near real time product providing precipitation data at the hourly temporal scale gridded at a nominal 4-km spatial resolution. In addition, remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model), which provides gridded precipitation estimates that are used as a baseline for multi-sensor QPE products comparison. The comparisons are performed at the annual, seasonal, monthly, and daily scales with focus on selected river basins (Southeastern US, Pacific Northwest, Great Plains). While, unconditional annual rain rates present a satisfying agreement between all products, results suggest that satellite QPE datasets exhibit important biases in particular at higher rain rates (≥4 mm/day). Conversely, on seasonal scales differences between remotely sensed data and ground surface observations can be greater than 50% and up to 90% for low daily accumulation (≤1 mm/day) such as in the Western US (summer) and Central US (winter). The conditional analysis performed using different daily rainfall accumulation thresholds (from low rainfall intensity to intense precipitation) shows that while intense events measured at the ground are infrequent (around 2% for daily accumulation above 2 inches/day), remotely sensed products displayed differences from 20-50% and up to 90-100%. A discussion on the impact of differing spatial and temporal resolutions with respect to the datasets ability to capture extreme precipitation events is also provided. Furthermore, this work is part of a broader effort to evaluate long-term multi-sensor QPEs in the perspective of developing Climate Data Records (CDRs) for precipitation.

High-resolution IKONOS satellite imagery for normalized difference vegetative index-related assessment applied to land clearance studies

NASA Astrophysics Data System (ADS)

Lavers, Chris R.; Mason, Travis

2017-07-01

High-resolution satellite imagery permits verification of human rights land clearance violations across international borders as a result of unstable regimes or socio-economic upheaval. Without direct access to these areas to validate allegations of human rights abuse, the use of remote sensing tools, techniques, and data is extremely important. Humanitarian assessment can benefit from software-based solutions, involving radiometrically calibrated normalized difference vegetation index and temporal change imagery. We discuss the introduction of a matrix filter approach for change detection studies to help assist rapid building detection over large search areas against a bright background to evaluate internally displaced people in the 2005 Porta Farm Zimbabwe clearances. Future wide-scale near real-time space-based monitoring with a range of digital filters would be of great benefit to international human rights observers and human rights networks.

Senegalese land surface change analysis and biophysical parameter estimation using NOAA AVHRR spectral data

NASA Technical Reports Server (NTRS)

Vukovich, Fred M.; Toll, David L.; Kennard, Ruth L.

1989-01-01

Surface biophysical estimates were derived from analysis of NOAA Advanced Very High Spectral Resolution (AVHRR) spectral data of the Senegalese area of west Africa. The parameters derived were of solar albedo, spectral visible and near-infrared band reflectance, spectral vegetative index, and ground temperature. Wet and dry linked AVHRR scenes from 1981 through 1985 in Senegal were analyzed for a semi-wet southerly site near Tambacounda and a predominantly dry northerly site near Podor. Related problems were studied to convert satellite derived radiance to biophysical estimates of the land surface. Problems studied were associated with sensor miscalibration, atmospheric and aerosol spatial variability, surface anisotropy of reflected radiation, narrow satellite band reflectance to broad solar band conversion, and ground emissivity correction. The middle-infrared reflectance was approximated with a visible AVHRR reflectance for improving solar albedo estimates. In addition, the spectral composition of solar irradiance (direct and diffuse radiation) between major spectral regions (i.e., ultraviolet, visible, near-infrared, and middle-infrared) was found to be insensitive to changes in the clear sky atmospheric optical depth in the narrow band to solar band conversion procedure. Solar albedo derived estimates for both sites were not found to change markedly with significant antecedent precipitation events or correspondingly from increases in green leaf vegetation density. The bright soil/substrate contributed to a high albedo for the dry related scenes, whereas the high internal leaf reflectance in green vegetation canopies in the near-infrared contributed to high solar albedo for the wet related scenes. The relationship between solar albedo and ground temperature was poor, indicating the solar albedo has little control of the ground temperature. The normalized difference vegetation index (NDVI) and the derived visible reflectance were more sensitive to antecedent rainfall amounts and green vegetation changes than were near-infrared changes. The information in the NDVI related to green leaf density changes primarily was from the visible reflectance. The contribution of the near-infrared reflectance to explaining green vegetation is largely reduced when there is a bright substrate.

Spatiotemporal Analysis of Urban Land Cover Changes in Kigali, Rwanda Using Multitemporal Landsat Data and Landscape Metrics

NASA Astrophysics Data System (ADS)

Mugiraneza, T.; Haas, J.; Ban, Y.

2017-11-01

Mapping urbanization and ensuing environmental impacts using satellite data combined with landscape metrics has become a hot research topic. The objectives of the study are to analyze the spatio-temporal evolution of urbanization patterns of Kigali, Rwanda over the last three decades (from 1984 to 2015) using multitemporal Landsat data and to assess the associated environmental impact using landscape metrics. Landsat images, Normalized Difference Vegetation Index (NDVI), Grey Level Co-occurrence Matrix (GLCM) variance texture and digital elevation model (DEM) data were classified using a support vector machine (SVM). Eight landscape indices were derived from classified images for urbanization environment impact assessment. Seven land cover classes were derived with an overall accuracy exceeding 88 % with Kappa Coefficients around 0.8. As most prominent changes, cropland was reduced considerably in favour of built-up areas that increased from 2,349 ha to 11,579 ha between 1984 and 2015. During those 31 years, the increased number of patches in most land cover classes illustrated landscape fragmentation, especially for forest. The landscape configuration indices demonstrate that in general the land cover pattern remained stable for cropland but it was highly changed in built-up areas. Satellite-based analysis and quantification of urbanization and its effects using landscape metrics are found to be interesting for grassroots and provide a cost-effective method for urban information production. This information can be used for e.g. potential design and implementation of early warning systems that cater for urbanization effects.

A comparative study of measurements from radiosondes, rocketsondes, and satellites

NASA Technical Reports Server (NTRS)

Nestler, M. S.

1983-01-01

Direct comparisons of operational products derived from measurements of radiance by satellites to measurements from conventional in situ sensors are important for the evaluation of satellite systems. However, errors in the in situ measurements themselves complicate such comparisons. Atmospheric temporal and spatial variability are also influential. These issues are investigated by means of a special field program composed of flights of dual radiosondes and multiple radiosondes launched near the time of NOAA-6 overpasses. Satellite derived mean layer temperatures, geopotential heights, and winds are compared with the same quantities determined from the in situ sensors. Of particular interest is the impact of in situ errors on these comparisons. It is shown that the radiosonde provides a precise pressure height relationship and therefore precise data for synoptic type use. Radar tracking of the radiosondes reveals, however, an imprecise pressure measurement which causes large differences between the actual altitude of the radiosonde and the altitude at which it is calculated to be. Radiosondes should be radar tracked and pressures calculated if the data are to be used for purposes other than synoptic use. Evaluation of rocketsonde data reveals a temperature precision of 1 to 2 K below about 55 km. Above 55 km, the precision decreases rapidly; rms differences of up to 11 K are obtained.

Global discrimination of land cover types from metrics derived from AVHRR pathfinder data

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

DeFries, R.; Hansen, M.; Townshend, J.

1995-12-01

Global data sets of land cover are a significant requirement for global biogeochemical and climate models. Remotely sensed satellite data is an increasingly attractive source for deriving these data sets due to the resulting internal consistency, reproducibility, and coverage in locations where ground knowledge is sparse. Seasonal changes in the greenness of vegetation, described in remotely sensed data as changes in the normalized difference vegetation index (NDVI) throughout the year, have been the basis for discriminating between cover types in previous attempts to derive land cover from AVHRR data at global and continental scales. This study examines the use ofmore » metrics derived from the NDVI temporal profile, as well as metrics derived from observations in red, infrared, and thermal bands, to improve discrimination between 12 cover types on a global scale. According to separability measures calculated from Bhattacharya distances, average separabilities improved by using 12 of the 16 metrics tested (1.97) compared to separabilities using 12 monthly NDVI values alone (1.88). Overall, the most robust metrics for discriminating between cover types were: mean NDVI, maximum NDVI, NDVI amplitude, AVHRR Band 2 (near-infrared reflectance) and Band 1 (red reflectance) corresponding to the time of maximum NDVI, and maximum land surface temperature. Deciduous and evergreen vegetation can be distinguished by mean NDVI, maximum NDVI, NDVI amplitude, and maximum land surface temperature. Needleleaf and broadleaf vegetation can be distinguished by either mean NDVI and NDVI amplitude or maximum NDVI and NDVI amplitude.« less

An Integrative Observing and Modeling Approach for the Physiological Understanding of Sun-Induced Chlorophyll Fluorescence in Japan

NASA Astrophysics Data System (ADS)

Kobayashi, H.; Kato, T.; Saitoh, Y.; Noda, H.; Kikosaka, K.; Ichii, K.; Nasahara, K. N.

2016-12-01

Satellite-derived sun-induced chlorophyll fluorescence (SIF) is expected to provides a pathway to link leaf level photosynthesis to global GPP. Existing studies have stressed how well the satellite-derived SIF is correlated with the eddy covariance and/or modeled GPPs. There are some challenges in SIF interpretation because the satellite-derived SIF is a mixture of fluorescence emission from sunlit and shaded leaves and multiple scatterings of fluorescence within plant canopies. In this presentation, we show observation and modeling results around Japan and discuss how the integrative observing and modeling approach potentially overcomes the gaps in-between satellite SIF and photosynthesis reaction within leaves. We have analyzed ground-based SIF monitoring systems "Phenological Eye Network (PEN)". PEN covers several eddy flux sites in Japan and is equipped with spectroradiometer (MS-700) since 2003 (at an earliest site). The computed seasonal SIF variations in the different ecosystems show environmental dependency of SIF and GPP. Another ground-based system we are now developing is the vegetation lidar system named LIFS (Laser-Induced Fluorescence Spectrum), which can offer eco-physiological information of plants. LIFS is consisted of a pulsed UV (355 nm) laser, a telescope, a spectrometer/filter, and a gated image-intensified CCD detector. This system has been using to remotely monitor tree growth status, chlorophyll contents in leaves and so on. The physical and physiological theories are necessary for understanding the observed SIF under various environmental conditions. We have been developing leaf to plant canopy scale photosynthesis and SIF models as precise as possible. The developed model has been used to understand how the leaf-level SIF emission can be related to the canopy scale SIF, which enables to investigate the top of canopy SIF observed from ground-based and satellite-derived SIF measurements.

Electrophysiological evidence of doubly innervated branched muscle fibers in the human brachioradialis muscle.

PubMed

Lateva, Zoia C; McGill, Kevin C

2007-12-01

Motor-unit action potentials (MUAPs) with unstable satellite (late-latency) components are found in EMG signals from the brachioradialis muscles of normal subjects. We analyzed the morphology and blocking behavior of these MUAPs to determine their anatomical origin. EMG signals were recorded from the brachioradialis muscles of 5 normal subjects during moderate-level isometric contractions. MUAP waveforms, discharge patterns, and blocking were determined using computer-aided EMG decomposition. Twelve MUAPs with unstable satellite potentials were detected, always two together in the same signal. Each MUAP also had a second unstable component associated with its main spike. The blocking behavior of the unstable components depended on how close together the two MUAPs were when they discharged. The latencies and blocking behavior indicate that the unstable components came from branched muscle fibers innervated by two different motoneurons. The satellite potentials were due to action potentials that traveled to the branching point along one branch and back along the other. The blockings were due to action-potential collisions when both motoneurons discharged close together in time. Animal studies suggest that branched muscle fibers may be a normal characteristic of series-fibered muscles. This study adds to our understanding of these muscles in humans.

A Drone-based Tropical Forest Experiment to Estimate Vegetation Properties

NASA Astrophysics Data System (ADS)

Henke, D.

2017-12-01

In mid-latitudes, remote sensing technology is intensively used to monitor vegetation properties. However, in the tropics, high cloud-cover and saturation effects of vegetation indices (VI) hamper the reliability of vegetation parameters derived from satellite data. A drone experiment over the Barro Colorado Island (BCI), Panama, with high temporal repetition rates was conducted in spring 2017 to investigate the robustness and stability of remotely sensed vegetation parameters in tropical environments. For this purpose, three 10-day flight windows in February, March and April were selected and drone flights were repeated on daily intervals when weather conditions and equipment allowed it. In total, 18 days were recorded with two different optical cameras on sensefly's eBee drone: one red, green, blue (RGB) camera and one camera with near infra-red (NIR), green and blue channels. When possible, the data were acquired at the same time of day. Pix4D and Agisoft software were used to calculate the Normalized Difference VI (NDVI) and forest structure. In addition, leave samples were collected ones per month from 16 different plant species and the relative water content was measured as ground reference. Further data sources for the analysis are phenocam images (RGB & NIR) on BCI and satellite images of MODIS (NDVI; Enhanced VI EVI) and Sentinel-1 (radar backscatter). The attached figure illustrates the main data collected on BCI. Initial results suggest that the coefficient of determination (R2) is relatively high between ground samples and drone data, Sentinel-1 backscatter and MODIS EVI with R2 values ranging from 0.4 to 0.6; on the contrary, R2 values between ground measurements and MODIS NDVI or phenocam images are below 0.2. As the experiment took place mainly during dry season on BCI, cloud-cover rates are less dominate than during wet season. Under these conditions, MODIS EVI, which is less vulnerable to saturation effects, seems to be more reliable than MODIS NDVI. During wet season, Sentinel-1 backscatter might be the most reliable satellite option to derive vegetation parameters in the tropics. For a more robust conclusion, additional data takes over several years and during dry as well as wet season are needed to confirm initial findings presented here.

Precision Orbit Derived Atmospheric Density: Development and Performance

NASA Astrophysics Data System (ADS)

McLaughlin, C.; Hiatt, A.; Lechtenberg, T.; Fattig, E.; Mehta, P.

2012-09-01

Precision orbit ephemerides (POE) are used to estimate atmospheric density along the orbits of CHAMP (Challenging Minisatellite Payload) and GRACE (Gravity Recovery and Climate Experiment). The densities are calibrated against accelerometer derived densities and considering ballistic coefficient estimation results. The 14-hour density solutions are stitched together using a linear weighted blending technique to obtain continuous solutions over the entire mission life of CHAMP and through 2011 for GRACE. POE derived densities outperform the High Accuracy Satellite Drag Model (HASDM), Jacchia 71 model, and NRLMSISE-2000 model densities when comparing cross correlation and RMS with accelerometer derived densities. Drag is the largest error source for estimating and predicting orbits for low Earth orbit satellites. This is one of the major areas that should be addressed to improve overall space surveillance capabilities; in particular, catalog maintenance. Generally, density is the largest error source in satellite drag calculations and current empirical density models such as Jacchia 71 and NRLMSISE-2000 have significant errors. Dynamic calibration of the atmosphere (DCA) has provided measurable improvements to the empirical density models and accelerometer derived densities of extremely high precision are available for a few satellites. However, DCA generally relies on observations of limited accuracy and accelerometer derived densities are extremely limited in terms of measurement coverage at any given time. The goal of this research is to provide an additional data source using satellites that have precision orbits available using Global Positioning System measurements and/or satellite laser ranging. These measurements strike a balance between the global coverage provided by DCA and the precise measurements of accelerometers. The temporal resolution of the POE derived density estimates is around 20-30 minutes, which is significantly worse than that of accelerometer derived density estimates. However, major variations in density are observed in the POE derived densities. These POE derived densities in combination with other data sources can be assimilated into physics based general circulation models of the thermosphere and ionosphere with the possibility of providing improved density forecasts for satellite drag analysis. POE derived density estimates were initially developed using CHAMP and GRACE data so comparisons could be made with accelerometer derived density estimates. This paper presents the results of the most extensive calibration of POE derived densities compared to accelerometer derived densities and provides the reasoning for selecting certain parameters in the estimation process. The factors taken into account for these selections are the cross correlation and RMS performance compared to the accelerometer derived densities and the output of the ballistic coefficient estimation that occurs simultaneously with the density estimation. This paper also presents the complete data set of CHAMP and GRACE results and shows that the POE derived densities match the accelerometer densities better than empirical models or DCA. This paves the way to expand the POE derived densities to include other satellites with quality GPS and/or satellite laser ranging observations.

The role of distinct parameters of interplanetary shocks in their propagation into and within the Earth's dayside magnetosphere, and their impact on magnetospheric particle populations

NASA Astrophysics Data System (ADS)

Colpitts, C. A.; Cattell, C. A.

2016-12-01

Interplanetary (IP) shocks are abrupt changes in the solar wind velocity and/or magnetic field. When an IP shock impacts the Earth's magnetosphere, it can trigger a number of responses including geomagnetic storms and substorms that affect radiation to satellites and aircraft, and ground currents that disrupt the power grid. There are a wide variety of IP shocks, and they interact with the magnetosphere in different ways depending on their orientation, speed and other factors. The distinct individual characteristics of IP shocks can have a dramatic effect on their impact on the near-earth environment. While some research has been done on the impact of shock parameters on their geo-effectiveness, these studies primarily utilized ground magnetometer derived indices such as Dst, AE and SME or signals at geosynchronous satellites. The current unprecedented satellite coverage of the magnetosphere, particularly on the dayside, presents an opportunity to directly measure how different shocks propagate into and within the magnetosphere, and how they affect the various particle populations therein. Initial case studies reveal that smaller shocks can have unexpected impacts in the dayside magnetosphere, including unusual particle and electric field signatures, depending on shock parameters. We have recently compiled a database of sudden impulses from 2012-2016, and the location of satellites in the dayside magnetosphere at the impulse times. We are currently combining and comparing this with existing databases compiled at UNH, Harvard and others, as well as solar wind data from ACE, Wind and other solar wind monitors, to generate a complete and accurate list of IP shocks, cataloguing parameters such as the type of shock (CME, CIR etc.), strength (Mach number, solar wind velocity etc.) and shock normal angle. We are investigating the magnetospheric response to these shocks using GOES, ARTEMIS and Cluster data, augmented with RBSP and MMS data where available, to determine what effect the various shock parameters have on their propagation through and impact on the magnetosphere. We will present several case studies from our database that show how different parameters affect how shocks propagate in the dayside and how they affect the particles therein.

Particulate matter concentration mapping from MODIS satellite data: a Vietnamese case study

NASA Astrophysics Data System (ADS)

Nguyen, Thanh T. N.; Bui, Hung Q.; Pham, Ha V.; Luu, Hung V.; Man, Chuc D.; Pham, Hai N.; Le, Ha T.; Nguyen, Thuy T.

2015-09-01

Particulate Matter (PM) pollution is one of the most important air quality concerns in Vietnam. In this study, we integrate ground-based measurements, meteorological and satellite data to map temporal PM concentrations at a 10 × 10 km grid for the entire of Vietnam. We specifically used MODIS Aqua and Terra data and developed statistically-significant regression models to map and extend the ground-based PM concentrations. We validated our models over diverse geographic provinces i.e., North East, Red River Delta, North Central Coast and South Central Coast in Vietnam. Validation suggested good results for satellite-derived PM2.5 data compared to ground-based PM2.5 (n = 285, r2 = 0.411, RMSE = 20.299 μg m-3 and RE = 39.789%). Further, validation of satellite-derived PM2.5 on two independent datasets for North East and South Central Coast suggested similar results (n = 40, r2 = 0.455, RMSE = 21.512 μg m-3, RE = 45.236% and n = 45, r2 = 0.444, RMSE = 8.551 μg m-3, RE = 46.446% respectively). Also, our satellite-derived PM2.5 maps were able to replicate seasonal and spatial trends of ground-based measurements in four different regions. Our results highlight the potential use of MODIS datasets for PM estimation at a regional scale in Vietnam. However, model limitation in capturing maximal or minimal PM2.5 peaks needs further investigations on ground data, atmospheric conditions and physical aspects.

Climate and anthropogenic impacts on forest vegetation derived from satellite data

NASA Astrophysics Data System (ADS)

Zoran, M.; Savastru, R.; Savastru, D.; Tautan, M.; Miclos, S.; Baschir, L.

2010-09-01

Vegetation and climate interact through a series of complex feedbacks, which are not very well understood. The patterns of forest vegetation are largely determined by temperature, precipitation, solar irradiance, soil conditions and CO2 concentration. Vegetation impacts climate directly through moisture, energy, and momentum exchanges with the atmosphere and indirectly through biogeochemical processes that alter atmospheric CO2 concentration. Changes in forest vegetation land cover/use alter the surface albedo and radiation fluxes, leading to a local temperature change and eventually a vegetation response. This albedo (energy) feedback is particularly important when forests mask snow cover. Forest vegetation-climate feedback regimes are designated based on the temporal correlations between the vegetation and the surface temperature and precipitation. The different feedback regimes are linked to the relative importance of vegetation and soil moisture in determining land-atmosphere interactions. Forest vegetation phenology constitutes an efficient bio-indicator of impacts of climate and anthropogenic changes and a key parameter for understanding and modeling vegetation-climate interactions. Climate variability represents the ensemble of net radiation, precipitation, wind and temperature characteristic for a region in a certain time scale (e.g.monthly, seasonal annual). The temporal and/or spatial sensitivity of forest vegetation dynamics to climate variability is used to characterize the quantitative relationship between these two quantities in temporal and/or spatial scales. So, climate variability has a great impact on the forest vegetation dynamics. Satellite remote sensing is a very useful tool to assess the main phenological events based on tracking significant changes on temporal trajectories of Normalized Difference Vegetation Index (NDVIs), which requires NDVI time-series with good time resolution, over homogeneous area, cloud-free and not affected by atmospheric and geometric effects and variations in sensor characteristics (calibration, spectral responses). Spatio-temporal forest vegetation dynamics have been quantified as the total amount of vegetation (mean NDVI) and the seasonal difference (annual NDVI amplitude) by a time series analysis of NDVI satellite images over 1989 - 2009 period for a forest ecosystem placed in the North-Eastern part of Bucharest town, Romania, from IKONOS and LANDSAT TM and ETM satellite images and meteorological data. A climate indicator (CI) was created from meteorological data (precipitation over net radiation). The relationships between the vegetation dynamics and the CI have been determined spatially and temporally. The driest test regions prove to be the most sensitive to climate impact. The spatial and temporal patterns of the mean NDVI are the same, while they are partially different for the seasonal difference. For investigated test area, considerable NDVI decline was observed for drought events during 2003 and 2007 years. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics. Specific aim of this paper was to assess, forecast, and mitigate the risks of climatic changes on forest systems and its biodiversity as well as on adjacent environment areas and to provide early warning strategies on the basis of spectral information derived from satellite data regarding atmospheric effects of forest biome degradation .

Satellite-derived attributes of cloud vortex systems and their application to climate studies

NASA Technical Reports Server (NTRS)

Carleton, Andrew M.

1987-01-01

Defense Meteorological Satellite Program (DMSP) visible and infrared mosaics are analyzed in conjunction with synoptic meteorological observations of sea level pressure (SLP) and upper-air height to derive composite patterns of cyclonic cloud vortices for the Northern Hemisphere. The patterns reveal variations in the structure and implied dynamics of cyclonic systems at different stages of development that include: (1) increasing vertical symmetry of the lower-level and upper-air circulations and (2) decreasing lower-tropospheric thicknesses and temperature advection, associated with increasing age of the vortex. Cloud vortices are more intense in winter than in summer and typically reach maximum intensity in the short-lived prespiral signature stage. There are major structural differences among frontal wave, polar air, and 'instant occlusion' cyclogenesis types. Cyclones in the dissipation stage may reintensify (deepen), as denoted by the appearance in the imagery of an asymmetric cloud band or a tightened spiral vortex. The satellite-derived statistics on cloud vortex intensity, which are seasonal- and latitude- as well as type-dependent, are applied to a preliminary examination of the synoptic manifestations of seasonal climate variability. An apparently close relationship is found, for two winter and spring seasons, between Northern Hemisphere cyclonic activity and variations in cryosphere variables, particularly the extent of Arctic sea ice. The results may indicate that increased snow and ice extent accompany a southward displacement of cyclonic activity and/or a predominance of deeper systems. However, there is also a strong regional dependence to the ice-synoptics feedback. This study demonstrates the utility of high resolution meteorological satellite imagery for studies of climate variations (climate dynamics).

Latitudinal dependence on the frequency of Pi2 pulsations near the plasmapause using THEMIS satellites and Asian-Oceanian SuperDARN radars

NASA Astrophysics Data System (ADS)

Teramoto, Mariko; Nishitani, Nozomu; Nishimura, Yukitoshi; Nagatsuma, Tsutomu

2016-02-01

We herein describe a harmonic Pi2 wave that started at 09:12 UT on August 19, 2010, with data that were obtained simultaneously at 19:00-20:00 MLT by three mid-latitude Asian-Oceanian Super Dual Auroral Radar Network (SuperDARN) radars (Unwin, Tiger, and Hokkaido radars), three Time History of Events and Macroscale Interactions during Substorms (THEMIS) satellites (THEMIS A, THEMIS D, and THEMIS E), and ground-based magnetometers at low and high latitudes. All THEMIS satellites, which were located in the plasmasphere, observed Pi2 pulsations dominantly in the magnetic compressional ( B //) and electric azimuthal ( E A) components, i.e., the fast-mode component. The spectrum of Pi2 pulsations in the B // and E A components contained two spectral peaks at approximately 12 to 14 mHz ( f 1, fundamental) and 23 to 25 mHz ( f 2, second harmonic). The Poynting flux derived from the electric and magnetic fields indicated that these pulsations were waves propagating earthward and duskward. Doppler variations ( V) from the 6-s or 8-s resolution camping beams of the Tiger and Unwin SuperDARN radars, which are associated with Pi2 pulsations in the eastward electric field component in the ionosphere, observed Pi2 pulsations within and near the footprint of the plasmapause, whose location was estimated by the THEMIS satellites. The latitudinal profile of f 2 power normalized by f 1 power for Doppler velocities indicated that the enhancement of the normalized f 2 power was the largest near the plasmapause at an altitude-adjusted corrected geomagnetic (AACGM) latitude of 60° to 65°. Based on these features, we suggest that compressional waves propagate duskward away from the midnight sector, where the harmonic cavity mode is generated.

Handling the satellite inter-frequency biases in triple-frequency observations

NASA Astrophysics Data System (ADS)

Zhao, Lewen; Ye, Shirong; Song, Jia

2017-04-01

The new generation of GNSS satellites, including BDS, Galileo, modernized GPS, and GLONASS, transmit navigation sdata at more frequencies. Multi-frequency signals open new prospects for precise positioning, but satellite code and phase inter-frequency biases (IFB) induced by the third frequency need to be handled. Satellite code IFB can be corrected using products estimated by different strategies, the theoretical and numerical compatibility of these methods need to be proved. Furthermore, a new type of phase IFB, which changes with the relative sun-spacecraft-earth geometry, has been observed. It is necessary to investigate the cause and possible impacts of phase Time-variant IFB (TIFB). Therefore, we present systematic analysis to illustrate the relevancy between satellite clocks and phase TIFB, and compare the handling strategies of the code and phase IFB in triple-frequency positioning. First, the un-differenced L1/L2 satellite clock corrections considering the hardware delays are derived. And IFB induced by the dual-frequency satellite clocks to triple-frequency PPP model is detailed. The analysis shows that estimated satellite clocks actually contain the time-variant phase hardware delays, which can be compensated in L1/L2 ionosphere-free combinations. However, the time-variant hardware delays will lead to TIFB if the third frequency is used. Then, the methods used to correct the code and phase IFB are discussed. Standard point positioning (SPP) and precise point positioning (PPP) using BDS observations are carried out to validate the improvement of different IFB correction strategies. Experiments show that code IFB derived from DCB or geometry-free and ionosphere-free combination show an agreement of 0.3 ns for all satellites. Positioning results and error distribution with two different code IFB correcting strategies achieve similar tendency, which shows their substitutability. The original and wavelet filtered phase TIFB long-term series show significant periodical characteristic for most GEO and IGSO satellites, with the magnitude varies between - 5 cm and 5 cm. Finally, BDS L1/L3 kinematic PPP is conducted with code IFB corrected with DCB combinations, and TIFB corrected with filtered series. Results show that the IFB corrected L1/L3 PPP can achieve comparable convergence and positioning accuracy as L1/L2 combinations in static and kinematic mode.

Cross Calibration of TOMS, SBUV/2 and SCIAMACHY Radiances from Ground Observations

NASA Technical Reports Server (NTRS)

Hilsenrath, Ernest; Bhartia, P. K.; Bojkov, B.; Kowaleski, M.; Labow, G.; Ahmad, Z.

2002-01-01

We have shown that validation of radiances is a very effective means for correcting absolute accuracy and long term drifts of backscatter type satellite measurements. This method by-passes the algorithms used for both satellite and ground based measurements which are normally used to validate and correct the satellite data. A new method for satellite validation is planned which will compliment measurements from the existing ground-based networks. This method will employ very accurate comparisons between ground based zenith sky radiances and satellite nadir radiances. These comparisons will rely heavily on the experience derived from the Shuttle SBUV (SSBUV) program which provided a reference standard of radiance measurements for SBUV/2, TOMS, and GOME. This new measurement program, called 'Skyrad', employs two well established capabilities at the Goddard Space Flight Center, 1) the SSBUV calibration facilities and 2) the radiative transfer codes used for the TOMS and SBUV/2 algorithms and their subsequent refinements. Radiative transfer calculations show that ground based zenith sky and satellite nadir backscatter ultraviolet comparisons can be made very accurately under certain viewing conditions. The Skyrad instruments (SSBUV, Brewer spectrophotometers, and possibly others) will be calibrated and maintained to a precision of a few tenths of a percent. Skyrad data will then enable long term calibration of upcoming satellite instruments such as QuickTOMS, SBUV/2s and SCIAMACHY with a high degree of precision. This technique can be further employed to monitor the performance of future instruments such as GOMEZ, OMI, and OMPS. Additional information is included in the original extended abstract.

APPLYING SATELLITE IMAGERY TO TRIAGE ASSESSMENT OF ECOSYSTEM HEALTH

EPA Science Inventory

Considerable evidence documents that certain changes in vegetation and soils result in irreversibly degraded rangeland ecosystems. We used Advanced Very High Resolution Radiometer (AVHRR)imagery to develop calibration patterns of change in the Normalized Difference Vegetation Ind...

Estimating Density Using Precision Satellite Orbits from Multiple Satellites

NASA Astrophysics Data System (ADS)

McLaughlin, Craig A.; Lechtenberg, Travis; Fattig, Eric; Krishna, Dhaval Mysore

2012-06-01

This article examines atmospheric densities estimated using precision orbit ephemerides (POE) from several satellites including CHAMP, GRACE, and TerraSAR-X. The results of the calibration of atmospheric densities along the CHAMP and GRACE-A orbits derived using POEs with those derived using accelerometers are compared for various levels of solar and geomagnetic activity to examine the consistency in calibration between the two satellites. Densities from CHAMP and GRACE are compared when GRACE is orbiting nearly directly above CHAMP. In addition, the densities derived simultaneously from CHAMP, GRACE-A, and TerraSAR-X are compared to the Jacchia 1971 and NRLMSISE-00 model densities to observe altitude effects and consistency in the offsets from the empirical models among all three satellites.

Simultaneous assimilation of ozone profiles from multiple UV-VIS satellite instruments

NASA Astrophysics Data System (ADS)

van Peet, Jacob C. A.; van der A, Ronald J.; Kelder, Hennie M.; Levelt, Pieternel F.

2018-02-01

A three-dimensional global ozone distribution has been derived from assimilation of ozone profiles that were observed by satellites. By simultaneous assimilation of ozone profiles retrieved from the nadir looking satellite instruments Global Ozone Monitoring Experiment 2 (GOME-2) and Ozone Monitoring Instrument (OMI), which measure the atmosphere at different times of the day, the quality of the derived atmospheric ozone field has been improved. The assimilation is using an extended Kalman filter in which chemical transport model TM5 has been used for the forecast. The combined assimilation of both GOME-2 and OMI improves upon the assimilation results of a single sensor. The new assimilation system has been demonstrated by processing 4 years of data from 2008 to 2011. Validation of the assimilation output by comparison with sondes shows that biases vary between -5 and +10 % between the surface and 100 hPa. The biases for the combined assimilation vary between -3 and +3 % in the region between 100 and 10 hPa where GOME-2 and OMI are most sensitive. This is a strong improvement compared to direct retrievals of ozone profiles from satellite observations.

Extracting fields snow coverage information with HJ-1A/B satellites data

NASA Astrophysics Data System (ADS)

Dong, Wenquan; Meng, Jihua

2015-10-01

The distribution and change of snow coverage are sensitive factors of climate change. In northeast part of China, farmlands are still covered with snow in spring. Since sowing activity can only be done when the snow melted, fields snow coverage monitoring provides reference for the determination of sowing date. Because of the restriction of the sensors and application requirements, current researches on remote sensing of snow focus more on the study of musicale and large scale, rather than the study of small scale, and especially research on snow melting period is rarely reported.HJ-1A/B satellites are parts of little satellite constellation, focusing on environment and disaster monitoring and meteorological forecast. Compared to other data sources, HJ-1A/B satellites both have comparatively higher temporal and spatial resolution and are more conducive to monitor the variations of melting snow coverage at small watershed. This paper was based on HJ-1A/1B data, taking Hongxing farm of Bei'an, Heilongjiang Province, China as the study area. In this paper, we exploited the methods for extraction of snow cover information on farmland in two cases, both HJ-1A/1B CCD with HJ-1B IRS data and just HJ-1A/1B CCD data. The reason we chose the two cases is that, the two optical satellites HJ-1A/B are capable of providing a whole territory coverage period in visible light spectrum in two days, infrared spectrum in four days. So sometimes we can only obtain CCD image. In this case, the method of normalized snow index cannot be used to extract snow coverage information. Using HJ-1A/1B CCD with HJ-1B IRS data, combined with the theory of snow remote sensing monitoring, this paper analyzed spectral response characteristics of HJ-1A/1B satellites data, then the widely used Normalized Difference Snow Index(NDSI) and S3 Index were quoted to the HJ-1A/1B satellites data. The NDSI uses reflectance values of Red and SWIR spectral bands of HJ-1B, and S3 index uses reflectance values of NIR, Red and SWIR spectral bands. With multi-temporal HJ satellite data, the optimal threshold of normalized snow index was determined to divide the farmland into snow covering area, melting snow area and non-snow area. The results are quite similar to each other and of high accuracy, and the melting snow coverage can be well extracted by two types of normalized snow index. When we can only obtain CCD image, we use supervised classification method to extract melting snow coverage. With this method, the accuracy of fields snow coverage extraction is slightly lower than that using normalized snow index methods mentioned above. And in mountain area, the snow coverage area is slightly larger than that is extracted by normalized snow index methods, because the shadows make the color of snow in the valley darker, the supervised classification method divides it into non-snow coverage area, while the normalized snow index method well weakened the effect of shadow. This study shows that extraction accuracy in both cases is assessed, and both of them can meet the needs of practical applications. HJ-1A/1B satellites are conducive to monitor the variations of melting snow coverage over farmland, and they can provide reference for the determination of sowing date.

Evaluation of atmospheric profiles derived from single- and zero-difference excess phase processing of BeiDou radio occultation data from the FY-3C GNOS mission

NASA Astrophysics Data System (ADS)

Bai, Weihua; Liu, Congliang; Meng, Xiangguang; Sun, Yueqiang; Kirchengast, Gottfried; Du, Qifei; Wang, Xianyi; Yang, Guanglin; Liao, Mi; Yang, Zhongdong; Zhao, Danyang; Xia, Junming; Cai, Yuerong; Liu, Lijun; Wang, Dongwei

2018-02-01

The Global Navigation Satellite System (GNSS) Occultation Sounder (GNOS) is one of the new-generation payloads onboard the Chinese FengYun 3 (FY-3) series of operational meteorological satellites for sounding the Earth's neutral atmosphere and ionosphere. The GNOS was designed for acquiring setting and rising radio occultation (RO) data by using GNSS signals from both the Chinese BeiDou System (BDS) and the US Global Positioning System (GPS). An ultra-stable oscillator with 1 s stability (Allan deviation) at the level of 10-12 was installed on the FY-3C GNOS, and thus both zero-difference and single-difference excess phase processing methods should be feasible for FY-3C GNOS observations. In this study we focus on evaluating zero-difference processing of BDS RO data vs. single-difference processing, in order to investigate the zero-difference feasibility for this new instrument, which after its launch in September 2013 started to use BDS signals from five geostationary orbit (GEO) satellites, five inclined geosynchronous orbit (IGSO) satellites and four medium Earth orbit (MEO) satellites. We used a 3-month set of GNOS BDS RO data (October to December 2013) for the evaluation and compared atmospheric bending angle and refractivity profiles, derived from single- and zero-difference excess phase data, against co-located profiles from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses. We also compared against co-located refractivity profiles from radiosondes. The statistical evaluation against these reference data shows that the results from single- and zero-difference processing are reasonably consistent in both bias and standard deviation, clearly demonstrating the feasibility of zero differencing for GNOS BDS RO observations. The average bias (and standard deviation) of the bending angle and refractivity profiles were found to be about 0.05 to 0.2 % (and 0.7 to 1.6 %) over the upper troposphere and lower stratosphere. Zero differencing was found to perform slightly better, as may be expected from its lower vulnerability to noise. The validation results indicate that GNOS can provide, on top of GPS RO profiles, accurate and precise BDS RO profiles both from single- and zero-difference processing. The GNOS observations by the series of FY-3 satellites are thus expected to provide important contributions to numerical weather prediction and global climate change analysis.

A statistical evaluation and comparison of VISSR Atmospheric Sounder (VAS) data and corresponding rawinsonde measurements

NASA Technical Reports Server (NTRS)

1984-01-01

Three mesoscale sounding data sets from the VISSR Atmospheric Sounder (VAS) produced using different retrieval techniques were evaluated of corresponding ground truth rawinsonde data for 6-7 March 1982. Mean, standard deviations, and RMS differences between the satellite and rawinsonde parameters were calculated over gridded fields in central Texas and Oklahoma. Large differences exist between each satellite data set and the ground truth data. Biases in the satellite temperature and moisture profiles seem extremely dependent upon the three dimensional structure of the atmosphere and range from 1 deg to 3 deg C for temperature and 3 deg to 6 deg C for dewpoint temperature. Atmospheric gradients of basic and derived parameters determined from the VAS data sets produced an adequate representation of the mesoscale environment but their magnitudes were often reduced by 30 to 50%.

Hybrid method for building extraction in vegetation-rich urban areas from very high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Jayasekare, Ajith S.; Wickramasuriya, Rohan; Namazi-Rad, Mohammad-Reza; Perez, Pascal; Singh, Gaurav

2017-07-01

A continuous update of building information is necessary in today's urban planning. Digital images acquired by remote sensing platforms at appropriate spatial and temporal resolutions provide an excellent data source to achieve this. In particular, high-resolution satellite images are often used to retrieve objects such as rooftops using feature extraction. However, high-resolution images acquired over built-up areas are associated with noises such as shadows that reduce the accuracy of feature extraction. Feature extraction heavily relies on the reflectance purity of objects, which is difficult to perfect in complex urban landscapes. An attempt was made to increase the reflectance purity of building rooftops affected by shadows. In addition to the multispectral (MS) image, derivatives thereof namely, normalized difference vegetation index and principle component (PC) images were incorporated in generating the probability image. This hybrid probability image generation ensured that the effect of shadows on rooftop extraction, particularly on light-colored roofs, is largely eliminated. The PC image was also used for image segmentation, which further increased the accuracy compared to segmentation performed on an MS image. Results show that the presented method can achieve higher rooftop extraction accuracy (70.4%) in vegetation-rich urban areas compared to traditional methods.

Response of vegetation NDVI to climatic extremes in the arid region of Central Asia: a case study in Xinjiang, China

NASA Astrophysics Data System (ADS)

Yao, Junqiang; Chen, Yaning; Zhao, Yong; Mao, Weiyi; Xu, Xinbing; Liu, Yang; Yang, Qing

2018-02-01

Observed data showed the climatic transition from warm-dry to warm-wet in Xinjiang during the past 30 years and will probably affect vegetation dynamics. Here, we analyze the interannual change of vegetation index based on the satellite-derived normalized difference vegetation index (NDVI) with temperature and precipitation extreme over the Xinjiang, using the 8-km NDVI third-generation (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) from 1982 to 2010. Few previous studies analyzed the link between climate extremes and vegetation response. From the satellite-based results, annual NDVI significantly increased in the first two decades (1981-1998) and then decreased after 1998. We show that the NDVI decrease over the past decade may conjointly be triggered by the increases of temperature and precipitation extremes. The correlation analyses demonstrated that the trends of NDVI was close to the trend of extreme precipitation; that is, consecutive dry days (CDD) and torrential rainfall days (R24) positively correlated with NDVI during 1998-2010. For the temperature extreme, while the decreases of NDVI correlate positively with warmer mean minimum temperature ( Tnav), it correlates negatively with the number of warmest night days ( Rwn). The results suggest that the climatic extremes have possible negative effects on the ecosystem.

SACRA - global data sets of satellite-derived crop calendars for agricultural simulations: an estimation of a high-resolution crop calendar using satellite-sensed NDVI

NASA Astrophysics Data System (ADS)

Kotsuki, S.; Tanaka, K.

2015-01-01

To date, many studies have performed numerical estimations of food production and agricultural water demand to understand the present and future supply-demand relationship. A crop calendar (CC) is an essential input datum to estimate food production and agricultural water demand accurately with the numerical estimations. CC defines the date or month when farmers plant and harvest in cropland. This study aims to develop a new global data set of a satellite-derived crop calendar for agricultural simulations (SACRA) and reveal advantages and disadvantages of the satellite-derived CC compared to other global products. We estimate global CC at a spatial resolution of 5 min (≈10 km) using the satellite-sensed NDVI data, which corresponds well to vegetation growth and death on the land surface. We first demonstrate that SACRA shows similar spatial pattern in planting date compared to a census-based product. Moreover, SACRA reflects a variety of CC in the same administrative unit, since it uses high-resolution satellite data. However, a disadvantage is that the mixture of several crops in a grid is not considered in SACRA. We also address that the cultivation period of SACRA clearly corresponds to the time series of NDVI. Therefore, accuracy of SACRA depends on the accuracy of NDVI used for the CC estimation. Although SACRA shows different CC from a census-based product in some regions, multiple usages of the two products are useful to take into consideration the uncertainty of the CC. An advantage of SACRA compared to the census-based products is that SACRA provides not only planting/harvesting dates but also a peak date from the time series of NDVI data.

Satellite Vegetation Index Data as a Tool to Forecast Population Dynamics of Medically Important Mosquitoes at Military Installations in the Continental United States

DTIC Science & Technology

2008-07-01

for the two installations. We obtained monthly North American normalized differ- ence vegetation index ( NDVI ) satellite climate data sets for 1981-2005...from the Goddard Space Flight Center."*-" The NDVI measures the greenness of the earth, capturing in one index the combined effects of temperature...humidity, insola- tion, elevation, soils, land use. and precipitation on vegeta- tion. There is an almost-linear relationship between NDVI values and

Time series of Essential Climate Variables from Satellite Data

NASA Astrophysics Data System (ADS)

Werscheck, M.

2010-09-01

Climate change is a fact. We need to know how the climate system will develop in future and how this will affect workaday life. To do this we need climate models for prediction of the future on all time scales, and models to assess the impact of the prediction results to the various sectors of social and economic life. With this knowledge we can take measures to mitigate the causes and adapt to changes. Prerequisite for this is a careful and thorough monitoring of the climate systems. Satellite data are an increasing & valuable source of information to observe the climate system. For many decades now satellite data are available to derive information about our planet earth. EUMETSAT is the European Organisation in charge of the exploitation of satellite data for meteorology and (since the year 2000) climatology. Within the EUMETSAT Satellite Application Facility (SAF) Network, comprising 8 initiatives to derive geophysical parameters from satellite, the Satellite Application Facility on Climate Monitoring (CM SAF) is especially dedicated to provide climate relevant information from satellite data. Many products as e.g. water vapour, radiation at surface and top of atmosphere, cloud properties are available, some of these for more then 2 decades. Just recently the European Space Agency (ESA) launched the Climate Change Initiative (CCI) to derive Essential Climate Variables (ECVs) from satellite data, including e.g. cloud properties, aerosol, ozone, sea surface temperature etc.. The presentation will give an overview on some relevant European activities to derive Essential Climate Variables from satellite data and the links to Global Climate Observing System (GCOS), the Global Satellite Intercalibration System (GSICS) as well as the Sustained Co-ordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE CM).

Geomagnetic cutoffs: A review for space dosimetry applications

NASA Astrophysics Data System (ADS)

Smart, D. F.; Shea, M. A.

1994-10-01

The earth's magnetic field acts as a shield against charged particle radiation from interplanetary space, technically described as the geomagnetic cutoff. The cutoff rigidity problem (except for the dipole special case) has 'no solution in closed form'. The dipole case yields the Stormer equation which has been repeatedly applied to the earth in hopes of providing useful approximations of cutoff rigidities. Unfortunately the earth's magnetic field has significant deviations from dipole geometry, and the Stormer cutoffs are not adequate for most applications. By application of massive digital computer power it is possible to determine realistic geomagnetic cutoffs derived from high order simulation of the geomagnetic field. Using this technique, 'world-grids' of directional cutoffs for the earth's surface and for a limited number of satellite altitudes have been derived. However, this approach is so expensive and time comsuming it is impractical for most spacecraft orbits, and approximations must be used. The world grids of cutoff rigidities are extensively used as lookup tables, normalization points and interpolation aids to estimate the effective geomagnetic cutoff rigidity of a specific location in space. We review the various options for estimating the cutoff rigidity for earth-orbiting satellites.

Spatio-temporal Root Zone Soil Moisture Estimation for Indo - Gangetic Basin from Satellite Derived (AMSR-2 and SMOS) Surface Soil Moisture

NASA Astrophysics Data System (ADS)

Sure, A.; Dikshit, O.

2017-12-01

Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.

Validation of GOES-10 Satellite-derived Cloud and Radiative Properties for the MASRAD ARM Mobile Facility Deployment

NASA Technical Reports Server (NTRS)

Khaiyer, M. M.; Doelling, D. R.; Palikonda, R.; Mordeen, M. L.; Minnis, P.

2007-01-01

This poster presentation reviews the process used to validate the GOES-10 satellite derived cloud and radiative properties. The ARM Mobile Facility (AMF) deployment at Pt Reyes, CA as part of the Marine Stratus Radiation Aerosol and Drizzle experiment (MASRAD), 14 March - 14 September 2005 provided an excellent chance to validate satellite cloud-property retrievals with the AMF's flexible suite of ground-based remote sensing instruments. For this comparison, NASA LaRC GOES10 satellite retrievals covering this region and period were re-processed using an updated version of the Visible Infrared Solar-Infrared Split-Window Technique (VISST), which uses data taken at 4 wavelengths (0.65, 3.9,11 and 12 m resolution), and computes broadband fluxes using improved CERES (Clouds and Earth's Radiant Energy System)-GOES-10 narrowband-to-broadband flux conversion coefficients. To validate MASRAD GOES-10 satellite-derived cloud property data, VISST-derived cloud amounts, heights, liquid water paths are compared with similar quantities derived from available ARM ground-based instrumentation and with CERES fluxes from Terra.

Integration of Satellite-Derived Cloud Phase, Cloud Top Height, and Liquid Water Path into an Operational Aircraft Icing Nowcasting System

NASA Technical Reports Server (NTRS)

Haggerty, Julie; McDonough, Frank; Black, Jennifer; Landott, Scott; Wolff, Cory; Mueller, Steven; Minnis, Patrick; Smith, William, Jr.

2008-01-01

Operational products used by the U.S. Federal Aviation Administration to alert pilots of hazardous icing provide nowcast and short-term forecast estimates of the potential for the presence of supercooled liquid water and supercooled large droplets. The Current Icing Product (CIP) system employs basic satellite-derived information, including a cloud mask and cloud top temperature estimates, together with multiple other data sources to produce a gridded, three-dimensional, hourly depiction of icing probability and severity. Advanced satellite-derived cloud products developed at the NASA Langley Research Center (LaRC) provide a more detailed description of cloud properties (primarily at cloud top) compared to the basic satellite-derived information used currently in CIP. Cloud hydrometeor phase, liquid water path, cloud effective temperature, and cloud top height as estimated by the LaRC algorithms are into the CIP fuzzy logic scheme and a confidence value is determined. Examples of CIP products before and after the integration of the LaRC satellite-derived products will be presented at the conference.

Validation of CERES-MODIS Arctic cloud properties using CloudSat/CALIPSO and ARM NSA observations

NASA Astrophysics Data System (ADS)

Giannecchini, K.; Dong, X.; Xi, B.; Minnis, P.; Kato, S.

2011-12-01

The traditional passive satellite studies of cloud properties in the Arctic are often affected by the complex surface features present across the region. Nominal visual and thermal contrast exists between Arctic clouds and the snow- and ice-covered surfaces beneath them, which can lead to difficulties in satellite retrievals of cloud properties. However, the addition of active sensors to the A-Train constellation of satellites has increased the availability of validation sources for cloud properties derived from passive sensors in the data-sparse high-latitude regions. In this study, Arctic cloud fraction and cloud heights derived from the NASA CERES team (CERES-MODIS) have been compared with CloudSat/CALIPSO and DOE ARM NSA radar-lidar observations over Barrow, AK, for the two-year period from 2007 to 2008. An Arctic-wide comparison of cloud fraction and height between CERES-MODIS and CloudSat/CALIPSO was then conducted for the same time period. The CERES-MODIS cloud properties, which include cloud fraction and cloud effective heights, were retrieved using the 4-channel VISST (Visible Infrared Solar-Infrared Split-window Technique) [Minnis et al.,1995]. CloudSat/CALIPSO cloud fraction and cloud-base and -top heights were from version RelB1 data products determined by both the 94 GHz radar onboard CloudSat and the lidar on CALIPSO with a vertical resolution of 30 m below 8.2 km and 60 m above. To match the surface and satellite observations/retrievals, the ARM surface observations were averaged into 3-hour intervals centered at the time of the satellite overpass, while satellite observations were averaged within a 3°x3° grid box centered on the Barrow site. The preliminary results have shown that all observed CFs have peaks during April-May and September-October, and dips during winter months (January-February) and summer months (June-July) during the study period of 2007-2008. ARM radar-lidar and CloudSat/CALIPSO show generally good agreement in CF (0.79 vs. 0.74), while CERES-MODIS derived values are much lower (0.60). CERES-MODIS derived cloud effective height (2.7 km) falls between the CloudSat/CALIPSO derived cloud base (0.6 km) and top (6.4 km) and the ARM ceilometers and MMCR derived cloud base (0.9 km) and radar derived cloud top (5.8 km). When extended to the entire Arctic, although the CERES-MODIS and Cloudsat/CALIPSO derived annual mean CFs agree within a few percents, there are significant differences over several regions, and the maximum cloud heights derived from CloudSat/CALIPSO (13.4 km) and CERES-MODIS (10.7 km) show the largest disagreement during early spring.

Assessment of the Relative Accuracy of Hemispheric-Scale Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Kelly, Richard E.; Riggs, George A.; Chang, Alfred T. C.; Foster, James L.; Houser, Paul (Technical Monitor)

2001-01-01

There are several hemispheric-scale satellite-derived snow-cover maps available, but none has been fully validated. For the period October 23 - December 25, 2000, we compare snow maps of North America derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the National Oceanic and Atmospheric Administration (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC), which both rely on satellite data from the visible and near-infrared parts of the spectrum; we also compare MODIS and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) passive-microwave snow maps. The maps derived from visible and near-infrared data are more accurate for mapping snow cover than are the passive-microwave-derived maps, however discrepancies exist as to the location and extent of the snow cover among those maps. The large (approx. 30 km) footprint of the SSM/I data and the difficulty in distinguishing wet and shallow snow from wet or snow-free ground, reveal differences up to 5.32 million sq km in the amount of snow mapped using MODIS versus SSM/I data. Algorithms that utilize both visible and passive-microwave data, which would take advantage of the all-weather mapping ability of the passive-microwave data, will be refined following the launch of the Advanced Microwave Scanning Radiometer (AMSR) in the fall of 2001.

Effects of diurnal adjustment on biases and trends derived from inter-sensor calibrated AMSU-A data

NASA Astrophysics Data System (ADS)

Chen, H.; Zou, X.; Qin, Z.

2018-03-01

Measurements of brightness temperatures from Advanced Microwave Sounding Unit-A (AMSU-A) temperature sounding instruments onboard NOAA Polarorbiting Operational Environmental Satellites (POES) have been extensively used for studying atmospheric temperature trends over the past several decades. Intersensor biases, orbital drifts and diurnal variations of atmospheric and surface temperatures must be considered before using a merged long-term time series of AMSU-A measurements from NOAA-15, -18, -19 and MetOp-A.We study the impacts of the orbital drift and orbital differences of local equator crossing times (LECTs) on temperature trends derivable from AMSU-A using near-nadir observations from NOAA-15, NOAA-18, NOAA-19, and MetOp-A during 1998-2014 over the Amazon rainforest. The double difference method is firstly applied to estimation of inter-sensor biases between any two satellites during their overlapping time period. The inter-calibrated observations are then used to generate a monthly mean diurnal cycle of brightness temperature for each AMSU-A channel. A diurnal correction is finally applied each channel to obtain AMSU-A data valid at the same local time. Impacts of the inter-sensor bias correction and diurnal correction on the AMSU-A derived long-term atmospheric temperature trends are separately quantified and compared with those derived from original data. It is shown that the orbital drift and differences of LECTamong different POESs induce a large uncertainty in AMSU-A derived long-term warming/cooling trends. After applying an inter-sensor bias correction and a diurnal correction, the warming trends at different local times, which are approximately the same, are smaller by half than the trends derived without applying these corrections.

Analysis of smoke and cloud impact on seasonal and interannual variations in normalized difference vegetation index in Amazon

NASA Astrophysics Data System (ADS)

Kobayashi, H.; Dye, D. G.

2004-12-01

Normalized difference vegetation index (NDVI) derived from National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) is a unique measurement of long-term variations in global vegetation dynamics. The NDVI data have been used for the detection of the seasonal and interannual variations in vegetation. However, as reported in several studies, NDVI decreases with the increase in clouds and/or smoke aerosol contaminated in the pixels. This study assesses the smoke and clouds effect on long-term Global Inventory Modeling and Mapping Studies (GIMMS) and Pathfinder AVHRR Land (PAL) NDVI data in Amazon. This knowledge will help developing the correction method in the tropics in the future. To assess the smoke and cloud effects on GIMMS and PAL, we used another satellite-derived data sets; NDVI derived from SPOT/VEGETATION (VGT) data and Aerosol Index (AI) derived from Total Ozone Mapping Spectrometer (TOMS). Since April 1998, VGT has measured the earth surface globally including in Amazon. The advantage of the VGT is that it has blue channel where the smoke and cloud can be easily detected. By analyzing the VGT NDVI and comparing with the AVHRR-based NDVI, we inferred smoke and cloud effect on the AVHRR-based NDVI. From the results of the VGT analysis, we found the large NDVI seasonality in South and Southeastern Amazon. In these areas, the NDVI gradually increased from April to July and decreased from August to October. However the sufficient NDVI data were not existed from August to November when the smoke and cloud pixels were masked using blue reflectance. Thus it is said that the smoke and clouds mainly cause the large decreases in NDVI between August and November and NDVI has little vegetation signature in these months. Also we examined the interannual variations in NDVI and smoke aerosol. Then the decrease in NDVI is well consistent with the increase in the increase in AI. Our results suggest that the months between April and July are the most reliable season to monitor the vegetation.

Burn severity mapping in Australia 2009

USGS Publications Warehouse

McKinley, Randy; Clark, J.; Lecker, Jennifer

2012-01-01

In 2009, the Victoria Department of Sustainability and Environment estimated approximately 430,000 hectares of Victoria Australia were burned by numerous bushfires. Burned Area Emergency Response (BAER) teams from the United States were deployed to Victoria to assist local fire managers. The U.S. Geological Survey Earth Resources Observation and Science Center (USGS/EROS) and U.S. Forest Service Remote Sensing Applications Center (USFS/RSAC) aided the support effort by providing satellite-derived "soil burn severity " maps for over 280,000 burned hectares. In the United States, BAER teams are assembled to make rapid assessments of burned lands to identify potential hazards to public health and property. An early step in the assessment process is the creation of a soil burn severity map used to identify hazard areas and prioritize treatment locations. These maps are developed primarily using Landsat satellite imagery and the differenced Normalized Burn Ratio (dNBR) algorithm.

The prediction of tropopause height from clusters of brightness temperatures and its application in the stratified regression temperature retrievals using microwave and infrared satellite measurements

NASA Technical Reports Server (NTRS)

Munteanu, M. J.; Piraino, P.; Jakubowicz, O.

1984-01-01

A total of 1575 radiosondes and the corresponding simulated brightness temperatures were used in an effort to derive a temperature retrieval based on the clusters of brightness temperatures. The 8 simulated channels, namely, 3 MSU and 5 IR of the TIROS-N satellite are used by the GLAS temperature retrieval method. The 3 MSU and 5 IR brightness temperatures were clustered into 17 cluster groups and a regression for the prediction of the tropopause height in mb was generated. The overall r.m.s. for the tropopause prediction is excellent, namely, around 16 mb for the summer and 23 mb for the winter. The correct cluster of brightness temperatures can be identified 98% of the time by the method of discriminatory classification if it is approximately a normal distribution or, in general, by the method of the nearest neighbor.

Case studies using GOES infrared data and a planetary boundary layer model to infer regional scale variations in soil moisture. M.S. Thesis

NASA Technical Reports Server (NTRS)

Rose, F. G.

1983-01-01

Modeled temperature data from a one-dimensional, time-dependent, initial value, planetary boundary layer model for 16 separate model runs with varying initial values of moisture availability are applied, by the use of a regression equation, to longwave infrared GOES satellite data to infer moisture availability over a regional area in the central U.S. This was done for several days during the summers of 1978 and 1980 where a large gradient in the antecedent precipitation index (API) represented the boundary between a drought area and a region of near normal precipitation. Correlations between satellite derived moisture availability and API were found to exist. Errors from the presence of clouds, water vapor and other spatial inhomogeneities made the use of the measurement for anything except the relative degree of moisture availability dubious.

Validation of Satellite Derived Cloud Properties Over the Southeastern Pacific

NASA Astrophysics Data System (ADS)

Ayers, J.; Minnis, P.; Zuidema, P.; Sun-Mack, S.; Palikonda, R.; Nguyen, L.; Fairall, C.

2005-12-01

Satellite measurements of cloud properties and the radiation budget are essential for understanding meso- and large-scale processes that determine the variability in climate over the southeastern Pacific. Of particular interest in this region is the prevalent stratocumulus cloud deck. The stratocumulus albedos are directly related to cloud microphysical properties that need to be accurately characterized in Global Climate Models (GCMs) to properly estimate the Earth's radiation budget. Meteorological observations in this region are sparse causing large uncertainties in initialized model fields. Remote sensing from satellites can provide a wealth of information about the clouds in this region, but it is vital to validate the remotely sensed parameters and to understand their relationship to other parameters that are not directly observed by the satellites. The variety of measurements from the R/V Roger Revelle during the 2003 STRATUS cruise and from the R/V Ron Brown during EPIC 2001 and the 2004 STRATUS cruises are suitable for validating and improving the interpretation of the satellite derived cloud properties. In this study, satellite-derived cloud properties including coverage, height, optical depth, and liquid water path are compared with in situ measurements taken during the EPIC and STRATUS cruises. The remotely sensed values are derived from Geostationary Operational Environmental Satellite (GOES) imager data, Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites, and from the Visible and Infrared Scanner (VIRS) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The products from this study will include regional monthly cloud climatologies derived from the GOES data for the 2003 and 2004 cruises as well as micro and macro physical cloud property retrievals centered over the ship tracks from MODIS and VIRS.

Assessing the Impact of Earth Radiation Pressure Acceleration on Low-Earth Orbit Satellites

NASA Astrophysics Data System (ADS)

Vielberg, Kristin; Forootan, Ehsan; Lück, Christina; Kusche, Jürgen; Börger, Klaus

2017-04-01

The orbits of satellites are influenced by several external forces. The main non-gravitational forces besides thermospheric drag, acting on the surface of satellites, are accelerations due to the Earth and Solar Radiation Pres- sure (SRP and ERP, respectively). The sun radiates visible and infrared light reaching the satellite directly, which causes the SRP. Earth also emits and reflects the sunlight back into space, where it acts on satellites. This is known as ERP acceleration. The influence of ERP increases with decreasing distance to the Earth, and for low-earth orbit (LEO) satellites ERP must be taken into account in orbit and gravity computations. Estimating acceler- ations requires knowledge about energy emitted from the Earth, which can be derived from satellite remote sensing data, and also by considering the shape and surface material of a satellite. In this sensitivity study, we assess ERP accelerations based on different input albedo and emission fields and their modelling for the satellite missions Challenging Mini-Satellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE). As input fields, monthly 1°x1° products of Clouds and the Earth's Radiant En- ergy System (CERES), L3 are considered. Albedo and emission models are generated as latitude-dependent, as well as in terms of spherical harmonics. The impact of different albedo and emission models as well as the macro model and the altitude of satellites on ERP accelerations will be discussed.

A new approach for the construction of gridded emission inventories from satellite data

NASA Astrophysics Data System (ADS)

Kourtidis, Konstantinos; Georgoulias, Aristeidis; Mijling, Bas; van der A, Ronald; Zhang, Qiang; Ding, Jieying

2017-04-01

We present a new method for the derivation of anthropogenic emission estimates for SO2. The method, which we term Enhancement Ratio Method (ERM), uses observed relationships between measured OMI satellite tropospheric columnar levels of SO2 and NOx in each 0.25 deg X 0.25 deg grid box at low wind speeds, and the Daily Emission estimates Constrained by Satellite Observations (DECSO) versions v1 and v3a NOx emission estimates to scale the SO2 emissions. The method is applied over China, and emission estimates for SO2 are derived for different seasons and years (2007-2011), thus allowing an insight into the interannual evolution of the emissions. The inventory shows a large decrease of emissions during 2007-2009 and a modest increase between 2010-2011. The evolution in emission strength over time calculated here is in general agreement with bottom-up inventories, although differences exist, not only between the current inventory and other inventories but also among the bottom up inventories themselves. The gridded emission estimates derived appear to be consistent, both in their spatial distribution and their magnitude, with the Multi-resolution Emission Inventory for China (MEIC). The total emissions correlate very well with most existing inventories. This research has been financed under the FP7 Programme MarcoPolo (Grand Number 606953, Theme SPA.2013.3.2-01).

Relationship between LiDAR-derived forest canopy height and Landsat images

Treesearch

Cristina Pascual; Antonio Garcia-Abril; Warren B. Cohen; Susana Martin-Fernandez

2010-01-01

The mean and standard deviation (SD) of light detection and ranging (LiDAR)-derived canopy height are related to forest structure. However, LiDAR data typically cover a limited area and have a high economic cost compared with satellite optical imagery. Optical images may be required to extrapolate LiDAR height measurements across a broad landscape. Different spectral...

Gravitational field modes GEM 3 and 4

NASA Technical Reports Server (NTRS)

Lerch, F. J.; Wagner, C. A.; Putney, B. H.; Sandson, M. L.; Brownd, J. E.; Richardson, J. A.; Taylor, W. A.

1972-01-01

A refinement in the satellite geopotential solution for a Goddard Earth Model (GEM 3) was obtained. The solution includes the addition of two low inclination satellites, SAS at 3 deg and PEOLE at 15 deg, and is based upon 27 close earth satellites containing some 400,000 observations of electronic, laser, and optical data. In addition, a new combination satellite/gravimetry solution (GEM 4) was derived. The new model includes 61 center of mass tracking station locations with data from GRARR, Laser, MOTS, Baker-Nunn, and NWL Tranet Doppler tracking sites. Improvement was obtained for the zonal coefficients of the new models and is shown by tests on the long period perturbations of the orbits. Individual zonal coefficients agree very closely among different models that contain low inclination satellites. Tests of models with surface gravity data show that the GEM 3 satellite model has significantly better agreement with the gravimetry data than the GEM 1 satellite model, and that it also has better agreement with the gravimetry data than the 1969 SAO Standard Earth 2 model.

The Lopsidedness of Satellite Galaxy Systems in ΛCDM Simulations

NASA Astrophysics Data System (ADS)

Pawlowski, Marcel S.; Ibata, Rodrigo A.; Bullock, James S.

2017-12-01

The spatial distribution of satellite galaxies around pairs of galaxies in the Sloan Digital Sky Survey (SDSS) have been found to bulge significantly toward the respective partner. Highly anisotropic, planar distributions of satellite galaxies are in conflict with expectations derived from cosmological simulations. Does the lopsided distribution of satellite systems around host galaxy pairs constitute a similar challenge to the standard model of cosmology? We investigate whether such satellite distributions are present around stacked pairs of hosts extracted from the ΛCDM simulations Millennium-I, Millennium-II, Exploring the Local Volume in Simulations, and Illustris-1. By utilizing this set of simulations covering different volumes, resolutions, and physics, we implicitly test whether a lopsided signal exists for different ranges of satellite galaxy masses, and whether the inclusion of hydrodynamical effects produces significantly different results. All simulations display a lopsidedness similar to the observed situation. The signal is highly significant for simulations containing a sufficient number of hosts and resolved satellite galaxies (up to 5 σ for Millennium-II). We find a projected signal that is up to twice as strong as that reported for the SDSS systems for certain opening angles (∼16% more satellites in the direction between the pair than expected for uniform distributions). Considering that the SDSS signal is a lower limit owing to likely back- and foreground contamination, the ΛCDM simulations appear to be consistent with this particular empirical property of galaxy pairs.

LCS-1: a high-resolution global model of the lithospheric magnetic field derived from CHAMP and Swarm satellite observations

NASA Astrophysics Data System (ADS)

Olsen, Nils; Ravat, Dhananjay; Finlay, Christopher C.; Kother, Livia K.

2017-12-01

We derive a new model, named LCS-1, of Earth's lithospheric field based on four years (2006 September-2010 September) of magnetic observations taken by the CHAMP satellite at altitudes lower than 350 km, as well as almost three years (2014 April-2016 December) of measurements taken by the two lower Swarm satellites Alpha and Charlie. The model is determined entirely from magnetic 'gradient' data (approximated by finite differences): the north-south gradient is approximated by first differences of 15 s along-track data (for CHAMP and each of the two Swarm satellites), while the east-west gradient is approximated by the difference between observations taken by Swarm Alpha and Charlie. In total, we used 6.2 mio data points. The model is parametrized by 35 000 equivalent point sources located on an almost equal-area grid at a depth of 100 km below the surface (WGS84 ellipsoid). The amplitudes of these point sources are determined by minimizing the misfit to the magnetic satellite 'gradient' data together with the global average of |Br| at the ellipsoid surface (i.e. applying an L1 model regularization of Br). In a final step, we transform the point-source representation to a spherical harmonic expansion. The model shows very good agreement with previous satellite-derived lithospheric field models at low degree (degree correlation above 0.8 for degrees n ≤ 133). Comparison with independent near-surface aeromagnetic data from Australia yields good agreement (coherence >0.55) at horizontal wavelengths down to at least 250 km, corresponding to spherical harmonic degree n ≈ 160. The LCS-1 vertical component and field intensity anomaly maps at Earth's surface show similar features to those exhibited by the WDMAM2 and EMM2015 lithospheric field models truncated at degree 185 in regions where they include near-surface data and provide unprecedented detail where they do not. Example regions of improvement include the Bangui anomaly region in central Africa, the west African cratons, the East African Rift region, the Bay of Bengal, the southern 90°E ridge, the Cretaceous quiet zone south of the Walvis Ridge and the younger parts of the South Atlantic.

Photosynthetic Efficiency of Northern Forest Ecosystems Using a MODIS-Derived Photochemical Reflectance Index (PRI)

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Huemmrich, K. F.; Landis, D. R.; Black, T. A.; Barr, A. G.; McCaughey, J. H.

2016-01-01

This study evaluates a direct remote sensing approach from space for the determination of ecosystem photosynthetic light use efficiency (LUE), through measurement of vegetation reflectance changes expressed with the Photochemical Reflectance Index (PRI). The PRI is a normalized difference index based on spectral changes at a physiologically active wavelength (approximately 531 nanometers) as compared to a reference waveband, and is only available from a very few satellites. These include the two Moderate-Resolution Imaging Spectroradiometers (MODIS) on the Aqua and Terra satellites each of which have a narrow (10-nanometer) ocean band centered at 531 nanometers. We examined several PRI variations computed with candidate reference bands, since MODIS lacks the traditional 570-nanometer reference band. The PRI computed using MODIS land band 1 (620-670 nanometers) gave the best performance for daily LUE estimation. Through rigorous statistical analyses over a large image collection (n equals 420), the success of relating in situ daily tower-derived LUE to MODIS observations for northern forests was strongly influenced by satellite viewing geometry. LUE was calculated from CO2 fluxes (moles per moles of carbon absorbed quanta) measured at instrumented Canadian Carbon Program flux towers in four Canadian forests: a mature fir site in British Columbia, mature aspen and black spruce sites in Saskatchewan, and a mixed deciduous/coniferous forest site in Ontario. All aspects of the viewing geometry had significant effects on the MODIS-PRI, including the view zenith angle (VZA), the view azimuth angle, and the displacement of the view azimuth relative to the solar principal plane, in addition to illumination related variables.Nevertheless, we show that forward scatter sector views (VZA, 16 degrees-45 degrees) provided the strongest relationships to daily LUE, especially those collected in the early afternoon by Aqua (r squared = 0.83, RMSE (root mean square error) equals 0.003 moles per moles of carbon absorbed quanta). Nadir (VZA, 0 degrees plus or minus 15 degrees) and backscatter views (VZA, -16 degrees to -45 degrees) had lower performance in estimating LUE (nadir: r squared approximately equal to 0.62-0.67; backscatter: r squared approximately equal to 0.54-0.59) and similar estimation error (RMSE equals 0.004-0.005).When directional effects were not considered, only a moderately successful MODIS-PRI vs. LUE relationship (r squared equals 0.34, RMSE equals 0.007) was obtained in the full dataset (all views & sites, both satellites), but site-specific relationships were able to discriminate between coniferous and deciduous forests. Overall, MODIS-PRI values from Terra (late morning) were higher than those from Aqua (early afternoon), before/after the onset of diurnal stress responses expressed spectrally. Therefore, we identified ninety-two Terra-Aqua "same day" pairs, for which the sum of Terra morning and Aqua afternoon MODIS-PRI values (PRI (sub sum) using all available directional observations was linearly correlated with daily tower LUE (r squared equals 0.622, RMSE equals 0.013) and independent of site differences or meteorological information. Our study highlights the value of off-nadir directional reflectance observations, and the value of pairing morning and afternoon satellite observations to monitor stress responses that inhibit carbon uptake in Canadian forest ecosystems. In addition, we show that MODIS-PRI values, when derived from either: (i) forward views only, or (ii) Terra/Aqua same day (any view) combined observations, provided more accurate estimates of tower-measured daily LUE than those derived from either nadir or backscatter views or those calculated by the widely used semi-operational MODIS GPP model (MOD17) which is based on a theoretical maximum LUE and environmental data. Consequently, we demonstrate the importance of diurnal as well as off-nadir satellite observations for detecting vegetation physiological processes.

Effects of assimilating precipitation zones derived from satellite and lightning data on numerical simulations of tropical-like Mediterranean storms

NASA Astrophysics Data System (ADS)

Fita, L.; Romero, R.; Luque, A.; Ramis, C.

2009-08-01

The scarcity of meteorological observations in maritime areas is a well-known problem that can be an important limitation in the study of different phenomena. Tropical-like storms or medicanes developed over the Mediterranean sea are intense storms with some similarities to the tropical ones. Although they do not reach the hurricane intensity, their potential for damage is very high, due to the densely populated Mediterranean coastal regions. In this study, the two notable cases of medicane development which occurred in the western Mediterranean basin in September 1996 and October 2003, are considered. The capability of mesoscale numerical models to simulate general aspects of such a phenomena has been previously shown. With the aim of improving the numerical results, an adjustment of the humidity vertical profiles in MM5 simulations is performed by means of satellite derived precipitation. Convective and stratiform precipitation types obtained from satellite images are used to individually adjust the profiles. Lightning hits are employed to identify convective grid points. The adjustment of the vertical humidity profiles is carried out in the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses used as initial conditions for the simulations. Analyses nudging to ECMWF analyses and to the satellite-based humidity-corrected version of these analyses has also been applied using Four Dimensional Data Assimilation (FDDA). An additional adjustment is applied as observation nudging of satellite/lightning information at different time and spatial resolutions. Statistical parameters are proposed and tested as an objective way to intercompare satellite-derived and simulated trajectories. Simulations of medicanes exhibit a strong sensitivity to vertical humidity profiles. Trajectories of the storms are improved or worsened by using FDDA. A case dependence is obtained on the characteristics of the humidity-corrected medicanes. FDDA sensitivity on temporal and spatial resolution of the assimilated data also presents a case dependence. It also shows a significant sensitivity of the results of the observation nudging to the specific choice of the values of coefficient weight and vertical ratio of the ingested observations.

Global Patterns of Lightning Properties Derived by OTD and LIS

NASA Technical Reports Server (NTRS)

Beirle, Steffen; Koshak, W.; Blakeslee, R.; Wagner, T.

2014-01-01

The satellite instruments Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) provide unique empirical data about the frequency of lightning flashes around the globe (OTD), and the tropics (LIS), which 5 has been used before to compile a well received global climatology of flash rate densities. Here we present a statistical analysis of various additional lightning properties derived from OTD/LIS, i.e. the number of so-called "events" and "groups" per flash, as well as 10 the mean flash duration, footprint and radiance. These normalized quantities, which can be associated with the flash "strength", show consistent spatial patterns; most strikingly, oceanic flashes show higher values than continental flashes for all properties. Over land, regions with high (Eastern US) 15 and low (India) flash strength can be clearly identified. We discuss possible causes and implications of the observed regional differences. Although a direct quantitative interpretation of the investigated flash properties is difficult, the observed spatial patterns provide valuable information for the 20 interpretation and application of climatological flash rates. Due to the systematic regional variations of physical flash characteristics, viewing conditions, and/or measurement sensitivities, parametrisations of lightning NOx based on total flash rate densities alone are probably affected by regional biases.

Predicting the Magnetic Field of Earth-impacting CMEs

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

Kay, C.; Gopalswamy, N.; Reinard, A.

Predicting the impact of coronal mass ejections (CMEs) and the southward component of their magnetic field is one of the key goals of space weather forecasting. We present a new model, the ForeCAT In situ Data Observer (FIDO), for predicting the in situ magnetic field of CMEs. We first simulate a CME using ForeCAT, a model for CME deflection and rotation resulting from the background solar magnetic forces. Using the CME position and orientation from ForeCAT, we then determine the passage of the CME over a simulated spacecraft. We model the CME’s magnetic field using a force-free flux rope andmore » we determine the in situ magnetic profile at the synthetic spacecraft. We show that FIDO can reproduce the general behavior of four observed CMEs. FIDO results are very sensitive to the CME’s position and orientation, and we show that the uncertainty in a CME’s position and orientation from coronagraph images corresponds to a wide range of in situ magnitudes and even polarities. This small range of positions and orientations also includes CMEs that entirely miss the satellite. We show that two derived parameters (the normalized angular distance between the CME nose and satellite position and the angular difference between the CME tilt and the position angle of the satellite with respect to the CME nose) can be used to reliably determine whether an impact or miss occurs. We find that the same criteria separate the impacts and misses for cases representing all four observed CMEs.« less

Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data

PubMed Central

van Leeuwen, Willem J. D.

2008-01-01

This study examines how satellite based time-series vegetation greenness data and phenological measurements can be used to monitor and quantify vegetation recovery after wildfire disturbances and examine how pre-fire fuel reduction restoration treatments impact fire severity and impact vegetation recovery trajectories. Pairs of wildfire affected sites and a nearby unburned reference site were chosen to measure the post-disturbance recovery in relation to climate variation. All site pairs were chosen in forested uplands in Arizona and were restricted to the area of the Rodeo-Chediski fire that occurred in 2002. Fuel reduction treatments were performed in 1999 and 2001. The inter-annual and seasonal vegetation dynamics before, during, and after wildfire events can be monitored using a time series of biweekly composited MODIS NDVI (Moderate Resolution Imaging Spectroradiometer - Normalized Difference Vegetation Index) data. Time series analysis methods included difference metrics, smoothing filters, and fitting functions that were applied to extract seasonal and inter-annual change and phenological metrics from the NDVI time series data from 2000 to 2007. Pre- and post-fire Landsat data were used to compute the Normalized Burn Ratio (NBR) and examine burn severity at the selected sites. The phenological metrics (pheno-metrics) included the timing and greenness (i.e. NDVI) for the start, peak and end of the growing season as well as proxy measures for the rate of green-up and senescence and the annual vegetation productivity. Pre-fire fuel reduction treatments resulted in lower fire severity, which reduced annual productivity much less than untreated areas within the Rodeo-Chediski fire perimeter. The seasonal metrics were shown to be useful for estimating the rate of post-fire disturbance recovery and the timing of phenological greenness phases. The use of satellite time series NDVI data and derived pheno-metrics show potential for tracking vegetation cover dynamics and successional changes in response to drought, wildfire disturbances, and forest restoration treatments in fire-suppressed forests. PMID:27879809

Comparisons of Satellite Soil Moisture, an Energy Balance Model Driven by LST Data and Point Measurements

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Rudari, Roberto; Boni, Giorgio; Puca, Silvia

2013-04-01

Soil moisture plays a fundamental role in the partitioning of mass and energy fluxes between land surface and atmosphere, thereby influencing climate and weather, and it is important in determining the rainfall-runoff response of catchments; moreover, in hydrological modelling and flood forecasting, a correct definition of moisture conditions is a key factor for accurate predictions. Different sources of information for the estimation of the soil moisture state are currently available: satellite data, point measurements and model predictions. All are affected by intrinsic uncertainty. Among different satellite sensors that can be used for soil moisture estimation three major groups can be distinguished: passive microwave sensors (e.g., SSMI), active sensors (e.g. SAR, Scatterometers), and optical sensors (e.g. Spectroradiometers). The last two families, mainly because of their temporal and spatial resolution seem the most suitable for hydrological applications In this work soil moisture point measurements from 10 sensors in the Italian territory are compared of with the satellite products both from the HSAF project SM-OBS-2, derived from the ASCAT scatterometer, and from ACHAB, an operative energy balance model that assimilate LST data derived from MSG and furnishes daily an evaporative fraction index related to soil moisture content for all the Italian region. Distributed comparison of the ACHAB and SM-OBS-2 on the whole Italian territory are performed too.

On the use of the GRACE normal equation of inter-satellite tracking data for estimation of soil moisture and groundwater in Australia

NASA Astrophysics Data System (ADS)

Tangdamrongsub, Natthachet; Han, Shin-Chan; Decker, Mark; Yeo, In-Young; Kim, Hyungjun

2018-03-01

An accurate estimation of soil moisture and groundwater is essential for monitoring the availability of water supply in domestic and agricultural sectors. In order to improve the water storage estimates, previous studies assimilated terrestrial water storage variation (ΔTWS) derived from the Gravity Recovery and Climate Experiment (GRACE) into land surface models (LSMs). However, the GRACE-derived ΔTWS was generally computed from the high-level products (e.g. time-variable gravity fields, i.e. level 2, and land grid from the level 3 product). The gridded data products are subjected to several drawbacks such as signal attenuation and/or distortion caused by a posteriori filters and a lack of error covariance information. The post-processing of GRACE data might lead to the undesired alteration of the signal and its statistical property. This study uses the GRACE least-squares normal equation data to exploit the GRACE information rigorously and negate these limitations. Our approach combines GRACE's least-squares normal equation (obtained from ITSG-Grace2016 product) with the results from the Community Atmosphere Biosphere Land Exchange (CABLE) model to improve soil moisture and groundwater estimates. This study demonstrates, for the first time, an importance of using the GRACE raw data. The GRACE-combined (GC) approach is developed for optimal least-squares combination and the approach is applied to estimate the soil moisture and groundwater over 10 Australian river basins. The results are validated against the satellite soil moisture observation and the in situ groundwater data. Comparing to CABLE, we demonstrate the GC approach delivers evident improvement of water storage estimates, consistently from all basins, yielding better agreement on seasonal and inter-annual timescales. Significant improvement is found in groundwater storage while marginal improvement is observed in surface soil moisture estimates.

The impact of customer-contractor interactions on spacecraft innovation: Insights from communication satellite history

NASA Astrophysics Data System (ADS)

Szajnfarber, Zoe; Stringfellow, Margaret V.; Weigel, Annalisa L.

2010-11-01

This paper captures a first detailed attempt to quantitatively analyze the innovation history of the space sector. Building on a communication satellite innovation metric and a spacecraft innovation framework developed as part of an ongoing project, this paper presents a preliminary model of global communication satellite innovation. In addition to innovation being a function of the rate of performance normalized by price, innovation was found to be strongly influenced by characteristics of the customer-contractor contractual relationship. Specifically, Department of Defense contracts tend to result in a lower level of innovation on average as compared to other customers. Also, particular customer-contractor pairs perform differently and exhibit a second order relationship in time.

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

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.

Derivation of Coefficients for the Bidirectional Reflection Distribution Function from AVHRR-data over Europe, under Consideration of the Helmholtz Reciprocity Law

NASA Astrophysics Data System (ADS)

Billing, H.; Koslowsky, D.

In the AVHRR data of the polar orbiting NOAA Satellites, directional reflectance under a certain view from satellite and a certain illumination by the sun is measured. Due to the nearly sunsynchroneous orbit of the NOAA satellite, each area is seen under different viewing angles in successive days. Only after approximately 9 days, the conditions are again similar. Areas, seen in specular direction, may appear only half as bright, as if seen in antispecular direction. This deviation from a Lambertian reflector is a function of the surface roughness and the degree of coverage with vegetation. The NOAA afternoon satellites drift by half an hour from year to year. Thus even data from the same season, but different years, are seen under different illumination conditions. To derive the bidirectional reflection distribution function in dependence on satellite viewing angle and solar illumination becomes a very complicated procedure. Using the Helmholtz reciprocity principle (HRP), i.e. the symetrie in viewing and illumination, reduces the problem by one dimension. For different bidimensional reflection laws it will be tested, whether they can be formulated to fullfill the HRP. Via regression, the parameters will be deduced for time series of AVHRR data of 10 years from NOAA 11,14,16 and 17. Brdfunctions, suggested by Rao as well as a law, suggested by Ba seem to become unstable for low sun resp. large viewing zenit angles. Only brdfs with 4 coefficients can fit the observed distributions. A nonlinear temporal angular model (NTAM), suggested by Latifovic,Cihlar and Chen, seems to be suitable to describe even the hot spot and the dependence on plant growth. The coefficients of these brdf-function will be derived via regression for monthly series of cloud free data for the European area, where AVHRR data in full resolution are received in Berlin. Using these coefficients, monthly maps of surface roughness are produced for the above area for the time since 1985. Ba, M.B., Deschamps, P.-Y.,Frouin, R. 1995. Error reduction in NOAA satellite monitoring of the land surface vegetation during FIFE. J. Geophys. Res., 100: 25537-25548. Rao, C.R.N., Chen, J., 1994. Post-launch calibration of the visible and near infrared channels of the advanced very high resolution radiometer on NOAA-7,- 9, and -11 spacecraft. NOAA Technical Report NESDIS 78. Latifovic, R., Chilar, J., Chen, J., 2003. A Comparison of BRDF Models for the Normalisation of Satellite Optical Data to a Standard Sun-Target- Sensor Geometry. IEEE Transactions on Geoscience and Remote Sensing, Vol.41, No.8, 1889 - 1898.

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.

Quantifying soil burn severity for hydrologic modeling to assess post-fire effects on sediment delivery

NASA Astrophysics Data System (ADS)

Dobre, Mariana; Brooks, Erin; Lew, Roger; Kolden, Crystal; Quinn, Dylan; Elliot, William; Robichaud, Pete

2017-04-01

Soil erosion is a secondary fire effect with great implications for many ecosystem resources. Depending on the burn severity, topography, and the weather immediately after the fire, soil erosion can impact municipal water supplies, degrade water quality, and reduce reservoirs' storage capacity. Scientists and managers use field and remotely sensed data to quickly assess post-fire burn severity in ecologically-sensitive areas. From these assessments, mitigation activities are implemented to minimize post-fire flood and soil erosion and to facilitate post-fire vegetation recovery. Alternatively, land managers can use fire behavior and spread models (e.g. FlamMap, FARSITE, FOFEM, or CONSUME) to identify sensitive areas a priori, and apply strategies such as fuel reduction treatments to proactively minimize the risk of wildfire spread and increased burn severity. There is a growing interest in linking fire behavior and spread models with hydrology-based soil erosion models to provide site-specific assessment of mitigation treatments on post-fire runoff and erosion. The challenge remains, however, that many burn severity mapping and modeling products quantify vegetation loss rather than measuring soil burn severity. Wildfire burn severity is spatially heterogeneous and depends on the pre-fire vegetation cover, fuel load, topography, and weather. Severities also differ depending on the variable of interest (e.g. soil, vegetation). In the United States, Burned Area Reflectance Classification (BARC) maps, derived from Landsat satellite images, are used as an initial burn severity assessment. BARC maps are classified from either a Normalized Burn Ratio (NBR) or differenced Normalized Burned Ratio (dNBR) scene into four classes (Unburned, Low, Moderate, and High severity). The development of soil burn severity maps requires further manual field validation efforts to transform the BARC maps into a product more applicable for post-fire soil rehabilitation activities. Alternative spectral indices and modeled output approaches may prove better predictors of soil burn severity and hydrologic effects, but these have not yet been assessed in a model framework. In this project we compare field-verified soil burn severity maps to satellite-derived and modeled burn severity maps. We quantify the extent to which there are systematic differences in these mapping products. We then use the Water Erosion Prediction Project (WEPP) hydrologic soil erosion model to assess sediment delivery from these fires using the predicted and observed soil burn severity maps. Finally, we discuss differences in observed and predicted soil burn severity maps and application to watersheds in the Pacific Northwest to estimate post-fire sediment delivery.

Integration of Ground, Buoys, Satellite and Model data to map the Changes in Meteorological Parameters Associated with Harvey Hurricane

NASA Astrophysics Data System (ADS)

Chauhan, A.; Sarkar, S.; Singh, R. P.

2017-12-01

The coastal areas have dense onshore and marine observation network and are also routinely monitored by constellation of satellites. The monitoring of ocean, land and atmosphere through a range of meteorological parameters, provides information about the land and ocean surface. Satellite data also provide information at different pressure levels that help to access the development of tropical storms and formation of hurricanes at different categories. Integration of ground, buoys, satellite and model data showing the changes in meteorological parameters during the landfall stages of hurricane Harvey will be discussed. Hurricane Harvey was one of the deadliest hurricanes at the Gulf coast which caused intense flooding from the precipitation. The various observation networks helped city administrators to evacuate the coastal areas, that minimized the loss of lives compared to the Galveston hurricane of 1900 which took 10,000 lives. Comparison of meteorological parameters derived from buoys, ground stations and satellites associated with Harvey and 2005 Katrina hurricane present some of the interesting features of the two hurricanes.

Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate.

PubMed

Dierssen, Heidi M

2010-10-05

Phytoplankton biomass and productivity have been continuously monitored from ocean color satellites for over a decade. Yet, the most widely used empirical approach for estimating chlorophyll a (Chl) from satellites can be in error by a factor of 5 or more. Such variability is due to differences in absorption and backscattering properties of phytoplankton and related concentrations of colored-dissolved organic matter (CDOM) and minerals. The empirical algorithms have built-in assumptions that follow the basic precept of biological oceanography--namely, oligotrophic regions with low phytoplankton biomass are populated with small phytoplankton, whereas more productive regions contain larger bloom-forming phytoplankton. With a changing world ocean, phytoplankton composition may shift in response to altered environmental forcing, and CDOM and mineral concentrations may become uncoupled from phytoplankton stocks, creating further uncertainty and error in the empirical approaches. Hence, caution is warranted when using empirically derived Chl to infer climate-related changes in ocean biology. The Southern Ocean is already experiencing climatic shifts and shows substantial errors in satellite-derived Chl for different phytoplankton assemblages. Accurate global assessments of phytoplankton will require improved technology and modeling, enhanced field observations, and ongoing validation of our "eyes in space."

Assimilation of remote sensing observations into a continuous distributed hydrological model: impacts on the hydrologic cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Campo, Lorenzo; Cenci, Luca; Silvestro, Francesco; Delogu, Fabio; Boni, Giorgio; Rudari, Roberto

2015-04-01

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 in-situ data. This work investigates the impact of the assimilation of different remote sensing products on the hydrological cycle by using a continuous physically based distributed hydrological model. Three soil moisture products derived by ASCAT (Advanced SCATterometer) are used to update the model state variables. The satellite-derived products are assimilated into the hydrological model using different assimilation techniques: a simple nudging and the Ensemble Kalman Filter. Moreover two assimilation strategies are evaluated to assess the impact of assimilating the satellite products at model spatial resolution or at the satellite scale. The experiments are carried out for three Italian catchments on multi year period. The benefits on the model predictions of discharge, LST, evapotranspiration and soil moisture dynamics are tested and discussed.

Highly ionized atoms observed with Copernicus. [stellar O lines

NASA Technical Reports Server (NTRS)

York, D. G.

1974-01-01

Based on high-resolution observations using the Princeton satellite/spectrometer on Copernicus the O VI doublet is discussed in detail, in conjunction with data for the resonance lines of N V, Si IV, and S IV reported in five stars. The temperature, density, and the possible extent of the O VI producing region are discussed. The ratio N(S IV)/N(O VI) is used to derive a lower limit to the temperature in the O VI producing region. In near pressure equilibrium with normal interstellar clouds and H II regions minimum densities are found to be consistent with a hot plasma.

Evaluating the relationships between solar-induced chlorophyll fluorescence from Orbiting Carbon Observatory-2 and gross primary productivity from eddy covariance flux towers

NASA Astrophysics Data System (ADS)

Li, X.; Xiao, J.; He, B.

2017-12-01

Solar-induced chlorophyll fluorescence (SIF) opens a new perspective on the monitoring of vegetation photosynthesis from space, and has been recently used to estimate gross primary productivity (GPP). However, previous studies on SIF were mainly based on satellite observations from the Greenhouse Gases Observing Satellite (GOSAT) and Global Ozone Monitoring Experiment-2 (GOME-2), and the evaluation of these coarse-resolution SIF measurements using GPP derived from eddy covariance (EC) flux towers has been hindered by the scale mismatch between satellite and tower footprints. We use new far-red SIF observations from the Orbiting Carbon Observatory-2 (OCO-2) satellite with much finer spatial resolution and GPP data from EC flux towers from 2014 to 2016 to examine the relationship between GPP and SIF for temperate forests. The OCO-2 SIF tracked tower GPP fairly well, and had strong correlation with tower GPP at both retrieval bands (757nm and 771nm) and both instantaneous (mid-day) and daily timescales. Daily SIF at 757nm (SIF757) exhibited much stronger correlation with tower GPP compared to MODIS enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) derived from either Terra or Aqua and had a similarly strong relationship as EVI based on the bidirectional reflectance distribution function (BRDF) corrected reflectance product (Terra+Aqua). Absorbed photosynthetically active radiation (APAR) explained 85% of the variance in SIF757, while the product of APAR and two environmental scalars - fTmin and fVPD (representing minimum temperature stress and water stress) explained slightly higher variance (92%) in SIF757. This suggests that SIF mainly depends on APAR and also contains information on light use efficiency (LUE) reflecting environmental stresses and physiological or biochemical variations of vegetation. The hyperbolic model based on SIF757 estimated GPP well (R2=0.81, p<0.0001; RMSE=1.11 gC m-2 d-1), and its performance was comparable to or slightly better than that of a LUE-based GPP model - the MODSI GPP algorithm. Our findings demonstrate the strong predictive ability of OCO-2 SIF in estimating GPP for temperate forests and its potential in future ecosystem functioning and carbon cycling studies.

Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images.

PubMed

Liu, Meiling; Wang, Tiejun; Skidmore, Andrew K; Liu, Xiangnan

2018-05-05

Regional-level information on heavy metal pollution in agro-ecosystems is essential for food security because excessive levels of heavy metals in crops may pose risks to humans. However, collecting this information over large areas is inherently costly. This paper investigates the possibility of applying multi-temporal Sentinel-2 satellite images to detect heavy metal-induced stress (i.e., Cd stress) in rice crops in four study areas in Zhuzhou City, Hunan Province, China. For this purpose, we compared seven Sentinel-2 images acquired in 2016 and 2017 with in situ measured hyper-spectral data, chlorophyll content, rice leaf area index, and heavy metal concentrations in soil collected from 2014 to 2017. Vegetation indices (VIs) related to red edge bands were referred to as the sensitive indicators for screening stressed rice from unstressed rice. The coefficients of spatio-temporal variation (CSTV) derived from the VIs allowed us to discriminate crops exposed to pollution from heavy metals as well as environmental stressors. The results indicate that (i) the red edge chlorophyll index, the red edge position index, and the normalized difference red edge 2 index derived from multi-temporal Sentinel-2 images were good indicators for screening stressed rice from unstressed rice; (ii) Rice under Cd stress remained stable with lower CSTV values of VIs overall growth stages in the experimental region, whereas rice under other stressors (i.e., pests and disease) showed abrupt changes at some growth stages and presented "hot spots" with greater CSTV values; and (iii) the proposed spatio-temporal anomaly detection method was successful at detecting rice under Cd stress; and CSTVs of rice VIs stabilized regardless of whether they were applied to consecutive growth stages or to two different crop years. This study suggests that regional heavy metal stress may be accurately detected using multi-temporal Sentinel-2 images, using VIs sensitive to the spatio-temporal characteristics of crops. Copyright © 2018 Elsevier B.V. All rights reserved.

Drone based estimation of actual evapotranspiration over different forest types

NASA Astrophysics Data System (ADS)

Marzahn, Philip; Gampe, David; Castro, Saulo; Vega-Araya, Mauricio; Sanchez-Azofeifa, Arturo; Ludwig, Ralf

2017-04-01

Actual evapotranspiration (Eta) plays an important role in surface-atmosphere interactions. Traditionally, Eta is measured by means of lysimeters, eddy-covariance systems or fiber optics, providing estimates which are spatially restricted to a footprint from a few square meters up to several hectares . In the past, several methods have been developed to derive Eta by means of multi-spectral remote sensing data using thermal and VIS/NIR satellite imagery of the land surface. As such approaches do have their justification on coarser scales, they do not provide Eta information on the fine resolution plant level over large areas which is mandatory for the detection of water stress or tree mortality. In this study, we present a comparison of a drone based assessment of Eta with eddy-covariance measurements over two different forest types - a deciduous forest in Alberta, Canada and a tropical dry forest in Costa Rica. Drone based estimates of Eta were calculated applying the Triangle-Method proposed by Jiang and Islam (1999). The Triangle-Method estimates actual evapotranspiration (Eta) by means of the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) provided by two camera systems (MicaSense RedEdge, FLIR TAU2 640) flown simultaneously on an octocopter. . Results indicate a high transferability of the original approach from Jiang and Islam (1999) developed for coarse to medium resolution satellite imagery tothe high resolution drone data, leading to a deviation in Eta estimates of 10% compared to the eddy-covariance measurements. In addition, the spatial footprint of the eddy-covariance measurement can be detected with this approach, by showing the spatial heterogeneities of Eta due to the spatial distribution of different trees and understory vegetation.

Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.

2010-12-01

High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

Estimating carbon fluxes on small rotationally grazed pastures

USDA-ARS?s Scientific Manuscript database

Satellite-based Normalized Difference Vegetation Index (NDVI) data have been extensively used for estimating gross primary productivity (GPP) and yield of grazing lands throughout the world. Large-scale estimates of GPP are a necessary component of efforts to monitor the soil carbon balance of grazi...

One-dimensional nonlinear instability study of a slightly viscoelastic, perfectly conducting liquid jet under a radial electric field

NASA Astrophysics Data System (ADS)

Li, Fang; Yin, Xie-Yuan; Yin, Xie-Zhen

2016-05-01

A one-dimensional electrified viscoelastic model is built to study the nonlinear behavior of a slightly viscoelastic, perfectly conducting liquid jet under a radial electric field. The equations are solved numerically using an implicit finite difference scheme together with a boundary element method. The electrified viscoelastic jet is found to evolve into a beads-on-string structure in the presence of the radial electric field. Although the radial electric field greatly enhances the linear instability of the jet, its influence on the decay of the filament thickness is limited during the nonlinear evolution of the jet. On the other hand, the radial electric field induces axial non-uniformity of the first normal stress difference within the filament. The first normal stress difference in the center region of the filament may be greatly decreased by the radial electric field. The regions with/without satellite droplets are illuminated on the χ (the electrical Bond number)-k (the dimensionless wave number) plane. Satellite droplets may be formed for larger wave numbers at larger radial electric fields.

Enhancement of Chlorophyll Concentration and Growing Harmful Algal Bloom Along the California Coast

NASA Astrophysics Data System (ADS)

Aceves, Joselyn; Singh, Ramesh

2016-07-01

We have carried out detailed analysis of satellite and ground data at different locations, Cal Poly, Goleta, Newport, Santa Monica, and Scripps piers and Monterey, Stearns and Santa Cruz wharfs along the California coast for the period 2008-2015. The sea surface temperature and chlorophyll concentrations derived from satellite data are analyzed together with ground observations of nitrogen, phosphorus, domoic acids and harmful algal blooms. The frequency of harmful algal blooms are found to increase in recent years depending upon the enhancement of chlorophyll concentrations and the discharges along the coast and dynamics of the sea surface temperature. The frequency of harmful algal blooms is higher in the northern California compared to southern California. The anthropogenic activities along the coast have increased which are associated with the forest fires and long range transport of dusts from Asia. The aerosol optical depth derived from satellite data during summer months seems to play an important role in the frequency of harmful algal blooms.

Inter-Comparison of GOES-8 Imager and Sounder Skin Temperature Retrievals

NASA Technical Reports Server (NTRS)

Haines, Stephanie L.; Suggs, Ronnie J.; Jedlovec, Gary J.; Arnold, James E. (Technical Monitor)

2001-01-01

Skin temperature (ST) retrievals derived from geostationary satellite observations have both high temporal and spatial resolutions and are therefore useful for applications such as assimilation into mesoscale forecast models, nowcasting, and diagnostic studies. Our retrieval method uses a Physical Split Window technique requiring at least two channels within the longwave infrared window. On current GOES satellites, including GOES-11, there are two Imager channels within the required spectral interval. However, beginning with the GOES-M satellite the 12-um channel will be removed, leaving only one longwave channel. The Sounder instrument will continue to have three channels within the longwave window, and therefore ST retrievals will be derived from Sounder measurements. This research compares retrievals from the two instruments and evaluates the effects of the spatial resolution and sensor calibration differences on the retrievals. Both Imager and Sounder retrievals are compared to ground-truth data to evaluate the overall accuracy of the technique. An analysis of GOES-8 and GOES-11 intercomparisons is also presented.

Recent Progress on the Second Generation CMORPH: LEO-IR Based Precipitation Estimates and Cloud Motion Vector

NASA Astrophysics Data System (ADS)

Xie, Pingping; Joyce, Robert; Wu, Shaorong

2015-04-01

As reported at the EGU General Assembly of 2014, a prototype system was developed for the second generation CMORPH to produce global analyses of 30-min precipitation on a 0.05olat/lon grid over the entire globe from pole to pole through integration of information from satellite observations as well as numerical model simulations. The second generation CMORPH is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) as well as LEO platforms, and precipitation simulations from numerical global models. Key to the success of the 2nd generation CMORPH, among a couple of other elements, are the development of a LEO-IR based precipitation estimation to fill in the polar gaps and objectively analyzed cloud motion vectors to capture the cloud movements of various spatial scales over the entire globe. In this presentation, we report our recent work on the refinement for these two important algorithm components. The prototype algorithm for the LEO IR precipitation estimation is refined to achieve improved quantitative accuracy and consistency with PMW retrievals. AVHRR IR TBB data from all LEO satellites are first remapped to a 0.05olat/lon grid over the entire globe and in a 30-min interval. Temporally and spatially co-located data pairs of the LEO TBB and inter-calibrated combined satellite PMW retrievals (MWCOMB) are then collected to construct tables. Precipitation at a grid box is derived from the TBB through matching the PDF tables for the TBB and the MWCOMB. This procedure is implemented for different season, latitude band and underlying surface types to account for the variations in the cloud - precipitation relationship. At the meantime, a sub-system is developed to construct analyzed fields of cloud motion vectors from the GEO/LEO IR based precipitation estimates and the CFS Reanalysis (CFSR) precipitation fields. Motion vectors are first derived separately from the satellite IR based precipitation estimates and the CFSR precipitation fields. These individually derived motion vectors are then combined through a 2D-VAR technique to form an analyzed field of cloud motion vectors over the entire globe. Error function is experimented to best reflect the performance of the satellite IR based estimates and the CFSR in capturing the movements of precipitating cloud systems over different regions and for different seasons. Quantitative experiments are conducted to optimize the LEO IR based precipitation estimation technique and the 2D-VAR based motion vector analysis system. Detailed results will be reported at the EGU.

Comparison of precision orbit derived density estimates for CHAMP and GRACE satellites

NASA Astrophysics Data System (ADS)

Fattig, Eric Dale

Current atmospheric density models cannot adequately represent the density variations observed by satellites in Low Earth Orbit (LEO). Using an optimal orbit determination process, precision orbit ephemerides (POE) are used as measurement data to generate corrections to density values obtained from existing atmospheric models. Densities obtained using these corrections are then compared to density data derived from the onboard accelerometers of satellites, specifically the CHAMP and GRACE satellites. This comparison takes two forms, cross correlation analysis and root mean square analysis. The densities obtained from the POE method are nearly always superior to the empirical models, both in matching the trends observed by the accelerometer (cross correlation), and the magnitudes of the accelerometer derived density (root mean square). In addition, this method consistently produces better results than those achieved by the High Accuracy Satellite Drag Model (HASDM). For satellites orbiting Earth that pass through Earth's upper atmosphere, drag is the primary source of uncertainty in orbit determination and prediction. Variations in density, which are often not modeled or are inaccurately modeled, cause difficulty in properly calculating the drag acting on a satellite. These density variations are the result of many factors; however, the Sun is the main driver in upper atmospheric density changes. The Sun influences the densities in Earth's atmosphere through solar heating of the atmosphere, as well as through geomagnetic heating resulting from the solar wind. Data are examined for fourteen hour time spans between November 2004 and July 2009 for both the CHAMP and GRACE satellites. This data spans all available levels of solar and geomagnetic activity, which does not include data in the elevated and high solar activity bins due to the nature of the solar cycle. Density solutions are generated from corrections to five different baseline atmospheric models, as well as nine combinations of density and ballistic coefficient correlated half-lives. These half-lives are varied among values of 1.8, 18, and 180 minutes. A total of forty-five sets of results emerge from the orbit determination process for all combinations of baseline density model and half-lives. Each time period is examined for both CHAMP and GRACE-A, and the results are analyzed. Results are averaged from all solutions periods for 2004--2007. In addition, results are averaged after binning according to solar and geomagnetic activity levels. For any given day in this period, a ballistic coefficient correlated half-life of 1.8 minutes yields the best correlation and root mean square values for both CHAMP and GRACE. For CHAMP, a density correlated half-life of 18 minutes is best for higher levels of solar and geomagnetic activity, while for lower levels 180 minutes is usually superior. For GRACE, 180 minutes is nearly always best. The three Jacchia-based atmospheric models yield very similar results. The CIRA 1972 or Jacchia 1971 models as baseline consistently produce the best results for both satellites, though results obtained for Jacchia-Roberts are very similar to the other Jacchia-based models. Data are examined in a similar manner for the extended solar minimum period during 2008 and 2009, albeit with a much smaller sampling of data. With the exception of some atypical results, similar combinations of half-lives and baseline atmospheric model produce the best results. A greater sampling of data will aid in characterizing density in a period of especially low solar activity. In general, cross correlation values for CHAMP and GRACE revealed that the POE method matched trends observed by the accelerometers very well. However, one period of time deviated from this trend for the GRACE-A satellite. Between late October 2005 and January 2006, correlations for GRACE-A were very low. Special examination of the surrounding months revealed the extent of time this period covered. Half-life and baseline model combinations that produced the best results during this time were similar to those during normal periods. Plotting these periods revealed very short period density variations in the accelerometer that could not be reproduced by the empirical models, HASDM, or the POE method. Finally, densities produced using precision orbit data for the GRACE-B satellite were shown to be nearly indistinguishable from those produced by GRACE-A. Plots of the densities produced for both satellites during the same time periods revealed this fact. Multiple days were examined covering all possible ranges of solar and geomagnetic activity. In addition, the period in which GRACE-A correlations were low was studied. No significant differences existed between GRACE-A and GRACE-B for all of the days examined.

Downward longwave surface radiation from sun-synchronous satellite data - Validation of methodology

NASA Technical Reports Server (NTRS)

Darnell, W. L.; Gupta, S. K.; Staylor, W. F.

1986-01-01

An extensive study has been carried out to validate a satellite technique for estimating downward longwave radiation at the surface. The technique, mostly developed earlier, uses operational sun-synchronous satellite data and a radiative transfer model to provide the surface flux estimates. The satellite-derived fluxes were compared directly with corresponding ground-measured fluxes at four different sites in the United States for a common one-year period. This provided a study of seasonal variations as well as a diversity of meteorological conditions. Dome heating errors in the ground-measured fluxes were also investigated and were corrected prior to the comparisons. Comparison of the monthly averaged fluxes from the satellite and ground sources for all four sites for the entire year showed a correlation coefficient of 0.98 and a standard error of estimate of 10 W/sq m. A brief description of the technique is provided, and the results validating the technique are presented.

Electron content of the ionosphere and the plasma sphere on the basis of ATS-6-Data, NNSS-data, and ionograms. [Navy Navigation Satellite System

NASA Technical Reports Server (NTRS)

Leitinger, R.; Hartmann, G. K.; Davies, K.

1976-01-01

The reported investigation takes into account data obtained with the aid of the geostationary satellite ATS-6, the satellites of the U.S. navy navigation system (NNSS) at an altitude between 900 and 1200 km, and the satellites ISIS 1 and ISIS 2. The altitude range between ground and ATS-6 is divided into two regions, including the 'ionosphere', involving the region with an upper limit of 2000 km, and the 'plasma sphere', involving the region above an altitude of 2000 km. Data concerning the electron content obtained from different sources are compared, taking into account discrepancies between ionogram-derived values and values computed on the basis of satellite measurements. Attention is also given to the vertical electron content of the ionosphere on the basis of a combination of data obtained with the aid of the ATS-6 and the NNSS.

Reconciling Ground-Based and Space-Based Estimates of the Frequency of Occurrence and Radiative Effect of Clouds around Darwin, Australia

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

Protat, Alain; Young, Stuart; McFarlane, Sally A.

2014-02-01

The objective of this paper is to investigate whether estimates of the cloud frequency of occurrence and associated cloud radiative forcing as derived from ground-based and satellite active remote sensing and radiative transfer calculations can be reconciled over a well instrumented active remote sensing site located in Darwin, Australia, despite the very different viewing geometry and instrument characteristics. It is found that the ground-based radar-lidar combination at Darwin does not detect most of the cirrus clouds above 10 km (due to limited lidar detection capability and signal obscuration by low-level clouds) and that the CloudSat radar - Cloud-Aerosol Lidar withmore » Orthogonal Polarization (CALIOP) combination underreports the hydrometeor frequency of occurrence below 2 km height, due to instrument limitations at these heights. The radiative impact associated with these differences in cloud frequency of occurrence is large on the surface downwelling shortwave fluxes (ground and satellite) and the top-of atmosphere upwelling shortwave and longwave fluxes (ground). Good agreement is found for other radiative fluxes. Large differences in radiative heating rate as derived from ground and satellite radar-lidar instruments and RT calculations are also found above 10 km (up to 0.35 Kday-1 for the shortwave and 0.8 Kday-1 for the longwave). Given that the ground-based and satellite estimates of cloud frequency of occurrence and radiative impact cannot be fully reconciled over Darwin, caution should be exercised when evaluating the representation of clouds and cloud-radiation interactions in large-scale models and limitations of each set of instrumentation should be considered when interpreting model-observations differences.« less

Evaluation of the Precision of Satellite-Derived Sea Surface Temperature Fields

NASA Astrophysics Data System (ADS)

Wu, F.; Cornillon, P. C.; Guan, L.

2016-02-01

A great deal of attention has been focused on the temporal accuracy of satellite-derived sea surface temperature (SST) fields with little attention being given to their spatial precision. Specifically, the primary measure of the quality of SST fields has been the bias and variance of selected values minus co-located (in space and time) in situ values. Contributing values, determined by the location of the in situ values and the necessity that the satellite-derived values be cloud free, are generally widely separated in space and time hence provide little information related to the pixel-to-pixel uncertainty in the retrievals. But the main contribution to the uncertainty in satellite-derived SST retrievals relates to atmospheric contamination and because the spatial scales of atmospheric features are, in general, large compared with the pixel separation of modern infra-red sensors, the pixel-to-pixel uncertainty is often smaller than the accuracy determined from in situ match-ups. This makes selection of satellite-derived datasets for the study of submesoscale processes, for which the spatial structure of the upper ocean is significant, problematic. In this presentation we present a methodology to characterize the spatial precision of satellite-derived SST fields. The method is based on an examination of the high wavenumber tail of the 2-D spectrum of SST fields in the Sargasso Sea, a low energy region of the ocean close to the track of the MV Oleander, a container ship making weekly roundtrips between New York and Bermuda, with engine intake temperatures sampled every 75 m along track. Important spectral characteristics are the point at which the satellite-derived spectra separate from the Oleander spectra and the spectral slope following separation. In this presentation a number of high resolution 375 m to 10 km SST datasets are evaluated based on this approach.

Lead Detection and Mapping with Reference to Relationships Between Scale, Sensor Characteristics, Surface Conditions and Atmospheric Properties

DTIC Science & Technology

1993-10-01

satellite-derived products and to understand in a more quantitative manner the benefits of different sensor systems . While there have been studies of...radiation balance of the surface/atmosphere system in the Arctic (Curry et al., 1990; Curry et al., 1989a; Curry et al., 1989b) and 4) high level cirrus...characteristics of the target or imaging system . In such a context it assumes that a given lead pixel is completely within the FOV of the satellite

Application of normal fluorescence and stability-indicating derivative synchronous fluorescence spectroscopy for the determination of gliquidone in presence of its fluorescent alkaline degradation product

NASA Astrophysics Data System (ADS)

El-ghobashy, Mohamed R.; Yehia, Ali M.; Helmy, Aya H.; Youssef, Nadia F.

2018-01-01

Simple, smart and sensitive normal fluorescence and stability-indicating derivative synchronous spectrofluorimetric methods have been developed and validated for the determination of gliquidone in the drug substance and drug product. Normal spectrofluorimetric method of gliquidone was established in methanol at λ excitation 225 nm and λ emission 400 nm in concentration range 0.2-3 μg/ml with LOD equal 0.028. The fluorescence quantum yield of gliquidone was calculated using quinine sulfate as a reference and found to be 0.542. Stability-indicating first and third derivative synchronous fluorescence spectroscopy were successfully utilized to overcome the overlapped spectra in normal fluorescence of gliquidone and its alkaline degradation product. Derivative synchronous methods are based on using the synchronous fluorescence of gliquidone and its degradation product in methanol at Δ λ50 nm. Peak amplitude in the first derivative of synchronous fluorescence spectra was measured at 309 nm where degradation product showed zero-crossing without interference. The peak amplitudes in the third derivative of synchronous fluorescence spectra, peak to trough were measured at 316,329 nm where degradation product showed zero-crossing. The different experimental parameters affecting the normal and synchronous fluorescence intensity of gliquidone were studied and optimized. Moreover, the cited methods have been validated as per ICH guidelines. The peak amplitude-concentration plots of the derivative synchronous fluorescence were linear over the concentration range 0.05-2 μg/ml for gliquidone. Limits of detection were 0.020 and 0.022 in first and third derivative synchronous spectra, respectively. The adopted methods were successfully applied to commercial tablets and the results demonstrated that the derivative synchronous fluorescence spectroscopy is a powerful stability-indicating method, suitable for routine use with a short analysis time. Statistical comparison between the results obtained by normal fluorescence and derivative synchronous methods and the official one using student's t-test and F-ratio showed no significant difference regarding accuracy and precision.

Dynamics of the Seychelles-Chagos Thermocline Ridge

NASA Astrophysics Data System (ADS)

Bulusu, S.

2016-02-01

The southwest tropical Indian Ocean (SWTIO) features a unique, seasonal upwelling of the thermocline also known as the Seychelles-Chagos Thermocline Ridge (SCTR). More recently, this ridge or "dome"-like feature in the thermocline depth at (55°E-65°E, 5°S-12°S) in the SWTIO has been linked to interannual variability in the semi-annual Indian Ocean monsoon seasons as well as the Madden-Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO). The SCTR is a region where the MJO is associated with strong SST variability. Normally more cyclones are found generated in this SCTR region when the thermocline is deeper, which has a positive relation to the arrival of a downwelling Rossby wave from the southeast tropical Indian Ocean. Previous studies have focused their efforts solely on sea surface temperature (SST) because they determined salinity variability to be low, but with the Soil Moisture and Ocean Salinity (SMOS), and Aquarius salinity missions new insight can be shed on the effects that the seasonal upwelling of the thermocline has on Sea Surface Salinity (SSS). Seasonal SSS anomalies these missions will reveal the magnitude of seasonal SSS variability, while Argo depth profiles will show the link between changes in subsurface salinity and temperature structure. A seasonal increase in SST and a decrease in SSS associated with the downwelling of the thermocline have also been shown to occasionally generate MJO events, an extremely important part of climate variability in the Indian ocean. Satellite derives salinity and Argo data can help link changes in surface and subsurface salinity structure to the generation of the important MJO events. This study uses satellite derived salinity from Soil Moisture and Ocean Salinity (SMOS), and Aquarius to see if these satellites can yield new information on seasonal and interannual surface variability. In this study barrier layer thickness (BLT) estimates will be derived from satellite measurements using a multilinear regression model (MRM). This study will help to improve monsoon modeling and forecasting, two areas that remain highly inaccurate after decades of research work.

Handling of subpixel structures in the application of satellite derived irradiance data for solar energy system analysis - a review

NASA Astrophysics Data System (ADS)

Beyer, Hans Georg

2016-04-01

With the increasing availability of satellite derived irradiance information, this type of data set is more and more in use for the design and operation of solar energy systems, most notably PV- and CSP-systems. By this, the need for data measured on-site is reduced. However, due to basic limitations of the satellite-derived data, several requirements put by the intended application cannot be coped with this data type directly. Traw satellite information has to be enhanced in both space and time resolution by additional information to be fully applicable for all aspects of the modelling od solar energy systems. To cope with this problem, several individual and collaborative projects had been performed in the recent years or are ongoing. Approaches are on one hand based on pasting synthesized high-resolution data into the low-resolution original sets. Pre-requite is an appropriate model, validated against real world data. For the case of irradiance data, these models can be extracted either directly from ground measured data sets or from data referring to the cloud situation as gained from the images of sky cameras or from monte -carlo initialized physical models. The current models refer to the spatial structure of the cloud fields. Dynamics are imposed by moving the cloud structures according to a large scale cloud motion vector, either extracted from the dynamics interfered from consecutive satellite images or taken from a meso-scale meteorological model. Dynamic irradiance information is then derived from the cloud field structure and the cloud motion vector. This contribution, which is linked to subtask A - Solar Resource Applications for High Penetration of Solar Technologies - of IEA SHC task 46, will present the different approaches and discuss examples in view of validation, need for auxiliary information and respective general applicability.

The Invigoration of Deep Convective Clouds Over the Atlantic: Aerosol Effect, Meteorology or Retrieval Artifact?

NASA Technical Reports Server (NTRS)

Koren, Ilan; Feingold, Graham; Remer, Lorraine A.

2010-01-01

Associations between cloud properties and aerosol loading are frequently observed in products derived from satellite measurements. These observed trends between clouds and aerosol optical depth suggest aerosol modification of cloud dynamics, yet there are uncertainties involved in satellite retrievals that have the potential to lead to incorrect conclusions. Two of the most challenging problems are addressed here: the potential for retrieved aerosol optical depth to be cloud-contaminated, and as a result, artificially correlated with cloud parameters; and the potential for correlations between aerosol and cloud parameters to be erroneously considered to be causal. Here these issues are tackled directly by studying the effects of the aerosol on convective clouds in the tropical Atlantic Ocean using satellite remote sensing, a chemical transport model, and a reanalysis of meteorological fields. Results show that there is a robust positive correlation between cloud fraction or cloud top height and the aerosol optical depth, regardless of whether a stringent filtering of aerosol measurements in the vicinity of clouds is applied, or not. These same positive correlations emerge when replacing the observed aerosol field with that derived from a chemical transport model. Model-reanalysis data is used to address the causality question by providing meteorological context for the satellite observations. A correlation exercise between the full suite of meteorological fields derived from model reanalysis and satellite-derived cloud fields shows that observed cloud top height and cloud fraction correlate best with model pressure updraft velocity and relative humidity. Observed aerosol optical depth does correlate with meteorological parameters but usually different parameters from those that correlate with observed cloud fields. The result is a near-orthogonal influence of aerosol and meteorological fields on cloud top height and cloud fraction. The results strengthen the case that the aerosol does play a role in invigorating convective clouds.

Optimization of Ocean Color Algorithms: Application to Satellite Data Merging

NASA Technical Reports Server (NTRS)

Ritorena, Stephane; Siegel, David A.; Morel, Andre

2004-01-01

The objective of the program is to develop and validate a procedure for ocean color data merging, which is one of the major goals of the SIMBIOS project. As part of the SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) from different sensors to generate a unified product. Instead, our procedure uses the normalized water-leaving radiances L((sub wN)(lambda)) from single or multiple sensors and uses them in the inversion of a semi-analytical ocean color model that allows the retrieval of several ocean color variables simultaneously. Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. chlorophyll): 1) It works with single or multiple data sources regardless of their specific bands; 2) It exploits band redundancies and band differences; 3) It accounts for uncertainties in the L((sub wN)(lambda)) data; 4) It provides uncertainty estimates for the retrieved variables.

Rectangular rotation of spherical harmonic expansion of arbitrary high degree and order

NASA Astrophysics Data System (ADS)

Fukushima, Toshio

2017-08-01

In order to move the polar singularity of arbitrary spherical harmonic expansion to a point on the equator, we rotate the expansion around the y-axis by 90° such that the x-axis becomes a new pole. The expansion coefficients are transformed by multiplying a special value of Wigner D-matrix and a normalization factor. The transformation matrix is unchanged whether the coefficients are 4 π fully normalized or Schmidt quasi-normalized. The matrix is recursively computed by the so-called X-number formulation (Fukushima in J Geodesy 86: 271-285, 2012a). As an example, we obtained 2190× 2190 coefficients of the rectangular rotated spherical harmonic expansion of EGM2008. A proper combination of the original and the rotated expansions will be useful in (i) integrating the polar orbits of artificial satellites precisely and (ii) synthesizing/analyzing the gravitational/geomagnetic potentials and their derivatives accurately in the high latitude regions including the arctic and antarctic area.

Differences in visible and near-IR responses, and derived vegetation indices, for the NOAA-9 and NOAA-10 AVHRRs: a case study

USGS Publications Warehouse

Gallo, Kevin P.; Eidenshink, Jeffery C.

1988-01-01

This study evaluates the differences in the visible and near-IR responses of the Advanced Very High Resolution Radiometers (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA)-9 and -10 satellites for coincident sample locations. The study also evaluates the differences in vegetation indices computed from those data. Data were acquired of the southeast portion of the United States for the 6 December 1986 daylight orbits of NOAA-9 and NOAA-10 satellites. The results suggest that, with appropriate gain and offset, the vegetation indices of the two sensor systems may be interchangeable for assessment of land surfaces.

Combining Satellite and in Situ Data with Models to Support Climate Data Records in Ocean Biology

NASA Technical Reports Server (NTRS)

Gregg, Watson

2011-01-01

The satellite ocean color data record spans multiple decades and, like most long-term satellite observations of the Earth, comes from many sensors. Unfortunately, global and regional chlorophyll estimates from the overlapping missions show substantial biases, limiting their use in combination to construct consistent data records. SeaWiFS and MODIS-Aqua differed by 13% globally in overlapping time segments, 2003-2007. For perspective, the maximum change in annual means over the entire Sea WiFS mission era was about 3%, and this included an El NinoLa Nina transition. These discrepancies lead to different estimates of trends depending upon whether one uses SeaWiFS alone for the 1998-2007 (no significant change), or whether MODIS is substituted for the 2003-2007 period (18% decline, P less than 0.05). Understanding the effects of climate change on the global oceans is difficult if different satellite data sets cannot be brought into conformity. The differences arise from two causes: 1) different sensors see chlorophyll differently, and 2) different sensors see different chlorophyll. In the first case, differences in sensor band locations, bandwidths, sensitivity, and time of observation lead to different estimates of chlorophyll even from the same location and day. In the second, differences in orbit and sensitivities to aerosols lead to sampling differences. A new approach to ocean color using in situ data from the public archives forces different satellite data to agree to within interannual variability. The global difference between Sea WiFS and MODIS is 0.6% for 2003-2007 using this approach. It also produces a trend using the combination of SeaWiFS and MODIS that agrees with SeaWiFS alone for 1998-2007. This is a major step to reducing errors produced by the first cause, sensor-related discrepancies. For differences that arise from sampling, data assimilation is applied. The underlying geographically complete fields derived from a free-running model is unaffected by solar zenith angle requirements and obscuration from clouds and aerosols. Combined with in situ dataenhanced satellite data, the model is forced into consistency using data assimilation. This approach eliminates sampling discrepancies from satellites. Combining the reduced differences of satellite data sets using in situ data, and the removal of sampling biases using data assimilation, we generate consistent data records of ocean color. These data records can support investigations of long-term effects of climate change on ocean biology over multiple satellites, and can improve the consistency of future satellite data sets.

Examining the utility of satellite-based wind sheltering estimates for lake hydrodynamic modeling

USGS Publications Warehouse

Van Den Hoek, Jamon; Read, Jordan S.; Winslow, Luke A.; Montesano, Paul; Markfort, Corey D.

2015-01-01

Satellite-based measurements of vegetation canopy structure have been in common use for the last decade but have never been used to estimate canopy's impact on wind sheltering of individual lakes. Wind sheltering is caused by slower winds in the wake of topography and shoreline obstacles (e.g. forest canopy) and influences heat loss and the flux of wind-driven mixing energy into lakes, which control lake temperatures and indirectly structure lake ecosystem processes, including carbon cycling and thermal habitat partitioning. Lakeshore wind sheltering has often been parameterized by lake surface area but such empirical relationships are only based on forested lakeshores and overlook the contributions of local land cover and terrain to wind sheltering. This study is the first to examine the utility of satellite imagery-derived broad-scale estimates of wind sheltering across a diversity of land covers. Using 30 m spatial resolution ASTER GDEM2 elevation data, the mean sheltering height, hs, being the combination of local topographic rise and canopy height above the lake surface, is calculated within 100 m-wide buffers surrounding 76,000 lakes in the U.S. state of Wisconsin. Uncertainty of GDEM2-derived hs was compared to SRTM-, high-resolution G-LiHT lidar-, and ICESat-derived estimates of hs, respective influences of land cover type and buffer width on hsare examined; and the effect of including satellite-based hs on the accuracy of a statewide lake hydrodynamic model was discussed. Though GDEM2 hs uncertainty was comparable to or better than other satellite-based measures of hs, its higher spatial resolution and broader spatial coverage allowed more lakes to be included in modeling efforts. GDEM2 was shown to offer superior utility for estimating hs compared to other satellite-derived data, but was limited by its consistent underestimation of hs, inability to detect within-buffer hs variability, and differing accuracy across land cover types. Nonetheless, considering a GDEM2 hs-derived wind sheltering potential improved the modeled lake temperature root mean square error for non-forested lakes by 0.72 °C compared to a commonly used wind sheltering model based on lake area alone. While results from this study show promise, the limitations of near-global GDEM2 data in timeliness, temporal and spatial resolution, and vertical accuracy were apparent. As hydrodynamic modeling and high-resolution topographic mapping efforts both expand, future remote sensing-derived vegetation structure data must be improved to meet wind sheltering accuracy requirements to expand our understanding of lake processes.

Evaluation Of The Potential Of Gravity Anomalies From Satellite Altimetry By Merging With Gravity Data From Various Sources - Application To Coastal Areas

NASA Astrophysics Data System (ADS)

Fernandes, M. J.; Bastos, L.; Tomé, P.

The region of the Azores archipelago is a natural laboratory for gravity field studies, due to its peculiar geodynamic and oceanographic features, related to rough structures in the gravity field. As a consequence, gravity data from various sources have been collected in the scope of various observation campaigns. The available data set comprises marine, airborne and satellite derived gravity anoma- lies. The satellite data have been derived by altimetric inversion of satellite altimeter data (Topex/Poseidon and ERS), to which processing methods tuned for optimal data recovery in coastal areas have been applied. Marine and airborne data along coinci- dent profiles, some of them coincident with satellite tracks, were collected during an observation campaign that took place in the Azores in 1997, in the scope of the Eu- ropean Union project AGMASCO. In addition, gravity anomalies from an integrated GPS/INS system installed aboard an aircraft, have also been computed from the posi- tion and navigation data collected during the AGMASCO campaign. This paper presents a comparison study between all available data sets. In particular, the improvement of the satellite derived anomalies near the shoreline is assessed with respect to existing satellite derived models and with the high resolution geopotential model GPM98. The impact of these data sets in the regional geoid improvement will also be presented.

The effect of temperature on apoptosis and adipogenesis on skeletal muscle satellite cells derived from different muscle types

PubMed Central

Harding, Rachel L; Clark, Daniel L; Halevy, Orna; Coy, Cynthia S; Yahav, Shlomo; Velleman, Sandra G

2015-01-01

Satellite cells are multipotential stem cells that mediate postnatal muscle growth and respond differently to temperature based upon aerobic versus anaerobic fiber-type origin. The objective of this study was to determine how temperatures below and above the control, 38°C, affect the fate of satellite cells isolated from the anaerobic pectoralis major (p. major) or mixed fiber biceps femoris (b. femoris). At all sampling times, p. major and b. femoris cells accumulated less lipid when incubated at low temperatures and more lipid at elevated temperatures compared to the control. Satellite cells isolated from the p. major were more sensitive to temperature as they accumulated more lipid at elevated temperatures compared to b. femoris cells. Expression of adipogenic genes, CCAAT/enhancer-binding protein β (C/EBPβ) and proliferator-activated receptor gamma (PPARγ) were different within satellite cells isolated from the p. major or b. femoris. At 72 h of proliferation, C/EBPβ expression increased with increasing temperature in both cell types, while PPARγ expression decreased with increasing temperature in p. major satellite cells. At 48 h of differentiation, both C/EBPβ and PPARγ expression increased in the p. major and decreased in the b. femoris, with increasing temperature. Flow cytometry measured apoptotic markers for early apoptosis (Annexin-V-PE) or late apoptosis (7-AAD), showing less than 1% of apoptotic satellite cells throughout all experimental conditions, therefore, apoptosis was considered biologically not significant. The results support that anaerobic p. major satellite cells are more predisposed to adipogenic conversion than aerobic b. femoris cells when thermally challenged. PMID:26341996

Post-hurricane forest damage assessment using satellite remote sensing

Treesearch

W. Wang; J.J. Qu; X. Hao; Y. Liu; J.A. Stanturf

2010-01-01

This study developed a rapid assessment algorithm for post-hurricane forest damage estimation using moderate resolution imaging spectroradiometer (MODIS) measurements. The performance of five commonly used vegetation indices as post-hurricane forest damage indicators was investigated through statistical analysis. The Normalized Difference Infrared Index (NDII) was...

Standardized principal components for vegetation variability monitoring across space and time

NASA Astrophysics Data System (ADS)

Mathew, T. R.; Vohora, V. K.

2016-08-01

Vegetation at any given location changes through time and in space. In what quantity it changes, where and when can help us in identifying sources of ecosystem stress, which is very useful for understanding changes in biodiversity and its effect on climate change. Such changes known for a region are important in prioritizing management. The present study considers the dynamics of savanna vegetation in Kruger National Park (KNP) through the use of temporal satellite remote sensing images. Spatial variability of vegetation is a key characteristic of savanna landscapes and its importance to biodiversity has been demonstrated by field-based studies. The data used for the study were sourced from the U.S. Agency for International Development where AVHRR derived Normalized Difference Vegetation Index (NDVI) images available at spatial resolutions of 8 km and at dekadal scales. The study area was extracted from these images for the time-period 1984-2002. Maximum value composites were derived for individual months resulting in an image dataset of 216 NDVI images. Vegetation dynamics across spatio-temporal domains were analyzed using standardized principal components analysis (SPCA) on the NDVI time-series. Each individual image variability in the time-series is considered. The outcome of this study demonstrated promising results - the variability of vegetation change in the area across space and time, and also indicated changes in landscape on 6 individual principal components (PCs) showing differences not only in magnitude, but also in pattern, of different selected eco-zones with constantly changing and evolving ecosystem.

Bayesian model for matching the radiometric measurements of aerospace and field ocean color sensors.

PubMed

Salama, Mhd Suhyb; Su, Zhongbo

2010-01-01

A Bayesian model is developed to match aerospace ocean color observation to field measurements and derive the spatial variability of match-up sites. The performance of the model is tested against populations of synthesized spectra and full and reduced resolutions of MERIS data. The model derived the scale difference between synthesized satellite pixel and point measurements with R(2) > 0.88 and relative error < 21% in the spectral range from 400 nm to 695 nm. The sub-pixel variabilities of reduced resolution MERIS image are derived with less than 12% of relative errors in heterogeneous region. The method is generic and applicable to different sensors.

Using satellite remote sensing to model and map the distribution of Bicknell's thrush (Catharus bicknelli) in the White Mountains of New Hampshire

NASA Astrophysics Data System (ADS)

Hale, Stephen Roy

Landsat-7 Enhanced Thematic Mapper satellite imagery was used to model Bicknell's Thrush (Catharus bicknelli) distribution in the White Mountains of New Hampshire. The proof-of-concept was established for using satellite imagery in species-habitat modeling, where for the first time imagery spectral features were used to estimate a species-habitat model variable. The model predicted rising probabilities of thrush presence with decreasing dominant vegetation height, increasing elevation, and decreasing distance to nearest Fir Sapling cover type. To solve the model at all locations required regressor estimates at every pixel, which were not available for the dominant vegetation height and elevation variables. Topographically normalized imagery features Normalized Difference Vegetation Index and Band 1 (blue) were used to estimate dominant vegetation height using multiple linear regression; and a Digital Elevation Model was used to estimate elevation. Distance to nearest Fir Sapling cover type was obtained for each pixel from a land cover map specifically constructed for this project. The Bicknell's Thrush habitat model was derived using logistic regression, which produced the probability of detecting a singing male based on the pattern of model covariates. Model validation using Bicknell's Thrush data not used in model calibration, revealed that the model accurately estimated thrush presence at probabilities ranging from 0 to <0.40 and from 0.50 to <0.60. Probabilities from 0.40 to <0.50 and greater than 0.60 significantly underestimated and overestimated presence, respectively. Applying the model to the study area illuminated an important implication for Bicknell's Thrush conservation. The model predicted increasing numbers of presences and increasing relative density with rising elevation, with which exists a concomitant decrease in land area. Greater land area of lower density habitats may account for more total individuals and reproductive output than higher density less abundant land area. Efforts to conserve areas of highest individual density under the assumption that density reflects habitat quality could target the smallest fraction of the total population.

Examining spring and autumn phenology in a temperate deciduous urban woodlot

NASA Astrophysics Data System (ADS)

Yu, Rong

This dissertation is an intensive phenological study in a temperate deciduous urban woodlot over six consecutive years (2007-2012). It explores three important topics related to spring and autumn phenology, as well as ground and remote sensing phenology. First, it examines key climatic factors influencing spring and autumn phenology by conducting phenological observations four days a week and recording daily microclimate measurements. Second, it investigates the differences in phenological responses between an urban woodlot and a rural forest by employing comparative basswood phenological data. Finally, it bridges ground visual phenology and remote sensing derived phenological changes by using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from the Moderate Resolution Imaging Spectro-radiometer (MODIS). The primary outcomes are as follows: 1) empirical spatial regression models for two dominant tree species - basswood and white ash - have been built and analyzed to detect spatial patterns and possible causes of phenological change; the results show that local urban settings significantly affect phenology; 2) empirical phenological progression models have been built for each species and the community as a whole to examine how phenology develops in spring and autumn; the results indicate that the critical factor influencing spring phenology is AGDD (accumulated growing degree-days) and for autumn phenology, ACDD (accumulated chilling degree-days) and day length; and 3) satellite derived phenological changes have been compared with ground visual community phenology in both spring and autumn seasons, and the results confirm that both NDVI and EVI depict vegetation dynamics well and therefore have corresponding phenological meanings.

The Global Precipitation Climatology Project: First Algorithm Intercomparison Project

NASA Technical Reports Server (NTRS)

Arkin, Phillip A.; Xie, Pingping

1994-01-01

The Global Precipitation Climatology Project (GPCP) was established by the World Climate Research Program to produce global analyses of the area- and time-averaged precipitation for use in climate research. To achieve the required spatial coverage, the GPCP uses simple rainfall estimates derived from IR and microwave satellite observations. In this paper, we describe the GPCP and its first Algorithm Intercomparison Project (AIP/1), which compared a variety of rainfall estimates derived from Geostationary Meteorological Satellite visible and IR observations and Special Sensor Microwave/Imager (SSM/I) microwave observations with rainfall derived from a combination of radar and raingage data over the Japanese islands and the adjacent ocean regions during the June and mid-July through mid-August periods of 1989. To investigate potential improvements in the use of satellite IR data for the estimation of large-scale rainfall for the GPCP, the relationship between rainfall and the fractional coverage of cold clouds in the AIP/1 dataset is examined. Linear regressions between fractional coverage and rainfall are analyzed for a number of latitude-longitude areas and for a range of averaging times. The results show distinct differences in the character of the relationship for different portions of the area. These results suggest that the simple IR-based estimation technique currently used in the GPCP can be used to estimate rainfall for global tropical and subtropical areas, provided that a method for adjusting the proportional coefficient for varying areas and seasons can be determined.

A new method to compute lunisolar perturbations in satellite motions

NASA Technical Reports Server (NTRS)

Kozai, Y.

1973-01-01

A new method to compute lunisolar perturbations in satellite motion is proposed. The disturbing function is expressed by the orbital elements of the satellite and the geocentric polar coordinates of the moon and the sun. The secular and long periodic perturbations are derived by numerical integrations, and the short periodic perturbations are derived analytically. The perturbations due to the tides can be included in the same way. In the Appendix, the motion of the orbital plane for a synchronous satellite is discussed; it is concluded that the inclination cannot stay below 7 deg.

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.

Application of a Topological Metric for Assessing Numerical Ocean Models with Satellite Observations

NASA Astrophysics Data System (ADS)

Morey, S. L.; Dukhovskoy, D. S.; Hiester, H. R.; Garcia-Pineda, O. G.; MacDonald, I. R.

2015-12-01

Satellite-based sensors provide a vast amount of observational data over the world ocean. Active microwave radars measure changes in sea surface height and backscattering from surface waves. Data from passive radiometers sensing emissions in multiple spectral bands can directly measure surface temperature, be combined with other data sources to estimate salinity, or processed to derive estimates of optically significant quantities, such as concentrations of biochemical properties. Estimates of the hydrographic variables can be readily used for assimilation or assessment of hydrodynamic ocean models. Optical data, however, have been underutilized in ocean circulation modeling. Qualitative assessments of oceanic fronts and other features commonly associated with changes in optically significant quantities are often made through visual comparison. This project applies a topological approach, borrowed from the field of computer image recognition, to quantitatively evaluate ocean model simulations of features that are related to quantities inferred from satellite imagery. The Modified Hausdorff Distance (MHD) provides a measure of the similarity of two shapes. Examples of applications of the MHD to assess ocean circulation models are presented. The first application assesses several models' representation of the freshwater plume structure from the Mississippi River, which is associated with a significant expression of color, using a satellite-derived ocean color index. Even though the variables being compared (salinity and ocean color index) differ, the MHD allows contours of the fields to be compared topologically. The second application assesses simulations of surface oil transport driven by winds and ocean model currents using surface oil maps derived from synthetic aperture radar backscatter data. In this case, maps of time composited oil coverage are compared between the simulations and satellite observations.

A semi-empirical model for estimating surface solar radiation from satellite data

NASA Astrophysics Data System (ADS)

Janjai, Serm; Pattarapanitchai, Somjet; Wattan, Rungrat; Masiri, Itsara; Buntoung, Sumaman; Promsen, Worrapass; Tohsing, Korntip

2013-05-01

This paper presents a semi-empirical model for estimating surface solar radiation from satellite data for a tropical environment. The model expresses solar irradiance as a semi-empirical function of cloud index, aerosol optical depth, precipitable water, total column ozone and air mass. The cloud index data were derived from MTSAT-1R satellite, whereas the aerosol optical depth data were obtained from MODIS/Terra satellite. The total column ozone data were derived from OMI/AURA satellite and the precipitable water data were obtained from NCEP/NCAR. A five year period (2006-2010) of these data and global solar irradiance measured at four sites in Thailand namely, Chiang Mai (18.78 °N, 98.98 °E), Nakhon Pathom (13.82 °N, 100.04 °E), Ubon Ratchathani (15.25 °N, 104.87 °E) and Songkhla (7.20 °N, 100.60 °E), were used to derive the coefficients of the model. To evaluate its performance, the model was used to calculate solar radiation at four sites in Thailand namely, Phisanulok (16.93 °N, 100.24 °E), Kanchanaburi (14.02 °N, 99.54 °E), Nongkhai (17.87 °N, 102.72 °E) and Surat Thani (9.13 °N, 99.15 °E) and the results were compared with solar radiation measured at these sites. It was found that the root mean square difference (RMSD) between measured and calculated values of hourly solar radiation was in the range of 25.5-29.4%. The RMSD is reduced to 10.9-17.0% for the case of monthly average hourly radiation. The proposed model has the advantage in terms of the simplicity for applications and reasonable accuracy of the results.

Drought impacts on photosynthesis, isoprene emission and atmospheric formaldehyde in a mid-latitude forest

NASA Astrophysics Data System (ADS)

Zheng, Yiqi; Unger, Nadine; Tadić, Jovan M.; Seco, Roger; Guenther, Alex B.; Barkley, Michael P.; Potosnak, Mark J.; Murray, Lee T.; Michalak, Anna M.; Qiu, Xuemei; Kim, Saewung; Karl, Thomas; Gu, Lianhong; Pallardy, Stephen G.

2017-10-01

Isoprene plays a critical role in air quality and climate. Photosynthesis (gross primary productivity, GPP) and formaldehyde (HCHO) are both related to isoprene emission at large spatiotemporal scales, but neither is a perfect proxy. We apply multiple satellite products and site-level measurements to examine the impact of water deficit on the three interlinked variables at the Missouri Ozarks site during a 20-day mild dryness stress in summer 2011 and a 3-month severe drought in summer 2012. Isoprene emission shows opposite responses to the short- and long-term droughts, while GPP was substantially reduced in both cases. In 2012, both remote-sensed solar-induced fluorescence (SIF) and satellite HCHO column qualitatively capture reductions in flux-derived GPP and isoprene emission, respectively, on weekly to monthly time scales, but with muted responses. For instance, as flux-derived GPP approaches zero in late summer 2012, SIF drops by 29-33% (July) and 19-27% (August) relative to year 2011. A possible explanation is that electron transport and photosystem activity are maintained to a certain extent under the drought stress. Similarly, flux tower isoprene emissions in July 2012 are 54% lower than July 2011, while the relative reductions in July for 3 independent satellite-derived HCHO data products are 27%, 12% and 6%, respectively. We attribute the muted HCHO response to a photochemical feedback whereby reduced isoprene emission increases the oxidation capacity available to generate HCHO from other volatile organic compound sources. Satellite SIF offers a potential alternative indirect method to monitor isoprene variability at large spatiotemporal scales from space, although further research is needed under different environmental conditions and regions. Our analysis indicates that fairly moderate reductions in satellite SIF and HCHO column may imply severe drought conditions at the surface.

Performance of DS-CDMA with imperfect power control operating over a low earth orbiting satellite link

NASA Astrophysics Data System (ADS)

Vojcic, Branimir R.; Pickholtz, Raymond L.; Milstein, Laurence B.

1994-05-01

The analysis of both performance and capacity of direct sequence CDMA in terrestrial cellular systems has been addressed in the technical literature. It has been suggested that CDMA be used as a multiple access method for satellite systems as well, in particular for multispot beam Low Earth Orbit Satellites (LEOS). One is tempted to argue that since CDMA works well on terrestrial links, it will nominally work as well on satellite links. However, because there are fundamental differences in the characteristics of the two channels, such as larger time delays from the mobile to the base station and smaller multipath delay spreads on the satellite channels, the performance of CDMA on satellite links cannot always be accurately predicted from its performance on terrestrial channels. In this paper, we analytically derive the performance of a CDMA system which operates over a low earth orbiting satellite channel. We incorporate such effects as imperfect power control and dual-order diversity to obtain the average probability of error of a single user.

Stem Cells for Skeletal Muscle Tissue Engineering.

PubMed

Pantelic, Molly N; Larkin, Lisa M

2018-04-19

Volumetric muscle loss (VML) is a debilitating condition wherein muscle loss overwhelms the body's normal physiological repair mechanism. VML is particularly common among military service members who have sustained war injuries. Because of the high social and medical cost associated with VML and suboptimal current surgical treatments, there is great interest in developing better VML therapies. Skeletal muscle tissue engineering (SMTE) is a promising alternative to traditional VML surgical treatments that use autogenic tissue grafts, and rather uses isolated stem cells with myogenic potential to generate de novo skeletal muscle tissues to treat VML. Satellite cells are the native precursors to skeletal muscle tissue, and are thus the most commonly studied starting source for SMTE. However, satellite cells are difficult to isolate and purify, and it is presently unknown whether they would be a practical source in clinical SMTE applications. Alternative myogenic stem cells, including adipose-derived stem cells, bone marrow-derived mesenchymal stem cells, perivascular stem cells, umbilical cord mesenchymal stem cells, induced pluripotent stem cells, and embryonic stem cells, each have myogenic potential and have been identified as possible starting sources for SMTE, although they have yet to be studied in detail for this purpose. These alternative stem cell varieties offer unique advantages and disadvantages that are worth exploring further to advance the SMTE field toward highly functional, safe, and practical VML treatments. The following review summarizes the current state of satellite cell-based SMTE, details the properties and practical advantages of alternative myogenic stem cells, and offers guidance to tissue engineers on how alternative myogenic stem cells can be incorporated into SMTE research.

Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling.

PubMed

Soni, Kirti; Parmar, Kulwinder Singh; Kapoor, Sangeeta; Kumar, Nishant

2016-05-15

A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend. Copyright © 2016 Elsevier B.V. All rights reserved.

Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter

NASA Technical Reports Server (NTRS)

Hilker, Thomas; Lyapustin, Alexei I.; Tucker, Compton J.; Sellers, Piers J.; Hall, Forrest G.; Wang, Yujie

2012-01-01

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.

Global long-term ozone trends derived from different observed and modelled data sets

NASA Astrophysics Data System (ADS)

Coldewey-Egbers, M.; Loyola, D.; Zimmer, W.; van Roozendael, M.; Lerot, C.; Dameris, M.; Garny, H.; Braesicke, P.; Koukouli, M.; Balis, D.

2012-04-01

The long-term behaviour of stratospheric ozone amounts during the past three decades is investigated on a global scale using different observed and modelled data sets. Three European satellite sensors GOME/ERS-2, SCIAMACHY/ENVISAT, and GOME-2/METOP are combined and a merged global monthly mean total ozone product has been prepared using an inter-satellite calibration approach. The data set covers the 16-years period from June 1995 to June 2011 and it exhibits an excellent long-term stability, which is required for such trend studies. A multiple linear least-squares regression algorithm using different explanatory variables is applied to the time series and statistically significant positive trends are detected in the northern mid latitudes and subtropics. Global trends are also estimated using a second satellite-based Merged Ozone Data set (MOD) provided by NASA. For few selected geographical regions ozone trends are additionally calculated using well-maintained measurements of individual Dobson/Brewer ground-based instruments. A reasonable agreement in the spatial patterns of the trends is found amongst the European satellite, the NASA satellite, and the ground-based observations. Furthermore, two long-term simulations obtained with the Chemistry-Climate Models E39C-A provided by German Aerospace Center and UMUKCA-UCAM provided by University of Cambridge are analysed.

Chlorophyll-a retrieval in the Philippine waters

NASA Astrophysics Data System (ADS)

Perez, G. J. P.; Leonardo, E. M.; Felix, M. J.

2017-12-01

Satellite-based monitoring of chlorophyll-a (Chl-a) concentration has been widely used for estimating plankton biomass, detecting harmful algal blooms, predicting pelagic fish abundance, and water quality assessment. Chl-a concentrations at 1 km spatial resolution can be retrieved from MODIS onboard Aqua and Terra satellites. However, with this resolution, MODIS has scarce Chl-a retrieval in coastal and inland waters, which are relevant for archipelagic countries such as the Philippines. These gaps on Chl-a retrieval can be filled by sensors with higher spatial resolution, such as the OLI of Landsat 8. In this study, assessment of Chl-a concentration derived from MODIS/Aqua and OLI/Landsat 8 imageries across the open, coastal and inland waters of the Philippines was done. Validation activities were conducted at eight different sites around the Philippines for the period October 2016 to April 2017. Water samples filtered on the field were processed in the laboratory for Chl-a extraction. In situ remote sensing reflectance was derived from radiometric measurements and ancillary information, such as bathymetry and turbidity, were also measured. Correlation between in situ and satellite-derived Chl-a concentration using the blue-green ratio yielded relatively high R2 values of 0.51 to 0.90. This is despite an observed overestimation for both MODIS and OLI-derived values, especially in turbid and coastal waters. The overestimation of Chl-a may be attributed to inaccuracies in i) remote sensing reflectance (Rrs) retrieval and/or ii) empirical model used in calculating Chl-a concentration. However, a good 1:1 correspondence between the satellite and in situ maximum Rrs band ratio was established. This implies that the overestimation is largely due to the inaccuracies from the default coefficients used in the empirical model. New coefficients were then derived from the correlation analysis of both in situ-measured Chl-a concentration and maximum Rrs band ratio. This results to a significant improvement on calculated RMSE of satellite-derived Chl-a values. Meanwhile, it was observed that the blue-green band ratio has low Chl-a predictive capability in turbid waters. A more accurate estimation was found using the NIR and red band ratios for turbid waters with covarying Chl-a concentration and low sediment load.

"Using Satellite Remote Sensing to Derive Numeric Criteria in Coastal and Inland Waters of the United States"

NASA Astrophysics Data System (ADS)

Crawford, T. N.; Schaeffer, B. A.

2016-12-01

Anthropogenic nutrient pollution is a major stressor of aquatic ecosystems around the world. In the United States, states and tribes can adopt numeric water quality values (i.e. criteria) into their water quality management standards to protect aquatic life from eutrophication impacts. However, budget and resource constraints have limited the ability of many states and tribes to collect the water quality monitoring data needed to derive numeric criteria. Over the last few decades, satellite technology has provided water quality measurements on a global scale over long time periods. Water quality managers are finding the data provided by satellite technology useful in managing eutrophication impacts in coastal waters, estuaries, lakes, and reservoirs. In recent years EPA has worked with states and tribes to derive remotely sensed numeric Chl-a criteria for coastal waters with limited field-based data. This approach is now being expanded and used to derive Chl-a criteria in freshwater systems across the United States. This presentation will cover EPA's approach to derive numeric Chl-a criteria using satellite remote sensing, recommendations to improve satellite sensors to expand applications, potential areas of interest, and the challenges of using remote sensing to establish water quality management goals, as well as provide a case in which this approach has been applied.

Statistics of velocity gradients in two-dimensional Navier-Stokes and ocean turbulence.

PubMed

Schorghofer, Norbert; Gille, Sarah T

2002-02-01

Probability density functions and conditional averages of velocity gradients derived from upper ocean observations are compared with results from forced simulations of the two-dimensional Navier-Stokes equations. Ocean data are derived from TOPEX satellite altimeter measurements. The simulations use rapid forcing on large scales, characteristic of surface winds. The probability distributions of transverse velocity derivatives from the ocean observations agree with the forced simulations, although they differ from unforced simulations reported elsewhere. The distribution and cross correlation of velocity derivatives provide clear evidence that large coherent eddies play only a minor role in generating the observed statistics.

Extravehicular Crewman Work System (ECWS) study program. Volume 3: Satellite service

NASA Technical Reports Server (NTRS)

Wilde, R. C.

1980-01-01

The satellite service portion of the Extravehicular Crewman Work System Study defines requirements and service equipment concepts for performing satellite service from the space shuttle orbiter. Both normal and contingency orbital satellite service is required. Service oriented satellite design practices are required to provide on orbit satellite service capability for the wide variety of satellites at the subsystem level. Development of additional satellite service equipment is required. The existing space transportation system provides a limited capability for performing satellite service tasks in the shuttle payload bay area.

The biomass burning contribution to climate-carbon-cycle feedback

NASA Astrophysics Data System (ADS)

Harrison, Sandy P.; Bartlein, Patrick J.; Brovkin, Victor; Houweling, Sander; Kloster, Silvia; Prentice, I. Colin

2018-05-01

Temperature exerts strong controls on the incidence and severity of fire. All else equal, warming is expected to increase fire-related carbon emissions, and thereby atmospheric CO2. But the magnitude of this feedback is very poorly known. We use a single-box model of the land biosphere to quantify this positive feedback from satellite-based estimates of biomass burning emissions for 2000-2014 CE and from sedimentary charcoal records for the millennium before the industrial period. We derive an estimate of the centennial-scale feedback strength of 6.5 ± 3.4 ppm CO2 per degree of land temperature increase, based on the satellite data. However, this estimate is poorly constrained, and is largely driven by the well-documented dependence of tropical deforestation and peat fires (primarily anthropogenic) on climate variability patterns linked to the El Niño-Southern Oscillation. Palaeo-data from pre-industrial times provide the opportunity to assess the fire-related climate-carbon-cycle feedback over a longer period, with less pervasive human impacts. Past biomass burning can be quantified based on variations in either the concentration and isotopic composition of methane in ice cores (with assumptions about the isotopic signatures of different methane sources) or the abundances of charcoal preserved in sediments, which reflect landscape-scale changes in burnt biomass. These two data sources are shown here to be coherent with one another. The more numerous data from sedimentary charcoal, expressed as normalized anomalies (fractional deviations from the long-term mean), are then used - together with an estimate of mean biomass burning derived from methane isotope data - to infer a feedback strength of 5.6 ± 3.2 ppm CO2 per degree of land temperature and (for a climate sensitivity of 2.8 K) a gain of 0.09 ± 0.05. This finding indicates that the positive carbon cycle feedback from increased fire provides a substantial contribution to the overall climate-carbon-cycle feedback on centennial timescales. Although the feedback estimates from palaeo- and satellite-era data are in agreement, this is likely fortuitous because of the pervasive influence of human activities on fire regimes during recent decades.

Asian dust aerosol: Optical effect on satellite ocean color signal and a scheme of its correction

NASA Astrophysics Data System (ADS)

Fukushima, H.; Toratani, M.

1997-07-01

The paper first exhibits the influence of the Asian dust aerosol (KOSA) on a coastal zone color scanner (CZCS) image which records erroneously low or negative satellite-derived water-leaving radiance especially in a shorter wavelength region. This suggests the presence of spectrally dependent absorption which was disregarded in the past atmospheric correction algorithms. On the basis of the analysis of the scene, a semiempirical optical model of the Asian dust aerosol that relates aerosol single scattering albedo (ωA) to the spectral ratio of aerosol optical thickness between 550 nm and 670 nm is developed. Then, as a modification to a standard CZCS atmospheric correction algorithm (NASA standard algorithm), a scheme which estimates pixel-wise aerosol optical thickness, and in turn ωA, is proposed. The assumption of constant normalized water-leaving radiance at 550 nm is adopted together with a model of aerosol scattering phase function. The scheme is combined to the standard algorithm, performing atmospheric correction just the same as the standard version with a fixed Angstrom coefficient except in the case where the presence of Asian dust aerosol is detected by the lowered satellite-derived Angstrom exponent. Some of the model parameter values are determined so that the scheme does not produce any spatial discontinuity with the standard scheme. The algorithm was tested against the Japanese Asian dust CZCS scene with parameter values of the spectral dependency of ωA, first statistically determined and second optimized for selected pixels. Analysis suggests that the parameter values depend on the assumed Angstrom coefficient for standard algorithm, at the same time defining the spatial extent of the area to apply the Asian dust scheme. The algorithm was also tested for a Saharan dust scene, showing the relevance of the scheme but with different parameter setting. Finally, the algorithm was applied to a data set of 25 CZCS scenes to produce a monthly composite of pigment concentration for April 1981. Through these analyses, the modified algorithm is considered robust in the sense that it operates most compatibly with the standard algorithm yet performs adaptively in response to the magnitude of the dust effect.

Phenology of Succession: Tracking the Recovery of Dryland Forests after Wildfire Events

NASA Astrophysics Data System (ADS)

Walker, J.; Brown, J. F.; Sankey, J. B.; Wallace, C.; Weltzin, J. F.

2016-12-01

The frequency, size, and intensity of forest wildfires in the U.S. Southwest have increased over the past 30 years. In the coming decades, burn effects and altered climatic conditions may increasingly divert vegetation recovery trajectories from pre-disturbance forested ecosystems toward grassland or shrub woodlands. Dryland herbaceous and woody vegetation species exhibit different phenological responses to precipitation, resulting in temporal and spatial shifts in landscape phenology patterns as the proportions of plant functional groups change over time. We have developed time series of Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) greenness measures derived from satellite imagery from 1984 - 2015 to record the phenological signatures that characterize recovery trajectories towards predominantly grassland, shrubland, or forest land cover types. We leveraged the data and computational resources available through the Google Earth Engine cloud-based platform to analyze time series of Landsat Thematic Mapper and Enhanced Thematic Mapper Plus imagery collected over maturing (40 years or more post-fire) dryland forests in Arizona and New Mexico, USA. These time series provided the basis for long-term comparisons of phenology behavior in different successional trajectories and enabled the assessment of climatic influence on the eventual outcomes.

Simultaneous determination of aerosol optical thickness and water-leaving radiance from multispectral measurements in coastal waters

NASA Astrophysics Data System (ADS)

Shi, Chong; Nakajima, Teruyuki

2018-03-01

Retrieval of aerosol optical properties and water-leaving radiance over ocean is challenging since the latter mostly accounts for ˜ 10 % of the satellite-observed signal and can be easily influenced by the atmospheric scattering. Such an effort would be more difficult in turbid coastal waters due to the existence of optically complex oceanic substances or high aerosol loading. In an effort to solve such problems, we present an optimization approach for the simultaneous determination of aerosol optical thickness (AOT) and normalized water-leaving radiance (nLw) from multispectral satellite measurements. In this algorithm, a coupled atmosphere-ocean radiative transfer model combined with a comprehensive bio-optical oceanic module is used to jointly simulate the satellite-observed reflectance at the top of atmosphere and water-leaving radiance just above the ocean surface. Then, an optimal estimation method is adopted to retrieve AOT and nLw iteratively. The algorithm is validated using Aerosol Robotic Network - Ocean Color (AERONET-OC) products selected from eight OC sites distributed over different waters, consisting of observations that covered glint and non-glint conditions from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Results show a good consistency between retrieved and in situ measurements at each site. It is demonstrated that more accurate AOTs are determined based on the simultaneous retrieval method, particularly in shorter wavelengths and sunglint conditions, where the averaged percentage difference (APD) of retrieved AOT is generally reduced by approximate 10 % in visible bands compared with those derived from the standard atmospheric correction (AC) scheme, since all the spectral measurements can be used jointly to increase the information content in the inversion of AOT, and the wind speed is also simultaneously retrieved to compensate the specular reflectance error estimated from the rough ocean surface model. For the retrieval of nLw, atmospheric overcorrection can be avoided in order to have a significant improvement of the inversion of nLw at 412 nm. Furthermore, generally better estimates of band ratios of nLw(443) / nLw(554) and nLw(488) / nLw(554) are obtained using the simultaneous retrieval approach with lower root mean square errors and relative differences than those derived from the standard AC approach in comparison to the AERONET-OC products, as well as the APD values of retrieved Chl which decreased by about 5 %. On the other hand, the standard AC scheme yields a more accurate retrieval of nLw at 488 nm, prompting a further optimization of the oceanic bio-optical module of the current model.

EIGEN-5C - the new GeoForschungsZentrum Potsdam / Groupe de Recherche de Geodesie Spatiale combined gravity field model

NASA Astrophysics Data System (ADS)

Foerste, C.; Flechtner, F.; Stubenvoll, R.; Rothacher, M.; Kusche, J.; Neumayer, H. K.; Biancale, R.; Lemoine, J.; Barthelmes, F.; Bruinsma, S.; Koenig, R.; Dahle, C.

2008-12-01

Global gravity field models play a fundamental role in geodesy and Earth sciences, ranging from practical purposes, like precise orbit determination, to applications in geosciences, like investigations of the density structure of the Earth's interior. In this presentation we report on the latest, recently released EIGEN-model, EIGEN-5C (EIGEN = European Improved Gravity model of the Earth by New techniques) and its associated satellite-only model EIGEN-5S. The global gravity field model EIGEN-5C is complete to degree and order 360 (corresponding to half-wavelength of 55 km) and was jointly elaborated by GFZ Potsdam and CNES/GRGS Toulouse. As its precursor EIGEN-GL04C (released in March 2006), this model is inferred from a combination of GRACE and LAGEOS satellite tracking data with surface gravity data, based on the accumulation of normal equations. However, this new model presents remarkable changes and improvements compared to its precursors. EIGEN-5C incorporates a further extended GRACE and LAGEOS data set, covering almost the entire GRACE period from mid 2002 to end of 2007, but also newly available gravity anomaly data sets for Europe and Australia. New processing features are the complete reprocessing of the GRACE and LAGEOS data using the recent RL04 standards and background models by GFZ (combined with the GRACE/LAGEOS 10-days time series derived at GRGS based on nearly identical standards and background models) and a further extension of the full normal equations (in contrast to block diagonal form) derived from terrestrial data to a maximum degree and order of 280 (which was restricted to 179 for EIGEN-GL04C). In particular, this presentation focuses on the inter-comparison of this latest EIGEN model with the recently presented EGM08 model, which was developed by the National Geospatial-Intelligence Agency (NGA) of the USA. The EIGEN-5C model and its associated satellite-only model EIGEN-5S are available for download at the ICGEM data base (International Center for Global Earth Models) at GFZ Potsdam via the following URL: http://icgem.gfz-potsdam.de/ICGEM/ potsdam.de/ICGEM/

Corn response to climate stress detected with satellite-based NDVI time series

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

Wang, Ruoyu; Cherkauer, Keith; Bowling, Laura

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmentalmore » factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

Corn response to climate stress detected with satellite-based NDVI time series

DOE PAGES

Wang, Ruoyu; Cherkauer, Keith; Bowling, Laura

2016-03-23

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmentalmore » factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

Environmental Factors Correlated with Culturable Enterococci Concentrations in Tropical Recreational Waters: A Case Study in Escambron Beach, San Juan, Puerto Rico.

PubMed

Laureano-Rosario, Abdiel E; Symonds, Erin M; Rueda-Roa, Digna; Otis, Daniel; Muller-Karger, Frank E

2017-12-19

Enterococci concentration variability at Escambron Beach, San Juan, Puerto Rico, was examined in the context of environmental conditions observed during 2005-2015. Satellite-derived sea surface temperature (SST), turbidity, direct normal irradiance, and dew point were combined with local precipitation, winds, and mean sea level (MSL) observations in a stepwise multiple regression analyses (Akaike Information Criteria model selection). Precipitation, MSL, irradiance, SST, and turbidity explained 20% of the variation in observed enterococci concentrations based upon these analyses. Changes in these parameters preceded increases in enterococci concentrations by 24 h up to 11 days, particularly during positive anomalies of turbidity, SST, and 480-960 mm of accumulated (4 days) precipitation, which relates to bacterial ecology. Weaker, yet still significant, increases in enterococci concentrations were also observed during positive dew point anomalies. Enterococci concentrations decreased with elevated irradiance and MSL anomalies. Unsafe enterococci concentrations per US EPA recreational water quality guidelines occurred when 4-day cumulative precipitation ranged 481-960 mm; irradiance < 667 W·m -2 ; daily average turbidity anomaly >0.005 sr -1 ; SST anomaly >0.8 °C; and 3-day average MSL anomaly <-18.8 cm. This case study shows that satellite-derived environmental data can be used to inform future water quality studies and protect human health.

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.

Side-effects of a bad attitude: How GNSS spacecraft orientation errors affect solar radiation pressure modelling

NASA Astrophysics Data System (ADS)

Dilssner, Florian; Springer, Tim; Schönemann, Erik; Zandbergen, Rene; Enderle, Werner

2015-04-01

Solar radiation pressure (SRP) is the largest non-gravitational perturbation for Global Navigation Satellite System (GNSS) satellites, and can therefore have substantial impact on their orbital dynamics. Various SRP force models have been developed over the past 30 years for the purpose of precise orbit determination. They all rely upon the assumption that the satellites continuously maintain a Sun-Nadir pointing attitude with the navigation antenna boresight (body-fixed z-axis) pointing towards Earth center, and the solar panel rotation axis (body-fixed y-axis) being normal to the Sun direction. However, in reality, this is not perfectly the case. Reasons for a non-nominal spacecraft attitude may be eclipse maneuvers, commanded attitude biases and Sun/horizon sensor measurement errors, for example due to mounting misalignment or incorrectly calibrated sensor electronics. In this work the effect of GNSS spacecraft orientation errors on SRP modelling is investigated. Simplified mathematical functions describing the SRP force acting on the solar arrays in the presence of yaw-, pitch- and roll-biases are derived. Special attention is paid to the yaw-bias and its relationship to the SRP dynamics, particular in direction of the spacecraft y-axis ("y-bias force"). Analytical and experimental results gathered from orbit and attitude analyses of GPS Block II/IIA/IIF satellites demonstrate how sensitive the SRP coefficients are to changes in yaw.

Normalization and calibration of geostationary satellite radiances for the International Satellite Cloud Climatology Project

NASA Technical Reports Server (NTRS)

Desormeaux, Yves; Rossow, William B.; Brest, Christopher L.; Campbell, G. G.

1993-01-01

Procedures are described for normalizing the radiometric calibration of image radiances obtained from geostationary weather satellites that contributed data to the International Satellite Cloud Climatology Project. The key step is comparison of coincident and collocated measurements made by each satellite and the concurrent AVHRR on the 'afternoon' NOAA polar-orbiting weather satellite at the same viewing geometry. The results of this comparison allow transfer of the AVHRR absolute calibration, which has been established over the whole series, to the radiometers on the geostationary satellites. Results are given for Meteosat-2, 3, and 4, for GOES-5, 6, and 7, for GMS-2, 3, and 4 and for Insat-1B. The relative stability of the calibrations of these radiance data is estimated to be within +/- 3 percent; the uncertainty of the absolute calibrations is estimated to be less than 10 percent. The remaining uncertainties are at least two times smaller than for the original radiance data.

Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

NASA Astrophysics Data System (ADS)

Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye

2016-10-01

Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30 years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998-2015), PERSIANN-CDR (1983-2015) and CHIRPS 2.0 (1981-2015) over Chile to assess their performance across the country and evaluate their applicability for agricultural drought evaluation when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in-situ rainfall measurements across Chile were initially compared to the satellite-based precipitation estimates. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite-based estimates. Nine statistics were used to evaluate the performance of satellite products to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze these datasets to better understand their similarities and differences in characterizing rainfall patterns across Chile. Monthly analysis showed that all satellite products highly overestimated precipitation in the arid North zone. However, there were no major difference between all three products from North to South-Central zones. Though, in the South zone, PERSIANN-CDR shows the lowest fit with high underestimation, further CHIRPS 2.0 and TMPA 3B43 v7 had better agreement with in-situ measurements. The accuracy of satellite products were highly dependent on the amount of monthly rainfall with the best results found during winter seasons and in zones (Central to South) with higher amounts of precipitation. PERSIANN-CDR and CHIRPS 2.0 were used to derive SPI at time-scale of 1, 3 and 6 months, both satellite products presented similar results when it was compared in-situ against satellite SPI's. Because of its higher spatial resolution that allows better characterizing of spatial variation in precipitation pattern, the CHIRPS 2.0 was used to mapping the SPI-3 over Chile. The results of this study show that in order to use the CHIRPS 2.0 and PERSIANN-CDR data sets in Chile to monitor spatial patterns in the rainfall and drought intensity conditions, these products should be calibrated to adjust for the overestimation/underestimation of precipitation geographically specially in the North zone and seasonally during the summer and spring months in the other zones.

Globally Increased Crop Growth and Cropping Intensity from the Long-Term Satellite-Based Observations

NASA Astrophysics Data System (ADS)

Chen, Bin

2018-04-01

Understanding the spatiotemporal change trend of global crop growth and multiple cropping system under climate change scenarios is a critical requirement for supporting the food security issue that maintains the function of human society. Many studies have predicted the effects of climate changes on crop production using a combination of filed studies and models, but there has been limited evidence relating decadal-scale climate change to global crop growth and the spatiotemporal distribution of multiple cropping system. Using long-term satellite-derived Normalized Difference Vegetation Index (NDVI) and observed climate data from 1982 to 2012, we investigated the crop growth trend, spatiotemporal pattern trend of agricultural cropping intensity, and their potential correlations with respect to the climate change drivers at a global scale. Results show that 82.97 % of global cropland maximum NDVI witnesses an increased trend while 17.03 % of that shows a decreased trend over the past three decades. The spatial distribution of multiple cropping system is observed to expand from lower latitude to higher latitude, and the increased cropping intensity is also witnessed globally. In terms of regional major crop zones, results show that all nine selected zones have an obvious upward trend of crop maximum NDVI (p < 0.001), and as for climatic drivers, the gradual temperature and precipitation changes have had a measurable impact on the crop growth trend.

Increased wetness confounds Landsat-derived NDVI trends in the central Alaska North Slope region, 1985-2011

NASA Astrophysics Data System (ADS)

Raynolds, Martha K.; Walker, Donald A.

2016-08-01

Satellite data from the circumpolar Arctic have shown increases in vegetation indices correlated to warming air temperatures (e.g. Bhatt et al 2013 Remote Sensing 5 4229-54). However, more information is needed at finer scales to relate the satellite trends to vegetation changes on the ground. We examined changes using Landsat TM and ETM+ data between 1985 and 2011 in the central Alaska North Slope region, where the vegetation and landscapes are relatively well-known and mapped. We calculated trends in the normalized difference vegetation index (NDVI) and tasseled-cap transformation indices, and related them to high-resolution aerial photographs, ground studies, and vegetation maps. Significant, mostly negative, changes in NDVI occurred in 7.3% of the area, with greater change in aquatic and barren types. Large reflectance changes due to erosion, deposition and lake drainage were evident. Oil industry-related changes such as construction of artificial islands, roads, and gravel pads were also easily identified. Regional trends showed decreases in NDVI for most vegetation types, but increases in tasseled-cap greenness (56% of study area, greatest for vegetation types with high shrub cover) and tasseled-cap wetness (11% of area), consistent with documented degradation of polygon ice wedges, indicating that increasing cover of water may be masking increases in vegetation when summarized using the water-sensitive NDVI.

Exploring the Potential of PROBA-V for Evapotranspiration Monitoring in Wetlands

NASA Astrophysics Data System (ADS)

Barrios, Jose Miguel; Ghilain, Nicolas; Arboleda, Alirio; Gellens-Meulenberghs, Francoise

2016-08-01

This study aims at deriving daily evapotranspiration (ET) estimates at a convenient spatial resolution for ecosystem monitoring. The methodological approach was based on the computation of the energy balance over the study sites. The study explored the potential of integrating remote sensing (RS) products derived from the Meteosat Second Generation (MSG) satellite -in virtue of their high temporal resolution- and Proba-V data, supplying moderate spatial resolution data. This strategy was tested for the year 2014 on three wetlands sites located in Europe where eddy covariance measurements were available for validation. The modelled results correlated well with the validation data and showed the added value of combining the strengths of different satellite missions. The results open interesting perspectives for refining this approach with the upcoming Sentinel-3 datasets.

Guidance of magnetic space tug

NASA Astrophysics Data System (ADS)

Fabacher, Emilien; Lizy-Destrez, Stéphanie; Alazard, Daniel; Ankersen, Finn; Profizi, Alexandre

2017-07-01

Magnetic tugging of a target satellite without thrust capacity can be interesting in various contexts, as for example End-Of-Life management, or to complete launchers capabilities. The aim is to gradually modify the orbit of the target by constantly exerting on it a magnetic force. To do so, the chaser is assumed equipped with a steerable magnetic dipole, able to create both forces and torques on the magnetic torque rods carried by the target. The chaser is also supposed to carry electric thrusters, creating a continuous force which modifies the orbit of the whole formation composed of chaser and target. The relative motions of both satellites are derived, in order to assess the feasibility of such a concept. Relative configuration (attitudes and position) trajectories are derived, which are compliant with the dynamics, and enable the chaser to tug the target. Considering targets in Low Earth Orbit (LEO), the magnetic field of the Earth is taken into account, modeled by the International Geomagnetic Reference Field (IGRF). The position of the magnetic torque rod of the target may not be located at its center of mass. This lever-arm is taken into account in the dynamics. As for every Electro-Magnetic Formation Flight concept developed in the literature, satellites involved in magnetic tugging are constantly subjected to torques, created by the Earth magnetic field and by the magnetic fields created by the other satellites in the formation. In this study, the solution chosen to face this problem is to take into account the attitude equilibrium of the satellites early in the guidance phase, in order to avoid having to wave the dipole, as it is generally done. Promising results are presented for different types of orbit, showing that the concept could be feasible in many different scenarios.

Investigation of the Fire Radiative Energy Biomass Combustion Coefficient: A Comparison of Polar and Geostationary Satellite Retrievals Over the Conterminous United States

NASA Astrophysics Data System (ADS)

Li, Fangjun; Zhang, Xiaoyang; Kondragunta, Shobha; Roy, David P.

2018-02-01

Biomass burning substantially contributes to atmospheric aerosol and greenhouse gas emissions that influence climate and air quality. Fire radiative energy (FRE) (units: MJ) has been demonstrated to be linearly related to biomass consumption (units: kg) with potential for improving biomass burning emission estimation. The scalar constant, termed herein as the FRE biomass combustion coefficient (FBCC) (units: kg/MJ), which converts FRE to biomass consumption, has been estimated using field and laboratory experiments, varying from 0.368 to 0.453 kg/MJ. However, quite different FBCC values, especially for satellite-based approaches, have been reported. This study investigated the FBCC with respect to 445 wildfires that occurred from 2011 to 2012 across the Conterminous United States (CONUS) considering both polar-orbiting and geostationary satellite data. The FBCC was derived by comparing satellite FRE estimates with biomass consumption for the CONUS. FRE was estimated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellite (GOES); biomass consumption was estimated using Landsat-derived burned areas with fuel loadings from the Fuel Characteristic Classification System and using combustion completeness parameterized by Landsat burn severity and Fuel Characteristic Classification System fuelbed type. The reported results confirm the linearity of the empirical relationship between FRE and biomass consumption for wildfires. The CONUS FBCC was 0.374 kg/MJ for GOES FRE, 0.266 kg/MJ for MODIS FRE, and 0.320 kg/MJ considering both GOES and MODIS FRE. Limited sensitivity analyses, comparing MODIS and GOES FRE with biomass consumption estimated in three different ways, indicated that the FBCC varied from 0.301 to 0.458 kg/MJ.

Normalization of time-series satellite reflectance data to a standard sun-target-sensor geometry using a semi-empirical model

NASA Astrophysics Data System (ADS)

Zhao, Yongguang; Li, Chuanrong; Ma, Lingling; Tang, Lingli; Wang, Ning; Zhou, Chuncheng; Qian, Yonggang

2017-10-01

Time series of satellite reflectance data have been widely used to characterize environmental phenomena, describe trends in vegetation dynamics and study climate change. However, several sensors with wide spatial coverage and high observation frequency are usually designed to have large field of view (FOV), which cause variations in the sun-targetsensor geometry in time-series reflectance data. In this study, on the basis of semiempirical kernel-driven BRDF model, a new semi-empirical model was proposed to normalize the sun-target-sensor geometry of remote sensing image. To evaluate the proposed model, bidirectional reflectance under different canopy growth conditions simulated by Discrete Anisotropic Radiative Transfer (DART) model were used. The semi-empirical model was first fitted by using all simulated bidirectional reflectance. Experimental result showed a good fit between the bidirectional reflectance estimated by the proposed model and the simulated value. Then, MODIS time-series reflectance data was normalized to a common sun-target-sensor geometry by the proposed model. The experimental results showed the proposed model yielded good fits between the observed and estimated values. The noise-like fluctuations in time-series reflectance data was also reduced after the sun-target-sensor normalization process.

Earthquake damage mapping by using remotely sensed data: the Haiti case study

NASA Astrophysics Data System (ADS)

Romaniello, Vito; Piscini, Alessandro; Bignami, Christian; Anniballe, Roberta; Stramondo, Salvatore

2017-01-01

This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west-south-west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback-Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

Synergizing High-Resolution EOS Terra Satellite Data and S-POLKa Radar Reflectivity to Assess Trade Wind Cumuli Precipitation

NASA Astrophysics Data System (ADS)

Snodgrass, E. R.; di Girolamo, L.; Rauber, R.; Zhao, G.

2005-12-01

During the RICO field campaign, the EOS Terra Spacecraft and NCAR's S-POLKa radar collected coincident high-resolution visible and near-IR satellite data and dual-polarized S-band and Ka-band radar reflectivity data to understand trade wind cumuli cloud distribution and precipitation. In this paper, the comparison of the trade wind cloud field's satellite-derived cloud properties and radar-derived precipitation characteristics are presented. Specifically, these results focus on the relationship between radar reflectivity and derived rain rate to the satellite visible radiance, cloud fraction, height and thickness. Also results concerning the relationship between cloud area estimated by satellite and cloud boundary estimated by radar Bragg and Rayleigh scattering will be presented. The resolution effects between visible satellite data from the ASTER instrument at 15m ground-resolution and the S-POLKa radar data will be reviewed. The potential applications of these results to the estimation of trade wind cumuli's role in returning water to the ocean through precipitation, and to cloud and climate model parameterization will be discussed.

Monitoring tropical cyclone intensity using wind fields derived from short-interval satellite images

NASA Technical Reports Server (NTRS)

Rodgers, E. B.; Gentry, R. C.

1981-01-01

Rapid scan visible images from the Visible Infrared Spin Scan Radiometer sensor on board SMS-2 and GOES-1 were used to derive high resolution upper and lower tropospheric environmental wind fields around three western Atlantic tropical cyclones (1975-78). These wind fields were used to derive upper and lower tropospheric areal mean relative vorticity and their differences, the net relative angular momentum balance and upper tropospheric mass outflow. These kinematic parameters were shown by studies using composite rawinsonde data to be strongly related to tropical cyclone formation and intensity changes. Also, the role of forced synoptic scale subsidence in tropical cyclone formation was examined. The studies showed that satellite-derived lower and upper tropospheric wind fields can be used to monitor and possibly predict tropical cyclone formation and intensity changes. These kinematic analyses showed that future changes in tropical cyclone intensity are mainly related to the "spin-up" of the storms by the net horizontal transport of relative angular momentum caused by convergence of cyclonic vorticity in the lower troposphere and to a lesser extent the divergence of anticyclone vorticity in the upper troposphere.

Satellite-based phenology detection in broadleaf forests in South-Western Germany

NASA Astrophysics Data System (ADS)

Misra, Gourav; Buras, Allan; Menzel, Annette

2016-04-01

Many techniques exist for extracting phenological information from time series of satellite data. However, there have been only a few successful attempts to temporarily match satellite-derived observations with ground based phenological observations (Fisher et al., 2006; Hamunyela et al., 2013; Galiano et al., 2015). Such studies are primarily plagued with problems relating to shorter time series of satellite data including spatial and temporal resolution issues. A great challenge is to correlate spatially continuous and pixel-based satellite information with spatially discontinuous and point-based, mostly species-specific, ground observations of phenology. Moreover, the minute differences in phenology observed by ground volunteers might not be sufficient to produce changes in satellite-measured reflectance of vegetation, which also exposes the difference in the definitions of phenology (Badeck et al., 2004; White et al., 2014). In this study Start of Season (SOS) was determined for broadleaf forests at a site in south-western Germany using MODIS-sensor time series of Normalised Difference Vegetation Index (NDVI) data for the years covering 2001 to 2013. The NDVI time series raster data was masked for broadleaf forests using Corine Land Cover dataset, filtered and corrected for snow and cloud contaminations, smoothed with a Gaussian filter and interpolated to daily values. Several SOS techniques cited in literature, namely thresholds of amplitudes (20%, 50%, 60% and 75%), rates of change (1st, 2nd and 3rd derivative) and delayed moving average (DMA) were tested for determination of satellite SOS. The different satellite SOS were then compared with a species-rich ground based phenology information (e.g. understory leaf unfolding, broad leaf unfolding and greening of evergreen tree species). Working with all the pixels at a finer resolution, it is seen that the temporal trends in understory and broad leaf species are well captured. Initial analyses show promising results and suggest that different satellite SOS extraction techniques work well for specific phases of ground phenology information. More than half of the broadleaf pixels show an earliness in SOS which matches with the trend in ground phenology. References 1. F.-W. Badeck, A. Bondeau, K. Bottcher, D. Doktor, W. Lucht, J. Schaber, and S. Sitch, 2004, "Responses of spring phenology to climate change," New Phytologist, vol. 162, no. 2, pp. 295-309. 2. E. Hamunyela, J. Verbesselt, G. Roerink, and M. Herold, 2013, "Trends in Spring Phenology of Western European Deciduous Forests," Remote Sensing, vol. 5, no. 12, pp. 6159-6179. 3. V. F. Rodriguez-Galiano, J. Dash, and P. M. Atkinson, 2015, "Intercomparison of satellite sensor land surface phenology and ground phenology in Europe: Inter-annual comparison and modelling," Geophysical Research Letters, vol. 42, no. 7, pp. 2253-2260. 4. J. Fisher, J. Mustard, and M. Vadeboncoeur, 2006, "Green leaf phenology at Landsat resolution: Scaling from the field to the satellite," Remote Sensing of Environment, vol. 100, no. 2, pp. 265-279. 5. K. White, J. Pontius, and P. Schaberg, 2014, "Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty," Remote Sensing of Environment, vol. 148, pp. 97-107.

Measurement-based perturbation theory and differential equation parameter estimation with applications to satellite gravimetry

NASA Astrophysics Data System (ADS)

Xu, Peiliang

2018-06-01

The numerical integration method has been routinely used by major institutions worldwide, for example, NASA Goddard Space Flight Center and German Research Center for Geosciences (GFZ), to produce global gravitational models from satellite tracking measurements of CHAMP and/or GRACE types. Such Earth's gravitational products have found widest possible multidisciplinary applications in Earth Sciences. The method is essentially implemented by solving the differential equations of the partial derivatives of the orbit of a satellite with respect to the unknown harmonic coefficients under the conditions of zero initial values. From the mathematical and statistical point of view, satellite gravimetry from satellite tracking is essentially the problem of estimating unknown parameters in the Newton's nonlinear differential equations from satellite tracking measurements. We prove that zero initial values for the partial derivatives are incorrect mathematically and not permitted physically. The numerical integration method, as currently implemented and used in mathematics and statistics, chemistry and physics, and satellite gravimetry, is groundless, mathematically and physically. Given the Newton's nonlinear governing differential equations of satellite motion with unknown equation parameters and unknown initial conditions, we develop three methods to derive new local solutions around a nominal reference orbit, which are linked to measurements to estimate the unknown corrections to approximate values of the unknown parameters and the unknown initial conditions. Bearing in mind that satellite orbits can now be tracked almost continuously at unprecedented accuracy, we propose the measurement-based perturbation theory and derive global uniformly convergent solutions to the Newton's nonlinear governing differential equations of satellite motion for the next generation of global gravitational models. Since the solutions are global uniformly convergent, theoretically speaking, they are able to extract smallest possible gravitational signals from modern and future satellite tracking measurements, leading to the production of global high-precision, high-resolution gravitational models. By directly turning the nonlinear differential equations of satellite motion into the nonlinear integral equations, and recognizing the fact that satellite orbits are measured with random errors, we further reformulate the links between satellite tracking measurements and the global uniformly convergent solutions to the Newton's governing differential equations as a condition adjustment model with unknown parameters, or equivalently, the weighted least squares estimation of unknown differential equation parameters with equality constraints, for the reconstruction of global high-precision, high-resolution gravitational models from modern (and future) satellite tracking measurements.

An approach for using AVHRR data to monitor U.S. great plains grasslands

USGS Publications Warehouse

Reed, B.C.; Loveland, Thomas R.; Tieszen, L.L.

1996-01-01

Environmental monitoring requires regular observations regarding the status of the landscape- The concept behind most monitoring efforts using satellite data involve deriving normalized difference vegetation index (NDVI) values or accumulating the NDVI over a specified time period. These efforts attempt to estimate the continuous growth of green biomass by using continuous additions of NDVI as a surrogate measure. To build upon this concept, this study proposes three refinements; 1) use an objective definition of the current growing season to adjust the time window during which the NDVI is accumulated, 2) accumulate only the NDVI values which are affected by green vegetation, and 3) base monitoring units upon land cover type. These refinements improve the sensitivity of detecting interannual vegetation variability, reduce the need for extensive and detailed knowledge of ground conditions and crop calendars, provide a framework in which several types of monitoring can take place over diverse land cover types, and provide an objective time frame during which monitoring takes place.

Intercomparison of wind speeds inferred by the SASS, altimeter, and SMMR

NASA Technical Reports Server (NTRS)

Wentz, F. J.; Cardone, V. J.; Fedor, L. S.

1982-01-01

The operational theory, control algorithms, and comparisons with surface-determined wind speeds for the scatterometer (SASS), altimeter (ALT), and passive microwave radiometer (SMMR) on board the Seasat satellite are presented. Radiative scattering combining specular reflections and Bragg resonance scattering are noted to occur at tilting waves and sea foam, two conditions highly correlated with wind speed. SASS scans swaths of 70, 200, and 700 km from nadir, the SMMR covers a 150 km strip. Normalized radar sections are derived from the SASS and ALT telemetry, and brightness temperature from the SMMR. ALT winds were found to be biased about 3 m/sec low, while intercomparison between the SMMR and SASS data showed a mean difference of 0.3 m/sec with a standard deviation from measured winds of 1.7 m/sec or less. The effects of land thermal emissions, rain, and sun glint are discussed, and good viewing conditions are concluded to result in 2 m/sec accuracy.

Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA

Treesearch

Jay D. Miller; Eric E. Knapp; Carl H. Key; Carl N. Skinner; Clint J. Isbell; R. Max Creasy; Joseph W. Sherlock

2009-01-01

Multispectral satellite data have become a common tool used in the mapping of wildland fire effects. Fire severity, defined as the degree to which a site has been altered, is often the variable mapped. The Normalized Burn Ratio (NBR) used in an absolute difference change detection protocol (dNBR), has become the remote sensing method of choice for US Federal land...

Moisture convergence using satellite-derived wind fields - A severe local storm case study

NASA Technical Reports Server (NTRS)

Negri, A. J.; Vonder Haar, T. H.

1980-01-01

Five-minute interval 1-km resolution SMS visible channel data were used to derive low-level wind fields by tracking small cumulus clouds on NASA's Atmospheric and Oceanographic Information Processing System. The satellite-derived wind fields were combined with surface mixing ratios to derive horizontal moisture convergence in the prestorm environment of April 24, 1975. Storms began developing in an area extending from southwest Oklahoma to eastern Tennessee 2 h subsequent to the time of the derived fields. The maximum moisture convergence was computed to be 0.0022 g/kg per sec and areas of low-level convergence of moisture were in general indicative of regions of severe storm genesis. The resultant moisture convergence fields derived from two wind sets 20 min apart were spatially consistent and reflected the mesoscale forcing of ensuing storm development. Results are discussed with regard to possible limitations in quantifying the relationship between low-level flow and between low-level flow and satellite-derived cumulus motion in an antecedent storm environment.

Development of hybrid fog detection algorithm (FDA) using satellite and ground observation data for nighttime

NASA Astrophysics Data System (ADS)

Kim, So-Hyeong; Han, Ji-Hae; Suh, Myoung-Seok

2017-04-01

In this study, we developed a hybrid fog detection algorithm (FDA) using AHI/Himawari-8 satellite and ground observation data for nighttime. In order to detect fog at nighttime, Dual Channel Difference (DCD) method based on the emissivity difference between SWIR and IR1 is most widely used. DCD is good at discriminating fog from other things (middle/high clouds, clear sea and land). However, it is difficult to distinguish fog from low clouds. In order to separate the low clouds from the pixels that satisfy the thresholds of fog in the DCD test, we conducted supplementary tests such as normalized local standard derivation (NLSD) of BT11 and the difference of fog top temperature (BT11) and air temperature (Ta) from NWP data (SST from OSTIA data). These tests are based on the larger homogeneity of fog top than low cloud tops and the similarity of fog top temperature and Ta (SST). Threshold values for the three tests were optimized through ROC analysis for the selected fog cases. In addition, considering the spatial continuity of fog, post-processing was performed to detect the missed pixels, in particular, at edge of fog or sub-pixel size fog. The final fog detection results are presented by fog probability (0 100 %). Validation was conducted by comparing fog detection probability with the ground observed visibility data from KMA. The validation results showed that POD and FAR are ranged from 0.70 0.94 and 0.45 0.72, respectively. The quantitative validation and visual inspection indicate that current FDA has a tendency to over-detect the fog. So, more works which reducing the FAR is needed. In the future, we will also validate sea fog using CALIPSO data.

Comparison of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Earth Observing One (EO-1) Advanced Land Imager

NASA Technical Reports Server (NTRS)

Pedelty, Jeffrey A.; Morisette, Jeffrey T.; Smith, James A.

2004-01-01

We compare images from the Enhanced Thematic Mapper Plus (ETM+) sensor on Landsat-7 and the Advanced Land Imager (ALI) instrument on Earth Observing One (EO-1) over a test site in Rochester, New York. The site contains a variety of features, ranging from water of varying depths, deciduous/coniferous forest, and grass fields, to urban areas. Nearly coincident cloud-free images were collected one minute apart on 25 August 2001. We also compare images of a forest site near Howland, Maine, that were collected on 7 September, 2001. We atmospherically corrected each pair of images with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) atmosphere model, using aerosol optical thickness and water vapor column density measured by in situ Cimel sun photometers within the Aerosol Robotic Network (AERONET), along with ozone density derived from the Total Ozone Mapping Spectrometer (TOMS) on the Earth Probe satellite. We present true-color composites from each instrument that show excellent qualitative agreement between the multispectral sensors, along with grey-scale images that demonstrate a significantly improved ALI panchromatic band. We quantitatively compare ALI and ETM+ reflectance spectra of a grassy field in Rochester and find < or equal to 6% differences in the visible/near infrared and approx. 2% differences in the short wave infrared. Spectral comparisons of forest sites in Rochester and Howland yield similar percentage agreement except for band 1, which has very low reflectance. Principal component analyses and comparison of normalized difference vegetation index histograms for each sensor indicate that the ALI is able to reproduce the information content in the ETM+ but with superior signal-to-noise performance due to its increased 12-bit quantization.

A novel cross-satellite based assessment of the spatio-temporal development of a cyanobacterial harmful algal bloom

NASA Astrophysics Data System (ADS)

Page, Benjamin P.; Kumar, Abhishek; Mishra, Deepak R.

2018-04-01

As the frequency of cyanobacterial harmful algal blooms (CyanoHABs) become more common in recreational lakes and water supply reservoirs, demand for rapid detection and temporal monitoring will be imminent for effective management. The goal of this study was to demonstrate a novel and potentially operational cross-satellite based protocol for synoptic monitoring of rapidly evolving and increasingly common CyanoHABs in inland waters. The analysis involved a novel way to cross-calibrate a chlorophyll-a (Chl-a) detection model for the Landsat-8 OLI sensor from the relationship between the normalized difference chlorophyll index and the floating algal index derived from Sentinel-2A on a coinciding overpass date during the summer CyanoHAB bloom in Utah Lake. This aided in the construction of a time-series phenology of the Utah Lake CyanoHAB event. Spatio-temporal cyanobacterial density maps from both Sentinel-2A and Landsat-8 sensors revealed that the bloom started in the first week of July 2016 (July 3rd, mean cell count: 9163 cells/mL), reached peak in mid-July (July 15th, mean cell count: 108176 cells/mL), and reduced in August (August 24th, mean cell count: 9145 cells/mL). Analysis of physical and meteorological factors suggested a complex interaction between landscape processes (high surface runoff), climatic conditions (high temperature, high rainfall followed by negligible rainfall, stable wind), and water quality (low water level, high Chl-a) which created a supportive environment for triggering these blooms in Utah Lake. This cross satellite-based monitoring methods can be a great tool for regular monitoring and will reduce the budget cost for monitoring and predicting CyanoHABs in large lakes.

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.

Characterizing Seasonal Drought, Water Supply Pattern and Their Impact on Vegetation Growth Using Satellite Soil Moisture Data, GRACE Water Storage and Precipitation Observations

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Du, J.; Kim, Y.; Njoku, E. G.; Colliander, A.

2016-12-01

We combine soil moisture (SM) data from AMSR-E, AMSR-2 and SMAP, terrestrial water storage (TWS) changes from GRACE and precipitation measurements from GPCP to delineate and characterize drought and water supply pattern and its impact on vegetation growth. GRACE TWS provides spatially continuous observations of total terrestrial water storage changes and regional drought extent, persistence and severity, while satellite derived soil moisture estimates provide enhanced delineation of plant-available soil moisture. Together these data provide complementary metrics quantifying available plant water supply and have important implications for water resource management. We use these data to investigate the supply changes from different water components in relation to satellite based vegetation productivity metrics from MODIS, before, during and following the major drought events observed in the continental US during the past 13 years. We observe consistent trends and significant correlations between monthly time series of TWS, SM, and vegetation productivity. In Texas and surrounding semi-arid areas, we find that the spatial pattern of the vegetation-moisture relation follows the gradient in mean annual precipitation. In Texas, GRACE TWS and surface SM show strong coupling and similar characteristic time scale in relatively normal years, while during the 2011 onward hydrological drought, GRACE TWS manifests a longer time scale than that of surface SM, implying stronger drought persistence in deeper water storage. In the Missouri watershed, we find a spatially varying vegetation-moisture relationship where in the drier northwestern portion of the basin, the inter-annual variability in summer vegetation productivity is closely associated with changes in carry-on GRACE TWS from spring, whereas in the moist southeastern portion of the basin, summer precipitation is the dominant controlling factor on vegetation growth.

Some European capabilities in satellite cinema exhibition

NASA Astrophysics Data System (ADS)

Bock, Wolfgang

1990-08-01

The likely performance envelope and architecture for satellite cinema systems are derived from simple practical assumptions. A case is made for possible transatlantic cooperation towards establishing a satellite cinema standard.

Algorithm Development and Validation for Satellite-Derived Distributions of DOC and CDOM in the US Middle Atlantic Bight

NASA Technical Reports Server (NTRS)

Mannino, Antonio; Russ, Mary E.; Hooker, Stanford B.

2007-01-01

In coastal ocean waters, distributions of dissolved organic carbon (DOC) and chromophoric dissolved organic matter (CDOM) vary seasonally and interannually due to multiple source inputs and removal processes. We conducted several oceanographic cruises within the continental margin of the U.S. Middle Atlantic Bight (MAB) to collect field measurements in order to develop algorithms to retrieve CDOM and DOC from NASA's MODIS-Aqua and SeaWiFS satellite sensors. In order to develop empirical algorithms for CDOM and DOC, we correlated the CDOM absorption coefficient (a(sub cdom)) with in situ radiometry (remote sensing reflectance, Rrs, band ratios) and then correlated DOC to Rrs band ratios through the CDOM to DOC relationships. Our validation analyses demonstrate successful retrieval of DOC and CDOM from coastal ocean waters using the MODIS-Aqua and SeaWiFS satellite sensors with mean absolute percent differences from field measurements of < 9 %for DOC, 20% for a(sub cdom)(355)1,6 % for a(sub cdom)(443), and 12% for the CDOM spectral slope. To our knowledge, the algorithms presented here represent the first validated algorithms for satellite retrieval of a(sub cdom) DOC, and CDOM spectral slope in the coastal ocean. The satellite-derived DOC and a(sub cdom) products demonstrate the seasonal net ecosystem production of DOC and photooxidation of CDOM from spring to fall. With accurate satellite retrievals of CDOM and DOC, we will be able to apply satellite observations to investigate interannual and decadal-scale variability in surface CDOM and DOC within continental margins and monitor impacts of climate change and anthropogenic activities on coastal ecosystems.

Surface currents in the Bohai Sea derived from the Korean Geostationary Ocean Color Imager (GOCI)

NASA Astrophysics Data System (ADS)

Jiang, L.; Wang, M.

2016-02-01

The first geostationary ocean color satellite sensor, the Geostationary Ocean Color Imager (GOCI) onboard the Korean Communication, Ocean, and Meteorological Satellite can monitor and measure ocean phenomena over an area of 2500 × 2500 km2 around the western Pacific region centered at 36°N and 130°E. Hourly measurements during the day around 9:00 to 16:00 local time are a unique capability of GOCI to monitor ocean features of higher temporal variability. In this presentation, we show some recent results of GOCI-derived ocean surface currents in the Bohai Sea using the Maximum Cross-Correlation (MCC) feature tracking method and compare the results with altimetry-inversed tidal current observations produced from Oregon State University (OSU) Tidal Inversion Software (OTIS). The performance of the GOCI-based MCC method is assessed and the discrepancies between the GOCI- and OTIS-derived currents are evaluated. A series of sensitivity studies are conducted with images from various satellite products and of various time differences, MCC adjustable parameters, and influence from other forcings such as wind, to find the best setups for optimal MCC performance. Our results demonstrate that GOCI can effectively provide real-time monitoring of not only water optical, biological, and biogeochemical variability, but also the physical dynamics in the region.

Theoretical algorithms for satellite-derived sea surface temperatures

NASA Astrophysics Data System (ADS)

Barton, I. J.; Zavody, A. M.; O'Brien, D. M.; Cutten, D. R.; Saunders, R. W.; Llewellyn-Jones, D. T.

1989-03-01

Reliable climate forecasting using numerical models of the ocean-atmosphere system requires accurate data sets of sea surface temperature (SST) and surface wind stress. Global sets of these data will be supplied by the instruments to fly on the ERS 1 satellite in 1990. One of these instruments, the Along-Track Scanning Radiometer (ATSR), has been specifically designed to provide SST in cloud-free areas with an accuracy of 0.3 K. The expected capabilities of the ATSR can be assessed using transmission models of infrared radiative transfer through the atmosphere. The performances of several different models are compared by estimating the infrared brightness temperatures measured by the NOAA 9 AVHRR for three standard atmospheres. Of these, a computationally quick spectral band model is used to derive typical AVHRR and ATSR SST algorithms in the form of linear equations. These algorithms show that a low-noise 3.7-μm channel is required to give the best satellite-derived SST and that the design accuracy of the ATSR is likely to be achievable. The inclusion of extra water vapor information in the analysis did not improve the accuracy of multiwavelength SST algorithms, but some improvement was noted with the multiangle technique. Further modeling is required with atmospheric data that include both aerosol variations and abnormal vertical profiles of water vapor and temperature.

Continent-Wide Estimates of Antarctic Strain Rates from Landsat 8-Derived Velocity Grids and Their Application to Ice Shelf Studies

NASA Astrophysics Data System (ADS)

Alley, K. E.; Scambos, T.; Anderson, R. S.; Rajaram, H.; Pope, A.; Haran, T.

2017-12-01

Strain rates are fundamental measures of ice flow used in a wide variety of glaciological applications including investigations of bed properties, calculations of basal mass balance on ice shelves, application to Glen's flow law, and many other studies. However, despite their extensive application, strain rates are calculated using widely varying methods and length scales, and the calculation details are often not specified. In this study, we compare the results of nominal and logarithmic strain-rate calculations based on a satellite-derived velocity field of the Antarctic ice sheet generated from Landsat 8 satellite data. Our comparison highlights the differences between the two commonly used approaches in the glaciological literature. We evaluate the errors introduced by each code and their impacts on the results. We also demonstrate the importance of choosing and specifying a length scale over which strain-rate calculations are made, which can have large local impacts on other derived quantities such as basal mass balance on ice shelves. We present strain-rate data products calculated using an approximate viscous length-scale with satellite observations of ice velocity for the Antarctic continent. Finally, we explore the applications of comprehensive strain-rate maps to future ice shelf studies, including investigations of ice fracture, calving patterns, and stability analyses.

Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery

NASA Astrophysics Data System (ADS)

Navarro, Gabriel; Caballero, Isabel; Silva, Gustavo; Parra, Pedro-Cecilio; Vázquez, Águeda; Caldeira, Rui

2017-06-01

A forest fire started on August 8th, 2016 in several places on Madeira Island causing damage and casualties. As of August 10th the local media had reported the death of three people, over 200 people injured, over 950 habitants evacuated, and 50 houses damaged. This study presents the preliminary results of the assessment of several spectral indices to evaluate the burn severity of Madeira fires during August 2016. These spectral indices were calculated using the new European satellite Sentinel-2A launched in June 2015. The study confirmed the advantages of several spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVIreXn) using red-edge spectral bands to assess the post-fire conditions. Results showed high correlation between NDVI, GNDVI, NBR and NDVIre1n spectral indices and the analysis performed by Copernicus Emergency Management Service (EMSR175), considered as the reference truth. Regarding the red-edge spectral indices, the NDVIre1n (using band B5, 705 nm) presented better results compared with B6 (740 nm) and B7 (783 nm) bands. These preliminary results allow us to assume that Sentinel-2 will be a valuable tool for post-fire monitoring. In the future, the two twin Sentinel-2 satellites will offer global coverage of the Madeira Archipelago every five days, therefore allowing the simultaneous study of the evolution of the burnt area and reforestation information with high spatial (up to 10 m) and temporal resolution (5 days).

A computationally efficient approach for isolating satellite phase fractional cycle biases based on Kalman filter

NASA Astrophysics Data System (ADS)

Xiao, Guorui; Mayer, Michael; Heck, Bernhard; Sui, Lifen; Cong, Mingri

2017-04-01

Integer ambiguity resolution (AR) can significantly shorten the convergence time and improve the accuracy of Precise Point Positioning (PPP). Phase fractional cycle biases (FCB) originating from satellites destroy the integer nature of carrier phase ambiguities. To isolate the satellite FCB, observations from a global reference network are required. Firstly, float ambiguities containing FCBs are obtained by PPP processing. Secondly, the least squares method (LSM) is adopted to recover FCBs from all the float ambiguities. Finally, the estimated FCB products can be applied by the user to achieve PPP-AR. During the estimation of FCB, the LSM step can be very time-consuming, considering the large number of observations from hundreds of stations and thousands of epochs. In addition, iterations are required to deal with the one-cycle inconsistency among observations. Since the integer ambiguities are derived by directly rounding float ambiguities, the one-cycle inconsistency arises whenever the fractional parts of float ambiguities exceed the rounding boundary (e.g., 0.5 and -0.5). The iterations of LSM and the large number of observations require a long time to finish the estimation. Consequently, only a sparse global network containing a limited number of stations was processed in former research. In this paper, we propose to isolate the FCB based on a Kalman filter. The large number of observations is handled epoch-by-epoch, which significantly reduces the dimension of the involved matrix and accelerates the computation. In addition, it is also suitable for real-time applications. As for the one-cycle inconsistency, a pre-elimination method is developed to avoid the iteration of the whole process. According to the analysis of the derived satellite FCB products, we find that both wide-lane (WL) and narrow-lane (NL) FCB are very stable over time (e.g., WL FCB over several days rsp. NL FCB over tens of minutes). The stability implies that the satellite FCB can be removed by previous estimation. After subtraction of the satellite FCB, the receiver FCB can be determined. Theoretically, the receiver FCBs derived from different satellite observations should be the same for a single station. Thereby, the one-cycle inconsistency among satellites can be detected and eliminated by adjusting the corresponding receiver FCB. Here, stations can be handled individually to obtain "clean" FCB observations. In an experiment, 24 h observations from 200 stations are processed to estimate GPS FCB. The process finishes in one hour using a personal computer. The estimated WL FCB has a good consistency with existing WL FCB products (e.g., CNES, WHU-SGG). All differences are within ± 0.1 cycles, which indicates the correctness of the proposed approach. For NL FCB, all differences are within ± 0.2 cycles. Concerning the NL wavelength (10.7 cm), the slightly worse NL FCB may be ascribed to different PPP processing strategies. The state-based approach of the Kalman filter also allows for a more realistic modeling of stochastic parameters, which will be investigated in future research.

Validation of Cloud Parameters Derived from Geostationary Satellites, AVHRR, MODIS, and VIIRS Using SatCORPS Algorithms

NASA Technical Reports Server (NTRS)

Minnis, P.; Sun-Mack, S.; Bedka, K. M.; Yost, C. R.; Trepte, Q. Z.; Smith, W. L., Jr.; Painemal, D.; Chen, Y.; Palikonda, R.; Dong, X.;

The effect of the arbitrary level assignment of satellite cloud motion wind vectors on wind analyses in the pre-thunderstorm environment

NASA Technical Reports Server (NTRS)

Peslen, C. A.; Koch, S. E.; Uccellini, L. W.

1985-01-01

The impact of satellite-derived cloud motion vectors on SESAME rawinsonde wind fields was studied in two separate cases. The effect of wind and moisture gradients on the arbitrary assignment of the satellite data is assessed to coordinate surfaces in a severe storm environment marked by strong vertical wind shear. Objective analyses of SESAME rawinsonde winds and combined winds are produced and differences between these two analyzed fields are used to make an assessment of coordinate level choice. It is shown that the standard method of arbitrarily assigning wind vectors to a low level coordinate surface yields systematic differences between the rawinsonde and combined wind analyses. Arbitrary assignment of cloud motions to the 0.9 sigma surface produces smaller differences than assignment to the 825 mb pressure surface. Systematic differences occur near moisture discontinuities and in regions of horizontal and vertical wind shears. The differences between the combined and SESAME wind fields are made smallest by vertically interpolating cloud motions to either a pressure or sigma surface.

Investigating the auroral electrojets with low altitude polar orbiting satellites

NASA Astrophysics Data System (ADS)

Moretto, T.; Olsen, N.; Ritter, P.; Lu, G.

2002-07-01

Three geomagnetic satellite missions currently provide high precision magnetic field measurements from low altitude polar orbiting spacecraft. We demonstrate how these data can be used to determine the intensity and location of the horizontal currents that flow in the ionosphere, predominantly in the auroral electrojets. First, we examine the results during a recent geomagnetic storm. The currents derived from two satellites at different altitudes are in very good agreement, which verifies good stability of the method. Further, a very high degree of correlation (correlation coefficients of 0.8 0.9) is observed between the amplitudes of the derived currents and the commonly used auroral electrojet indices based on magnetic measurements at ground. This points to the potential of defining an auroral activity index based on the satellite observations, which could be useful for space weather monitoring. A specific advantage of the satellite observations over the ground-based magnetic measurements is their coverage of the Southern Hemisphere, as well as the Northern. We utilize this in an investigation of the ionospheric currents observed in both polar regions during a period of unusually steady interplanetary magnetic field with a large negative Y-component. A pronounced asymmetry is found between the currents in the two hemispheres, which indicates real inter-hemispheric differences beyond the mirror-asymmetry between hemispheres that earlier studies have revealed. The method is also applied to another event for which the combined measurements of the three satellites provide a comprehensive view of the current systems. The analysis hereof reveals some surprising results concerning the connection between solar wind driver and the resulting ionospheric currents. Specifically, preconditioning of the magnetosphere (history of the interplanetary magnetic field) is seen to play an important role, and in the winther hemisphere, it seems to be harder to drive currents on the nightside than on the dayside.

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.

CyAN satellite-derived Cyanobacteria products in support of Public Health Protection

EPA Science Inventory

The timely distribution of satellite-derived cyanoHAB data is necessary for adaptive water quality management decision-making and for targeted deployment of existing government and non-government water quality monitoring resources. The Cyanobacteria Assessment Network (CyAN) is a...

Seeing is believing I: The use of thermal sensing from satellite imagery to predict crop yield

NASA Astrophysics Data System (ADS)

B, Potgieter A.; D, Rodriguez; B, Power; J, Mclean; P, Davis

2014-02-01

Volatility in crop production has been part of the Australian environment since cropping began with the arrival of the first European settlers. Climate variability is the main factor affecting crop production at national, state and local scales. At field level spatial patterns on yield production are also determined by spatially changing soil properties in interaction with seasonal climate conditions and weather patterns at critical stages in the crop development. Here we used a combination of field level weather records, canopy characteristics, and satellite information to determine the spatial performance of a large field of wheat. The main objective of this research is to determine the ability of remote sensing technologies to capture yield losses due to water stress at the canopy level. The yield, canopy characteristics (i.e. canopy temperature and ground cover) and seasonal conditions of a field of wheat (~1400ha) (-29.402° South and 149.508°, New South Wales, Australia) were continuously monitored during the winter of 2011. Weather and crop variables were continuously monitored by installing three automatic weather stations in a transect covering different positions and soils in the landscape. Weather variables included rainfall, minimum and maximum temperatures and relative humidity, and crop characteristics included ground cover and canopy temperature. Satellite imagery Landsat TM 5 and 7 was collected at five different stages in the crop cycle. Weather variables and crop characteristics were used to calculate a crop stress index (CSI) at point and field scale (39 fields). Field data was used to validate a spatial satellite image derived index. Spatial yield data was downloaded from the harvester at the different locations in the field. We used the thermal band (land surface temperature, LST) and enhanced vegetation index (EVI) bands from the MODIS (250 m for visible bands and 1km for thermal band) and a derived EVI from Landsat TM 7 (25 m for visible and 90m for thermal) satellite platforms. Results showed that spatial variations in crop yield were related to a satellite derived canopy stress index (CSIsat) and a moisture stress index (MSIsat). A weather station level canopy stress index (CSIws) calculated at midday was correlated to the CSIsat at late morning. In addition, a strong linear relationship was observed between EVI and LST at point scale throughout the crop growth period. Differences were smallest at anthesis when the canopy closure was highest. This suggests that LST imagery data around flowering could be used to calculate crop stress over large areas of the crop. The harvested yield was related (R2 = 0.67) to CSIsat using a fix date across all fields. This relationship improved (R2 = 0.92) using both indices from all five dates across all fields during the crop growth period. Here we successfully showed that satellite derived crop attributes (CSIsat and MSIsat) can account for most of the variability in final crop yield and that they can be used to predict crop yield at field scales. Applications of these results could enhance the ability of producers to hedge their financial on -farm crop production losses due to in-season water stress by taking crop insurance. This is likely to further improve their adaptive capacity and thus strengthening the long-term viability of the industry domestically and elsewhere.

First Satellite Observations of Lower Tropospheric Ammonia and Methanol

NASA Technical Reports Server (NTRS)

Beer, Reinhard; Shephard, Mark W.; Kulawik, Susan S.; Clough, Shepard A.; Eldering, Annmarie; Bowman, Kevin W.; Sander, Stanley P.; Fisher, Brendan M.; Payne, Vivienne H.; Luo, Mingzhao;

First satellite observations of lower tropospheric ammonia and methanol

NASA Astrophysics Data System (ADS)

Beer, Reinhard; Shephard, Mark W.; Kulawik, Susan S.; Clough, Shepard A.; Eldering, Annmarie; Bowman, Kevin W.; Sander, Stanley P.; Fisher, Brendan M.; Payne, Vivienne H.; Luo, Mingzhao; Osterman, Gregory B.; Worden, John R.

2008-05-01

The Tropospheric Emission Spectrometer (TES) on the EOS Aura satellite makes global measurements of infrared radiances which are used to derive profiles of species such as O3, CO, H2O, HDO and CH4 as routine standard products. In addition, TES has a variety of special modes that provide denser spatial mapping over a limited geographical area. A continuous-coverage mode (called ``transect'', about 460 km long) has now been used to detect additional molecules indicative of regional air pollution. On 10 July 2007 at about 05:37 UTC (13:24 LMST) TES conducted such a transect observation over the Beijing area in northeast China. Examination of the residual spectral radiances following the retrieval of the TES standard products revealed surprisingly strong features attributable to enhanced concentrations of ammonia (NH3) and methanol (CH3OH), well above the normal background levels. This is the first time that these molecules have been detected in space-based nadir viewing measurements that penetrate into the lower atmosphere.

The melting sea ice of Arctic polar cap in the summer solstice month and the role of ocean

NASA Astrophysics Data System (ADS)

Lee, S.; Yi, Y.

2014-12-01

The Arctic sea ice is becoming smaller and thinner than climatological standard normal and more fragmented in the early summer. We investigated the widely changing Arctic sea ice using the daily sea ice concentration data. Sea ice data is generated from brightness temperature data derived from the sensors: Defense Meteorological Satellite Program (DMSP)-F13 Special Sensor Microwave/Imagers (SSM/Is), the DMSP-F17 Special Sensor Microwave Imager/Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA Earth Observing System (EOS) Aqua satellite. We tried to figure out appearance of arctic sea ice melting region of polar cap from the data of passive microwave sensors. It is hard to explain polar sea ice melting only by atmosphere effects like surface air temperature or wind. Thus, our hypothesis explaining this phenomenon is that the heat from deep undersea in Arctic Ocean ridges and the hydrothermal vents might be contributing to the melting of Arctic sea ice.

A diagnostic approach to obtaining planetary boundary layer winds using satellite-derived thermal data

NASA Technical Reports Server (NTRS)

Belt, Carol L.; Fuelberg, Henry E.

1984-01-01

The feasibility of using satellite derived thermal data to generate realistic synoptic scale winds within the planetary boundary layer (PBL) is examined. Diagnostic modified Ekman wind equations from the Air Force Global Weather Central (AFGWC) Boundary Layer Model are used to compute winds at seven levels within the PBL transition layer (50 m to 1600 m AGL). Satellite derived winds based on 62 predawn TIROS-N soundings are compared to similarly derived wind fields based on 39 AVE-SESAME II rawinsonde (RAOB) soundings taken 2 h later. Actual wind fields are also used as a basis for comparison. Qualitative and statistical comparisons show that the Ekman winds from both sources are in very close agreement, with an average vector correlation coefficient of 0.815. Best results are obtained at 300 m AGL. Satellite winds tend to be slightly weaker than their RAOB counterparts and exhibit a greater degree of cross-isobaric flow. The modified Ekman winds show a significant improvement over geostrophic values at levels nearest the surface.

Investigating the relationship between deforestation and changes in hydrology across the island of Hispaniola

NASA Astrophysics Data System (ADS)

Hakimdavar, R.

2013-05-01

Over recent decades, Haiti and the Dominican Republic have reported changes in reservoir water levels - while some areas have experienced increases others have seen decreasing trends, especially reservoirs located in the Dominican Republic - leading to, among other things, regional flooding and shortages in hydroelectricity output. We investigate whether extensive deforestation, particularly in the western part of Hispaniola - shared by the two nations - is driving these changes by affecting the regional water balance. Due to a lack of available spatiotemporal environmental data, remotely sensed vegetation and precipitation information is used along with estimated evapotranspiration rates to study regional hydro-climatologic fluctuations over three decades. Changes in vegetative cover, precipitation, and evapotranspiration across the island are investigated using 25 years of Normalized Difference Vegetation Index (NDVI) data, historical satellite and gauge precipitation records, and estimated surface temperature and solar radiation. NDVI values are derived from imagery obtained by NOAA's 8 km resolution AVHRR instrument. Monthly precipitation is collected from several different sources, including NASA and NOAA precipitation satellites, as well as local rain gauges. Evapotranspiration is estimated using an energy balance approach. Preliminary results indicate a general decrease in rainfall over the eastern part of the island during the past three decades, with little change observed across the western half. NDVI and precipitation anomalies across the island are not well correlated, suggesting that deforestation is likely not the cause of regional changes in precipitation. The results of this work hold potentially important implications for future land-use and water infrastructure planning for both nations.

Remotely sensed vegetation indices for seasonal crop yields predictions in the Czech Republic

NASA Astrophysics Data System (ADS)

Hlavinka, Petr; Semerádová, Daniela; Balek, Jan; Bohovic, Roman; Žalud, Zdeněk; Trnka, Miroslav

2015-04-01

Remotely sensed vegetation indices by satellites are valuable tool for vegetation conditions assessment also in the case of field crops. This study is based on the use of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard Terra satellite. Data available from the year 2000 were analyzed and tested for seasonal yields predictions within selected districts of the Czech Republic (Central Europe). Namely the yields of spring barley, winter wheat and oilseed winter rape during the period from 2000 to 2014 were assessed. Observed yields from 14 districts (NUTS 4) were collected and thus 210 seasons were included. Selected districts differ considerably in their soil fertility and terrain configuration and represent transect across various agroclimatic conditions (from warm and dry to relative cool and wet regions). Two approaches were tested: 1) using of composite remotely sensed data (available in 16 day time step) provided by the USGS (https://lpdaac.usgs.gov/); 2) using daily remotely sensed data in combination with originally developed smoothing method. The yields were successfully predicted based on established regression models (remotely sensed data used as independent parameter). Besides others the impact of severe drought episodes within vegetation were identified and yield reductions at district level predicted (even before harvest). As a result the periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above normal yields of field crops could be predicted by proposed method within study region up to 30 days prior to the harvest. It could be concluded that remotely sensed vegetation conditions assessment should be important part of early warning systems focused on drought. Such information should be widely available for various users (decision makers, farmers, etc.) in order to improve planning, business strategies but also to target the drought relief in case of major drought event. This study was funded by project "Building up a multidisciplinary scientific team focused on drought" No. CZ.1.07/2.3.00/20.0248, project supported by Czech National Agency of Agricultural Research No. QJ1310123 "Crop modelling as a tool for increasing the production potential and food security of the Czech Republic under Climate Change".

The effect of temperature on apoptosis and adipogenesis on skeletal muscle satellite cells derived from different muscle types.

PubMed

Harding, Rachel L; Clark, Daniel L; Halevy, Orna; Coy, Cynthia S; Yahav, Shlomo; Velleman, Sandra G

2015-09-01

Satellite cells are multipotential stem cells that mediate postnatal muscle growth and respond differently to temperature based upon aerobic versus anaerobic fiber-type origin. The objective of this study was to determine how temperatures below and above the control, 38°C, affect the fate of satellite cells isolated from the anaerobic pectoralis major (p. major) or mixed fiber biceps femoris (b. femoris). At all sampling times, p. major and b. femoris cells accumulated less lipid when incubated at low temperatures and more lipid at elevated temperatures compared to the control. Satellite cells isolated from the p. major were more sensitive to temperature as they accumulated more lipid at elevated temperatures compared to b. femoris cells. Expression of adipogenic genes, CCAAT/enhancer-binding protein β (C/EBPβ) and proliferator-activated receptor gamma (PPARγ) were different within satellite cells isolated from the p. major or b. femoris. At 72 h of proliferation, C/EBPβ expression increased with increasing temperature in both cell types, while PPARγ expression decreased with increasing temperature in p. major satellite cells. At 48 h of differentiation, both C/EBPβ and PPARγ expression increased in the p. major and decreased in the b. femoris, with increasing temperature. Flow cytometry measured apoptotic markers for early apoptosis (Annexin-V-PE) or late apoptosis (7-AAD), showing less than 1% of apoptotic satellite cells throughout all experimental conditions, therefore, apoptosis was considered biologically not significant. The results support that anaerobic p. major satellite cells are more predisposed to adipogenic conversion than aerobic b. femoris cells when thermally challenged. © 2015 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society.

Comparison of monthly rain rates derived from GPI and SSM/I using probability distribution functions

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Chang, Alfred T. C.; Janowiak, John

1993-01-01

Three years of monthly rain rates over 5 deg x 5 deg latitude-longitude boxes have been calculated for oceanic regions 50 deg N-50 deg S from measurements taken by the Special Sensor Microwave/Imager on board the Defense Meteorological Satellite Program satellites using the technique developed by Wilheit et al. (1987, 1991). The annual and seasonal zonal-mean rain rates are larger than Jaeger's (1983) climatological estimates but are smaller than those estimated from the GOES precipitation index (GPI) for the same period. Regional comparison with the GPI showed that these rain rates are smaller in the north Indian Ocean and in the southern extratropics where the GPI is known to overestimate. The differences are also dominated by a jump at 170 deg W in the GPI rain rates across the mid-Pacific Ocean. This jump is attributed to the fusion of different satellite measurements in producing the GPI.

Paracentric inversion of Yq and review of the literature.

PubMed

Aiello, V; Astolfi, N; Gruppioni, R; Buldrini, B; Prontera, P; Bonfatti, A; Sensi, A; Calzolari, E

2007-01-01

We report on the second prenatal diagnosis of familial paracentric inversion of the long arm of Y chromosome [46, X, inv(Y)(q11.2q12)]. The anomaly was detected through an amniocentesis performed because of advanced maternal age. The inversion has been detected by standard GTG banding methods and better characterized by FISH with painting probe and specific satellite probes DYZ1 and DYZ3. The inversion derived from phenotypically normal father. Pregnancy was uneventful and an healthy child was born. We discuss the issue concerning genetic prenatal counselling of this rare condition and we report the clinical follow up of the child.

Whistler mode waves observed by MGF search coil magnetometer -Polarization and wave normal features of upstream waves near the bow-shock

NASA Astrophysics Data System (ADS)

Hayashi, K.; Matsui, H.; Kawano, H.; Yamamoto, T.; Kokubun, S.

1994-12-01

Whistler mode waves observed in the upstream region very close to the bow-shock is focused from the initial survey for magnetic fed data in a frequency range between 1Hz and 50Hz observed by the search coil magnetometer on board the Geotail satellite. Based on the three component wave form data polarization and wave-normal characteristics of foreshock waves is first shown as dynamic spectra for the whole Fourier components of the 50 Hz band width. Intense whistler mode waves generated in the foot region of the bow-shock are found strongly controlled in the observed polarization dependent on the angle between directions of the wave propagation and the solar wind flow but not very dependent on frequency. Our simple scheme to derive the ware characteristics which is effective to survey large amount of data continuously growing is also introduced.

Vegetation extraction from high-resolution satellite imagery using the Normalized Difference Vegetation Index (NDVI)

NASA Astrophysics Data System (ADS)

AlShamsi, Meera R.

2016-10-01

Over the past years, there has been various urban development all over the UAE. Dubai is one of the cities that experienced rapid growth in both development and population. That growth can have a negative effect on the surrounding environment. Hence, there has been a necessity to protect the environment from these fast pace changes. One of the major impacts this growth can have is on vegetation. As technology is evolving day by day, there is a possibility to monitor changes that are happening on different areas in the world using satellite imagery. The data from these imageries can be utilized to identify vegetation in different areas of an image through a process called vegetation detection. Being able to detect and monitor vegetation is very beneficial for municipal planning and management, and environment authorities. Through this, analysts can monitor vegetation growth in various areas and analyze these changes. By utilizing satellite imagery with the necessary data, different types of vegetation can be studied and analyzed, such as parks, farms, and artificial grass in sports fields. In this paper, vegetation features are detected and extracted through SAFIY system (i.e. the Smart Application for Feature extraction and 3D modeling using high resolution satellite ImagerY) by using high-resolution satellite imagery from DubaiSat-2 and DEIMOS-2 satellites, which provide panchromatic images of 1m resolution and spectral bands (red, green, blue and near infrared) of 4m resolution. SAFIY system is a joint collaboration between MBRSC and DEIMOS Space UK. It uses image-processing algorithms to extract different features (roads, water, vegetation, and buildings) to generate vector maps data. The process to extract green areas (vegetation) utilize spectral information (such as, the red and near infrared bands) from the satellite images. These detected vegetation features will be extracted as vector data in SAFIY system and can be updated and edited by end-users, such as governmental entities and municipalities.

Improving Assessment of Lifetime Solar Ultraviolet Radiation Exposure in Epidemiologic Studies: Comparison of Ultraviolet Exposure Assessment Methods in a Nationwide United States Occupational Cohort.

PubMed

Little, Mark P; Tatalovich, Zaria; Linet, Martha S; Fang, Michelle; Kendall, Gerald M; Kimlin, Michael G

2018-06-13

Solar ultraviolet radiation is the primary risk factor for skin cancers and sun-related eye disorders. Estimates of individual ambient ultraviolet irradiance derived from ground-based solar measurements and from satellite measurements have rarely been compared. Using self-reported residential history from 67,189 persons in a nationwide occupational US radiologic technologists cohort, we estimated ambient solar irradiance using data from ground-based meters and noontime satellite measurements. The mean distance-moved from city of longest residence in childhood increased from 137.6 km at ages 13-19 to 870.3 km at ages ≥65, with corresponding increases in absolute latitude-difference moved. At ages 20/40/60/80, the Pearson/Spearman correlation coefficients of ground-based and satellite-derived solar potential ultraviolet exposure, using irradiance and cumulative radiant-exposure metrics, were high (=0.87-0.92). There was also moderate correlation (Pearson/Spearman correlation coefficients=0.51-0.60) between irradiance at birth and at last-known address, for ground-based and satellite data. Satellite-based lifetime estimates of ultraviolet radiation were generally 14-15% lower than ground-based estimates, albeit with substantial uncertainties, possibly because ground-based estimates incorporate fluctuations in cloud and ozone, which are incompletely incorporated in the single noontime satellite-overpass ultraviolet value. If confirmed elsewhere, the findings suggest that ground-based estimates may improve exposure-assessment accuracy and potentially provide new insights into ultraviolet-radiation-disease relationships in epidemiologic studies. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition

NASA Technical Reports Server (NTRS)

Khlopenkov, Konstantin V.; Duda, David; Thieman, Mandana; Sun-mack, Szedung; Su, Wenying; Minnis, Patrick; Bedka, Kristopher

2017-01-01

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC delivers adequate spatial resolution imagery but only in shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and longwave broadband windows. Accurate calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud properties retrievals from low earth orbit (LEO, including NASA Terra and Aqua MODIS, Suomi-NPP VIIRS, and NOAA AVHRR) and geosynchronous (GEO, including GOES east and west, METEOSAT, INSAT-3D, MTSAT-2, and Himawari-8) satellite imagers. The cloud properties are derived using the Clouds and the Earth's Radiant Energy System (CERES) mission Cloud Subsystem group algorithms. These properties have to be co-located with EPIC pixels to provide the scene identification and to select anisotropic directional models (ADMs), which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiance and cloud property parameters derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. Selection of satellite data for each 5-km pixel is based on an aggregated rating that incorporates five parameters: nominal satellite resolution, pixel time relative to the EPIC time, viewing zenith angle, distance from day/night terminator, and probability of sun glint. To provide a smoother transition in the merged output, in regions where candidate pixel data from two satellite sources have comparable aggregated rating, the selection decision is defined by the cumulative function of the normal distribution so that abrupt changes in the visual appearance of the composite data are avoided. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling.

Robust satellite techniques for oil spill detection and monitoring

NASA Astrophysics Data System (ADS)

Casciello, D.; Pergola, N.; Tramutoli, V.

Discharge of oil into the sea is one of the most dangerous, among technological hazards, for the maritime environment. In the last years maritime transport and exploitation of marine resources continued to increase; as a result, tanker accidents are nowadays increasingly frequent, continuously menacing the maritime security and safety. Satellite remote sensing could contribute in multiple ways, in particular for what concerns early warning and real-time (or near real-time) monitoring. Several satellite techniques exist, mainly based on the use of SAR (Synthetic Aperture Radar) technology, which are able to recognise, with sufficient accuracy, oil spills discharged into the sea. Unfortunately, such methods cannot be profitably used for real-time detection, because of the low observational frequency assured by present satellite platforms carrying SAR sensors (the mean repetition rate is something like 30 days). On the other hand, potential of optical sensors aboard meteorological satellites, was not yet fully exploited and no reliable techniques have been developed until now for this purpose. Main limit of proposed techniques can be found in the ``fixed threshold'' approach which makes such techniques difficult to implement without operator supervision and, generally, without an independent information on the oil spill presence that could drive the choice of the best threshold. A different methodological approach (RAT, Robust AVHRR Techniques) proposed by Tramutoli (1998) and already successfully applied to several natural and environmental emergencies related to volcanic eruptions, forest fires and seismic activity. In this paper its extension to near real-time detection and monitoring of oil spills by means of NOAA-AVHRR (Advanced Very High Resolution Radiometer) records will be described. Briefly, RAT approach is an automatic change-detection scheme that considers a satellite image as a space-time process, described at each place (x,y) and time t, by the value of the satellite derived measurements V(x,y,t). Generally speaking an Absolute Local Index of Change of the Environment (ALICE) is computed and this index permits to identify signal anomalies, in the space-time domain, as deviations from a normal state preliminarily defined, for each image pixel, (e.g. in terms of time average and standard deviation) on the base only of satellite observations collected during several year in the past, in similar observational conditions (same time of the day, same month of the year). By this way local (i.e. specific for the place and the time of observation) instead than fixed thresholds are automatically set by RAT which permit to discriminate signal anomalies from those variations due to natural or observational condition variability. Using AVHRR observations in the Thermal (TIR) and Middle (MIR) Infrared region, such an approach has been applied to the extended oil spill event, occurred at the end of January 1991 in the Persian Gulf. Preliminary results will be analysed that confirm as the suggested technique is able to detect and monitoring oil spills also in the most difficult observational conditions. Automatic implementation, intrinsic exportability on whatever geographic zone and/or satellite package, high sensitivity also to low intensity signals (i.e. small or thin spills), no need for ancillary information (different form satellite data at hand), seem the most promising merits of the proposed technique. Although these results should be confirmed by further analyses on different events and extended also to other AVHRR spectral bands (VIS, NIR), this work surely encourages to continue the research in this field. Moreover, the complete independence of the RAT approach on the specific sensor and/or satellite system, will ensure its full exportability on the new generation of Earth observation satellite sensors (e.g. SEVIRI-Spinning Enhanced Visible and Infrared Imager onboard Meteosat Second generation satellite, with a repetition rate of 15 minutes and 12 spectral bands) which, thanks to their improved capabilities, could actually guarantee timely, reliable and accurate information.

Transfer and distortion of atmospheric information in the satellite temperature retrieval problem

NASA Technical Reports Server (NTRS)

Thompson, O. E.

1981-01-01

A systematic approach to investigating the transfer of basic ambient temperature information and its distortion by satellite systems and subsequent analysis algorithms is discussed. The retrieval analysis cycle is derived, the variance spectrum of information is examined as it takes different forms in that process, and the quality and quantity of information existing at each stop is compared with the initial ambient temperature information. Temperature retrieval algorithms can smooth, add, or further distort information, depending on how stable the algorithm is, and how heavily influenced by a priori data.

Analysis of nonlinear internal waves observed by Landsat thematic mapper

NASA Astrophysics Data System (ADS)

Artale, V.; Levi, D.; Marullo, S.; Santoleri, R.

1990-09-01

In this work we test the compatibility between the theoretical parameters of a nonlinear wave model and the quantitative information that one can deduce from satellite-derived data. The theoretical parameters are obtained by applying an inverse problem to the solution of the Cauchy problem for the Korteweg-de Vries equation. Our results are applied to the case of internal wave patterns elaborated from two different satellite sensors at the south of Messina (the thematic mapper) and at the north of Messina (the synthetic aperture radar).

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA

USGS Publications Warehouse

Boyte, Stephen; Wylie, Bruce K.; Rigge, Matthew B.; Dahal, Devendra

2018-01-01

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.

Isolated Galaxies and Isolated Satellite Systems

NASA Astrophysics Data System (ADS)

Ann, H. B.; Park, C.; Choi, Y. Y.

2010-10-01

We search for isolated galaxies using a volume-limited sample of galaxies with 0.02 < z < 0.04742 from SDSS DR7 supplemented by bright galaxies. We devise a diagnostic tool to select isolated galaxies in different environments using the projected separation (rp) normalized by the virial radius of the nearest neighbor (rvir,nei) and the local background density. We find that the isolation condition of rp > rvir,nei and ρ < ρbar well segregates the CIG galaxies. We confirm the morphology conformity between the host and their satellites, which suggests the importance to galaxy evolution of hydrodynamic interactions among galaxies within their virial radii.

MOBY, A Radiometric Buoy for Performance Monitoring and Vicarious Calibration of Satellite Ocean Color Sensors: Measurement and Data Analysis Protocols. Chapter 2

NASA Technical Reports Server (NTRS)

Clark, Dennis K.; Yarbrough, Mark A.; Feinholz, Mike; Flora, Stephanie; Broenkow, William; Kim, Yong Sung; Johnson, B. Carol; Brown, Steven W.; Yuen, Marilyn; Mueller, James L.

2003-01-01

The Marine Optical Buoy (MOBY) is the centerpiece of the primary ocean measurement site for calibration of satellite ocean color sensors based on independent in situ measurements. Since late 1996, the time series of normalized water-leaving radiances L(sub WN)(lambda) determined from the array of radiometric sensors attached to MOBY are the primary basis for the on-orbit calibrations of the USA Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Japanese Ocean Color and Temperature Sensor (OCTS), the French Polarization Detection Environmental Radiometer (POLDER), the German Modular Optoelectronic Scanner on the Indian Research Satellite (IRS1-MOS), and the USA Moderate Resolution Imaging Spectrometer (MODIS). The MOBY vicarious calibration L(sub WN)(lambda) reference is an essential element in the international effort to develop a global, multi-year time series of consistently calibrated ocean color products using data from a wide variety of independent satellite sensors. A longstanding goal of the SeaWiFS and MODIS (Ocean) Science Teams is to determine satellite-derived L(sub WN)(labda) with a relative combined standard uncertainty of 5 %. Other satellite ocean color projects and the Sensor Intercomparison for Marine Biology and Interdisciplinary Oceanic Studies (SIMBIOS) project have also adopted this goal, at least implicitly. Because water-leaving radiance contributes at most 10 % of the total radiance measured by a satellite sensor above the atmosphere, a 5 % uncertainty in L(sub WN)(lambda) implies a 0.5 % uncertainty in the above-atmosphere radiance measurements. This level of uncertainty can only be approached using vicarious-calibration approaches as described below. In practice, this means that the satellite radiance responsivity is adjusted to achieve the best agreement, in a least-squares sense, for the L(sub WN)(lambda) results determined using the satellite and the independent optical sensors (e.g. MOBY). The end result of this approach is to implicitly absorb unquantified, but systematic, errors in the atmospheric correction, incident solar flux, and satellite sensor calibration into a single correction factor to produce consistency with the in situ data.

Geopotential Model Improvement Using POCM_4B Dynamic Ocean Topography Information: PGM2000A

NASA Technical Reports Server (NTRS)

Pavlis, N. K.; Chinn, D. S.; Cox, C. M.; Lemoine, Frank G.; Smith, David E. (Technical Monitor)

2000-01-01

The two-year mean (1993-1994) Dynamic Ocean Topography (DOT) field implied by the POCM_4B circulation model was used to develop normal equations for DOT, in a surface spherical harmonic representation. These normal equations were combined with normal equations from satellite tracking data, surface gravity data, and altimeter data from TOPEX/Poseidon and ERS-1. Several least-squares combination solutions were developed in this fashion, by varying parameters such as the maximum degree of the estimated DOT and the relative weights of the different data. The solutions were evaluated in terms of orbit fit residuals, GPS/Leveling-derived undulations, and independent DOT information from in situ WOCE hydrographic data. An optimal solution was developed in this fashion which was originally presented at the 1998 EGS meeting in Nice, France. This model, designated here PGM2000A, maintains the orbit and land geoid modeling performance of EGM96, while improving its marine geoid modeling capability. In addition, PGM2000A's error spectrum is considerably more realistic than those of other contemporary gravitational models and agrees well with the error spectrum of EGM96. We will present the development and evaluation of PGM2000A, with particular emphasis on the weighting of the DOT information implied by POCM_4B. We will also present an inter-comparison of PGM2000A with the GRIM5-C1 and TEG-4 models. Directions for future work and problematic areas will be identified.

Bayesian Model for Matching the Radiometric Measurements of Aerospace and Field Ocean Color Sensors

PubMed Central

Salama, Mhd. Suhyb; Su, Zhongbo

2010-01-01

A Bayesian model is developed to match aerospace ocean color observation to field measurements and derive the spatial variability of match-up sites. The performance of the model is tested against populations of synthesized spectra and full and reduced resolutions of MERIS data. The model derived the scale difference between synthesized satellite pixel and point measurements with R2 > 0.88 and relative error < 21% in the spectral range from 400 nm to 695 nm. The sub-pixel variabilities of reduced resolution MERIS image are derived with less than 12% of relative errors in heterogeneous region. The method is generic and applicable to different sensors. PMID:22163615

Climate-disease connections: Rift Valley Fever in Kenya

NASA Technical Reports Server (NTRS)

Anyamba, A.; Linthicum, K. J.; Tucker, C. J.

2001-01-01

All known Rift Valley fever(RVF) outbreaks in Kenya from 1950 to 1998 followed periods of abnormally high rainfall. On an interannual scale, periods of above normal rainfall in East Africa are associated with the warm phase of the El Nino/Southern Oscillation (ENSO) phenomenon. Anomalous rainfall floods mosquito-breeding habitats called dambos, which contain transovarially infected mosquito eggs. The eggs hatch Aedes mosquitoes that transmit the RVF virus preferentially to livestock and to humans as well. Analysis of historical data on RVF outbreaks and indicators of ENSO (including Pacific and Indian Ocean sea surface temperatures and the Southern Oscillation Index) indicates that more than three quarters of the RVF outbreaks have occurred during warm ENSO event periods. Mapping of ecological conditions using satellite normalized difference vegetation index (NDVI) data show that areas where outbreaks have occurred during the satellite recording period (1981-1998) show anomalous positive departures in vegetation greenness, an indicator of above-normal precipitation. This is particularly observed in arid areas of East Africa, which are predominantly impacted by this disease. These results indicate a close association between interannual climate variability and RVF outbreaks in Kenya.

Climate-disease connections: Rift Valley Fever in Kenya.

PubMed

Anyamba, A; Linthicum, K J; Tucker, C J

2001-01-01

All known Rift Valley fever(RVF) outbreaks in Kenya from 1950 to 1998 followed periods of abnormally high rainfall. On an interannual scale, periods of above normal rainfall in East Africa are associated with the warm phase of the El Niño/Southern Oscillation (ENSO) phenomenon. Anomalous rainfall floods mosquito-breeding habitats called dambos, which contain transovarially infected mosquito eggs. The eggs hatch Aedes mosquitoes that transmit the RVF virus preferentially to livestock and to humans as well. Analysis of historical data on RVF outbreaks and indicators of ENSO (including Pacific and Indian Ocean sea surface temperatures and the Southern Oscillation Index) indicates that more than three quarters of the RVF outbreaks have occurred during warm ENSO event periods. Mapping of ecological conditions using satellite normalized difference vegetation index (NDVI) data show that areas where outbreaks have occurred during the satellite recording period (1981-1998) show anomalous positive departures in vegetation greenness, an indicator of above-normal precipitation. This is particularly observed in arid areas of East Africa, which are predominantly impacted by this disease. These results indicate a close association between interannual climate variability and RVF outbreaks in Kenya.

Investigating SWOT's capabilities to detect meso and submesoscale eddies in the western Mediterranean

NASA Astrophysics Data System (ADS)

Gomez-Navarro, Laura; Pascual, Ananda; Fablet, Ronan; Mason, Evan

2017-04-01

The primary oceanographic objective of the future Surface Water Ocean Topography (SWOT) altimetric satellite is to characterize the mesoscale and submesoscale ocean circulation. The aim of this study is to assess the capabilities of SWOT to resolve the meso and submesoscale in the western Mediterranean. With ROMS model data as inputs for the SWOT simulator, pseudo-SWOT data were generated. These data were compared with the original ROMS model data and ADT data from present day altimetric satellites to assess the temporal and spatial resolution of SWOT in the western Mediterranean. We then addressed the removal of the satellite's noise in the pseudo-SWOT data using a Laplacian diffusion. We investigated different parameters of the filter by looking at their impact on the spatial spectra and RMSEs calculated from the simulator outputs. To further assess the satellites capabilities, we derived absolute geostrophic velocities and relative vorticity. Our numerical experiments show that the noise patterns affect the spectral content of the pseudo-SWOT fields below 30 km. The Laplacian diffusion improves the recovery of the spectral signature of the altimetric field, especially down to 20 km. With the help of this filter, we manage to observe small scale oceanic features in pseudo-SWOT data, and in its derived variables.

Advanced Satellite-Derived Wind Observations, Assimilation, and Targeting Strategies during TCS-08 for Developing Improved Operational Analysis and Prediction of Western Pacific Tropical Cyclones

DTIC Science & Technology

2012-09-30

influences on TC structure evolve up to landfall or extratropical transition. In particular, winds derived from geostationary satellites have been shown... extratropical transition, it is clear that a dedicated research effort is needed to optimize the satellite data processing strategies, assimilation...and applications to better understand the behavior of the near- storm environmental flow fields during these evolutionary TC stages. To our knowledge

Advanced Satellite-Derived Wind Observations, Assimilation, and Targeting Strategies during TCS-08 for Developing Improved Operational Analysis and Prediction of Western Pacific Tropical Cyclones

DTIC Science & Technology

2011-09-30

influences on TC structure evolve up to landfall or extratropical transition. In particular, winds derived from geostationary satellites have been...and extratropical transition, it is clear that a dedicated research effort is needed to optimize the satellite data processing strategies...assimilation, and applications to better understand the behavior of the near- storm environmental flow fields during these evolutionary TC stages. To our

An error analysis of tropical cyclone divergence and vorticity fields derived from satellite cloud winds on the Atmospheric and Oceanographic Information Processing System (AOIPS)

NASA Technical Reports Server (NTRS)

Hasler, A. F.; Rodgers, E. B.

1977-01-01

An advanced Man-Interactive image and data processing system (AOIPS) was developed to extract basic meteorological parameters from satellite data and to perform further analyses. The errors in the satellite derived cloud wind fields for tropical cyclones are investigated. The propagation of these errors through the AOIPS system and their effects on the analysis of horizontal divergence and relative vorticity are evaluated.

Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence: Insights from two contrasting extreme events

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

Sun, Ying; Fu, Rong; Dickinson, Robert

This study uses the droughts of 2011 in Texas and 2012 over the central Great Plains as case studies to explore the potential of satellite-observed solar-induced chlorophyll fluorescence (SIF) for monitoring drought dynamics. We find that the spatial patterns of negative SIF anomalies from the Global Ozone Monitoring Experiment 2 (GOME-2) closely resembled drought intensity maps from the U.S. Drought Monitor for both events. The drought-induced suppression of SIF occurred throughout 2011 but was exacerbated in summer in the Texas drought. This event was characterized by a persistent depletion of root zone soil moisture caused by yearlong below-normal precipitation. Inmore » contrast, for the central Great Plains drought, warmer temperatures and relatively normal precipitation boosted SIF in the spring of 2012; however, a sudden drop in precipitation coupled with unusually high temperatures rapidly depleted soil moisture through evapotranspiration, leading to a rapid onset of drought in early summer. Accordingly, SIF reversed from above to below normal. For both regions, the GOME-2 SIF anomalies were significantly correlated with those of root zone soil moisture, indicating that the former can potentially be used as proxy of the latter for monitoring agricultural droughts with different onset mechanisms. Further analyses indicate that the contrasting dynamics of SIF during these two extreme events were caused by changes in both fraction of absorbed photosynthetically active radiation fPAR and fluorescence yield, suggesting that satellite SIF is sensitive to both structural and physiological/biochemical variations of vegetation. Here, we conclude that the emerging satellite SIF has excellent potential for dynamic drought monitoring.« less

Drought onset mechanisms revealed by satellite solar-induced chlorophyll fluorescence: Insights from two contrasting extreme events

DOE PAGES

Sun, Ying; Fu, Rong; Dickinson, Robert; ...

2015-11-02

This study uses the droughts of 2011 in Texas and 2012 over the central Great Plains as case studies to explore the potential of satellite-observed solar-induced chlorophyll fluorescence (SIF) for monitoring drought dynamics. We find that the spatial patterns of negative SIF anomalies from the Global Ozone Monitoring Experiment 2 (GOME-2) closely resembled drought intensity maps from the U.S. Drought Monitor for both events. The drought-induced suppression of SIF occurred throughout 2011 but was exacerbated in summer in the Texas drought. This event was characterized by a persistent depletion of root zone soil moisture caused by yearlong below-normal precipitation. Inmore » contrast, for the central Great Plains drought, warmer temperatures and relatively normal precipitation boosted SIF in the spring of 2012; however, a sudden drop in precipitation coupled with unusually high temperatures rapidly depleted soil moisture through evapotranspiration, leading to a rapid onset of drought in early summer. Accordingly, SIF reversed from above to below normal. For both regions, the GOME-2 SIF anomalies were significantly correlated with those of root zone soil moisture, indicating that the former can potentially be used as proxy of the latter for monitoring agricultural droughts with different onset mechanisms. Further analyses indicate that the contrasting dynamics of SIF during these two extreme events were caused by changes in both fraction of absorbed photosynthetically active radiation fPAR and fluorescence yield, suggesting that satellite SIF is sensitive to both structural and physiological/biochemical variations of vegetation. Here, we conclude that the emerging satellite SIF has excellent potential for dynamic drought monitoring.« less

Comparison of total water vapour content in the Arctic derived from GNSS, AIRS, MODIS and SCIAMACHY

NASA Astrophysics Data System (ADS)

Alraddawi, Dunya; Sarkissian, Alain; Keckhut, Philippe; Bock, Olivier; Noël, Stefan; Bekki, Slimane; Irbah, Abdenour; Meftah, Mustapha; Claud, Chantal

2018-05-01

Atmospheric water vapour plays a key role in the Arctic radiation budget, hydrological cycle and hence climate, but its measurement with high accuracy remains an important challenge. Total column water vapour (TCWV) datasets derived from ground-based GNSS measurements are used to assess the quality of different existing satellite TCWV datasets, namely from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Atmospheric Infrared Sounder (AIRS) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The comparisons between GNSS and satellite data are carried out for three reference Arctic observation sites (Sodankylä, Ny-Ålesund and Thule) where long homogeneous GNSS time series of more than a decade (2001-2014) are available. We select hourly GNSS data that are coincident with overpasses of the different satellites over the three sites and then average them into monthly means that are compared with monthly mean satellite products for different seasons. The agreement between GNSS and satellite time series is generally within 5 % at all sites for most conditions. The weakest correlations are found during summer. Among all the satellite data, AIRS shows the best agreement with GNSS time series, though AIRS TCWV is often slightly too high in drier atmospheres (i.e. high-latitude stations during autumn and winter). SCIAMACHY TCWV data are generally drier than GNSS measurements at all the stations during the summer. This study suggests that these biases are associated with cloud cover, especially at Ny-Ålesund and Thule. The dry biases of MODIS and SCIAMACHY observations are most pronounced at Sodankylä during the snow season (from October to March). Regarding SCIAMACHY, this bias is possibly linked to the fact that the SCIAMACHY TCWV retrieval does not take accurately into account the variations in surface albedo, notably in the presence of snow with a nearby canopy as in Sodankylä. The MODIS bias at Sodankylä is found to be correlated with cloud cover fraction and is also expected to be affected by other atmospheric or surface albedo changes linked for instance to the presence of forests or anthropogenic emissions. Overall, the results point out that a better estimation of seasonally dependent surface albedo and a better consideration of vertically resolved cloud cover are recommended if biases in satellite measurements are to be reduced in the polar regions.

Exploiting Satellite Focal Plane Geometry for Automatic Extraction of Traffic Flow from Single Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Krauß, T.

2014-11-01

The focal plane assembly of most pushbroom scanner satellites is built up in a way that different multispectral or multispectral and panchromatic bands are not all acquired exactly at the same time. This effect is due to offsets of some millimeters of the CCD-lines in the focal plane. Exploiting this special configuration allows the detection of objects moving during this small time span. In this paper we present a method for automatic detection and extraction of moving objects - mainly traffic - from single very high resolution optical satellite imagery of different sensors. The sensors investigated are WorldView-2, RapidEye, Pléiades and also the new SkyBox satellites. Different sensors require different approaches for detecting moving objects. Since the objects are mapped on different positions only in different spectral bands also the change of spectral properties have to be taken into account. In case the main distance in the focal plane is between the multispectral and the panchromatic CCD-line like for Pléiades an approach for weighted integration to receive mostly identical images is investigated. Other approaches for RapidEye and WorldView-2 are also shown. From these intermediate bands difference images are calculated and a method for detecting the moving objects from these difference images is proposed. Based on these presented methods images from different sensors are processed and the results are assessed for detection quality - how many moving objects can be detected, how many are missed - and accuracy - how accurate is the derived speed and size of the objects. Finally the results are discussed and an outlook for possible improvements towards operational processing is presented.

Vegetation monitoring and classification using NOAA/AVHRR satellite data

NASA Technical Reports Server (NTRS)

Greegor, D. H., Jr.; Norwine, J. R.

1983-01-01

A vegetation gradient model, based on a new surface hydrologic index and NOAA/AVHRR meteorological satellite data, has been analyzed along a 1300 km east-west transect across the state of Texas. The model was developed to test the potential usefulness of such low-resolution data for vegetation stratification and monitoring. Normalized Difference values (ratio of AVHRR bands 1 and 2, considered to be an index of greenness) were determined and evaluated against climatological and vegetation characteristics at 50 sample locations (regular intervals of 0.25 deg longitude) along the transect on five days in 1980. Statistical treatment of the data indicate that a multivariate model incorporating satellite-measured spectral greenness values and a surface hydrologic factor offer promise as a new technique for regional-scale vegetation stratification and monitoring.

Using GPS and leveling data in local precise geoid determination and case study

NASA Astrophysics Data System (ADS)

Erol, B.; Çelik, R. N.; Erol, S.

2003-04-01

As an important result of developments in high technology, satellite based positioning system has become to use in geodesy and surveying professions. These developments made the measurement works more accurate, more practical and more economic. Today, one of the most recent used satellite based positioning system is GPS (Global Positioning System) and it serves to a very wide range of geodetic applications from monitoring earth crustal deformations till building the basis for a GIS (Geographical Information Systems). The most efficient way to utilize GPS measurement system for mentioned aims is having a reliable geodetic infrastructure in working area. Geodetic infrastructure is a extraterrestrial and time system and involved 4D geodetic reference networks. The forth element of mentioned geodetic reference system is time because having an accurate and reliable geodetic infrastructure is needed to up-date according to physical realities of the region. By the help of a well designed geodetic infrastructure accurate and reliable coordinates of a point can be generated economically every time in a global and up-to-date system. Geoid is one of the important parts of a geodetic infrastructure. As it is well known, geoid is the equipotential surface of the Earth's gravity field which best fits, in a least squares sense, global mean sea level and it is reference for physical height systems like orthometric and normal heights. In the most of the applications, vertical position of a point is expressed with orthometric or normal height. Orthometric or normal height is a physical concept and gives vertical position of a point uniquely. On the other hand, vertical position of a point is derived in a geometrical system according to GPS measurements. GPS datum is WGS84 and in this system, an ellipsoidal height of a point is calculated according to WGS84 ellipsoid. So, it is an necessity to transform the ellipsoidal heights to orthometric heights and this procedure is managed with the fundamental mathematical equation; N=h-H. In the equation, "h" is the ellipsoidal height of a point P, "H" is the orthometric height of the same point and "N" is "geoid undulation" value. Normally, "H" orthometric height derived from leveling measurements but these measurements are tiring applications. So, while having a geoid model in the region as the essential part of geodetic infrastructure, number leveling measurements can be reduced from the procedure and by this way time and labor is saved. Geoid determination is modeling of the data in such a way that geoid height can be obtained digital or analog at a point whose horizontal position is known. Geoid models can be developed for local, regional or global regions. Using satellite techniques, especially GPS, in geodetic measurements are increased importance of geoid. Because geoid is a natural tie between high precision geodetic coordinates and coordinates which obtained from satellites. There are several geoid determination methods according to used data and models. GPS/Leveling method, which is also known as geometric method, is one of these methods. This method is appropriate for local precise geoid determination in respectively small areas. In this paper, it is going to be given information about GPS/Leveling geoid determination method and mathematical models, which are used in geoid determination with this method. And Izmir local geoid model will be presented as a case study. Izmir is one of the west metropolitan cities of Turkey and located near Aegean Sea. The topography is extremely rough in the region. There are two different geoid determination studies which were carried out in 1996 and 2001 in Izmir. Both models were accomplished according to GPS/Leveling method. Those two geoid models of Izmir Metropolitan region are investigated in here, the conflict of them were discussed. The relation between distribution of common reference points and differences of geoid undulation values, which are calculated from both models separately, were analyzed and also effects of topography on conflict of both geoid model was investigated. The results of the study and suggestions are going to be given in the paper.

Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

NASA Astrophysics Data System (ADS)

Hardtke, Leonardo A.; Blanco, Paula D.; Valle, Héctor F. del; Metternicht, Graciela I.; Sione, Walter F.

2015-06-01

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g., ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l'Observation de la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000 km2 in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the normalized burned ratio index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. Additionally, the performance of the adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area (MCD45A1), the active fire algorithm (MOD14); and the L3JRC SPOT VEGETATION 1 km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R2 = 0.01-0.28, while our algorithm performed showed a stronger correlation coefficient (R2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

NASA Astrophysics Data System (ADS)

Zambrano, Francisco; Wardlow, Brian; Tadesse, Tsegaye; Lillo-Saavedra, Mario; Lagos, Octavio

2017-04-01

Precipitation is a key parameter for the study of climate change and variability and the detection and monitoring of natural disaster such as drought. Precipitation datasets that accurately capture the amount and spatial variability of rainfall is critical for drought monitoring and a wide range of other climate applications. This is challenging in many parts of the world, which often have a limited number of weather stations and/or historical data records. Satellite-derived precipitation products offer a viable alternative with several remotely sensed precipitation datasets now available with long historical data records (+30years), which include the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) datasets. This study presents a comparative analysis of three historical satellite-based precipitation datasets that include Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 version 7 (1998-2015), PERSIANN-CDR (1983-2015) and CHIRPS 2.0 (1981-2015) over Chile to assess their performance across the country and for the case of the two long-term products the applicability for agricultural drought were evaluated when used in the calculation of commonly used drought indicator as the Standardized Precipitation Index (SPI). In this analysis, 278 weather stations of in situ rainfall measurements across Chile were initially compared to the satellite data. The study area (Chile) was divided into five latitudinal zones: North, North-Central, Central, South-Central and South to determine if there were a regional difference among these satellite products, and nine statistics were used to evaluate their performance to estimate the amount and spatial distribution of historical rainfall across Chile. Hierarchical cluster analysis, k-means and singular value decomposition were used to analyze these datasets to better understand their similarities and differences in characterizing rainfall patterns across Chile. Monthly analysis showed that all satellite products highly overestimated rainfall in the arid North zone. However, there were no major difference between all three products from North to South-Central zones. Though, in the South zone, PERSIANN-CDR shows the lowest fit with high underestimation, while CHIRPS 2.0 and TMPA 3B43 v7 had better agreement with in situ measurements. The accuracy of satellite products were highly dependent on the amount of monthly rainfall with the best results found during winter seasons and in zones (Central to South) with higher amounts of precipitation. PERSIANN-CDR and CHIRPS 2.0 were used to derive SPI at time-scale of 1, 3 and 6 months, both satellite products presented similar results when it was compared in situ against satellite SPI's. Because of its higher spatial resolution that allows better characterizing of spatial variation in precipitation pattern, the CHIRPS 2.0 was used to mapping the SPI-3 over Chile. The results of this study show that in order to use the CHIRPS 2.0 and PERSIANN-CDR datasets in Chile to monitor spatial patterns in the rainfall and drought intensity conditions, these products should be calibrated to adjust for the overestimation/underestimation of rainfall geographically specially in the North zone and seasonally during the summer and spring months in the other zones.

A Spectrally Selective Attenuation Mechanism-Based Kpar Algorithm for Biomass Heating Effect Simulation in the Open Ocean

NASA Astrophysics Data System (ADS)

Chen, Jun; Zhang, Xiangguang; Xing, Xiaogang; Ishizaka, Joji; Yu, Zhifeng

2017-12-01

Quantifying the diffuse attenuation coefficient of the photosynthetically available radiation (Kpar) can improve our knowledge of euphotic depth (Zeu) and biomass heating effects in the upper layers of oceans. An algorithm to semianalytically derive Kpar from remote sensing reflectance (Rrs) is developed for the global open oceans. This algorithm includes the following two portions: (1) a neural network model for deriving the diffuse attention coefficients (Kd) that considers the residual error in satellite Rrs, and (2) a three band depth-dependent Kpar algorithm (TDKA) for describing the spectrally selective attenuation mechanism of underwater solar radiation in the open oceans. This algorithm is evaluated with both in situ PAR profile data and satellite images, and the results show that it can produce acceptable PAR profile estimations while clearly removing the impacts of satellite residual errors on Kpar estimations. Furthermore, the performance of the TDKA algorithm is evaluated by its applicability in Zeu derivation and mean temperature within a mixed layer depth (TML) simulation, and the results show that it can significantly decrease the uncertainty in both compared with the classical chlorophyll-a concentration-based Kpar algorithm. Finally, the TDKA algorithm is applied in simulating biomass heating effects in the Sargasso Sea near Bermuda, with new Kpar data it is found that the biomass heating effects can lead to a 3.4°C maximum positive difference in temperature in the upper layers but could result in a 0.67°C maximum negative difference in temperature in the deep layers.

Interference and Compatibility Studies Between Satellite Service Systems and Systems Using High Altitude Platform Stations

NASA Astrophysics Data System (ADS)

Milas, Vasilis; Koletta, Maria; Constantinou, Philip

2003-07-01

This paper provides the results of interference and compatibility studies in order to assess the sharing conditions between Fixed Satellite Service (FSS) and Fixed Service provided by High Altitude Platform Stations (HAPS) in the same operational frequency bands and discusses the most important operational parameters that have an impact on the interference calculations. To characterize interference phenomena between the two systems carrier to interference (C/I) ratios are evaluated. Simulation results under the scenario of a realistic deployment of HAPS and the use of different satellite configurations are presented. An interesting result derived from the simulations is that FSS/GSO Earth Stations and HAPS ground stations may coexist in the HAPS coverage area under certain considerations.

Annually and monthly resolved solar irradiance and atmospheric temperature data across the Hawaiian archipelago from 1998 to 2015 with interannual summary statistics.

PubMed

Bryce, Richard; Losada Carreño, Ignacio; Kumler, Andrew; Hodge, Bri-Mathias; Roberts, Billy; Brancucci Martinez-Anido, Carlo

2018-08-01

This article contains data and summary statistics of solar irradiance and dry bulb temperature across the Hawaiian archipelago resolved on a monthly basis and spanning years 1998-2015. This data was derived in association with an article titled "Consequences of Neglecting the Interannual Variability of the Solar Resource: A Case Study of Photovoltaic Power Among the Hawaiian Islands" (Bryce et al., 2018 [7]). The solar irradiance data is presented in terms of Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), and Global Horizontal Irradiance (GHI) and was obtained from the satellite-derived data contained in the National Solar Radiation Database (NSRDB). The temperature data is also obtained from this source. We have processed the NSRDB data and compiled these monthly resolved data sets, along with interannual summary statistics including the interannual coefficient of variability.

Near-earth asteroids - Possible sources from reflectance spectroscopy

NASA Technical Reports Server (NTRS)

Mcfadden, L. A.; Gaffey, M. J.; Mccord, T. B.

1985-01-01

The diversity of reflectance spectra noted among near-earth asteroids that were compared with selected asteroids, planets and satellites to determine possible source regions is indicative of different mineralogical composition and, accordingly, of more than one source region. Spectral signatures that are similar to those of main belt asteroids support models deriving some of these asteroids from the 5:2 Kirkwood gap and the Flora family, by way of gravitational perturbations. The differences in composition found between near-earth asteroids and planetary and satellite surfaces are in keeping with theoretical arguments that such bodies should not be sources. While some near-earth asteroids furnish portions of the earth's meteorite flux, other sources must also contribute.

Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment.

PubMed

Cole, Ella F; Long, Peter R; Zelazowski, Przemyslaw; Szulkin, Marta; Sheldon, Ben C

2015-11-01

Population-level studies of how tit species (Parus spp.) track the changing phenology of their caterpillar food source have provided a model system allowing inference into how populations can adjust to changing climates, but are often limited because they implicitly assume all individuals experience similar environments. Ecologists are increasingly using satellite-derived data to quantify aspects of animals' environments, but so far studies examining phenology have generally done so at large spatial scales. Considering the scale at which individuals experience their environment is likely to be key if we are to understand the ecological and evolutionary processes acting on reproductive phenology within populations. Here, we use time series of satellite images, with a resolution of 240 m, to quantify spatial variation in vegetation green-up for a 385-ha mixed-deciduous woodland. Using data spanning 13 years, we demonstrate that annual population-level measures of the timing of peak abundance of winter moth larvae (Operophtera brumata) and the timing of egg laying in great tits (Parus major) and blue tits (Cyanistes caeruleus) is related to satellite-derived spring vegetation phenology. We go on to show that timing of local vegetation green-up significantly explained individual differences in tit reproductive phenology within the population, and that the degree of synchrony between bird and vegetation phenology showed marked spatial variation across the woodland. Areas of high oak tree (Quercus robur) and hazel (Corylus avellana) density showed the strongest match between remote-sensed vegetation phenology and reproductive phenology in both species. Marked within-population variation in the extent to which phenology of different trophic levels match suggests that more attention should be given to small-scale processes when exploring the causes and consequences of phenological matching. We discuss how use of remotely sensed data to study within-population variation could broaden the scale and scope of studies exploring phenological synchrony between organisms and their environment.

Characterization of turbidity in Florida's Lake Okeechobee and Caloosahatchee and St. Lucie estuaries using MODIS-Aqua measurements.

PubMed

Wang, Menghua; Nim, Carl J; Son, Seunghyun; Shi, Wei

2012-10-15

This paper describes the use of ocean color remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to characterize turbidity in Lake Okeechobee and its primary drainage basins, the Caloosahatchee and St. Lucie estuaries from 2002 to 2010. Drainage modification and agricultural development in southern Florida transport sediments and nutrients from watershed agricultural areas to Lake Okeechobee. As a result of development around Lake Okeechobee and the estuaries that are connected to Lake Okeechobee, estuarine conditions have also been adversely impacted, resulting in salinity and nutrient fluctuations. The measurement of water turbidity in lacustrine and estuarine ecosystems allows researchers to understand important factors such as light limitation and the potential release of nutrients from re-suspended sediments. Based on a strong correlation between water turbidity and normalized water-leaving radiance at the near-infrared (NIR) band (nL(w)(869)), a new satellite water turbidity algorithm has been developed for Lake Okeechobee. This study has shown important applications with satellite-measured nL(w)(869) data for water quality monitoring and measurements for turbid inland lakes. MODIS-Aqua-measured water property data are derived using the shortwave infrared (SWIR)-based atmospheric correction algorithm in order to remotely obtain synoptic turbidity data in Lake Okeechobee and normalized water-leaving radiance using the red band (nL(w)(645)) in the Caloosahatchee and St. Lucie estuaries. We found varied, but distinct seasonal, spatial, and event driven turbidity trends in Lake Okeechobee and the Caloosahatchee and St. Lucie estuary regions. Wind waves and hurricanes have the largest influence on turbidity trends in Lake Okeechobee, while tides, currents, wind waves, and hurricanes influence the Caloosahatchee and St. Lucie estuarine areas. Published by Elsevier Ltd.

Estimation of daily mean air temperature from satellite derived radiometric data

NASA Technical Reports Server (NTRS)

Phinney, D.

1976-01-01

The Screwworm Eradication Data System (SEDS) at JSC utilizes satellite derived estimates of daily mean air temperature (DMAT) to monitor the effect of temperature on screwworm populations. The performance of the SEDS screwworm growth potential predictions depends in large part upon the accuracy of the DMAT estimates.

The role of landscape characteristics for forage maturation and nutritional benefits of migration in red deer.

PubMed

Mysterud, Atle; Vike, Brit Karen; Meisingset, Erling L; Rivrud, Inger Maren

2017-06-01

Large herbivores gain nutritional benefits from following the sequential flush of newly emergent, high-quality forage along environmental gradients in the landscape, termed green wave surfing. Which landscape characteristics underlie the environmental gradient causing the green wave and to what extent landscape characteristics alone explain individual variation in nutritional benefits remain unresolved questions. Here, we combine GPS data from 346 red deer ( Cervus elaphus ) from four partially migratory populations in Norway with the satellite-derived normalized difference vegetation index (NDVI), an index of plant phenology. We quantify whether migratory deer had access to higher quality forage than resident deer, how landscape characteristics within summer home ranges affected nutritional benefits, and whether differences in landscape characteristics could explain differences in nutritional gain between migratory and resident deer. We found that migratory red deer gained access to higher quality forage than resident deer but that this difference persisted even after controlling for landscape characteristics within the summer home ranges. There was a positive effect of elevation on access to high-quality forage, but only for migratory deer. We discuss how the landscape an ungulate inhabits may determine its responses to plant phenology and also highlight how individual behavior may influence nutritional gain beyond the effect of landscape.

Evaluation of methods to derive green-up dates based on daily NDVI satellite observations

NASA Astrophysics Data System (ADS)

Doktor, Daniel

2010-05-01

Bridging the gap between satellite derived green-up dates and in situ phenological observations has been the purpose of many studies over the last decades. Despite substantial advancements in satellite technology and data quality checks there is as yet no universally accepted method for extracting phenological metrics based on satellite derived vegetation indices. Dependent on the respective method derived green-up dates can vary up to serveral weeks using identical data sets. Consequently, it is difficult to compare various studies and to accurately determine an increased vegetation length due to changing temperature patterns as observed by ground phenological networks. Here, I compared how the characteristic NDVI increase over temperate deciduous forests in Germany in spring relates to respective budburst events observed on the ground. MODIS Terra daily surface reflectances with a 250 m resolution (2000-2008) were gathered to compute daily NDVI values. As ground truth, observations of the extensive phenological network of the German Weather Service were used. About 1500 observations per year and species (Beech, Oak and Birch) were available evenly distributed all over Germany. Two filtering methods were tested to reduce the noisy raw data. The first method only keeps NDVI values which are classified as ‚ideal global quality' and applies on those a temporal moving window where values are removed which differ more than 20% of the mean. The second method uses an adaptation of the BISE (Best Index Slope Extraction) algorithm. Subsequently, three functions were fitted to the selected observations: a simple linear interpolation, a sigmoidal function and a double logistic sigmoidal function allowing to approximate two temporally separated green-up signals. The green-up date was then determined at halfway between minimum and maximum (linear interpolation) or at the inflexion point of the sigmoidal curve. A number of global threshold values (NDVI 0.4,0.5,0.6) and varying definitions of the NDVI baseline during dormancy were also tested. In contrast to most past studies, I did not attempt to identify matched pairs of geographically coincident ground and satellite observations. Rather than comparing on an individual grid-cell basis I analysed and compared the statistical properties of distributions generated from ground and satellite observations. It has been noticed that remote sensing provides a statistical distribution of a random variable, not an exact representation of the state of the land surface or atmosphere at a particular pixel. The same holds true for ground observations as they sample from biological variability and landscapes with heterogeneous microclimates. First results reveal substantial differences between the applied methods. Based on the assumption that the satellite captures predominantly the greening-up of the canopy - which occurs about 2 weeks later than observed budburst dates - the double sigmoidal function combined with the BISE filtering procedure performed best.

A comparison and evaluation of satellite derived positions of tracking stations

NASA Technical Reports Server (NTRS)

Vincent, S. F.; Strange, W. E.; Marsh, J. G.

1971-01-01

A comparison is presented of sets of satellite tracking station coordinate values published in the past few years by a number of investigators, i.e. Goddard Space Flight Center, Smithsonian Astrophysical Observatory, Ohio State University, The Naval Weapons Laboratory, Air Force Cambridge Research Laboratories, and Wallops Island. The comparisons have been made in terms of latitude, longitude and height. The results of the various solutions have been compared directly and also with external standards such as local survey data and gravimetrically derived geoid heights. After taking into account systematic rotations, latitude and longitude agreement on a global basis is generally 15 meters or better, on the North American Datum agreement is generally better than 10 meters. Allowing for scale differences (of the order of 2 ppm) radial agreement is generally of the order of 10 meters.