Comparison of AVHRR and SMMR data for monitoring vegetation phenology on a continental scale

NASA Technical Reports Server (NTRS)

Justice, C. O.; Townshend, J. R. G.; Choudhury, B. J.

1989-01-01

AVHRR normalized difference vegetation index (NDVI) data for a one-year period were compared with Scanning Multichannel Microwave Radiometer microwave polarization difference temperature (MPDT) data for the study of vegetation phenology. It is shown that the MPDT response differs considerably from the seasonal NDVI pattern. The results do not support the hypothetical relationship between MPDT and leaf water content. It is found that only vegetation types with a substantial seasonal variation in the areal extent of vegetated cover show strong seasonality in MPDT data.

The 1990 conterminous U. S. AVHRR data set

USGS Publications Warehouse

Eidenshink, Jeffery C.

1992-01-01

The U.S. Geological Survey, using NOAA-ll Advanced Very High Resolution Radiometer (AVHRR) 1-km data, has produced a time series of 19 biweekly maximum normalized difference vegetation index (NDV!) composites of the conterminous United States for the 1990 growing season. Each biweekly composite included data from approximately 20 calibrated and georegistered daily overpasses. The output is a data set which includes all five calibrated AVHRR channels, NOV! values, three satellite/solar viewing angles, and date of observation pointer for each biweekly composite. The data set is intended for assessing seasonal variations in vegetation condition and provides a foundation for studying long-term changes in vegetation resulting from human interactions or global climate alterations.

A gradient model of vegetation and climate utilizing NOAA satellite imagery. Phase 1: Texas transect

NASA Technical Reports Server (NTRS)

Greegor, D. H.; Norwine, J.

1981-01-01

A new experimental climatological model/variable termed the sponge, a measure of moisture availability based on daily temperature maxima and minima and precipitation, is tested for potential biogeographic, ecological, and agro-climatological applications. Results, depicted in tabular and graphic from, suggest that, as a generalized climatic index, sponge's simplicity and sensitivity make particularly appropriate for trans-regional biogeographic studies (e.g., large-area and global vegetation monitoring). The feasibility of utilizing NOAA/AVHRR data for vegetation classification was investigated and a vegetation gradient model that utilizes sponge, and AVHRR pixel data (channels 1 and 2) were obtained for 12 locations. The normalized difference values for the AVHRR data when plotted against vegetation characteristics (biomass, net productivity, leaf area) and sponge values suggest that a multivariate gradient model incorporating AVHRR and sponge data may indeed be useful in global vegetation stratification and monitoring.

Analysis of urban regions using AVHRR thermal infrared data

USGS Publications Warehouse

Wright, Bruce

1993-01-01

Using 1-km AVHRR satellite data, relative temperature difference caused by conductivity and inertia were used to distinguish urban and non urban land covers. AVHRR data that were composited on a biweekly basis and distributed by the EROS Data Center in Sioux Falls, South Dakota, were used for the classification process. These composited images are based on the maximum normalized different vegetation index (NDVI) of each pixel during the 2-week period using channels 1 and 2. The resultant images are nearly cloud-free and reduce the need for extensive reclassification processing. Because of the physiographic differences between the Eastern and Western United States, the initial study was limited to the eastern half of the United States. In the East, the time of maximum difference between the urban surfaces and the vegetated non urban areas is the peak greenness period in late summer. A composite image of the Eastern United States for the 2-weel time period from August 30-Septmeber 16, 1991, was used for the extraction of the urban areas. Two channels of thermal data (channels 3 and 4) normalized for regional temperature differences and a composited NDVI image were classified using conventional image processing techniques. The results compare favorably with other large-scale urban area delineations.

Study to assess the importance of errors introduced by applying NOAA 6 and NOAA 7 AVHRR data as an estimator of vegetative vigor: Feasibility study of data normalization

NASA Technical Reports Server (NTRS)

Duggin, M. J. (Principal Investigator); Piwinski, D.

1982-01-01

The use of NOAA AVHRR data to map and monitor vegetation types and conditions in near real-time can be enhanced by using a portion of each GAC image that is larger than the central 25% now considered. Enlargement of the cloud free image data set can permit development of a series of algorithms for correcting imagery for ground reflectance and for atmospheric scattering anisotropy within certain accuracy limits. Empirical correction algorithms used to normalize digital radiance or VIN data must contain factors for growth stage and for instrument spectral response. While it is not possible to correct for random fluctuations in target radiance, it is possible to estimate the necessary radiance difference between targets in order to provide target discrimination and quantification within predetermined limits of accuracy. A major difficulty lies in the lack of documentation of preprocessing algorithms used on AVHRR digital data.

Monitoring start of season in Alaska with GLOBE, AVHRR, and MODIS data

NASA Astrophysics Data System (ADS)

Robin, Jessica; Dubayah, Ralph; Sparrow, Elena; Levine, Elissa

2008-03-01

This work evaluates whether continuity between Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) is achievable for monitoring phenological changes in Alaska. This work also evaluates whether NDVI can detect changes in start of the growing season (SOS) in this region. Six quadratic regression models with NDVI as a function of accumulated growing degree days (AGDD) were developed from 2001 through 2004 AVHRR and MODIS NDVI data sets for urban, mixed, and forested land covers. Model parameters determined NDVI values for start of the observational period as well as peak and length of the growing season. NDVI values for start of the growing season were determined from the model equations and field observations of SOS made by GLOBE students and researchers at University of Alaska Fairbanks. AGDD was computed from daily air temperature. AVHRR and MODIS models were significantly different from one another with differences in the start of the observational season as well as start, peak, and length of the growing season. Furthermore, AGDD for SOS was significantly lower during the 1990s than the 1980s. NDVI values at SOS did not detect this change. There are limitations with using NDVI to monitor phenological changes in these regions because of snow, the large extent of conifers, and clouds, which restrict the composite period. In addition, differing processing and spectral characteristics restrict continuity between AVHRR and MODIS NDVI data sets.

Estimation of carbon emissions from wildfires in Alaskan boreal forests using AVHRR data

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

Kasischke, E.S.; French, N.H.F.; Bourgeau-Chavez, L.L

1993-06-01

The objectives of this research study were to evaluate the utility of using AVHRR data for locating and measuring the areal extent of wildfires in the boreal forests of Alaska and to estimate the amount of carbon being released during these fires. Techniques were developed to using the normalized difference vegetation signature derived from AVHRR data to detect and measure the area of fires in Alaska. A model was developed to estimate the amount of biomass/carbon being stored in Alaskan boreal forests, and the amount of carbon released during fires. The AVHRR analysis resulted in detection of > 83% ofmore » all forest fires greater than 2,000 ha in size in the years 1990 and 1991. The areal estimate derived from AVHRR data were 75% of the area mapped by the Alaska Fire Service for these years. Using fire areas and locations for 1954 through 1992, it was determined that on average, 13.0 gm-C-m-2 of boreal forest area is released during fires every year. This estimate is two to six times greater than previous reported estimates. Our conclusions are that the analysis of AVHRR data represents a viable means for detecting and mapping fires in boreal regions on a global basis.« less

[Dendrolimus spp. damage monitoring by using NOAA/AVHRR data].

PubMed

Zhang, Yushu; Ban, Xianxiu; Chen, Pengshi; Feng, Rui; Ji, Ruipeng; Xiao, Yan

2005-05-01

This paper approached the feasibility of quantitatively monitoring Dendrolimus spp. damage by using NOAA/ AVHRR data. The damaged rate of needle leaf was used to represent Dendrolimus spp. harming degree, and < 30%, 30%-60% and > 60% of damaged rate was defined as low, medium and severe harming degree, respectively. The correlation equation of damaged rate and normalized vegetation index (NDVI) was established, based on the ground spectrum observation. The NDVI was 0.8823 when no damage occurred. A relative NDVI value of damaged to undamaged area was used to express the remote sensing index of low, medium and severe harming degree. The index was 1 for undamaged forest, and 0.78-1, 0.57-0.78 and < 0.57 for low, medium and severe harming degrees, respectively. The mixed pixels were separated by linear addable vertical vegetation index in the monitoring, and the quantitative monitoring and analysis was accomplished for years when the three damage degrees happened. It was shown that AVHRR data could be more available in quantitatively monitoring and analyzing serious damage, while low degree damage was difficult to distinguish by AVHRR data, due to the differences of surface properties and atmospheric influences, as well as the lower space resolution of NOAA/AVHRR. The damaged area estimated by AVHRR was 12.1%-14.3% lower than that by TM.

Trace gas emissions to the atmosphere by biomass burning in the west African savannas

NASA Technical Reports Server (NTRS)

Frouin, Robert J.; Iacobellis, Samuel F.; Razafimpanilo, Herisoa; Somerville, Richard C. J.

1994-01-01

Savanna fires and atmospheric carbon dioxide (CO2) detection and estimating burned area using Advanced Very High Resolution Radiometer_(AVHRR) reflectance data are investigated in this two part research project. The first part involves carbon dioxide flux estimates and a three-dimensional transport model to quantify the effect of north African savanna fires on atmospheric CO2 concentration, including CO2 spatial and temporal variability patterns and their significance to global emissions. The second article describes two methods used to determine burned area from AVHRR data. The article discusses the relationship between the percentage of burned area and AVHRR channel 2 reflectance (the linear method) and Normalized Difference Vegetation Index (NDVI) (the nonlinear method). A comparative performance analysis of each method is described.

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.

Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data. [Normalized Difference Vegetation Index

NASA Technical Reports Server (NTRS)

Nemani, Ramakrishna R.; Running, Steven W.

1989-01-01

Infrared surface temperatures from satellite sensors have been used to infer evaporation and soil moisture distribution over large areas. However, surface energy partitioning to latent versus sensible heat changes with surface vegetation cover and water availability. The hypothesis that the relationship between surface temperature and canopy density is sensitivite to seasonal changes in canopy resistance of conifer forests is presently tested. Surface temperature and canopy density were computed for a 20 x 25 km forested region in Montana, from the NOAA/AVHRR for 8 days during the summer of 1985. A forest ecosystem model, FOREST-BGC, simulated canopy resistance for the same period. For all eight days, surface temperatures had high association with canopy density, measured as Normalized Difference Vegetation Index, implying that latent heat exchange is the major cause of spatial variations in surface radiant tmeperatures.

Trace gas emissions to the atmosphere by biomass burning in the west African savannas. Final report, 1 October 1991-31 March 1994

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

Frouin, R.J.; Iacobellis, S.F.; Razafimpanilo, H.

1994-08-01

Savanna fires and atmospheric carbon dioxide (CO2) detection and estimating burned area using Advanced Very High Resolution Radiometer (AVHRR) reflectance data are investigated in this two part research project. The first part involves carbon dioxide flux estimates and a three-dimensional transport model to quantify the effect of North African savanna fires on atmospheric CO2 concentration, including CO2 spatial and temporal variability patterns and their significance to global emissions. The second article describes two methods used to determine burned area from AVHRR data. The article discusses the relationship between the percentage of burned area and AVHRR channel 2 reflectance (the linearmore » method) and Normalized Difference Vegetation Index (NDVI) (the nonlinear method). A comparative performance analysis of each method is described.« less

The 1 km AVHRR global land data set: first stages in implementation

USGS Publications Warehouse

Eidenshink, J.C.; Faundeen, J.L.

1994-01-01

The global land 1 km data set project represents an international effort to acquire, archive, process, and distribute 1 km AVHRR data of the entire global land surface in order to meet the needs of the international science community. A network of 26 high resolution picture transmission (HRPT) stations, along with data recorded by the National Oceanic and Atmospheric Administration (NOAA), has been acquiring daily global land coverage since 1 April 1992. A data set of over 30000 AVHRR images has been archived and made available for distribution by the United States Geological Survey, EROS Data Center and the European Space Agency. Under the guidance of the International Geosphere Biosphere programme, processing standards for the AVHRR data have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are related to the study of surface vegetation cover. A prototype 10-day composite was produced for the period of 21–30 June 1992. Production of an 18-month time series of 10-day composites is underway.

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...

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.

Identifying high production, low production and degraded rangelands in Senegal with normalized difference vegetation index data

USGS Publications Warehouse

Tappan, G. Gray; Wood, Lynette; Moore, Donald G.

1993-01-01

Seasonal herbaceous vegetation production on Senegal's native rangelands exhibits high spatial and temporal variability. This variability can be monitored using normalized difference vegetation index (NDVI) data computed from 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) image data. Although annual fluctuations in rainfall account for some of the variability, numerous long-term production patterns are evident in the AVHRR time-series data. Different n productivity reflect variations in the region's climate, topography, soils, and land use. Areas of overgrazing and intensive cultivation have caused long-term soil and vegetation degradation. Rangelands of high and low productivity, and degraded rangelands were identified using NDVI. Time-series image data from 1987 though 1992 were used to map relative rangeland productivity. The results were compared to detailed resource maps on soils, vegetation and land use. Much of the variation in rangeland productivity correlated well to the known distribution of resources. The study developed an approach that identified a number of areas of degraded soils and low vegetation production.

Estimating solar radiation using NOAA/AVHRR and ground measurement data

NASA Astrophysics Data System (ADS)

Fallahi, Somayeh; Amanollahi, Jamil; Tzanis, Chris G.; Ramli, Mohammad Firuz

2018-01-01

Solar radiation (SR) data are commonly used in different areas of renewable energy research. Researchers are often compelled to predict SR at ground stations for areas with no proper equipment. The objective of this study was to test the accuracy of the artificial neural network (ANN) and multiple linear regression (MLR) models for estimating monthly average SR over Kurdistan Province, Iran. Input data of the models were two data series with similar longitude, latitude, altitude, and month (number of months) data, but there were differences between the monthly mean temperatures in the first data series obtained from AVHRR sensor of NOAA satellite (DS1) and in the second data series measured at ground stations (DS2). In order to retrieve land surface temperature (LST) from AVHRR sensor, emissivity of the area was considered and for that purpose normalized vegetation difference index (NDVI) calculated from channels 1 and 2 of AVHRR sensor was utilized. The acquired results showed that the ANN model with DS1 data input with R2 = 0.96, RMSE = 1.04, MAE = 1.1 in the training phase and R2 = 0.96, RMSE = 1.06, MAE = 1.15 in the testing phase achieved more satisfactory performance compared with MLR model. It can be concluded that ANN model with remote sensing data has the potential to predict SR in locations with no ground measurement stations.

USE OF REMOTELY SENSED DATA FOR PARAMETERIZING AND VALIDATING LAND-USE HYDROLOGIC MODELS

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 (AVHRR) flown on NOAA satellites. NDVI variability was compared with regi...

Monitoring Start of Season in Alaska

NASA Astrophysics Data System (ADS)

Robin, J.; Dubayah, R.; Sparrow, E.; Levine, E.

2006-12-01

In biomes that have distinct winter seasons, start of spring phenological events, specifically timing of budburst and green-up of leaves, coincides with transpiration. Seasons leave annual signatures that reflect the dynamic nature of the hydrologic cycle and link the different spheres of the Earth system. This paper evaluates whether continuity between AVHRR and MODIS normalized difference vegetation index (NDVI) is achievable for monitoring land surface phenology, specifically start of season (SOS), in Alaska. Additionally, two thresholds, one based on NDVI and the other on accumulated growing degree-days (GDD), are compared to determine which most accurately predicts SOS for Fairbanks. Ratio of maximum greenness at SOS was computed from biweekly AVHRR and MODIS composites for 2001 through 2004 for Anchorage and Fairbanks regions. SOS dates were determined from annual green-up observations made by GLOBE students. Results showed that different processing as well as spectral characteristics of each sensor restrict continuity between the two datasets. MODIS values were consistently higher and had less inter-annual variability during the height of the growing season than corresponding AVHRR values. Furthermore, a threshold of 131-175 accumulated GDD was a better predictor of SOS for Fairbanks than a NDVI threshold applied to AVHRR and MODIS datasets. The NDVI threshold was developed from biweekly AVHRR composites from 1982 through 2004 and corresponding annual green-up observations at University of Alaska-Fairbanks (UAF). The GDD threshold was developed from 20+ years of historic daily mean air temperature data and the same green-up observations. SOS dates computed with the GDD threshold most closely resembled actual green-up dates observed by GLOBE students and UAF researchers. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska.

Evaluation of Operational Albedo Algorithms For AVHRR, MODIS and VIIRS: Case Studies in Southern Africa

NASA Astrophysics Data System (ADS)

Privette, J. L.; Schaaf, C. B.; Saleous, N.; Liang, S.

2004-12-01

Shortwave broadband albedo is the fundamental surface variable that partitions solar irradiance into energy available to the land biophysical system and energy reflected back into the atmosphere. Albedo varies with land cover, vegetation phenological stage, surface wetness, solar angle, and atmospheric condition, among other variables. For these reasons, a consistent and normalized albedo time series is needed to accurately model weather, climate and ecological trends. Although an empirically-derived coarse-scale albedo from the 20-year NOAA AVHRR record (Sellers et al., 1996) is available, an operational moderate resolution global product first became available from NASA's MODIS sensor. The validated MODIS product now provides the benchmark upon which to compare albedo generated through 1) reprocessing of the historic AVHRR record and 2) operational processing of data from the future National Polar-Orbiting Environmental Satellite System's (NPOESS) Visible/Infrared Imager Radiometer Suite (VIIRS). Unfortunately, different instrument characteristics (e.g., spectral bands, spatial resolution), processing approaches (e.g., latency requirements, ancillary data availability) and even product definitions (black sky albedo, white sky albedo, actual or blue sky albedo) complicate the development of the desired multi-mission (AVHRR to MODIS to VIIRS) albedo time series -- a so-called Climate Data Record. This presentation will describe the different albedo algorithms used with AVHRR, MODIS and VIIRS, and compare their results against field measurements collected over two semi-arid sites in southern Africa. We also describe the MODIS-derived VIIRS proxy data we developed to predict NPOESS albedo characteristics. We conclude with a strategy to develop a seamless Climate Data Record from 1982- to 2020.

Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates

NASA Technical Reports Server (NTRS)

Running, Steven W.; Nemani, Ramakrishna R.

1988-01-01

Weekly AVHRR Normalized Difference Vegetation Index (NDVI) values for 1983-1984 for seven sites of diverse climate in North America were correlated with results of an ecosystem simulation model of a hypothetical forest stand for the corresponding period at each site. The tendency of raw NDVI data to overpredict photosynthesis and transpiration on water limited sites was shown to be partially corrected by using an aridity index of annual radiation/annual precipitation. The results suggest that estimates of vegetation productivity using the global vegetation index are only accurate as annual integrations, unless unsubsampled local area coverage NDVI data can be tested against forest photosynthesis, transpiration and aboveground net primary production data measured at shorter time intervals.

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.

[Analysis on the relationship between malaria epidemics and NOAA-AVHRR NDVI in Hainan province].

PubMed

Wen, Liang; Xu, De-zhong; Wang, Shan-qing; Li, Cai-xu; Zhang, Zhi-ying; Su, Yong-qiang

2005-04-01

To explore the relationship between malaria epidemics and NOAA-AVHRR NDVI. Data on malaria were collected in all 19 counties in Hainan province from Feb, 1995 to Jan, 1996. Values regarding normalized difference vegetation index (NDVI)-related indicators including mean and maximum values of NDVI, the area proportion of NDVI values of 145- and 145+, months with NDVI values of 135+, 140+, 145+, 150+ of these counties in this period were all extracted from NOAA-AVHRR images, using ERDAS8.5 software. The coefficients of correlation of malaria incidences and these NDVI-related indicator values were then calculated with SPSS 11.0. The incidence of malaria showed positive correlations to mean and maximum values of NDVI, the area proportion of NDVI values of 145+ and months with NDVI values of 135+, 140+, 145+, 150+ respectively, but having negative correlation to the area of NDVI values of 145-. The malaria epidemic regions were in accordance with those regions that the NDVI values of 145+ were continuing for 9 months or more. Malaria prevalence was associated with NOAA-AVHRR NDVI value which could be considered to be use for malaria surveillance in Hainan province.

Evaluating the Consistency of the 1982–1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II

PubMed Central

Alcaraz-Segura, Domingo; Liras, Elisa; Tabik, Siham; Paruelo, José; Cabello, Javier

2010-01-01

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations’ carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982–1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast. PMID:22205868

Evaluating the consistency of the 1982-1999 NDVI trends in the Iberian Peninsula across four time-series derived from the AVHRR sensor: LTDR, GIMMS, FASIR, and PAL-II.

PubMed

Alcaraz-Segura, Domingo; Liras, Elisa; Tabik, Siham; Paruelo, José; Cabello, Javier

2010-01-01

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations' carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982-1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast.

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.

Integrated NDVI images for Niger 1986-1987. [Normalized Difference Vegetation Index

NASA Technical Reports Server (NTRS)

Harrington, John A., Jr.; Wylie, Bruce K.; Tucker, Compton J.

1988-01-01

Two NOAA AVHRR images are presented which provide a comparison of the geographic distribution of an integration of the normalized difference vegetation index (NDVI) for the Sahel zone in Niger for the growing seasons of 1986 and 1987. The production of the images and the application of the images for resource management are discussed. Daily large area coverage with a spatial resolution of 1.1 km at nadir were transformed to the NDVI and geographically registered to produce the images.

Mapping the global land surface using 1 km AVHRR data

USGS Publications Warehouse

Lauer, D.T.; Eidenshink, J.C.

1998-01-01

The scientific requirements for mapping the global land surface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global land surface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.

BOREAS RSS-7 Regional LAI and FPAR Images From 10-Day AVHRR-LAC Composites

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Chen, Jing; Cihlar, Josef

2000-01-01

The BOReal Ecosystem-Atmosphere Study Remote Sensing Science (BOREAS RSS-7) team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of Leaf Area Index (LAI) from remotely sensed images. Advanced Very High Resolution Radiometer (AVHRR) level-4c 10-day composite Normalized Difference Vegetation Index (NDVI) images produced at CCRS were used to produce images of LAI and the Fraction of Photosynthetically Active Radiation (FPAR) absorbed by plant canopies for the three summer IFCs in 1994 across the BOREAS region. The algorithms were developed based on ground measurements and Landsat Thematic Mapper (TM) images. The data are stored in binary image format files.

Generating a Long-Term Land Data Record from the AVHRR and MODIS Instruments

NASA Technical Reports Server (NTRS)

Pedelty, Jeffrey; Devadiga, Sadashiva; Masuoka, Edward; Brown, Molly; Pinzon, Jorge; Tucker, Compton; Vermote, Eric; Prince, Stephen; Nagol, Jyotheshwar; Justice, Christopher;

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.

NOAA-AVHRR image mosaics applied to vegetation identification

NASA Astrophysics Data System (ADS)

de Almeida, Maria d. G.; Ruddorff, Bernardo F.; Shimabukuro, Yosio E.

2001-06-01

In this paper, the maximum-value composite of images procedure from Normalized Difference Vegetation Index is used to get a cloud free image mosaic. The image mosaic is used to identify vegetation targets such as tropical forest, savanna and caatinga as well to make the vegetation cover mapping of Minas Gerais state, Brazil.

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.

The Circumpolar Arctic Vegetation Map: AVHRR-derived base maps, environmental controls, and integrated mapping procedures

Treesearch

D. A. WALKER; W. A. GOULD; MAIERH. A.; M. K. RAYNOLDS

2002-01-01

A new false-colour-infrared image derived from biweekly 1993 and 1995 Advanced Very High Resolution Radiometer (AVHRR) data provides a snow-free and cloud-free base image for the interpretation of vegetation as part of a 1:7.5M-scale Circumpolar Arctic Vegetation Map (CAVM). A maximum-NDVI (Normalized DiVerence Vegetation Index) image prepared from the same data...

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...

Aerosol Correction for Remotely Sensed Sea Surface Temperatures From the NOAA AVHRR: Phase II

NASA Astrophysics Data System (ADS)

Nalli, N. R.; Ignatov, A.

2002-05-01

For over two decades, the National Oceanic and Atmospheric Administration (NOAA) has produced global retrievals of sea surface temperature (SST) using infrared (IR) data from the Advanced Very High Resolution Radiometer (AVHRR). The standard multichannel retrieval algorithms are derived from regression analyses of AVHRR window channel brightness temperatures against in situ buoy measurements under non-cloudy conditions thus providing a correction for IR attenuation due to molecular water vapor absorption. However, for atmospheric conditions with elevated aerosol levels (e.g., arising from dust, biomass burning and volcanic eruptions), such algorithms lead to significant negative biases in SST because of IR attenuation arising from aerosol absorption and scattering. This research presents the development of a 2nd-phase aerosol correction algorithm for daytime AVHRR SST. To accomplish this, a long-term (1990-1998), global AVHRR-buoy matchup database was created by merging the Pathfinder Atmospheres (PATMOS) and Oceans (PFMDB) data sets. The merged data are unique in that they include multi-year, global daytime estimates of aerosol optical depth (AOD) derived from AVHRR channels 1 and 2 (0.63 and 0.83 μ m, respectively), along with an effective Angstrom exponent derived from the AOD retrievals (Ignatov and Nalli, 2002). Recent enhancements in the aerosol data constitute an improvement over the Phase I algorithm (Nalli and Stowe, 2002) which relied only on channel 1 AOD and the ratio of normalized reflectance from channels 1 and 2. The Angstrom exponent and channel 2 AOD provide important statistical information about the particle size distribution of the aerosol. The SST bias can be parametrically expressed as a function of observed AVHRR channels 1 and 2 slant-path AOD, normalized reflectance ratio and the Angstrom exponent. Based upon these empirical relationships, aerosol correction equations are then derived for the daytime multichannel and nonlinear SST (MCSST and NLSST) algorithms. Separate sets of coefficients are utilized for two aerosol modes, these being stratospheric/tropospheric (e.g., volcanic aerosol) and tropospheric (e.g., dust, smoke). The algorithms are subsequently applied to retrospective PATMOS data to demonstrate the potential for climate applications. The minimization of cold biases in the AVHRR SST, as demonstrated in this work, should improve its overall utility for the general user community.

Using NOAA AVHRR data to assess flood damage in China.

PubMed

Wang, Quan; Watanabe, Masataka; Hayashi, Seiji; Murakami, Shogo

2003-03-01

The article used two NOAA-14 Advanced Very High Resolution Radiometer (AVHRR) datasets to assess flood damage in the middle and lower reaches of China's Changjiang River (Yangtze River) in 1998. As the AVHRR is an optical sensor, it cannot penetrate the clouds that frequently cover the land during the flood season, and this technology is greatly limited in flood monitoring. However the widely used normalized difference vegetation index (NDVI) can be used to monitor flooding, since water has a much lower NDVI value than other surface features. Though many factors other than flooding (e.g. atmospheric conditions, different sun-target-satellite angles, and cloud) can change NDVI values, inundated areas can be distinguished from other types of ground cover by changes in the NDVI value before and after the flood after eliminating the effects of other factors on NDVI. AVHRR data from 26 May and 22 August, 1998 were selected to represent the ground conditions before and after flooding. After accurate geometric correction by collecting GCPs, and atmospheric and angular corrections by using the 6S code, NDVI values for both days and their differences were calculated for cloud-free pixels. The difference in the NDVI values between these two times, together with the NDVI values and a land-use map, were used to identify inundated areas and to assess the area lost to the flood. The results show a total of 358,867 ha, with 207,556 ha of cultivated fields (paddy and non-irrigated field) inundated during the flood of 1998 in the middle and lower reaches of the Changjiang River Catchment; comparing with the reported total of 321,000 and 197,000 ha, respectively. The discrimination accuracy of this method was tested by comparing the results from two nearly simultaneous sets of remote-sensing data (NOAA's AVHRR data from 10 September, 1998, and JERS-1 synthetic aperture radar (SAR) data from 11 September, 1998, with a lag of about 18.5 hr) over a representative flooded region in the study area. The results showed that 67.26% of the total area identified as inundated using the NOAA data was also identified as inundated using the SAR data.

Relative sensitivity of Normalized Difference Vegetation Index (NDVI) and Microwave Polarization Difference Index (MPDI) for vegetation and desertification monitoring

NASA Technical Reports Server (NTRS)

Becker, Francois; Choudhury, Bhaskar J.

1988-01-01

A simple equation relating the Microwave Polarization Difference Index (MPDI) and the Normalized Difference Vegetation Index (NDVI) is proposed which represents well data obtained from Nimbus 7/SMMR at 37 GHz and NOAA/AVHRR Channels 1 and 2. It is found that there is a limit which is characteristic of a particular type of cover for which both indices are equally sensitive to the variation of vegetation, and below which MPDI is more efficient than NDVI. The results provide insight into the relationship between water content and chlorophyll absorption at pixel size scales.

Inter-Satellite Calibration Linkages for the Visible and Near-Infrared Channels of the Advanced Very High Resolution Radiometer on the NOAA-7, -9, and -11 Spacecraft. Revised

NASA Technical Reports Server (NTRS)

NagarajaRao, C. R.; Chen, J.

1996-01-01

The post-launch degradation of the visible (channel 1: 0.58- 068 microns) and near-infrared (channel 2: approx. 0.72 - l.l microns) channels of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-7, -9, and -11 Polar-orbiting Operational Environmental Satellites (POES) was estimated using the south-eastern part of the Libyan Desert as a radiometrically stable calibration target. The relative annual degradation rates, in per cent, for the two channels are, respectively: 3.6 and 4.3 (NOAA-7); 5.9 and 3.5 (NOAA-9); and 1.2 and 2.0 (NOAA-11). Using the relative degradation rates thus determined, in conjunction with absolute calibrations based on congruent path aircraft/satellite radiance measurements over White Sands, New Mexico (USA), the variation in time of the absolute gain or slope of the AVHRR on NOAA-9 was evaluated. Inter-satellite calibration linkages were established, using the AVHRR on NOAA-9 as a normalization standard. Formulae for the calculation of calibrated radiances and albedos (AVHRR usage), based on these interlinkages, are given for the three AVHRRs.

Study of atmospheric and bidirectional effects on surface reflectance and vegetation index time series: Application to NOAA AVHRR and preparation for future space missions

NASA Technical Reports Server (NTRS)

Frouin, Robert

1993-01-01

The objectives of the investigation, namely 'to characterize the atmospheric and directional effects on surface reflectance and vegetation index using the First International Satellite Cloud Climatology Project (ISLCSP) Field Experiment (FIFE) data set, develop new algorithms to obtain better Advanced Very High Resolution Radiometer (AVHRR) indices, and define possible improvements for future satellite missions', were addressed in three separate, yet complementary studies. First, it was shown, from theoretical calculations, that visible and near infrared reflectances combined linearly at optimum (one or two) viewing angles relate linearly to the fraction of photosynthetically available radiation absorbed by plants, f(sub par), can be used independently of the type of foliage and substrate, eliminate the effects of sub-pixel spatial heterogeneity, and improve the accuracy of the f(sub par) estimates when compared to the Normalized Difference Vegetation Index, NDVI. Second, it was demonstrated that NDVI, even though it is not a linear combination of radiances or reflectances, can be spatially integrated without significant loss of information from scales of 300 to 1000 m. Third, AVHRR visible and near-infrared reflectances over the FIFE site, separating temporal and bidirectional components and determining the model parameters through an original iterative scheme was successfully modeled. It appears that NDVI generated from the top-of-atmosphere reflectances normalized by the bidirectional effects (as determined in the scheme) is a better vegetation index than maximum NDVI. Details about the three studies are presented.

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.

Comparison of C-band and Ku-band scatterometry for medium-resolution tropical forest inventory

NASA Astrophysics Data System (ADS)

Hardin, Perry J.; Long, David G.

1993-08-01

Since 1978, AVHRR imagery from NOAA polar orbiters has provided coverage of tropical regions at this desirable resolution, but much of the imagery is plagued with heavy cloud cover typical of equatorial regions. Clearly a medium resolution radar sensor would be a useful addition to AVHRR, but none are planned to fly in the future. In contrast, scatterometers are an important radar component of many future earth remote sensing systems, but the inherent resolution of these instruments is too low (approximately equals 50 km) for monitoring earth's land surfaces. However, a recently developed image reconstruction technique can increase the spatial resolution of scatterometer data to levels (approximately equals 4 to 14 km) approaching AVHRR global area coverage (approximately equals 4 km). When reconstructed, scatterometer data may prove to be an important asset in evaluating equatorial land cover. In this paper, the authors compare the utility of reconstructed Seasat scatterometer (SASS), Ku-band microwave data to reconstructed ERS-1 C-band scatterometer imagery for discrimination and monitoring of tropical vegetation formations. In comparative classification experiments conducted on reconstructed images of Brasil, the ERS-1 C-band imagery was slightly superior to its reconstructed SASS Ku-band counterpart for discriminating between several equatorial land cover classes. A classification accuracy approaching .90 was achieved when the two scatterometer images were combined with an AVHRR normalized difference vegetation index (NDVI) image. The success of these experiments indicates that further research into reconstructed image applications to tropical forest monitoring is warranted.

Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements

PubMed Central

Fontana, Fabio; Rixen, Christian; Jonas, Tobias; Aberegg, Gabriel; Wunderle, Stefan

2008-01-01

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG). We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand large-scale vegetation growth dynamics above the tree line in the European Alps. PMID:27879852

Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements.

PubMed

Fontana, Fabio; Rixen, Christian; Jonas, Tobias; Aberegg, Gabriel; Wunderle, Stefan

2008-04-23

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG).We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand largescale vegetation growth dynamics above the tree line in the European Alps.

Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data

NASA Astrophysics Data System (ADS)

Steyaert, L. T.; Hall, F. G.; Loveland, T. R.

1997-12-01

A multitemporal 1 km advanced very high resolution radiometer (AVHRR) land cover analysis approach was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. The land cover classification was developed by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly normalized difference vegetation index (NDVI) image composites (April-September 1992). Quantitative areal proportions of the major boreal forest components were determined for a 821 km × 619 km region, ranging from the southern grasslands-boreal forest ecotone to the northern boreal transitional forest. The boreal wetlands (mostly lowland black spruce, tamarack, mosses, fens, and bogs) occupied approximately 33% of the region, while lakes accounted for another 13%. Upland mixed coniferous-deciduous forests represented 23% of the ecosystem. A SW-NE productivity gradient across the region is manifested by three levels of tree stand density for both the boreal wetland conifer and the mixed forest classes, which are generally aligned with isopleths of regional growing degree days. Approximately 30% of the region was directly affected by fire disturbance within the preceding 30-35 years, especially in the Canadian Shield Zone where large fire-regeneration patterns contribute to the heterogeneous boreal landscape. Intercomparisons with land cover classifications derived from 30-m Landsat Thematic Mapper (TM) data provided important insights into the relative accuracy of the 1 km AVHRR land cover classification. Primarily due to the multitemporal NDVI image compositing process, the 1 km AVHRR land cover classes have an effective spatial resolution in the 3-4 km range; therefore fens, bogs, small water bodies, and small patches of dry jack pine cannot be resolved within the wet conifer mosaic. Major differences in the 1-km AVHRR and 30-m Landsat TM-derived land cover classes are most likely due to differences in the spatial resolution of the data sets. In general, the 1 km AVHRR land cover classes are vegetation mosaics consisting of mixed combinations of the Landsat classes. Detailed mapping of the global boreal forest with this approach will benefit from algorithms for cloud screening and to atmospherically correct reflectance data for both aerosol and water vapor effects. We believe that this 1 km AVHRR land cover analysis provides new and useful information for regional water, energy, carbon, and trace gases studies in BOREAS, especially given the significant spatial variability in land cover type and associated biophysical land cover parameters (e.g., albedo, leaf area index, FPAR, and surface roughness). Multiresolution land cover comparisons (30 m, l km, and 100 km grid cells) also illustrated how heterogeneous landscape patterns are represented in land cover maps with differing spatial scales and provided insights on the requirements and challenges for parameterizing landscape heterogeneity as part of land surface process research.

Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data

USGS Publications Warehouse

Steyaert, L.T.; Hall, F.G.; Loveland, Thomas R.

1997-01-01

A multitemporal 1 km advanced very high resolution radiometer (AVHRR) land cover analysis approach was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. The land cover classification was developed by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly normalized difference vegetation index (NDVI) image composites (April-September 1992). Quantitative areal proportions of the major boreal forest components were determined for a 821 km ?? 619 km region, ranging from the southern grasslands-boreal forest ecotone to the northern boreal transitional forest. The boreal wetlands (mostly lowland black spruce, tamarack, mosses, fens, and bogs) occupied approximately 33% of the region, while lakes accounted for another 13%. Upland mixed coniferous-deciduous forests represented 23% of the ecosystem. A SW-NE productivity gradient across the region is manifested by three levels of tree stand density for both the boreal wetland conifer and the mixed forest classes, which are generally aligned with isopleths of regional growing degree days. Approximately 30% of the region was directly affected by fire disturbance within the preceding 30-35 years, especially in the Canadian Shield Zone where large fire-regeneration patterns contribute to the heterogeneous boreal landscape. Intercomparisons with land cover classifications derived from 30-m Landsat Thematic Mapper (TM) data provided important insights into the relative accuracy of the 1 km AVHRR land cover classification. Primarily due to the multitemporal NDVI image compositing process, the 1 km AVHRR land cover classes have an effective spatial resolution in the 3-4 km range; therefore fens, bogs, small water bodies, and small patches of dry jack pine cannot be resolved within the wet conifer mosaic. Major differences in the 1-km AVHRR and 30-m Landsat TM-derived land cover classes are most likely due to differences in the spatial resolution of the data sets. In general, the 1 km AVHRR land cover classes are vegetation mosaics consisting of mixed combinations of the Landsat classes. Detailed mapping of the global boreal forest with this approach will benefit from algorithms for cloud screening and to atmospherically correct reflectance data for both aerosol and water vapor effects. We believe that this 1 km AVHRR land cover analysis provides new and useful information for regional water, energy, carbon, and trace gases studies in BOREAS, especially given the significant spatial variability in land cover type and associated biophysical land cover parameters (e.g., albedo, leaf area index, FPAR, and surface roughness). Multiresolution land cover comparisons (30 m, 1 km, and 100 km grid cells) also illustrated how heterogeneous landscape patterns are represented in land cover maps with differing spatial scales and provided insights on the requirements and challenges for parameterizing landscape heterogeneity as part of land surface process research.

Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation

NASA Technical Reports Server (NTRS)

Emery, William J.; Yu, Yunyue; Wick, Gary A.; Schluessel, Peter; Reynolds, Richard W.

1994-01-01

A new satellite sea surface temperature (SST) algorithm is developed that uses nearly coincident measurements from the microwave special sensor microwave imager (SSM/I) to correct for atmospheric moisture attenuation of the infrared signal from the advanced very high resolution radiometer (AVHRR). This new SST algorithm is applied to AVHRR imagery from the South Pacific and Norwegian seas, which are then compared with simultaneous in situ (ship based) measurements of both skin and bulk SST. In addition, an SST algorithm using a quadratic product of the difference between the two AVHRR thermal infrared channels is compared with the in situ measurements. While the quadratic formulation provides a considerable improvement over the older cross product (CPSST) and multichannel (MCSST) algorithms, the SSM/I corrected SST (called the water vapor or WVSST) shows overall smaller errors when compared to both the skin and bulk in situ SST observations. Applied to individual AVHRR images, the WVSST reveals an SST difference pattern (CPSST-WVSST) similar in shape to the water vapor structure while the CPSST-quadratic SST difference appears unrelated in pattern to the nearly coincident water vapor pattern. An application of the WVSST to week-long composites of global area coverage (GAC) AVHRR data demonstrates again the manner in which the WVSST corrects the AVHRR for atmospheric moisture attenuation. By comparison the quadratic SST method underestimates the SST corrections in the lower latitudes and overestimates the SST in th e higher latitudes. Correlations between the AVHRR thermal channel differences and the SSM/I water vapor demonstrate the inability of the channel difference to represent water vapor in the midlatitude and high latitudes during summer. Compared against drifting buoy data the WVSST and the quadratic SST both exhibit the same general behavior with the relatively small differences with the buoy temperatures.

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.

Development of digital interactive processing system for NOAA satellites AVHRR data

NASA Astrophysics Data System (ADS)

Gupta, R. K.; Murthy, N. N.

The paper discusses the digital image processing system for NOAA/AVHRR data including Land applications - configured around VAX 11/750 host computer supported with FPS 100 Array Processor, Comtal graphic display and HP Plotting devices; wherein the system software for relational Data Base together with query and editing facilities, Man-Machine Interface using form, menu and prompt inputs including validation of user entries for data type and range; preprocessing software for data calibration, Sun-angle correction, Geometric Corrections for Earth curvature effect and Earth rotation offsets and Earth location of AVHRR image have been accomplished. The implemented image enhancement techniques such as grey level stretching, histogram equalization and convolution are discussed. The software implementation details for the computation of vegetative index and normalized vegetative index using NOAA/AVHRR channels 1 and 2 data together with output are presented; scientific background for such computations and obtainability of similar indices from Landsat/MSS data are also included. The paper concludes by specifying the further software developments planned and the progress envisaged in the field of vegetation index studies.

The absolute radiometric calibration of the advanced very high resolution radiometer

NASA Technical Reports Server (NTRS)

Slater, P. N.; Teillet, P. M.; Ding, Y.

1989-01-01

The measurement conditions are described for an intensive field campaign at White Sands Missile Range for the calibration of the AVHRRs on NOAA-9, NOAA-10 and NOAA-11, LANDSAT-4 TM and SPOT. Three different methods for calibration of AVHRRs by reference to a ground surface site are reported, and results from these methods are compared. Significant degradations in NOAA-9 and NOAA-10 AVHRR responsivities occurred since prelaunch calibrations were completed. As of February 1988, degradations in NOAA-9 AVHRR responsivities were on the order of 37 percent in channel and 41 percent in channel 2, and for the NOAA-10 AVHRR these degradations were 42 and 59 percent in channels 1 and 2, respectively.

Intercomparison of 30+ years of AVHRR and Landsat-5 TM Surface Reflectance using Multiple Pseudo-Invariant Calibration Sites

NASA Astrophysics Data System (ADS)

Santamaría-Artigas, A. E.; Franch, B.; Vermote, E.; Roger, J. C.; Justice, C. O.

2017-12-01

The 30+ years daily surface reflectance long term data record (LTDR) from the Advanced Very High Resolution Radiometer (AVHRR) is a valuable source of information for long-term studies of the Earth surface. This LTDR was generated by combining observations from multiple AVHRR sensors aboard different NOAA satellites starting from the early 1980s, and due to the lack of on-board calibration its quality should be evaluated. Previous studies have used observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) over pseudo-invariant calibration sites (PICS) as a calibrated reference to assess the performance of AVHRR products. However, this limits the evaluation to the period after MODIS launch. In this work, the AVHRR surface reflectance LTDR was evaluated against Landsat-5 Thematic Mapper (TM) data using observations from 4 well known pseudo-invariant calibration sites (i.e. Sonoran, Saharan, Sudan1, and Libya4) over an extended time period (1984-2011). For the intercomparison, AVHRR and TM observations of each site were extracted and averaged over a 20 km x 20 km area and aggregated to monthly mean values. In order to account for the spectral differences between sensors, Hyperion hyperspectral data from the Sonoran and Libya4 sites were convolved with sensor-specific relative spectral responses, and used to compute spectral band adjustment factors (SBAFs). Results of the intercomparison are reported in terms of the root mean square difference (RMSD) and determination coefficient (r2). In general, there is good agreement between the surface reflectance products from both sensors. The overall RMSD and r2 for all the sites and AVHRR/TM combinations were 0.03 and 0.85 for the red band, and 0.04 and 0.81 for the near-infrared band. These results show the strong performance of the AVHRR surface reflectance LTDR through all of the considered period. Thus, remarking its usefulness and value for long term Earth studies. Figure 1 shows the red (filled markers) and near-infrared (empty markers) surface reflectance from AVHRR and TM for the complete evaluation period over the Saharan (diamond), Libya4 (square), Sudan1 (triangle), and Sonoran (circle) PICS.

A Consistent AVHRR Visible Calibration Record Based on Multiple Methods Applicable for the NOAA Degrading Orbits. Part 2 ; Validation

NASA Technical Reports Server (NTRS)

Doelling, David R.; Bhatt, Rajendra; Scarino, Benjamin R.; Gopalan, Arun; Haney, Conor O.; Minnis, Patrick; Bedka, Kristopher M.

2016-01-01

Consistent cross-sensor Advanced Very High Resolution Radiometer (AVHRR) calibration coefficients are determined using desert, polar ice, and deep convective cloud (DCC) invariant Earth targets. The greatest AVHRR calibration challenge is the slow orbit degradation of the host satellite, which precesses toward a terminator orbit. This issue is solved by characterizing the invariant targets with NOAA-16 AVHRR observed radiances that have been referenced to the Aqua Moderate Resolution Imaging Spectrometer (MODIS) calibration using simultaneous nadir overpass (SNO) observations. Another benefit of the NOAA-16 invariant target-modeled reflectance method is that, because of the similarities among the AVHRR spectral response functions, a smaller spectral band adjustment factor is required than when establishing calibrations relative to a non-AVHRR reference instrument. The sensor- and band-specific calibration uncertainties, with respect to the calibration reference, are, on average, 2 percent and 3 percent for channels 1 and 2, respectively. The uncertainties are smaller for sensors that are in afternoon orbits, have longer records, and spend less time in terminator conditions. The multiple invariant targets referenced to Aqua MODIS (MITRAM) AVHRR calibration coefficients are evaluated for individual target consistency, compared against Aqua MODIS/AVHRR SNOs, and selected published calibration gains. The MITRAM and SNO relative calibration biases mostly agree to within 1 percent for channels 1 and 2, respectively. The individual invariant target and MITRAM sensor relative calibration biases are mostly consistent to within 1 percent and 2 percent for channels 1 and 2, respectively. The differences between the MITRAM and other published calibrations are mostly attributed to the reference instrument calibration differences.

Aerosol correction for remotely sensed sea surface temperatures from the National Oceanic and Atmospheric Administration advanced very high resolution radiometer

NASA Astrophysics Data System (ADS)

Nalli, Nicholas R.; Stowe, Larry L.

2002-10-01

This research presents the first-phase derivation and implementation of daytime aerosol correction algorithms for remotely sensed sea surface temperature (SST) from the advanced very high resolution radiometer (AVHRR) instrument flown onboard NOAA polar orbiting satellites. To accomplish this, a long-term (1990-1998), global AVHRR-buoy match-up database was created by merging the NOAA/NASA Pathfinder Atmospheres and Pathfinder Oceans data sets. The merged data set is unique in that it includes daytime estimates of aerosol optical depth (AOD) derived from AVHRR channel 1 (0.63 μm) under global conditions of significant aerosol loading. Histograms of retrieved AOD reveal monomodal, lognormal distributions for both tropospheric and stratospheric aerosol modes. It is then shown empirically that the SST depression caused under each aerosol mode can be expressed as a linear function in two predictors, these being the slant path AOD retrieved from AVHRR channel 1 along with the ratio of channels 1 and 2 normalized reflectances. On the basis of these relationships, parametric equations are derived to provide an aerosol correction for retrievals from the daytime NOAA operational multichannel and nonlinear SST algorithms. Separate sets of coefficients are utilized for two aerosol modes: tropospheric (i.e., dust, smoke, haze) and stratospheric/tropospheric (i.e., following a major volcanic eruption). The equations are shown to significantly reduce retrieved SST bias using an independent set of match-ups. Eliminating aerosol-induced bias in both real-time and retrospective processing will enhance the utility of the AVHRR SST for the general user community and in climate research.

Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s

USGS Publications Warehouse

Stow, D.; Daeschner, Scott; Hope, A.; Douglas, David C.; Petersen, A.; Myneni, Ranga B.; Zhou, L.; Oechel, W.

2003-01-01

The interannual variability and trend of above-ground photosynthetic activity of Arctic tundra vegetation in the 1990s is examined for the north slope region of Alaska, based on the seasonally integrated normalized difference vegetation index (SINDVI) derived from local area coverage (LAC) National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. Smaller SINDVI values occurred during the three years (1992-1994) following the volcanic eruption of Mt Pinatubo. Even after implementing corrections for this stratospheric aerosol effect and adjusting for changes in radiometric calibration coefficients, an apparent increasing trend of SINDVI in the 1990s is evident for the entire north slope. The most pronounced increase was observed for the foothills physiographical province.

Winter wheat production forecast in United States of America using AVHRR historical data and NCAR Growing Degree Day

NASA Astrophysics Data System (ADS)

Claverie, M.; Franch, B.; Vermote, E.; Becker-Reshef, I.; Justice, C. O.

2015-12-01

Wheat is one of the key cereals crop grown worldwide. Thus, accurate and timely forecasts of its production are critical for informing agricultural policies and investments, as well as increasing market efficiency and stability. Becker-Reshef et al. (2010) used an empirical generalized model for forecasting winter wheat production using combined BRDF-corrected daily surface reflectance from the Moderate resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) with detailed official crop statistics and crop type masks. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season, percent wheat within the CMG pixel, and the final yields. This method predicts the yield approximately one month to six weeks prior to harvest. Recently, Franch et al. (2015) included Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts between a month to a month and a half prior to the peak NDVI (i.e. 1-2.5 months prior to harvest), while conserving the accuracy of the original model. In this study, we apply these methods to historical data from the Advanced Very High Resolution Radiometer (AVHRR). We apply both the original and the modified model to United States of America from 1990 to 2014 and inter-compare the AVHRR results to MODIS from 2000 to 2014.

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

Comparison of the New LEAF Area INDEX (LAI 3G) with the Kazahstan-Wide LEAF Area INDEX DATA SET (GGRS-LAI) over Central ASIA

NASA Astrophysics Data System (ADS)

Kappas, M.; Propastin, P.; Degener, J.; Renchin, T.

2014-12-01

Long-term global data sets of Leaf Area Index (LAI) are important for monitoring global vegetation dynamics. LAI indicating phenological development of vegetation is an important state variable for modeling land surface processes. The comparison of long-term data sets is based on two recently available data sets both derived from AVHRR time series. The LAI 3g data set introduced by Zaichun Zhu et al. (2013) is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality MODIS LAI data. The second long-term data set is based on the 8 km spatial resolution GIMMS-AVHRR data (GGRS-data set by Propastin et al. 2012). The GGRS-LAI product uses a three-dimensional physical radiative transfer model which establishes relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site/region specific parameters, including the vegetation architecture variables such as leaf angle distribution, clumping index, and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. The comparison of both long-term data sets will be used to interpret their quality for scientific research in other disciplines. References:Propastin, P., Kappas, M. (2012). Retrieval of coarse-resolution leaf area index over the Republic of Kazakhstan using NOAA AVHRR satellite data and ground measurements," Remote Sensing, vol. 4, no. 1, pp. 220-246. Zaichun Zhu, Jian Bi, Yaozhong Pan, Sangram Ganguly, Alessandro Anav, Liang Xu, Arindam Samanta, Shilong Piao, Ramakrishna R. Nemani and Ranga B. Myneni (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sens. 2013, 5, 927-948; doi:10.3390/rs5020927

The Effect of Spatial and Spectral Resolution in Determining NDVI

NASA Astrophysics Data System (ADS)

Boelman, N. T.

2003-12-01

We explore the impact that varying spatial and spectral resolutions of several sensors (a field portable spectroradiometer, Landsat, MODIS and AVHRR) has in determining the average Normalized Difference Vegetation Index (NDVI) at Imnavait Creek, a small arctic tundra watershed located on the north slope of Alaska. We found that at the field-of-views (FOVs) of less than 20 m2 that were sampled, the average NDVI value for this watershed is 0.65, compared to 0.77 at FOVs equal to and greater than 20 m2. In addition, we found that at FOVs less than 20 m2, the average NDVI value calculated according to each of Landsat, MODIS and AVHRR band definitions (controlled by spectral resolution) was similar. However, at FOVs equal to and greater than 20 m2, the average NDVI value calculated according to AVHRR's broad-band definitions was significantly and consistently higher than that from both Landsat and MODIS's narrow-band NDVI values. We speculate that these differences in NDVI exist because high leaf-area-index vegetation communities associated with watertracks are commonly spaced between 10 and 20 m apart in arctic tundra landscapes and are often only included when spectral sampling is conducted at FOVs greater than tens of square meters. These results suggest that both spatial resolution alone and its interaction with spectral resolution have to be considered when interpreting commonly used global-scale NDVI datasets. This is because traditionally, the fundamental relationships established between NDVI and ecosystem parameters, such as CO2 fluxes, aboveground biomass and net primary productivity, have been established at scales less than 20 m2. Other ecosystems, such as landscapes with isolated tree islands in boreal forest-tundra ecotones, may exhibit similar scaling patterns that need to be considered when interpreting global-scale NDVI datasets.

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.

Application of AVHRR vegetation index to study atmosphere-biosphere exchange of CO2: Results from a 3-D tracer transport model

NASA Technical Reports Server (NTRS)

Fung, Inez Y.; Tucker, C. J.; Prentice, Katharine C.

1985-01-01

The 'normalized difference vegetation indices' (NVI) derived from AVHRR radiances are combined with field data of soil respiration and a global map of net primary productivity to prescribe, for the globe, the seasonal exchange of CO2 between the atmosphere and the terrestrial biosphere. The monthly fluxes of CO2 thus obtained are used as inputs to a 3-D tracer transport model which uses winds generated by a 3-D atmospheric general circulation model to advect CO2 as an inert constituent. Analysis of the 3-D model results shows reasonable agreement between the simulated and observed annual cycles of atmospheric CO2 at the locations of the remote monitoring stations. The application is shown of atmospheric CO2 distributions to calibrate the NVI in terms of carbon fluxes. The approach suggests that the NVI may be used to provide quantitative information about long term and global scale variations of photosynthetic activity and of atmospheric CO2 concentrations provided that variations in the atmospheric circulation and in atmospheric composition are known.

AVHRR-Based Polar Pathfinder Products: Evaluation, Enhancement, and Transition to MODIS

NASA Technical Reports Server (NTRS)

Fowler, Charles; Maslanik, James; Stone, Robert; Stroeve, Julienne; Emery, William

2001-01-01

The AVHRR-Based Polar Pathfinder (APP) products include calibrated AVHRR channel data, surface temperatures, albedo, satellite scan and solar geometries, and a cloud mask composited into twice- per-day images, and daily averaged fields of sea ice motion, for regions poleward of 50 deg. latitude. Our goals under this grant, in general, are four-fold: 1. To quantify the APP accuracy and sources of error by comparing Pathfinder products with field measurements. 2. To determine the consistency of mean fields and trends in comparison with longer time series of available station data and forecast model output. 3. To investigate the consistency of the products between the different AVHRR instruments over the 1982-present period of the NOAA program. 4. To compare an annual cycle of the AVHRR Pathfinder products with MODIS to establish a baseline for extending Pathfinder-type products into the new ESE period. Year One tasks include intercomparisons of the Pathfinder products with field measurements, testing of algorithm assumptions, collection of field data, and further validation and possible improvement of the multi-sensor ice motion fields. Achievements for these tasks are summarized below.

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.

Intercalibration of Two Polar Satellite Instruments Without Simultaneous Nadir Observations

NASA Astrophysics Data System (ADS)

Manninen, Terhikki; Riihela, Aku; Schaaf, Crystal; Key, Jeffrey; Lattanzio, Alessio

2016-08-01

A new intercalibration method for two polar satellite instruments is presented. It is based on statistical fitting of two data sets covering the same area during the same period, but not simultaneously. Deming regression with iterative weights is used. The accuracy of the method was better than about 0.5 % for the MODIS vs. MODIS and AVHRR vs. AVHRR test data sets. The intercalibration of AVHRR vs. MODIS red and NIR channels is carried out and showed a difference of reflectance values of 2% (red) and 6 % (NIR). The red channel intercalibration has slightly higher accuracy for all cases studied.

AVHRR-Based Polar Pathfinder Products: Evaluation, Enhancement and Transition to MODIS

NASA Technical Reports Server (NTRS)

Fowler, Charles; Masalanik, James; Stone, Robert; Stroeve, Julienne; Emery, William

2001-01-01

The Advanced Very High Resolution Radiometer (AVHRR)-Based Polar Pathfinder (APP) products include calibrated AVHRR channel data, surface temperatures, albedo, satellite scan and solar geometries, and cloud mask, all composited into twice-per-day images, and daily averaged fields of sea ice motion, for regions poleward of 50 latitude. Our general goals under this grant: (1) Quantify the APP accuracy and sources of error by comparing Pathfinder products with field measurements; (2) Determine the consistency of mean fields and trends in comparison with longer time series of available station data and forecast model output; (3) Investigate the consistency of the products between the different AVHRR instruments over the 1982-present period of the NOAA program; and (4) Compare and annual cycle of the APP products with MODIS to establish a baseline for extending Pathfinder-type products into the new ESE period.

Properties of PSCs and Cirrus Determined from AVHRR Data

NASA Technical Reports Server (NTRS)

Hervig, Mark; Pagan, Kathy; Foschi, Patricia G.

1999-01-01

Polar stratospheric clouds (PSCS) and cirrus have been investigated using thermal emission measurements at 10.8 and 12 micrometers wavelength (channels 4 and 5) from the Advanced Very High Resolution Radiometer (AVHRR). The AVHRR signal was evaluated from a theoretical basis to understand the emission from clear and cloudy skies, and models were developed to simulate the AVHRR signal. Signal simulations revealed that nitric acid PSCs are invisible to AVHRR, while ice PSCs and cirrus are readily detectable. Methods were developed to retrieve cloud optical depths, average temperatures, average effective radii, and ice water paths, from AVHRR channels 4 and 5. Properties of ice PSCs retrieved from AVHRR were compared to values derived from coincident radiosondes and from the Polar Ozone and Aerosol Measurement II instrument, showing good agreement.

Long-term record of top-of-atmosphere albedo generated from AVHRR data

NASA Astrophysics Data System (ADS)

Song, Z.

2017-12-01

Top-of-Atmosphere (TOA) albedo is a fundamental component of Earth's energy budget. Previously, long-term accurate TOA albedo products did not exist due to the lack of stable broadband observations. With a new albedo estimation methodology based on multispectral observations, TOA albedo since 1981 has been retrieved using data from the Advanced Very High Resolution Radiometer (AVHRR), which provides the longest record of satellite observations across the globe. To develop the long-term TOA albedo record, the instantaneous TOA albedo was calculated by the direct estimation method, which was built on training data pairs from coincident AVHRR TOA reflectance and Moderate Resolution Imaging Spectroradiometer (MODIS) TOA albedo. The instantaneous TOA albedo was then converted to daily mean and monthly mean albedo based on the diurnal curves from geostationary satellites. The TOA albedo results (AVHRR-TAL) were compared with Clouds and the Earth's Radiant Energy System (CERES) flux products for 2007. The monthly mean AVHRR-TAL agreed well with the CERES products, with a root mean square difference (RMSD) of 0.032 and a bias of 0.013. In addition, AVHRR-TAL showed similar seasonal variations to those seen in the CERES products. Further analysis on long-term time series showed good consistency between the two datasets (R2 > 0.95 and relative RMSD < 4%) from 2000 to 2015. Although some calibration issues remain to be solved, our datasets show the potential ability to observe the global TOA albedo from 1981 to the present.

Interpretation of Nimbus-7 37 GHz microwave brightness temperature data in semi-arid southern Africa

NASA Technical Reports Server (NTRS)

Prince, S. D.; Choudhury, B. J.

1989-01-01

Monthly 37 GHz microwave polarization difference temperatures (MPDT) derived from the Nimbus-7 scanning multichannel microwave radiometer (SMMR) for southern Africa from 1979 to 1985 are compared with rainfall and Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. MPDT rose sharply during a drought episode which occurred within the period included in the data. The rise was seen not only in the growing season, but also in the dry season MPDT when no actively photosynthetic, water-containing leaves are present. The results suggest that scattering of the emitted microwave radiation by dead and living vegetation is a more important factor than has previously been recognized.

The utility of estimating net primary productivity over Alaska using baseline AVHRR data

USGS Publications Warehouse

Markon, C.J.; Peterson, Kim M.

2002-01-01

Net primary productivity (NPP) is a fundamental ecological variable that provides information about the health and status of vegetation communities. The Normalized Difference Vegetation Index, or NDVI, derived from the Advanced Very High Resolution Radiometer (AVHRR) is increasingly being used to model or predict NPP, especially over large remote areas. In this article, seven seasonally based metrics calculated from a seven-year baseline NDVI dataset were used to model NPP over Alaska, USA. For each growing season, they included maximum, mean and summed NDVI, total days, product of total days and maximum NDVI, an integral estimate of NDVI and a summed product of NDVI and solar radiation. Field (plot) derived NPP estimates were assigned to 18 land cover classes from an Alaskan statewide land cover database. Linear relationships between NPP and each NDVI metric were analysed at four scales: plot, 1-km, 10-km and 20-km pixels. Results show moderate to poor relationship between any of the metrics and NPP estimates for all data sets and scales. Use of NDVI for estimating NPP may be possible, but caution is required due to data seasonality, the scaling process used and land surface heterogeneity.

Predicting the spatial distribution of Ochlerotatus albifasciatus (Diptera: Culicidae) abundance with NOAA imagery.

PubMed

Gleiser, R M; Gorla, D E

2007-12-01

Ochlerotatus albifasciatus is a vector of western equine encephalomyelitis in Argentina and a nuisance mosquito affecting beef and dairy production. The objective of this study was to analyze whether environmental proxy data derived from 1 km resolution NOAA-AVHRR images could be useful as a rapid tool for locating areas with higher potential for Oc. albifasciatus activity at a regional scale. Training sites for mosquito abundance categories were 3.3x3.3 km polygons over sampling sites. Abundance was classified into two categories according to a proposed threshold for economic losses. Data of channels 1, 2, 4 and 5 were used to calculate five biophysical variables: normalized differences vegetation index (NDVI), land surface temperature, total precipitable water, dew point and vapour saturation deficit. A discriminant analysis correctly classified 100% of the areas predicted to be above or below the economic threshold of 2500 mosquitoes per night of capture, respectively. Components of the NDVI, the total precipitable water and the dew point temperature contributed most to the function value. The results suggest that environmental data derived from AVHRR-NOAA could be useful for rapidly identifying adequate areas for mosquito development or activity.

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.

LAI, FAPAR and FCOVER products derived from AVHRR long time series: principles and evaluation

NASA Astrophysics Data System (ADS)

Verger, A.; Baret, F.; Weiss, M.; Lacaze, R.; Makhmara, H.; Pacholczyk, P.; Smets, B.; Kandasamy, S.; Vermote, E.

2012-04-01

Continuous and long term global monitoring of the terrestrial biosphere has draught an intense interest in the recent years in the context of climate and global change. Developing methodologies for generating historical data records from data collected with different satellite sensors over the past three decades by taking benefits from the improvements identified in the processing of the new generation sensors is a new central issue in remote sensing community. In this context, the Bio-geophysical Parameters (BioPar) service within Geoland2 project (http://www.geoland2.eu) aims at developing pre-operational infrastructures for providing global land products both in near real time and off-line mode with long time series. In this contribution, we describe the principles of the GEOLAND algorithm for generating long term datasets of three key biophysical variables, leaf area index (LAI), Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) and cover fraction (FCOVER), that play a key role in several processes, including photosynthesis, respiration and transpiration. LAI, FAPAR and FCOVER are produced globally from AVHRR Long Term Data Record (LTDR) for the 1981-2000 period at 0.05° spatial resolution and 10 days temporal sampling frequency. The proposed algorithm aims to ensure robustness of the derived long time series and consistency with the ones developed in the recent years, and particularly with GEOLAND products derived from VEGETATION sensor. The approach is based on the capacity of neural networks to learn a particular biophysical product (GEOLAND) from reflectances from another sensor (AVHRR normalized reflectances in the red and near infrared bands). Outliers due to possible cloud contamination or residual atmospheric correction are iteratively eliminated. Prior information based on the climatology is used to get more robust estimates. A specific gap filing and smoothing procedure was applied to generate continuous and smooth time series of decadal products. Finally, quality assessment information as well as tentative quantitative uncertainties were proposed. The comparison of the resulting AVHRR LTDR products with actual GEOLAND series derived from VEGETATION demonstrates that they are very consistent, providing continuous time series of global observations of LAI, FAPAR and FCOVER for the last 30-year period, with continuation after 2011.

Drivers and Impacts of Ecological Change on the Yukon-Kuskokwim Delta, Alaska

NASA Astrophysics Data System (ADS)

Frost, G. V., Jr.; Bhatt, U. S.; Jorgenson, T.; Macander, M. J.; Whitley, M. A.; Loehman, R.

2016-12-01

The Yukon-Kuskokwim Delta (YKD) region is one of the most biologically productive areas of the tundra biome and supports one of the largest indigenous human populations in the Arctic. Much of the YKD lies near sea-level, and the region's warm, thin permafrost is highly susceptible to thaw as temperatures warm. Sea-level rise, sea-ice loss, and changes in the frequency and intensity of storms make coastal ecosystems and infrastructure especially vulnerable. Multi-scale satellite records, coupled with a network of long-term monitoring plots, offer a means of characterizing disturbance processes, the scales at which they operate, and how they manifest in changes to vegetation and habitat. At the regional scale, Normalized Difference Vegetation Index (NDVI) trends have been idiosyncratic relative to circumpolar trends, with coarse-scale (12.5 km) AVHRR time-series indicating strong declines in NDVI that contrast starkly with increases elsewhere in the Arctic. There is evidence that this "browning" is linked to regional climate drivers, including an increase in summer cloudiness; however, interpretation of NDVI trends are complicated by the large extent of surface water on the YKD. Also, the region's wide coastal zone is subject to abrupt, nonlinear dynamics after episodic storms, flooding, and salt-kill of vegetation. The Landsat record offers a means to corroborate trends observed by AVHRR, and to link them with underlying landscape-scale drivers. Landsat excels at pinpointing disturbance "hotspots," as well as directional changes in vegetation at 30 m resolution. Long-term field plots in YKD coastal areas (1994-present) are ideal for characterizing changes to the region's most biologically productive habitats and subsistence areas. These plots indicate a range of vegetation responses across gradients of landscape age; salt-tolerant vegetation has been resilient on younger delta deposits, whereas changes are accelerating on older deposits underlain by permafrost. The Landsat record generally corroborates the browning observed by AVHRR in the YKD coastal zone, but some obvious increases in vegetation productivity (e.g., tall shrub increase) elsewhere in the region are not evident in the AVHRR record.

METSAT information content: Cloud screening and solar correction investigations on the influence of NOAA-6 advanced very high resolution radiometer derived vegetation assessment

NASA Technical Reports Server (NTRS)

Mathews, M. L.

1983-01-01

The development of the cloud indicator index (CII) for use with METSAT's advanced very high resolution radiometer (AVHRR) is described. The CII is very effective at identification of clouds. Also, explored are different solar correction and standard techniques and the impact of these corrections have on the information content of AVHRR data.

Spatial and Temporal Dust Source Variability in Northern China Identified Using Advanced Remote Sensing Analysis

NASA Technical Reports Server (NTRS)

Taramelli, A.; Pasqui, M.; Barbour, J.; Kirschbaum, D.; Bottai, L.; Busillo, C.; Calastrini, F.; Guarnieri, F.; Small, C.

2013-01-01

The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi-scale remote sensing analysis of vegetation changes. The primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust-producing sources. The representation of intra-annual and inter-annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter-annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas.

Comparison of TOMS and AVHRR volcanic ash retrievals from the August 1992 eruption of Mt. Spurr

USGS Publications Warehouse

Krotkov, N.A.; Torres, O.; Seftor, C.; Krueger, A.J.; Kostinski, A.; Rose, William I.; Bluth, G.J.S.; Schneider, D.; Schaefer, S.J.

1999-01-01

On August 19, 1992, the Advanced Very High Resolution Radiometer (AVHRR) onboard NOAA-12 and NASA's Total Ozone Mapping Spectrometer (TOMS) onboard the Nimbus-7 satellite simultaneously detected and mapped the ash cloud from the eruption of Mt. Spurr, Alaska. The spatial extent and geometry of the cloud derived from the two datasets are in good agreement and both AVHRR split window IR (11-12??m brightness temperature difference) and the TOMS UV Aerosol Index (0.34-0.38??m ultraviolet backscattering and absorption) methods give the same range of total cloud ash mass. Redundant methods for determination of ash masses in drifting volcanic clouds offer many advantages for potential application to the mitigation of aircraft hazards.

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.

Atmospheric effects on METSAT data

NASA Technical Reports Server (NTRS)

Johnson, W. R.

1983-01-01

When using the J. V. Dave dataset, two channels of simulated METSAT advanced very high resolution radiometer (AVHRR) data compare favorably with actual data. Simulated NOAA6 and NOAA7 AVHRR data are presented as radiance profiles of reflected solar energy through atmosphere with three different aerosol levels. Effects of the atmosphere on the data are presented as functions of satellite view angle or pixel position on scanline. Vegetative index simultations are also profiled.

Virtual Sensors: Using Data Mining to Efficiently Estimate Spectra

NASA Technical Reports Server (NTRS)

Srivastava, Ashok; Oza, Nikunj; Stroeve, Julienne

2004-01-01

Detecting clouds within a satellite image is essential for retrieving surface geophysical parameters, such as albedo and temperature, from optical and thermal imagery because the retrieval methods tend to be valid for clear skies only. Thus, routine satellite data processing requires reliable automated cloud detection algorithms that are applicable to many surface types. Unfortunately, cloud detection over snow and ice is difficult due to the lack of spectral contrast between clouds and snow. Snow and clouds are both highly reflective in the visible wavelen,ats and often show little contrast in the thermal Infrared. However, at 1.6 microns, the spectral signatures of snow and clouds differ enough to allow improved snow/ice/cloud discrimination. The recent Terra and Aqua Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensors have a channel (channel 6) at 1.6 microns. Presently the most comprehensive, long-term information on surface albedo and temperature over snow- and ice-covered surfaces comes from the Advanced Very High Resolution Radiometer ( AVHRR) sensor that has been providing imagery since July 1981. The earlier AVHRR sensors (e.g. AVHRR/2) did not however have a channel designed for discriminating clouds from snow, such as the 1.6 micron channel available on the more recent AVHRR/3 or the MODIS sensors. In the absence of the 1.6 micron channel, the AVHRR Polar Pathfinder (APP) product performs cloud detection using a combination of time-series analysis and multispectral threshold tests based on the satellite's measuring channels to produce a cloud mask. The method has been found to work reasonably well over sea ice, but not so well over the ice sheets. Thus, improving the cloud mask in the APP dataset would be extremely helpful toward increasing the accuracy of the albedo and temperature retrievals, as well as extending the time-series of albedo and temperature retrievals from the more recent sensors to the historical ones. In this work, we use data mining methods to construct a model of MODIS channel 6 as a function of other channels that are common to both MODIS and AVHRR. The idea is to use the model to generate the equivalent of MODIS channel 6 for AVHRR as a function of the AVHRR equivalents to MODIS channels. We call this a Virtual Sensor because it predicts unmeasured spectra. The goal is to use this virtual channel 6. to yield a cloud mask superior to what is currently used in APP . Our results show that several data mining methods such as multilayer perceptrons (MLPs), ensemble methods (e.g., bagging), and kernel methods (e.g., support vector machines) generate channel 6 for unseen MODIS images with high accuracy. Because the true channel 6 is not available for AVHRR images, we qualitatively assess the virtual channel 6 for several AVHRR images.

Long-term vegetation activity trends in the Iberian Peninsula and The Balearic Islands using high spatial resolution NOAA-AVHRR data (1981 - 2015).

NASA Astrophysics Data System (ADS)

Martin-Hernandez, Natalia; Vicente-Serrano, Sergio; Azorin-Molina, Cesar; Begueria-Portugues, Santiago; Reig-Gracia, Fergus; Zabalza-Martínez, Javier

2017-04-01

We have analysed trends in the Normalized Difference Vegetation Index (NDVI) in the Iberian Peninsula and The Balearic Islands over the period 1981 - 2015 using a new high resolution data set from the entire available NOAA - AVHRR images (IBERIAN NDVI dataset). After a complete processing including geocoding, calibration, cloud removal, topographic correction and temporal filtering, we obtained bi-weekly time series. To assess the accuracy of the new IBERIAN NDVI time-series, we have compared temporal variability and trends of NDVI series with those results reported by GIMMS 3g and MODIS (MOD13A3) NDVI datasets. In general, the IBERIAN NDVI showed high reliability with these two products but showing higher spatial resolution than the GIMMS dataset and covering two more decades than the MODIS dataset. Using the IBERIAN NDVI dataset, we analysed NDVI trends by means of the non-parametric Mann-Kendall test and Theil-Sen slope estimator. In average, vegetation trends in the study area show an increase over the last decades. However, there are local spatial differences: the main increase has been recorded in humid regions of the north of the Iberian Peninsula. The statistical techniques allow finding abrupt and gradual changes in different land cover types during the analysed period. These changes are related with human activity due to land transformations (from dry to irrigated land), land abandonment and forest recovery.

Cross comparison of the Collection 6 and Collection 6.1 Terra and Aqua MODIS Bands 1 and 2 using AVHRR N15 and N19

NASA Astrophysics Data System (ADS)

Chen, Xuexia; Wu, Aisheng; Xiong, Xiaoxiong J.; Chen, Na

2017-09-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key scientific instrument that was launched into Earth orbit by NASA in 1999 on board the Terra (EOS AM) satellite and in 2002 on board the Aqua (EOS PM) satellite. Terra and Aqua MODIS collect the entire Earth's images every 1 to 2 days in 36 spectral bands. MODIS band 1 (0.620- 0.670 μm) and band 2 (0.841-0.876 μm) have nadir spatial resolution of 250 m and their measurements are crucial to derive key land surface products. This study evaluates the performance of the Collection 6 (C6, and C6.1) L1B of both Terra and Aqua MODIS bands 1 and 2 using Simultaneous Nadir Overpass (SNO) data to compare with AVHRR/3 sensors. We examine the relative stability between Terra and Aqua MODIS in reference to NOAA N15 and N19 the Advanced Very High Resolution Radiometer (AVHRR/3). The comparisons for MODIS to AVHRR/3 are over a fifteenyear period from 2002 to 2017. Results from this study provide a quantitative assessment of Terra and Aqua MODIS band 1 and band 2 calibration stability and the relative differences through the NOAA N15 and N19 AVHRR/3 sensors.

Comparison of Cloud Detection Using the CERES-MODIS Ed4 and LaRC AVHRR Cloud Masks and CALIPSO Vertical Feature Mask

NASA Astrophysics Data System (ADS)

Trepte, Q. Z.; Minnis, P.; Palikonda, R.; Bedka, K. M.; Sun-Mack, S.

2011-12-01

Accurate detection of cloud amount and distribution using satellite observations is crucial in determining cloud radiative forcing and earth energy budget. The CERES-MODIS (CM) Edition 4 cloud mask is a global cloud detection algorithm for application to Terra and Aqua MODIS data with the aid of other ancillary data sets. It is used operationally for the NASA's Cloud and Earth's Radiant Energy System (CERES) project. The LaRC AVHRR cloud mask, which uses only five spectral channels, is based on a subset of the CM cloud mask which employs twelve MODIS channels. The LaRC mask is applied to AVHRR data for the NOAA Climate Data Record Program. Comparisons among the CM Ed4, and LaRC AVHRR cloud masks and the CALIPSO Vertical Feature Mask (VFM) constitute a powerful means for validating and improving cloud detection globally. They also help us understand the strengths and limitations of the various cloud retrievals which use either active and passive satellite sensors. In this paper, individual comparisons will be presented for different types of clouds over various surfaces, including daytime and nighttime, and polar and non-polar regions. Additionally, the statistics of the global, regional, and zonal cloud occurrence and amount from the CERES Ed4, AVHRR cloud masks and CALIPSO VFM will be discussed.

Comparison of data from the Scanning Multifrequency Microwave Radiometer (SMMR) with data from the Advanced Very High Resolution Radiometer (AVHRR) for terrestrial environmental monitoring - An overview

NASA Technical Reports Server (NTRS)

Townshend, J. R. G.; Choudhury, B. J.; Tucker, C. J.; Giddings, L.; Justice, C. O.

1989-01-01

Comparison between the microwave polarized difference temperature (MPDT) derived from 37 GHz band data and the normalized difference vegetation index (NDVI) derived from near-infrared and red bands, from several empirical investigations are summarized. These indicate the complementary character of the two measures in environmental monitoring. Overall the NDVI is more sensitive to green leaf activity, whereas the MPDT appears also to be related to other elements of the above-ground biomass. Monitoring of hydrological phenomena is carried out much more effectively by the MPDT. Further work is needed to explain spectral and temporal variation in MPDT both through modelling and field experiments.

The use of EO Optical data for the Italian Supersites volcanoes monitoring

NASA Astrophysics Data System (ADS)

Silvestri, Malvina

2016-04-01

This work describes the INGV experience in the capability to import many different EO optical data into in house developed systems and to maintain a repository where the acquired data have been stored. These data are used for generating selected products which are functional to face the different volcanic activity phases. Examples on the processing of long time series based EO data of Mt Etna activity and Campi Flegrei observation by using remote sensing techniques and at different spatial resolution data (ASTER - 90mt, AVHRR -1km, MODIS-1km, MSG SEVIRI-3km) are also showed. Both volcanoes belong to Italian Supersites initiative of the geohazard scientific community. In the frame of the EC FP7 MED-SUV project (call FP7 ENV.2012.6.4-2), this work wants to describe the main activities concerning the generation of brightness temperature map from the satellite data acquired in real-time from INGV MEOS Multi-mission Antenna (for MODIS, Moderate Resolution Imaging Spectroradiometer and geostationary satellite data) and AVHRR-TERASCAN (for AVHRR, Advanced Very High Resolution Radiometer data). The advantage of direct download of EO data by means INGV antennas (with particular attention to AVHRR and MODIS) even though low spatial resolution offers the possibility of a systematic data processing having a daily updating of information for prompt response and hazard mitigation. At the same time it has been necessary the use of large archives to inventory and monitor dynamic and dangerous phenomena, like volcanic activity, globally.

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.

Grassland Npp Monitoring Based on Multi-Source Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Cai, Y. R.; Zheng, J. H.; Du, M. J.; Mu, C.; Peng, J.

2018-04-01

Vegetation is an important part of the terrestrial ecosystem. It plays an important role in the energy and material exchange of the ground-atmosphere system and is a key part of the global carbon cycle process.Climate change has an important influence on the carbon cycle of terrestrial ecosystems. Net Primary Productivity (Net Primary Productivity)is an important parameter for evaluating global terrestrial ecosystems. For the Xinjiang region, the study of grassland NPP has gradually become a hot issue in the ecological environment.Increasing the estimation accuracy of NPP is of great significance to the development of the ecosystem in Xinjiang. Based on the third-generation GIMMS AVHRR NDVI global vegetation dataset and the MODIS NDVI (MOD13A3) collected each month by the United States Atmospheric and Oceanic Administration (NOAA),combining the advantages of different remotely sensed datasets, this paper obtained the maximum synthesis fusion for New normalized vegetation index (NDVI) time series in 2006-2015.Analysis of Net Primary Productivity of Grassland Vegetation in Xinjiang Using Improved CASA Model The method described in this article proves the feasibility of applying data processing, and the accuracy of the NPP calculation using the fusion processed NDVI has been greatly improved. The results show that: (1) The NPP calculated from the new normalized vegetation index (NDVI) obtained from the fusion of GIMMS AVHRR NDVI and MODIS NDVI is significantly higher than the NPP calculated from these two raw data; (2) The grassland NPP in Xinjiang Interannual changes show an overall increase trend; interannual changes in NPP have a certain relationship with precipitation.

A Comparison Between SST and AOT Derived from AVHRR and MODIS Data in the Frame of the CREPAD Program

NASA Astrophysics Data System (ADS)

Robles-Gonzalez, Cristina; Fernandez-Renau, Alix; Lopez Gordillo, Noelia; Sevilla, Angel Garcia; Suarez, Juana Santana

2010-12-01

Since 1997, the INTA-CREPAD (Centre for REception, Processing, Archiving and Dissemination of Earth Observation Data) program distributes freely some of the most demanded low-resolution remote sensing products: SST, Ocean Chl-a, NDVI, AOD... The data input for such products are captured at the Canary Space Station (Centro Espacial de Canarias, CEC). The data sensors received at the station and used in the CREPAD program are AVHRR, SEAWIFS and MODIS. In this study SST and AOD retrieved by CREPAD algorithms from AVHRR and the SEADAS derived SST and AOD from MODIS have compared. SST values agree very well within 0.1±0.5oC and the coefficient of correlation of the images is 0.9. AOD validation gives good results taking into account the differences in the algorithms used. Mean AOD difference at 0.630 μm is 0.01±0.05 and the correlation coefficient is 0.6.

Classification of simulated and actual NOAA-6 AVHRR data for hydrologic land-surface feature definition. [Advanced Very High Resolution Radiometer

NASA Technical Reports Server (NTRS)

Ormsby, J. P.

1982-01-01

An examination of the possibilities of using Landsat data to simulate NOAA-6 Advanced Very High Resolution Radiometer (AVHRR) data on two channels, as well as using actual NOAA-6 imagery, for large-scale hydrological studies is presented. A running average was obtained of 18 consecutive pixels of 1 km resolution taken by the Landsat scanners were scaled up to 8-bit data and investigated for different gray levels. AVHRR data comprising five channels of 10-bit, band-interleaved information covering 10 deg latitude were analyzed and a suitable pixel grid was chosen for comparison with the Landsat data in a supervised classification format, an unsupervised mode, and with ground truth. Landcover delineation was explored by removing snow, water, and cloud features from the cluster analysis, and resulted in less than 10% difference. Low resolution large-scale data was determined useful for characterizing some landcover features if weekly and/or monthly updates are maintained.

AVHRR-based drought-observing system for monitoring the environment and socioeconomic activities

NASA Astrophysics Data System (ADS)

Kogan, F.

From all natural disaster, drought is the least understandable and the most damaging environmental phenomenon. Although in pre-satellite era, climate data were used for drought monitoring, drought specifics created problems in early drought detection start/end, monitoring its expansion/contraction, intensity and area coverage and the most important, timely estimation of the impacts on the environment and socioeconomic activities. The latest prevented to take prompt measures in mitigating negative consequences of drought for the society. Advances in remote sensing of the past ten years, contributed to the development of comprehensive drought monitoring system and numerous applications, which helped to make decisions for monitoring the environment and predicting sustainable socioeconomic activities. This paper discusses satellite-based land-surface observing system, which provides wells of information used for monitoring such unusual natural disaster as drought. This system was developed from the observations of the Advanced Very High Resolution Radiometer (AVHRR) flown on NOAA operational polar-orbiting satellites. The AVHRR data were packed into the Global Vegetation Index (GVI) product, which have served the global community since 1981. The GVI provided reflectances and indices (4 km spacial resolution) every seven days for each 16 km map cell between 75EN and 55ES covering all land ecosystems. The data includes raw and calibrated radiances in the visible, near infrared and infrared spectral bands, processed (with eliminated high frequency noise) radiances, normalized difference vegetation index (NDVI), 20-year climatology, vegetation condition indices and also products, such as vegetation health, drought, vegetation fraction, fire risk etc. In the past ten years, users around the world used this information addressing different issues of drought impacts on socioeconomic activities and responded positively to real time drought information place regularly on the following web site http://orbit-net.nesdis.noaa.gov/crad/sat/surf/vci/. Drought assessments were compared with ground observations in twenty two countries around the world and showed good results in early drought detection and monitoring its development and impacts on the environment and socioeconomic activities, for assessment of biomass/crop production losses and fire risk. In addition, the AVHRR-based products showed potential in monitoring mosquito-born epidemics, amount of water required for irrigation, and predicting ENSO impacts on productivity of land ecosystems. These applications were used in agriculture, forestry, weather models, climatology. This presentation will be illustrated with many examples of data applications and also with explanations of data structure and use.

A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2004-01-01

The relationship between vegetation and climate in the grassland and cropland of the northern US Great Plains was investigated with Normalized Difference Vegetation Index (NDVI) (1989–1993) images derived from the Advanced Very High Resolution Radiometer (AVHRR), and climate data from automated weather stations. The relationship was quantified using a spatial regression technique that adjusts for spatial autocorrelation inherent in these data. Conventional regression techniques used frequently in previous studies are not adequate, because they are based on the assumption of independent observations. Six climate variables during the growing season; precipitation, potential evapotranspiration, daily maximum and minimum air temperature, soil temperature, solar irradiation were regressed on NDVI derived from a 10-km weather station buffer. The regression model identified precipitation and potential evapotranspiration as the most significant climatic variables, indicating that the water balance is the most important factor controlling vegetation condition at an annual timescale. The model indicates that 46% and 24% of variation in NDVI is accounted for by climate in grassland and cropland, respectively, indicating that grassland vegetation has a more pronounced response to climate variation than cropland. Other factors contributing to NDVI variation include environmental factors (soil, groundwater and terrain), human manipulation of crops, and sensor variation.

The Calibration of AVHRR Visible Dual Gain using Meteosat-8 for NOAA-16 to 18

NASA Technical Reports Server (NTRS)

Doelling, David R.; Garber, Donald P.; Avey, L. A.; Nguyen, Louis; Minnis, Patrick

2007-01-01

The NOAA AVHRR program has given the remote sensing community over 25 years of imager radiances to retrieve global cloud, vegetation, and aerosol properties. This dataset can be used for long-term climate research, if the AVHRR instrument is well calibrated. Unfortunately, the AVHRR instrument does not have onboard visible calibration and does degrade over time. Vicarious post-launch calibration is necessary to obtain cloud properties that are not biased over time. The recent AVHRR-3 instrument has a dual gain in the visible channels in order to achieve greater radiance resolution in the clear-sky. This has made vicarious calibration of the AVHRR-3 more difficult to unravel. Reference satellite radiances from well-calibrated instruments, usually equipped with solar diffusers, such as MODIS, have been used to successfully vicariously calibrate other visible instruments. Transfer of calibration from one satellite to another using co-angled, collocated, coincident radiances has been well validated. Terra or Aqua MODIS and AVHRR comparisons can only be performed over the poles during summer. However, geostationary satellites offer a transfer medium that captures both parts of the dual gain. This AVHRR-3 calibration strategy uses, calibrated with MODIS, Meteosat-8 radiances simultaneously to determine the dual gains using 50km regions. The dual gain coefficients will be compared with the nominal coefficients. Results will be shown for all visible channels for NOAA-17.

a New Algorithm for the Aod Inversion from Noaa/avhrr Data

NASA Astrophysics Data System (ADS)

Sun, L.; Li, R.; Yu, H.

2018-04-01

The advanced very high resolution radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration satellite is one of the earliest data applied in aerosol research. The dense dark vegetation (DDV) algorithm is a popular method for the present land aerosol retrieval. One of the most crucial steps in the DDV algorithm with AVHRR data is estimating the land surface reflectance (LSR). However, LSR cannot be easily estimated because of the lack of a 2.13 μm band. In this article, the moderate resolution imaging spectroradiometer (MODIS) vegetation index product (MYD13) is introduced to support the estimation of AVHRR LSR. The relationship between MODIS NDVI and the AVHRR LSR of the visible band is analysed to retrieve aerosol optical depth (AOD) from AVHRR data. Retrieval experiments are carried out in mid-eastern America. The AOD data from AErosol RObotic NETwork (AERONET) measurements are used to evaluate the aerosol retrieval from AVHRR data, the results indicate that about 74 % of the retrieved AOD are within the expected error range of ±(0.05 + 0.2), and a cross comparison of the AOD retrieval results with the MODIS aerosol product (MYD04) shows that the AOD datasets have a similar spatial distribution.

Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis

USGS Publications Warehouse

Budde, M.E.; Tappan, G.; Rowland, James; Lewis, J.; Tieszen, L.L.

2004-01-01

The researchers calculated seasonal integrated normalized difference vegetation index (NDVI) for each of 7 years using a time-series of 1-km data from the Advanced Very High Resolution Radiometer (AVHRR) (1992-93, 1995) and SPOT Vegetation (1998-2001) sensors. We used a local variance technique to identify each pixel as normal or either positively or negatively anomalous when compared to its surroundings. We then summarized the number of years that a given pixel was identified as an anomaly. The resulting anomaly maps were analysed using Landsat TM imagery and extensive ground knowledge to assess the results. This technique identified anomalies that can be linked to numerous anthropogenic impacts including agricultural and urban expansion, maintenance of protected areas and increased fallow. Local variance analysis is a reliable method for assessing vegetation degradation resulting from human pressures or increased land productivity from natural resource management practices. ?? 2004 Published by Elsevier Ltd.

Climate applications for NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature

NASA Astrophysics Data System (ADS)

Boyer, T.; Banzon, P. V. F.; Liu, G.; Saha, K.; Wilson, C.; Stachniewicz, J. S.

2015-12-01

Few sea surface temperature (SST) datasets from satellites have the long temporal span needed for climate studies. The NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) on a 1/4° grid, produced at National Centers for Environmental Information, is based primarily on SSTs from the Advanced Very High Resolution Radiometer (AVHRR), available from 1981 to the present. AVHRR data can contain biases, particularly when aerosols are present. Over the three decade span, the largest departure of AVHRR SSTs from buoy temperatures occurred during the Mt Pinatubo and El Chichon eruptions. Therefore, in DOISST, AVHRR SSTs are bias-adjusted to match in situ SSTs prior to interpolation. This produces a consistent time series of complete SST fields that is suitable for modelling and investigating local climate phenomena like El Nino or the Pacific warm blob in a long term context. Because many biological processes and animal distributions are temperature dependent, there are also many ecological uses of DOISST (e.g., coral bleaching thermal stress, fish and marine mammal distributions), thereby providing insights into resource management in a changing ocean. The advantages and limitations of using DOISST for different applications will be discussed.

Future opportunities and challenges in remote sensing of drought

USGS Publications Warehouse

Wardlow, Brian D.; Anderson, Martha C.; Sheffield, Justin; Doorn, Brad; Zhan, Xiwu; Rodell, Matt; Wardlow, Brian D.; Anderson, Martha C.; Verdin, James P.

2012-01-01

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health.

Future Opportunities and Challenges in Remote Sensing of Drought

NASA Technical Reports Server (NTRS)

Wardlow, Brian D.; Anderson, Martha C.; Sheffield, Justin; Doorn, Brad; Zhan, Xiwu; Rodell, Matt

2011-01-01

The value of satellite remote sensing for drought monitoring was first realized more than two decades ago with the application of Normalized Difference Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) for assessing the effect of drought on vegetation. Other indices such as the Vegetation Health Index (VHI) were also developed during this time period, and applied to AVHRR NDVI and brightness temperature data for routine global monitoring of drought conditions. These early efforts demonstrated the unique perspective that global imagers such as AVHRR could provide for operational drought monitoring through their near-daily, global observations of Earth's land surface. However, the advancement of satellite remote sensing of drought was limited by the relatively few spectral bands of operational global sensors such as AVHRR, along with a relatively short period of observational record. Remote sensing advancements are of paramount importance given the increasing demand for tools that can provide accurate, timely, and integrated information on drought conditions to facilitate proactive decision making (NIDIS, 2007). Satellite-based approaches are key to addressing significant gaps in the spatial and temporal coverage of current surface station instrument networks providing key moisture observations (e.g., rainfall, snow, soil moisture, ground water, and ET) over the United States and globally (NIDIS, 2007). Improved monitoring capabilities will be particularly important given increases in spatial extent, intensity, and duration of drought events observed in some regions of the world, as reported in the International Panel on Climate Change (IPCC) report (IPCC, 2007). The risk of drought is anticipated to further increase in some regions in response to climatic changes in the hydrologic cycle related to evaporation, precipitation, air temperature, and snow cover (Burke et al., 2006; IPCC, 2007; USGCRP, 2009). Numerous national, regional, and global efforts such as the Famine and Early Warning System (FEWS), National Integrated Drought Information System (NIDIS), and Group on Earth Observations (GEO), as well as the establishment of regional drought centers (e.g., European Drought Observatory) and geospatial visualization and monitoring systems (e.g, NASA SERVIR) have been undertaken to improve drought monitoring and early warning systems throughout the world. The suite of innovative remote sensing tools that have recently emerged will be looked upon to fill important data and knowledge gaps (NIDIS, 2007; NRC, 2007) to address a wide range of drought-related issues including food security, water scarcity, and human health.

Phenological classification of the United States: A geographic framework for extending multi-sensor time-series data

USGS Publications Warehouse

Gu, Yingxin; Brown, Jesslyn F.; Miura, Tomoaki; van Leeuwen, Willem J.D.; Reed, Bradley C.

2010-01-01

This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies.

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.

Evaluation of AVHRR Aerosol Properties Over Mainland China from Deepblue Algorithm

NASA Astrophysics Data System (ADS)

Xue, Y.; Che, Y.; She, L.

2017-12-01

Advanced Very High Resolution Radiometer (AVHRR) on-board NOAA series satellites is the only operational senor which keeps observing surface of the Earth and cloud over 30 years since 1979. Such long time coverage helps to expand the application of AVHRR to aerosol properties retrieval over both land and ocean successfully. Recently in 2017, the Deep Blue Project has published AVHRR `Deep Blue' dataset version 001 (V001) using `Deep Blue (DB)' algorithm(Sayer et al., 2017). This dataset includes not only aerosol properties over land but also oceanic aerosol product at three periods (NOAA-11: 1989-1990, NOAA-14: 1995-1999, NOAA-18: 2006-2011). We pay much of our attention to DB's performance over mainland China. Therefore, in the presenting paper, we focus on validating AVHRR/DB dataset over different land covers in China in 2007, 2008 and 2010. Both of data from ground-based networks from the Aerosol Robotic NETwork (AERONET) and China Aerosol Remote Sensing Network (CARSNET) are used as reference data. The collocation method is to match data at a time range of of satellite pass-by and at a spatial frame of pixels around ground-based site. Totally, data from 18 AERONET and 25 CARSNET are used as shown in figure, collocating 922 matches with AERONET and 2325 matches with CARSNET. Additionally, we introduced a corrected RMS error as main evaluation metric. As a result, AVHRR/DB underestimates AOD increasingly and more uncertainties and errors will be introduced with the growth of AOD. Otherwise, the performance of AVHRR/DB are better compared with AERONET data than with CARSNET data from RMSbc of 0.35 vs. 0.42. Their Rs (0.757 vs. 0.654) prove this characteristic too. For urban areas, the performances in Beijing are better than that in Xi'an from RMSbc, otherwise RMS in Xi'an (0.324) is lower than others' (0.346 and 0.383) mainly because of small AOD observed range and low R (0.624). For croplands, those performances are at same levels with RMSbc from 0.312 to 0.380 except Huimin with RMSbc = 0.445. For grasslands and sparsely vegetated areas, it lacks AERONET observation sites that only SACOL in central China. Obviously, the algorithm has best performance over Dunhuang site, where the RMSbc = 0.338 and highest R about 0.771. Over the rest of sites, the AVHRR/DB has serious problem in retrieving AOD, high dispersion or poor correlation.

Remote sensing of ephemeral water bodies in western Niger

USGS Publications Warehouse

Verdin, J.P.

1996-01-01

Research was undertaken to evaluate the feasibility of monitoring the small ephemeral water bodies of the Sahel with the 1.1 km resolution data of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Twenty-one lakes of western Niger with good ground observation records were selected for examination. Thematic Mapper images from 1988 were first analysed to determine surface areas and temperature differences between water and adjacent land. Six AVHRR scenes from the 1988-89 dry season were then studied. It was found that a lake can be monitored until its surface area drops below 10 ha, in most cases. Furthermore, with prior knowledge of the location and shape of a water body, its surface area can be estimated from AVHRR band 5 data to within about 10 ha. These results are explained by the sharp temperature contrast between water and land, on the order of 13?? C.

Monitoring Italian volcanoes by NOAA-AVHRR satellite data

NASA Astrophysics Data System (ADS)

Spinetti, C.; Buongiorno, M. F.; Amici, S.; Silvestri, M.; Lombardo, V.; Musacchio, M.; Doumaz, F.; Corradini, C.

2009-04-01

The INGV Remote Sensing unit is equipped with a NOAA-AVHRR receiving station that provides 4 to 10 images per day of the central Mediterranean area in the visible to thermal infrared bands. These data were acquired and processed in real time using automatic and semi-automatic procedures which outputs information collated in daily and weekly observation reports and outputs overview images in the DPC dedicated web page. Satellite information included the presence of hot spots as well as their temporal evolution in terms of temperature. An automatic procedure that calculate lava flow effusion rate has been developed. The procedure automatically sent alert via e-mail when an hot spot is present in the AVHRR data. Volcanic ash information AVHRR-derived has been also included in a separate system. These information concerned the presence of volcanic ash in air, an assessment of the area affected, as well as the plume dispersal direction, the ash plume altitude and the concentration of ash in air. The eruptions occurred both at Etna and Stromboli volcanoes in Sicily (Italy) has been surveyed by satellite. The different eruptions were characterized both by lava flow emissions and eruption of ash plumes with different impact to the surrounding villages and cities, causing problems to local communications and air traffic. Information provided by satellite sensors are communicate in observation reports integrating ground-based surveillance operated by INGV Catania Volcanology Observatory in agreement with the Italian Department of Civil Protection (DPC) responsible for volcanic risk and airports closure during the explosive phases.

Effects of instrument characteristics on cloud properties retrieved from satellite imagery data

NASA Technical Reports Server (NTRS)

Baldwin, D. G.; Coakley, J. A., Jr.; Zhang, M. S.

1986-01-01

The relationships between sensor resolution and derived cloud properties in satellite remote sensing were studied by comparisons of cloud characteristics determined by spatial coherence analysis of AVHRR and GOES data. The latter data were simulated from 11 microns AVHRR data and were assigned a resolution (8 sq km) half that of the AVHRR. Day and nighttime passes were considered for single-layer maritime cloud systems. Sample radiance vs local standard deviation plots of 1024 points are provided for the same area from AVHRR and GOES-East sensors, demonstrating a qualitative agreement.

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.

A Climate-Data Record of the "Clear-Sky" Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, D. K.; Comiso, J. C.; Digirolamo, N. E.; Stock, L. V.; Riggs, G. A.; Shuman, C. A.

2009-01-01

We are developing a climate-data record (CDR of daily "clear-sky" ice-surface temperature (IST) of the Greenland Ice Sheet, from 1982 to the present using Advanced Very High Resolution Radiometer (AVHRR) (1982 - present) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data (2000 - present) at a resolution of approximately 5 km. The CDR will be continued in the National Polar-orbiting Operational Environmental Satellite System Visible/Infrared Imager Radiometer Suite era. Two algorithms remain under consideration. One algorithm under consideration is based on the split-window technique used in the Polar Pathfinder dataset (Fowler et al., 2000 & 21007). Another algorithm under consideration, developed by Comiso (2006), uses a single channel of AVHRR data (channel 4) in conjunction with meteorological-station data to account for atmospheric effects and drift between AVHRR instruments. Known issues being addressed in the production of the CDR are: tune-series bias caused by cloud cover (surface temperatures can be different under clouds vs. clear areas) and cross-calibration in the overlap period between AVHRR instruments, and between AVHRR and MODIS instruments. Because of uncertainties, mainly due to clouds (Stroeve & Steffen, 1998; Wang and Key, 2005; Hall et al., 2008 and Koenig and Hall, submitted), time-series of satellite 1S'1" do not necessarily correspond to actual surface temperatures. The CDR will be validated by comparing results with automatic-,",eather station (AWS) data and with satellite-derived surface-temperature products. Regional "clear-sky" surface temperature increases in the Arctic, measured from AVHRR infrared data, range from 0.57+/-0.02 deg C (Wang and Key, 2005) to 0.72+/-0.10 deg C (Comiso, 2006) per decade since the early 1980s. Arctic warming has important implications for ice-sheet mass balance because much of the periphery of the Greenland Ice Sheet is already near 0 deg C during the melt season, and is thus vulnerable to rapid melting if temperatures continue to increase. References

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.

Advanced Very High Resolution Radiometer - AVHRR - NOAA Satellite

Science.gov Websites

Information System (NOAASIS); Office of Satellite and Product Operations » DOC » NOAA  » NESDIS » NOAASIS NOAA Satellite Information System Advanced Very High Resolution Radiometer - AVHRR The ) or the USGS AVHRR site. Satellite Products and Services Division Direct Services Branch Phone: 301

Long time-series of turbid coastal water using AVHRR: An example from Florida Bay, USA

USGS Publications Warehouse

Stumpf, R.P.; Frayer, M.L.

1997-01-01

The AVHRR can provide information on the reflectance of turbid case II water, permitting examination of large estuaries and plumes from major rivers. The AVHRR has been onboard several NOAA satellites, with afternoon overpasses since 1981, offering a long time-series to examine changes in coastal water. We are using AVHRR data starting in December 1989, to examine water clarity in Florida Bay, which has undergone a decline since the late 1980's. The processing involves obtaining a nominal reflectance for red light with standard corrections including those for Rayleigh and aerosol path radiances. Established relationships between reflectance and the water properties being measured in the Bay provide estimates of diffuse attenuation and light limitation for phytoplankton and seagrass productivity studies. Processing also includes monthly averages of reflectance and attenuation. The AVHRR data set describes spatial and temporal patterns, including resuspension of bottom sediments in the winter, and changes in water clarity. The AVHRR also indicates that Florida Bay has much higher reflectivity relative to attenuation than other southeastern US estuaries. ??2005 Copyright SPIE - The International Society for Optical Engineering.

Long time-series of turbid coastal water using AVHRR: an example from Florida Bay, USA

NASA Astrophysics Data System (ADS)

Stumpf, Richard P.; Frayer, M. L.

1997-02-01

The AVHRR can provide information on the reflectance of turbid case II water, permitting examination of large estuaries and plumes from major rivers. The AVHRR has been onboard several NOAA satellites, with afternoon overpasses since 1981, offering a long time-series to examine changes in coastal water. We are using AVHRR data starting in December 1989, to examine water clarity in Florida Bay, which has undergone a decline since the late 1980's. The processing involves obtaining a nominal reflectance for red light with standard corrections including those for Rayleigh and aerosol path radiances. Established relationships between reflectance and the water properties being measured in the Bay provide estimates of diffuse attenuation and light limitation for phytoplankton and seagrass productivity studies. Processing also includes monthly averages of reflectance and attenuation. The AVHRR data set describes spatial and temporal patterns, including resuspension of bottom sediments in the winter, and changes in water clarity. The AVHRR also indicates that Florida Bay has much higher reflectivity relative to attenuation than other southeastern US estuaries.

Analysis of forest disturbance using TM and AVHRR data

NASA Technical Reports Server (NTRS)

Spanner, Michael A.; Hlavka, Christine A.; Pierce, Lars L.

1989-01-01

A methodology that will be used to determine the proportions of undisturbed, successional vegetation and recently disturbed land cover within coniferous forests using remotely sensed data from the advanced very high resolution radiometer (AVHRR) is presented. The method uses thematic mapper (TM) data to determine the proportions of the three stages of forest disturbance and regrowth for each AVHRR pixel in the sample areas, and is then applied to interpret all AVHRR imagery. Preliminary results indicate that there are predictable relationships between TM spectral response and the disturbance classes. Analysis of ellipse plots from a TM classification of the disturbed forested landscape indicates that the forest classes are separable in the red (0.63-0.69 micron) and near-infrared (0.76-0.90 micron) bands, providing evidence that the proportion of disturbance classes may be determined from AVHRR data.

Spatial and Temporal Variation of PATMOS-x AVHRR Lake Surface Temperatures in the Laurentian Great Lakes

NASA Astrophysics Data System (ADS)

White, C.; Heidinger, A. K.; Ackerman, S. A.; McIntyre, P. B.

2017-12-01

A thirty-four year lake surface water temperature (LSWT) time series over the North American Great Lakes was extracted from NOAA's Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC). The time series was cloud-cleared using the NOAA Pathfinder Atmospheres Extended (PATMOS-x) climate dataset and the Clouds from AVHRR Extended System (CLAVR-x) processing system, and was subsampled to a regular 0.05° grid. LSWT coefficients for each AVHRR platform were fit to NOAA National Data Buoy Center buoys with historical records spanning 1982 to 2016. Satellite to buoy matchups indicate an RMSE of 0.72 K for the entire time series across all five lakes. An empirically fit diurnal correction was applied to correct for orbital drift and varying observation times of NOAA-7,9,11,12,14-19, Metop-1 and Metop-2. Ordinary linear regression slopes on monthly mean LSWT show strong spatial heterogeneity in the long-term LSWT trends both within each lake and between lakes. Differences in long-term trends using nighttime only, daytime only, and both day and night are examined. Additionally, a coastal upwelling signal can be identified from the time series along with the indication of an earlier onset of spring stratification.

Satellite-derived vertical profiles of temperature and dew point for mesoscale weather forecast

NASA Astrophysics Data System (ADS)

Masselink, Thomas; Schluessel, P.

1995-12-01

Weather forecast-models need spatially high resolutioned vertical profiles of temperature and dewpoint for their initialisation. These profiles can be supplied by a combination of data from the Tiros-N Operational Vertical Sounder (TOVS) and the imaging Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA polar orbiting sate!- lites. In cloudy cases the profiles derived from TOVS data only are of insufficient accuracy. The stanthrd deviations from radiosonde ascents or numerical weather analyses likely exceed 2 K in temperature and 5Kin dewpoint profiles. It will be shown that additional cloud information as retrieved from AVHIRR allows a significant improvement in theaccuracy of vertical profiles. The International TOVS Processing Package (ITPP) is coupled to an algorithm package called AVHRR Processing scheme Over cLouds, Land and Ocean (APOLLO) where parameters like cloud fraction and cloud-top temperature are determined with higher accuracy than obtained from TOVS retrieval alone. Furthermore, a split-window technique is applied to the cloud-free AVHRR imagery in order to derive more accurate surface temperatures than can be obtained from the pure TOVS retrieval. First results of the impact of AVHRR cloud detection on the quality of the profiles are presented. The temperature and humidity profiles of different retrieval approaches are validated against analyses of the European Centre for Medium-Range Weatherforecasts.

Evaluation of a technique for satellite-derived area estimation of forest fires

NASA Technical Reports Server (NTRS)

Cahoon, Donald R., Jr.; Stocks, Brian J.; Levine, Joel S.; Cofer, Wesley R., III; Chung, Charles C.

1992-01-01

The advanced very high resolution radiometer (AVHRR), has been found useful for the location and monitoring of both smoke and fires because of the daily observations, the large geographical coverage of the imagery, the spectral characteristics of the instrument, and the spatial resolution of the instrument. This paper will discuss the application of AVHRR data to assess the geographical extent of burning. Methods have been developed to estimate the surface area of burning by analyzing the surface area effected by fire with AVHRR imagery. Characteristics of the AVHRR instrument, its orbit, field of view, and archived data sets are discussed relative to the unique surface area of each pixel. The errors associated with this surface area estimation technique are determined using AVHRR-derived area estimates of target regions with known sizes. This technique is used to evaluate the area burned during the Yellowstone fires of 1988.

Water vapour correction of the daily 1 km AVHRR global land dataset: Part I validation and use of the Water Vapour input field

USGS Publications Warehouse

DeFelice, Thomas P.; Lloyd, D.; Meyer, D.J.; Baltzer, T. T.; Piraina, P.

2003-01-01

An atmospheric correction algorithm developed for the 1 km Advanced Very High Resolution Radiometer (AVHRR) global land dataset was modified to include a near real-time total column water vapour data input field to account for the natural variability of atmospheric water vapour. The real-time data input field used for this study is the Television and Infrared Observational Satellite (TIROS) Operational Vertical Sounder (TOVS) Pathfinder A global total column water vapour dataset. It was validated prior to its use in the AVHRR atmospheric correction process using two North American AVHRR scenes, namely 13 June and 28 November 1996. The validation results are consistent with those reported by others and entail a comparison between TOVS, radiosonde, experimental sounding, microwave radiometer, and data from a hand-held sunphotometer. The use of this data layer as input to the AVHRR atmospheric correction process is discussed.

The FAO/NASA/NLR Artemis system - An integrated concept for environmental monitoring by satellite in support of food/feed security and desert locust surveillance

NASA Technical Reports Server (NTRS)

Hielkema, J. U.; Howard, J. A.; Tucker, C. J.; Van Ingen Schenau, H. A.

1987-01-01

The African real time environmental monitoring using imaging satellites (Artemis) system, which should monitor precipitation and vegetation conditions on a continental scale, is presented. The hardware and software characteristics of the system are illustrated and the Artemis databases are outlined. Plans for the system include the use of hourly digital Meteosat data and daily NOAA/AVHRR data to study environmental conditions. Planned mapping activities include monthly rainfall anomaly maps, normalized difference vegetation index maps for ten day and monthly periods with a spatial resolution of 7.6 km, ten day crop/rangeland moisture availability maps, and desert locust potential breeding activity factor maps for a plague prevention program.

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

NASA Technical Reports Server (NTRS)

Key, Jeff; Maslanik, James; Steffen, Konrad

1994-01-01

During the first half of our second project year we have accomplished the following: (1) acquired a new AVHRR data set for the Beaufort Sea area spanning an entire year; (2) acquired additional ATSR data for the Arctic and Antarctic now totaling over seven months; (3) refined our AVHRR Arctic and Antarctic ice surface temperature (IST) retrieval algorithm, including work specific to Greenland; (4) developed ATSR retrieval algorithms for the Arctic and Antarctic, including work specific to Greenland; (5) investigated the effects of clouds and the atmosphere on passive microwave 'surface' temperature retrieval algorithms; (6) generated surface temperatures for the Beaufort Sea data set, both from AVHRR and SSM/I; and (7) continued work on compositing GAC data for coverage of the entire Arctic and Antarctic. During the second half of the year we will continue along these same lines, and will undertake a detailed validation study of the AVHRR and ATSR retrievals using LEADEX and the Beaufort Sea year-long data. Cloud masking methods used for the AVHRR will be modified for use with the ATSR. Methods of blending in situ and satellite-derived surface temperature data sets will be investigated.

Examination of Regional Trends in Cloud Properties over Surface Sites Derived from MODIS and AVHRR using the CERES Cloud Algorithm

NASA Astrophysics Data System (ADS)

Smith, W. L., Jr.; Minnis, P.; Bedka, K. M.; Sun-Mack, S.; Chen, Y.; Doelling, D. R.; Kato, S.; Rutan, D. A.

2017-12-01

Recent studies analyzing long-term measurements of surface insolation at ground sites suggest that decadal-scale trends of increasing (brightening) and decreasing (dimming) downward solar flux have occurred at various times over the last century. Regional variations have been reported that range from near 0 Wm-2/decade to as large as 9 Wm-2/decade depending on the location and time period analyzed. The more significant trends have been attributed to changes in overhead clouds and aerosols, although quantifying their relative impacts using independent observations has been difficult, owing in part to a lack of consistent long-term measurements of cloud properties. This paper examines new satellite based records of cloud properties derived from MODIS (2000-present) and AVHRR (1981- present) data to infer cloud property trends over a number of surface radiation sites across the globe. The MODIS cloud algorithm was developed for the NASA Clouds and the Earth's Radiant Energy System (CERES) project to provide a consistent record of cloud properties to help improve broadband radiation measurements and to better understand cloud radiative effects. The CERES-MODIS cloud algorithm has been modified to analyze other satellites including the AVHRR on the NOAA satellites. Compared to MODIS, obtaining consistent cloud properties over a long period from AVHRR is a much more significant challenge owing to the number of different satellites, instrument calibration uncertainties, orbital drift and other factors. Nevertheless, both the MODIS and AVHRR cloud properties will be analyzed to determine trends, and their level of consistency and correspondence with surface radiation trends derived from the ground-based radiometer data. It is anticipated that this initial study will contribute to an improved understanding of surface solar radiation trends and their relationship to clouds.

BOREAS AFM-12 1-km AVHRR Seasonal Land Cover Classification

NASA Technical Reports Server (NTRS)

Steyaert, Lou; Hall, Forrest G.; Newcomer, Jeffrey A. (Editor); Knapp, David E. (Editor); Loveland, Thomas R.; Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Airborne Fluxes and Meteorology (AFM)-12 team's efforts focused on regional scale Surface Vegetation and Atmosphere (SVAT) modeling to improve parameterization of the heterogeneous BOREAS landscape for use in larger scale Global Circulation Models (GCMs). This regional land cover data set was developed as part of a multitemporal one-kilometer Advanced Very High Resolution Radiometer (AVHRR) land cover analysis approach that was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. This land cover classification was derived by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly Normalized Difference Vegetation Index (NDVI) image composites (April-September 1992). This regional data set was developed for use by BOREAS investigators, especially those involved in simulation modeling, remote sensing algorithm development, and aircraft flux studies. Based on regional field data verification, this multitemporal one-kilometer AVHRR land cover mapping approach was effective in characterizing the biome-level land cover structure, embedded spatially heterogeneous landscape patterns, and other types of key land cover information of interest to BOREAS modelers.The land cover mosaics in this classification include: (1) wet conifer mosaic (low, medium, and high tree stand density), (2) mixed coniferous-deciduous forest (80% coniferous, codominant, and 80% deciduous), (3) recent visible bum, vegetation regeneration, or rock outcrops-bare ground-sparsely vegetated slow regeneration bum (four classes), (4) open water and grassland marshes, and (5) general agricultural land use/ grasslands (three classes). This land cover mapping approach did not detect small subpixel-scale landscape features such as fens, bogs, and small water bodies. Field observations and comparisons with Landsat Thematic Mapper (TM) suggest a minimum effective resolution of these land cover classes in the range of three to four kilometers, in part, because of the daily to monthly compositing process. In general, potential accuracy limitations are mitigated by the use of conservative parameterization rules such as aggregation of predominant land cover classes within minimum horizontal grid cell sizes of ten kilometers. The AFM-12 one-kilometer AVHRR seasonal land cover classification data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Overview of South‐east Asia land cover using a NOAA AVHRR one kilometer composite

USGS Publications Warehouse

Defourny, Pierre; Pradhan, Udai C.; Vinay, Sritharan; Johnson, Gary E.

1994-01-01

A cloud free AVHRR composite of South‐East Asia at one kilometer resolution has been produced from 38 selected daily NOAA‐11 AVHRR images. Geometric accuracy of about 1 pixel is achieved using a two‐step rectification algorithm (orbital model and transformation by ground control points). A spatial and spectral enhancement has been performed, the sea masked out and political boundaries included in the final product. This AVHRR composite is particularly useful for a comprehensive overview of land cover at a regional scale. Qualitative comparison between a monthly composite and the existing forest maps highlights the forest cover change and points out the hot spots where the maps have to be updated.

Determining the rate of forest conversion in Mato Grosso, Brazil, using Landsat MSS and AVHRR data

NASA Technical Reports Server (NTRS)

Nelson, Ross; Horning, Ned; Stone, Thomas A.

1987-01-01

AVHRR-LAC thermal data and Landsat MSS and TM spectral data were used to estimate the rate of forest clearing in Mato Grosso, Brazil, between 1981 and 1984. The Brazilian state was stratified into forest and nonforest. A list sampling procedure was used in the forest stratum to select Landsat MSS scenes for processing based on estimates of fire activity in the scenes. Fire activity in 1984 was estimated using AVHRR-LAC thermal data. State-wide estimates of forest conversion indicate that between 1981 and 1984, 353,966 ha + or - 77,000 ha (0.4 percent of the state area) were converted per year. No evidence of reforestation was found in this digital sample. The relationship between forest clearing rate (based on MSS-TM analysis) and fire activity (estimated using AVHRR data) was noisy (R-squared = 0.41). The results suggest that AVHRR data may be put to better use as a stratification tool than as a subsidiary variable in list sampling.

Procedures for using signals from one sensor as substitutes for signals of another

NASA Technical Reports Server (NTRS)

Suits, G.; Malila, W.; Weller, T.

1988-01-01

Long-term monitoring of surface conditions may require a transfer from using data from one satellite sensor to data from a different sensor having different spectral characteristics. Two general procedures for spectral signal substitution are described in this paper, a principal-components procedure and a complete multivariate regression procedure. They are evaluated through a simulation study of five satellite sensors (MSS, TM, AVHRR, CZCS, and HRV). For illustration, they are compared to another recently described procedure for relating AVHRR and MSS signals. The multivariate regression procedure is shown to be best. TM can accurately emulate the other sensors, but they, on the other hand, have difficulty in accurately emulating its shortwave infrared bands (TM5 and TM7).

Polar Geophysics Products Derived from AVHRR: The "AVHRR Polar Pathfinder

NASA Technical Reports Server (NTRS)

Maslanik, James; Fowler, Charles; Scambos, Theodore

1999-01-01

This NOAA/NASA Pathfinder effort was established to locate, acquire, and process Advanced Very High Resolution Radiometer (AVHRR) imagery into geo-located and calibrated radiances, cloud masks, surface clear-sky broadband albedo, clear-sky skin temperatures, satellite viewing times, and viewing and solar geometry for the, high-latitude portions of the northern and southern hemispheres (all area north of 48N and south of 53S). AVHRR GAC data for August 1981 - July 1998 were acquired, with some gaps remaining, and processed into twice-daily 5-km grids, with some products also provided at 25-km resolution. AVHRR LAC data for 3.5 years of coverage in the northern hemisphere and 2.75 years of coverage in the southern hemisphere were processed into 1.25-km grids for the same suite of products. The resulting data sets are presently being transferred to the National Snow and Ice Data Center (NSIDC) for archiving and distribution. Using these data, researchers now have at their disposal an extensive AVHRR data set for investigations of high-latitude processes. In addition, the data lend themselves to development and testing of algorithms. The products are particularly relevant for climate research and algorithm development as applied to relatively long time periods and large areas.

Analysis of Terrestrial Conditions and Dynamics

NASA Technical Reports Server (NTRS)

Goward, S. N.

1985-01-01

An ecological model is developed to estimate annual net primary productivity of vegetation in twelve major North American biomes. Three models are adapted and combined, each addressing a different factor known to govern primary productivity, i.e., photosynthesis, respiration, and moisture availability. Measures of intercepted photosynthetically active radiation (1PAR) for input to the photosynthesis model are derived from spectral vegetation index data. Normalized Difference Vegetation Index (NDVI) data are produced from NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) observations for April 1982 through March 1983. NDVI values are sampled from within the biomes at locations for which climatological data are available. Monthly estimates of Net Primary Productivity (NPP) for each sample location are generated and summed over the twelve month period. These monthly estimates are averaged to produce a single annual estimated NPP value for each biomes. Comparison of estimated NPP values with figures reported in the literature produces a correlation coefficient of 85.

MEDOKADS - A 20 Year's Daily AVHRR Data Series for Analysis of Land Surface Properties

NASA Astrophysics Data System (ADS)

Koslowsky, D.; Billing, H.; Bolle, H.-J.

2009-04-01

To derive primary data products from raw AVHRR data, like spectral reflectances or temperatures, it is necessary to correct for sensor degradation and changing hardware specifications, to re-sample the data into a grid of equal pixel size, to perform geographical registration, cloud-screening and normalization for illumination and observation geometry. A data set which resulted from the application of these corrections is the top of the atmosphere Mediterranean Extended One-Km AVHRR Data Set (MEDOKADS) which now covers a period of 20 years. To study land surface processes, the obtained spectral data have to be combined, radiometric corrections for atmospheric effects, emissivity corrections in the case of temperature measurements have to be applied, and the variable over-flight times have to be accounted for. By application of complex evaluation schemes then higher level products are generated, like vegetation indices, surface albedo, and surface energy fluxes. The ultimate goal is to provide the users community with problem-related information. This includes the quantification of changes and the determination of trends. Methods and tools to reach this goal as well as their limitations are discussed. To validate the data, extended field measurements have been performed in which the scaling between local ground measurements and large scale satellite data play a major role. A major problem remains the application of atmospheric corrections because of the not well known variable aerosol content. The supervision of the quality of the derived information leads to the concept of anchor stations at which surface and atmospheric properties should permanently be measured.

Polar cloud and surface classification using AVHRR imagery - An intercomparison of methods

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Goroch, A. K.; Rabindra, P.; Rangaraj, N.; Navar, M. S.

1992-01-01

Six Advanced Very High-Resolution Radiometer local area coverage (AVHRR LAC) arctic scenes are classified into ten classes. Three different classifiers are examined: (1) the traditional stepwise discriminant analysis (SDA) method; (2) the feed-forward back-propagation (FFBP) neural network; and (3) the probabilistic neural network (PNN). More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6 percent, 87.6 percent, and 87.0 percent for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1 percent.

Comparative analysis of multisensor satellite monitoring of Arctic sea-ice

USGS Publications Warehouse

Belchansky, G.I.; Mordvintsev, Ilia N.; Douglas, David C.

1999-01-01

This report represents comparative analysis of nearly coincident Russian OKEAN-01 polar orbiting satellite data, Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) imagery. OKEAN-01 ice concentration algorithms utilize active and passive microwave measurements and a linear mixture model for measured values of the brightness temperature and the radar backscatter. SSM/I and AVHRR ice concentrations were computed with NASA Team algorithm and visible and thermal-infrared wavelength AVHRR data, accordingly

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

NASA Technical Reports Server (NTRS)

Key, Jeff; Maslanik, James; Steffen, Konrad

1995-01-01

During the second phase project year we have made progress in the development and refinement of surface temperature retrieval algorithms and in product generation. More specifically, we have accomplished the following: (1) acquired a new advanced very high resolution radiometer (AVHRR) data set for the Beaufort Sea area spanning an entire year; (2) acquired additional along-track scanning radiometer(ATSR) data for the Arctic and Antarctic now totalling over eight months; (3) refined our AVHRR Arctic and Antarctic ice surface temperature (IST) retrieval algorithm, including work specific to Greenland; (4) developed ATSR retrieval algorithms for the Arctic and Antarctic, including work specific to Greenland; (5) developed cloud masking procedures for both AVHRR and ATSR; (6) generated a two-week bi-polar global area coverage (GAC) set of composite images from which IST is being estimated; (7) investigated the effects of clouds and the atmosphere on passive microwave 'surface' temperature retrieval algorithms; and (8) generated surface temperatures for the Beaufort Sea data set, both from AVHRR and special sensor microwave imager (SSM/I).

Surface radiant flux densities inferred from LAC and GAC AVHRR data

NASA Astrophysics Data System (ADS)

Berger, F.; Klaes, D.

To infer surface radiant flux densities from current (NOAA-AVHRR, ERS-1/2 ATSR) and future meteorological (Envisat AATSR, MSG, METOP) satellite data, the complex, modular analysis scheme SESAT (Strahlungs- und Energieflüsse aus Satellitendaten) could be developed (Berger, 2001). This scheme allows the determination of cloud types, optical and microphysical cloud properties as well as surface and TOA radiant flux densities. After testing of SESAT in Central Europe and the Baltic Sea catchment (more than 400scenes U including a detailed validation with various surface measurements) it could be applied to a large number of NOAA-16 AVHRR overpasses covering the globe.For the analysis, two different spatial resolutions U local area coverage (LAC) andwere considered. Therefore, all inferred results, like global area coverage (GAC) U cloud cover, cloud properties and radiant properties, could be intercompared. Specific emphasis could be made to the surface radiant flux densities (all radiative balance compoments), where results for different regions, like Southern America, Southern Africa, Northern America, Europe, and Indonesia, will be presented. Applying SESAT, energy flux densities, like latent and sensible heat flux densities could also be determined additionally. A statistical analysis of all results including a detailed discussion for the two spatial resolutions will close this study.

Estimating the beam attenuation coefficient in coastal waters from AVHRR imagery

NASA Astrophysics Data System (ADS)

Gould, Richard W.; Arnone, Robert A.

1997-09-01

This paper presents an algorithm to estimate particle beam attenuation at 660 nm ( cp660) in coastal areas using the red and near-infrared channels of the NOAA AVHRR satellite sensor. In situ reflectance spectra and cp660 measurements were collected at 23 stations in Case I and II waters during an April 1993 cruise in the northern Gulf of Mexico. The reflectance spectra were weighted by the spectral response of the AVHRR sensor and integrated over the channel 1 waveband to estimate the atmospherically corrected signal recorded by the satellite. An empirical relationship between integrated reflectance and cp660 values was derived with a linear correlation coefficient of 0.88. Because the AVHRR sensor requires a strong channel 1 signal, the algorithm is applicable in highly turbid areas ( cp660 > 1.5 m -1) where scattering from suspended sediment strongly controls the shape and magnitude of the red (550-650 nm) reflectance spectrum. The algorithm was tested on a data set collected 2 years later in different coastal waters in the northern Gulf of Mexico and satellite estimates of cp660 averaged within 37% of measured values. Application of the algorithm provides daily images of nearshore regions at 1 km resolution for evaluating processes affecting ocean color distribution patterns (tides, winds, currents, river discharge). Further validation and refinement of the algorithm are in progress to permit quantitative application in other coastal areas. Published by Elsevier Science Ltd

Canadian Boreal Forest Greening and Browning Trends: An Analysis of Biogeographic Patterns and the Relative Roles of Disturbance versus Climate Drivers

NASA Astrophysics Data System (ADS)

Sulla-menashe, D. J.; Woodcock, C. E.; Friedl, M. A.

2017-12-01

Recent studies have used satellite-derived normalized difference vegetation index (NDVI) time series derived from the Advanced Very High Resolution Radiometer (AVHRR) to explore geographic patterns in boreal forest greening and browning. A number of these studies indicate that boreal forests are experiencing widespread browning, and have suggested that these patterns reflect decreases in forest productivity induced by climate change. A key limitation of these studies, however, is their reliance on AVHRR data, which provides imagery with very coarse spatial resolution and lower radiometric quality relative to other available remote sensing time series. Here we use NDVI time series from Landsat, which has much higher radiometric quality and spatial resolution than AVHRR, to characterize spatial patterns in greening and browning across Canada's boreal forest and to explore the drivers behind the observed trends. Our results show that the majority of NDVI changes in Canada's boreal forest reflect disturbance-recovery dynamics not climate change impacts, that greening and browning trends outside of disturbed forests are consistent with expected ecological responses to regional changes in climate, and that observed NDVI changes are geographically limited and relatively small in magnitude. Consistent with biogeographic theory, greening and browning unrelated to disturbance tended to be located in ecotones near boundaries of the boreal forest bioclimatic envelope. We observe greening to be most prevalent in Eastern Canada, which is more humid, and browning to be most prevalent in Western Canada, where there is more moisture stress. We conclude that continued long-term climate change has the potential to significantly alter the character and function of Canada's boreal forest, but recent changes have been modest and near-term impacts are likely to be focused in or near ecotones. As part of a NASA funded project supporting the Arctic-Boreal Vulnerability Experiment (ABoVE), we have extended the scope of this study from a set of 46 sites to the entire ABoVE domain covering Alaska and Northwestern Canada (over 6 million square kilometers). Using the full Landsat record, we will also be investigating climate change impacts to the timing of leaf phenology and disturbance frequency in these rapidly warming regions.

Aerosol Retrievals over the Ocean using Channel 1 and 2 AVHRR Data: A Sensitivity Analysis and Preliminary Results

NASA Technical Reports Server (NTRS)

Mishchenko, Michael I.; Geogdzhayev, Igor V.; Cairns, Brian; Rossow, William B.; Lacis, Andrew A.

1999-01-01

This paper outlines the methodology of interpreting channel 1 and 2 AVHRR radiance data over the oceans and describes a detailed analysis of the sensitivity of monthly averages of retrieved aerosol parameters to the assumptions made in different retrieval algorithms. The analysis is based on using real AVHRR data and exploiting accurate numerical techniques for computing single and multiple scattering and spectral absorption of light in the vertically inhomogeneous atmosphere-ocean system. We show that two-channel algorithms can be expected to provide significantly more accurate and less biased retrievals of the aerosol optical thickness than one-channel algorithms and that imperfect cloud screening and calibration uncertainties are by far the largest sources of errors in the retrieved aerosol parameters. Both underestimating and overestimating aerosol absorption as well as the potentially strong variability of the real part of the aerosol refractive index may lead to regional and/or seasonal biases in optical thickness retrievals. The Angstrom exponent appears to be the most invariant aerosol size characteristic and should be retrieved along with optical thickness as the second aerosol parameter.

The Calibration of AVHRR/3 Visible Dual Gain Using Meteosat-8 as a MODIS Calibration Transfer Medium

NASA Technical Reports Server (NTRS)

Avey, Lance; Garber, Donald; Nguyen, Louis; Minnis, Patrick

2007-01-01

This viewgraph presentation reviews the NOAA-17 AVHRR visible channels calibrated against MET-8/MODIS using dual gain regression methods. The topics include: 1) Motivation; 2) Methodology; 3) Dual Gain Regression Methods; 4) Examples of Regression methods; 5) AVHRR/3 Regression Strategy; 6) Cross-Calibration Method; 7) Spectral Response Functions; 8) MET8/NOAA-17; 9) Example of gain ratio adjustment; 10) Effect of mixed low/high count FOV; 11) Monitor dual gains over time; and 12) Conclusions

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 2; Implementation, Analysis and Validation

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Samanta, Arindam; Schull, Mitchell A.; Shabanov, Nikolay V.; Milesi, Cristina; Nemani, Ramajrushna R,; Knyazikhin, Yuri; Myneni, Ranga B.

2008-01-01

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the intercomparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.

A Comparison of Seasonal and Interannual Variability of Soil Dust Aerosols Over the Atlantic Ocean as Inferred by the Toms AI and AVHRR AOT Retrievals

NASA Technical Reports Server (NTRS)

Cakmur, R. V.; Miller, R. L.; Tegen, Ina; Hansen, James E. (Technical Monitor)

2001-01-01

The seasonal cycle and interannual variability of two estimates of soil (or 'mineral') dust aerosols are compared: Advanced Very High Resolution Radiometer (AVHRR) aerosol optical thickness (AOT) and Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI), Both data sets, comprising more than a decade of global, daily images, are commonly used to evaluate aerosol transport models. The present comparison is based upon monthly averages, constructed from daily images of each data set for the period between 1984 and 1990, a period that excludes contamination from volcanic eruptions. The comparison focuses upon the Northern Hemisphere subtropical Atlantic Ocean, where soil dust aerosols make the largest contribution to the aerosol load, and are assumed to dominate the variability of each data set. While each retrieval is sensitive to a different aerosol radiative property - absorption for the TOMS AI versus reflectance for the AVHRR AOT - the seasonal cycles of dust loading implied by each retrieval are consistent, if seasonal variations in the height of the aerosol layer are taken into account when interpreting the TOMS AI. On interannual time scales, the correlation is low at most locations. It is suggested that the poor interannual correlation is at least partly a consequence of data availability. When the monthly averages are constructed using only days common to both data sets, the correlation is substantially increased: this consistency suggests that both TOMS and AVHRR accurately measure the aerosol load in any given scene. However, the two retrievals have only a few days in common per month so that these restricted monthly averages have a large uncertainty. Calculations suggest that at least 7 to 10 daily images are needed to estimate reliably the average dust load during any particular month, a threshold that is rarely satisfied by the AVHRR AOT due to the presence of clouds in the domain. By rebinning each data set onto a coarser grid, the availability of the AVHRR AOT is increased during any particular month, along with its interannual correlation with the TOMS AI The latter easily exceeds the sampling threshold due to its greater ability to infer the aerosol load in the presence of clouds. Whether the TOMS AI should be regarded as a more reliable indicator of interannual variability depends upon the extent of contamination by sub-pixel clouds.

[Comparison of GIMMS and MODIS normalized vegetation index composite data for Qing-Hai-Tibet Plateau].

PubMed

Du, Jia-Qiang; Shu, Jian-Min; Wang, Yue-Hui; Li, Ying-Chang; Zhang, Lin-Bo; Guo, Yang

2014-02-01

Consistent NDVI time series are basic and prerequisite in long-term monitoring of land surface properties. Advanced very high resolution radiometer (AVHRR) measurements provide the longest records of continuous global satellite measurements sensitive to live green vegetation, and moderate resolution imaging spectroradiometer (MODIS) is more recent typical with high spatial and temporal resolution. Understanding the relationship between the AVHRR-derived NDVI and MODIS NDVI is critical to continued long-term monitoring of ecological resources. NDVI time series acquired by the global inventory modeling and mapping studies (GIMMS) and Terra MODIS were compared over the same time periods from 2000 to 2006 at four scales of Qinghai-Tibet Plateau (whole region, sub-region, biome and pixel) to assess the level of agreement in terms of absolute values and dynamic change by independently assessing the performance of GIMMS and MODIS NDVI and using 495 Landsat samples of 20 km x20 km covering major land cover type. High correlations existed between the two datasets at the four scales, indicating their mostly equal capability of capturing seasonal and monthly phenological variations (mostly at 0. 001 significance level). Simi- larities of the two datasets differed significantly among different vegetation types. The relative low correlation coefficients and large difference of NDVI value between the two datasets were found among dense vegetation types including broadleaf forest and needleleaf forest, yet the correlations were strong and the deviations were small in more homogeneous vegetation types, such as meadow, steppe and crop. 82% of study area was characterized by strong consistency between GIMMS and MODIS NDVI at pixel scale. In the Landsat NDVI vs. GIMMS and MODIS NDVI comparison of absolute values, the MODIS NDVI performed slightly better than GIMMS NDVI, whereas in the comparison of temporal change values, the GIMMS data set performed best. Similar with comparison results of GIMMS and MODIS NDVI, the consistency across the three datasets was clearly different among various vegetation types. In dynamic changes, differences between Landsat and MODIS NDVI were smaller than Landsat NDVI vs. GIMMS NDVI for forest, but Landsat and GIMMS NDVI agreed better for grass and crop. The results suggested that spatial patterns and dynamic trends of GIMMS NDVI were found to be in overall acceptable agreement with MODIS NDVI. It might be feasible to successfully integrate historical GIMMS and more recent MODIS NDVI to provide continuity of NDVI products. The accuracy of merging AVHRR historical data recorded with more modern MODIS NDVI data strongly depends on vegetation type, season and phenological period, and spatial scale. The integration of the two datasets for needleleaf forest, broadleaf forest, and for all vegetation types in the phenological transition periods in spring and autumn should be treated with caution.

Reconstructed high-resolution scatterometer data: a comparison with AVHRR vegetation index images for regional-scale monitoring of tropical rain forests

NASA Astrophysics Data System (ADS)

Hardin, Perry J.; Long, David G.

1993-08-01

There is considerable interest in utilizing microwave and visible spectrum imagery for the assessment of tropical rain forests. Because rain forest spans large sub-continental areas, medium resolution (1 - 16 km) imagery will play an important role in providing a global perspective of any forest removal or change. Since 1978, AVHRR imagery from NOAA polar orbiters has provided coverage of tropical regions at this desirable resolution, but much of the imagery is plagued with heavy cloud cover typical of equatorial regions. In contrast, no historical source of active microwave imagery at native 1 - 16 km resolution exists for all the global rain forest regions. In this paper, the authors compare the utility of Seasat scatterometer (SASS) ku-band microwave data to early-date AVHRR vegetation index products for discrimination of tropical vegetation formations. When considered separately, both the AVHRR imagery and the SASS imagery could be used to distinguish between broad categories of equatorial land cover, but the AVHRR imagery was slightly superior. When combined, the two data sets provided discrimination capability superior than could be obtained by using either set alone.

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

NASA Technical Reports Server (NTRS)

Smith, William L.; Ebert, Elizabeth

1990-01-01

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed.

Use of NOAA-N satellites for land/water discrimination and flood monitoring

NASA Technical Reports Server (NTRS)

Tappan, G.; Horvath, N. C.; Doraiswamy, P. C.; Engman, T.; Goss, D. W. (Principal Investigator)

1983-01-01

A tool for monitoring the extent of major floods was developed using data collected by the NOAA-6 advanced very high resolution radiometer (AVHRR). A basic understanding of the spectral returns in AVHRR channels 1 and 2 for water, soil, and vegetation was reached using a large number of NOAA-6 scenes from different seasons and geographic locations. A look-up table classifier was developed based on analysis of the reflective channel relationships for each surface feature. The classifier automatically separated land from water and produced classification maps which were registered for a number of acquisitions, including coverage of a major flood on the Parana River of Argentina.

Preliminary comparison of landscape pattern-normalized difference vegetation index (NDVI) relationships to central plains stream conditions

USGS Publications Warehouse

Griffith, J.A.; Martinko, E.A.; Whistler, J.L.; Price, K.P.

2002-01-01

We explored relationships of water quality parameters with landscape pattern metrics (LPMs), land use-land cover (LULC) proportions, and the advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) or NDVI-derived metrics. Stream sites (271) in Nebraska, Kansas, and Missouri were sampled for water quality parameters, the index of biotic integrity, and a habitat index in either 1994 or 1995. Although a combination of LPMs (interspersion and juxtaposition index, patch density, and percent forest) within Ozark Highlands watersheds explained >60% of the variation in levels of nitrite-nitrate nitrogen and conductivity, in most cases the LPMs were not significantly correlated with the stream data. Several problems using landscape pattern metrics were noted: small watersheds having only one or two patches, collinearity with LULC data, and counterintuitive or inconsistent results that resulted from basic differences in land use-land cover patterns among ecoregions or from other factors determining water quality. The amount of variation explained in water quality parameters using multiple regression models that combined LULC and LPMs was generally lower than that from NDVI or vegetation phenology metrics derived from time-series NDVI data. A comparison of LPMs and NDVI indicated that NDVI had greater promise for monitoring landscapes for stream conditions within the study area.

Preliminary comparison of landscape pattern-normalized difference vegetation index (NDVI) relationships to Central Plains stream conditions.

PubMed

Griffith, Jerry A; Martinko, Edward A; Whistler, Jerry L; Price, Kevin P

2002-01-01

We explored relationships of water quality parameters with landscape pattern metrics (LPMs), land use-land cover (LULC) proportions, and the advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) or NDVI-derived metrics. Stream sites (271) in Nebraska, Kansas, and Missouri were sampled for water quality parameters, the index of biotic integrity, and a habitat index in either 1994 or 1995. Although a combination of LPMs (interspersion and juxtaposition index, patch density, and percent forest) within Ozark Highlands watersheds explained >60% of the variation in levels of nitrite-nitrate nitrogen and conductivity, in most cases the LPMs were not significantly correlated with the stream data. Several problems using landscape pattern metrics were noted: small watersheds having only one or two patches, collinearity with LULC data, and counterintuitive or inconsistent results that resulted from basic differences in land use-land cover patterns among ecoregions or from other factors determining water quality. The amount of variation explained in water quality parameters using multiple regression models that combined LULC and LPMs was generally lower than that from NDVI or vegetation phenology metrics derived from time-series NDVI data. A comparison of LPMs and NDVI indicated that NDVI had greater promise for monitoring landscapes for stream conditions within the study area.

Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

NASA Technical Reports Server (NTRS)

Tucker, Compton; Puma, Michael

2015-01-01

Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.

Calibrating Historical IR Sensors Using GEO, and AVHRR Infrared Tropical Mean Calibration Models

NASA Technical Reports Server (NTRS)

Scarino, Benjamin; Doelling, David R.; Minnis, Patrick; Gopalan, Arun; Haney, Conor; Bhatt, Rajendra

2014-01-01

Long-term, remote-sensing-based climate data records (CDRs) are highly dependent on having consistent, wellcalibrated satellite instrument measurements of the Earth's radiant energy. Therefore, by making historical satellite calibrations consistent with those of today's imagers, the Earth-observing community can benefit from a CDR that spans a minimum of 30 years. Most operational meteorological satellites rely on an onboard blackbody and space looks to provide on-orbit IR calibration, but neither target is traceable to absolute standards. The IR channels can also be affected by ice on the detector window, angle dependency of the scan mirror emissivity, stray-light, and detector-to-detector striping. Being able to quantify and correct such degradations would mean IR data from any satellite imager could contribute to a CDR. Recent efforts have focused on utilizing well-calibrated modern hyper-spectral sensors to intercalibrate concurrent operational IR imagers to a single reference. In order to consistently calibrate both historical and current IR imagers to the same reference, however, another strategy is needed. Large, well-characterized tropical-domain Earth targets have the potential of providing an Earth-view reference accuracy of within 0.5 K. To that effort, NASA Langley is developing an IR tropical mean calibration model in order to calibrate historical Advanced Very High Resolution Radiometer (AVHRR) instruments. Using Meteosat-9 (Met-9) as a reference, empirical models are built based on spatially/temporally binned Met-9 and AVHRR tropical IR brightness temperatures. By demonstrating the stability of the Met-9 tropical models, NOAA-18 AVHRR can be calibrated to Met-9 by matching the AVHRR monthly histogram averages with the Met-9 model. This method is validated with ray-matched AVHRR and Met-9 biasdifference time series. Establishing the validity of this empirical model will allow for the calibration of historical AVHRR sensors to within 0.5 K, and thereby establish a climate-quality IR data record.

BOREAS Level-4c AVHRR-LAC Ten-Day Composite Images: Surface Parameters

NASA Technical Reports Server (NTRS)

Cihlar, Josef; Chen, Jing; Huang, Fengting; Nickeson, Jaime; Newcomer, Jeffrey A.; Hall, Forrest G. (Editor)

2000-01-01

The BOReal Ecosystem-Atmosphere Study (BOREAS) Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. Manitoba Remote Sensing Center (MRSC) and BOREAS Information System (BORIS) personnel acquired, processed, and archived data from the Advanced Very High Resolution Radiometer (AVHRR) instruments on the NOAA-11 and -14 satellites. The AVHRR data were acquired by CCRS and were provided to BORIS for use by BOREAS researchers. These AVHRR level-4c data are gridded, 10-day composites of surface parameters produced from sets of single-day images. Temporally, the 10-day compositing periods begin 11-Apr-1994 and end 10-Sep-1994. Spatially, the data cover the entire BOREAS region. The data are stored in binary image format files. Note: Some of the data files on the BOREAS CD-ROMs have been compressed using the Gzip program.

BOREAS Level-4b AVHRR-LAC Ten-Day Composite Images: At-sensor Radiance

NASA Technical Reports Server (NTRS)

Cihlar, Josef; Chen, Jing; Nickerson, Jaime; Newcomer, Jeffrey A.; Huang, Feng-Ting; Hall, Forrest G. (Editor)

2000-01-01

The BOReal Ecosystem-Atmosphere Study (BOREAS) Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. Manitoba Remote Sensing Center (MRSC) and BOREAS Information System (BORIS) personnel acquired, processed, and archived data from the Advanced Very High Resolution Radiometer (AVHRR) instruments on the National Oceanic and Atmospheric Administration (NOAA-11) and -14 satellites. The AVHRR data were acquired by CCRS and were provided to BORIS for use by BOREAS researchers. These AVHRR level-4b data are gridded, 10-day composites of at-sensor radiance values produced from sets of single-day images. Temporally, the 10- day compositing periods begin 11-Apr-1994 and end 10-Sep-1994. Spatially, the data cover the entire BOREAS region. The data are stored in binary image format files.

Pattern recognition analysis of polar clouds during summer and winter

NASA Technical Reports Server (NTRS)

Ebert, Elizabeth E.

1992-01-01

A pattern recognition algorithm is demonstrated which classifies eighteen surface and cloud types in high-latitude AVHRR imagery based on several spectral and textural features, then estimates the cloud properties (fractional coverage, albedo, and brightness temperature) using a hybrid histogram and spatial coherence technique. The summertime version of the algorithm uses both visible and infrared data (AVHRR channels 1-4), while the wintertime version uses only infrared data (AVHRR channels 3-5). Three days of low-resolution AVHRR imagery from the Arctic and Antarctic during January and July 1984 were analyzed for cloud type and fractional coverage. The analysis showed significant amounts of high cloudiness in the Arctic during one day in winter. The Antarctic summer scene was characterized by heavy cloud cover in the southern ocean and relatively clear conditions in the continental interior. A large region of extremely low brightness temperatures in East Antarctica during winter suggests the presence of polar stratospheric cloud.

Evaluation of remotely sensed actual evapotranspiration data for modeling small scale irrigation in Ethiopia.

NASA Astrophysics Data System (ADS)

Taddele, Y. D.; Ayana, E.; Worqlul, A. W.; Srinivasan, R.; Gerik, T.; Clarke, N.

2017-12-01

The research presented in this paper is conducted in Ethiopia, which is located in the horn of Africa. Ethiopian economy largely depends on rainfed agriculture, which employs 80% of the labor force. The rainfed agriculture is frequently affected by droughts and dry spells. Small scale irrigation is considered as the lifeline for the livelihoods of smallholder farmers in Ethiopia. Biophysical models are highly used to determine the agricultural production, environmental sustainability, and socio-economic outcomes of small scale irrigation in Ethiopia. However, detailed spatially explicit data is not adequately available to calibrate and validate simulations from biophysical models. The Soil and Water Assessment Tool (SWAT) model was setup using finer resolution spatial and temporal data. The actual evapotranspiration (AET) estimation from the SWAT model was compared with two remotely sensed data, namely the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectrometer (MODIS). The performance of the monthly satellite data was evaluated with correlation coefficient (R2) over the different land use groups. The result indicated that over the long term and monthly the AVHRR AET captures the pattern of SWAT simulated AET reasonably well, especially on agricultural dominated landscapes. A comparison between SWAT simulated AET and AVHRR AET provided mixed results on grassland dominated landscapes and poor agreement on forest dominated landscapes. Results showed that the AVHRR AET products showed superior agreement with the SWAT simulated AET than MODIS AET. This suggests that remotely sensed products can be used as valuable tool in properly modeling small scale irrigation.

Development of a Climate-Data Record (CDR) of the Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Hall, Dorthy K.; Comiso, Josefino C.; Shuman, Christopher A.; DiGirolamo, Nicolo E.; Stock, Larry V.

2010-01-01

Regional "clear sky" surface temperature increases since the early 1980s in the Arctic, measured using Advanced Very High Resolution Radiometer (AVHRR) infrared data, range from 0.57+/-0.02 deg C to 72+/-0.10 deg C per decade. Arctic warming has important implications for ice-sheet mass balance because much of the periphery of the Greenland Ice Sheet is already near 0 deg C during the melt season, and is thus vulnerable to rapid melting if temperatures continue to increase. An increase in melting of the ice sheet would accelerate sea-level rise, an issue affecting potentially billions of people worldwide. To quantify the ice-surface temperature (IST) of the Greenland Ice Sheet, and to provide an IST dataset of Greenland for modelers that provides uncertainties, we are developing a climate-data record (CDR) of daily "clear-sky" IST of the Greenland Ice Sheet, from 1982 to the present using AVHRR (1982 - present) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data (2000 - present) at a resolution of approximately 5 km. Known issues being addressed in the production of the CDR are: time-series bias caused by cloud cover (surface temperatures can be different under clouds vs. clear areas) and cross-calibration in the overlap period between AVHRR instruments, and between AVHRR and MODIS instruments. Because of uncertainties, mainly due to clouds, time-series of satellite IST do not necessarily correspond with actual surface temperatures. The CDR will be validated by comparing results with automatic-weather station data and with satellite-derived surface-temperature products and biases will be calculated.

Multi-Decadal Pathfinder Data Sets of Global Land Biophysical Variables from AVHRR and MODIS and their Use in GCM Studies of Biogeophysics and Biogeochemistry

NASA Technical Reports Server (NTRS)

Myneni, Ranga

2003-01-01

The problem of how the scale, or spatial resolution, of reflectance data impacts retrievals of vegetation leaf area index (LAI) and fraction absorbed photosynthetically active radiation (PAR) has been investigated. We define the goal of scaling as the process by which it is established that LAI and FPAR values derived from coarse resolution sensor data equal the arithmetic average of values derived independently from fine resolution sensor data. The increasing probability of land cover mixtures with decreasing resolution is defined as heterogeneity, which is a key concept in scaling studies. The effect of pixel heterogeneity on spectral reflectances and LAI/FPAR retrievals is investigated with 1 km Advanced Very High Resolution Radiometer (AVHRR) data aggregated to different coarse spatial resolutions. It is shown that LAI retrieval errors at coarse resolution are inversely related to the proportion of the dominant land cover in such pixel. Further, large errors in LAI retrievals are incurred when forests are minority biomes in non-forest pixels compared to when forest biomes are mixed with one another, and vice-versa. A physically based technique for scaling with explicit spatial resolution dependent radiative transfer formulation is developed. The successful application of this theory to scaling LAI retrievals from AVHRR data of different resolutions is demonstrated

Processing techniques for global land 1-km AVHRR data

USGS Publications Warehouse

Eidenshink, Jeffery C.; Steinwand, Daniel R.; Wivell, Charles E.; Hollaren, Douglas M.; Meyer, David

1993-01-01

The U.S. Geological Survey's (USGS) Earth Resources Observation Systems (EROS) Data Center (EDC) in cooperation with several international science organizations has developed techniques for processing daily Advanced Very High Resolution Radiometer (AVHRR) 1-km data of the entire global land surface. These techniques include orbital stitching, geometric rectification, radiometric calibration, and atmospheric correction. An orbital stitching algorithm was developed to combine consecutive observations acquired along an orbit by ground receiving stations into contiguous half-orbital segments. The geometric rectification process uses an AVHRR satellite model that contains modules for forward mapping, forward terrain correction, and inverse mapping with terrain correction. The correction is accomplished by using the hydrologic features coastlines and lakes from the Digital Chart of the World. These features are rasterized into the satellite projection and are matched to the AVHRR imagery using binary edge correlation techniques. The resulting coefficients are related to six attitude correction parameters: roll, roll rate, pitch, pitch rate, yaw, and altitude. The image can then be precision corrected to a variety of map projections and user-selected image frames. Because the AVHRR lacks onboard calibration for the optical wavelengths, a series of time-variant calibration coefficients derived from vicarious calibration methods and are used to model the degradation profile of the instruments. Reducing atmospheric effects on AVHRR data is important. A method has been develop that will remove the effects of molecular scattering and absorption from clear sky observations, using climatological measurements of ozone. Other methods to remove the effects of water vapor and aerosols are being investigated.

Bone cement distribution is a potential predictor to the reconstructive effects of unilateral percutaneous kyphoplasty in OVCFs: a retrospective study.

PubMed

Lin, Jiachen; Qian, Lie; Jiang, Changqing; Chen, Xiuyuan; Feng, Fan; Lao, Lifeng

2018-06-07

Osteoporotic vertebral compression fracture (OVCF) is a common type of fracture, and percutaneous kyphoplasty (PKP) is an eligible solution to it. Previous studies have revealed that both the volume and filling pattern of bone cement correlate with the clinical outcomes after PKP procedure. However, the role of bone cement distribution remains to be illustrated. To retrospectively evaluate the relationship between the bone cement distribution and the clinical outcomes of unilateral PKP, we enrolled 73 OVCF patients receiving unilateral PKP treatment. All the intervened vertebrae were classified into three groups based on the bone cement distribution observed on postoperative X-ray films. Preoperative and postoperative radiographic parameters including the vertebral height and kyphotic Cobb angle were recorded, and anterior vertebral height restoration rate (AVHRR) and Cobb angle correction (CR) were then calculated to assess the vertebral height reconstruction. Preoperative and postoperative Oswestry Disability Index (ODI) and visual analogue scale (VAS) were adopted by interviewing patients to assess the mobility improvement and pain relief. Demographic data, body mass index (BMI), lumbar bone mineral density (evaluated by BMD T-score) of each patient, bone cement volume (BV), and bone cement extravasation (BE) were also recorded. Between- and within-group comparisons and multivariable correlation analysis were carried out to analyze the data. VAS and ODI scores were both significantly improved in all of the enrolled cases with no significant differences between groups. Among the three groups, the average age, AVHRR, and BV were significantly different. Occurrence of BE was significantly different between two of the three groups. AVHRR was demonstrated to correlate negatively with preoperative anterior vertebral height ratio and positively with preoperative Cobb angle, CR, diffusion score, and ODI changes. Bone cement distribution is a potential predictor to the reconstructive effects in unilateral PKP for OVCFs. Bone cement distribution is associated with AVHRR and BV, as well as the risk of BE occurrence. Greater bone cement distribution may indicate better vertebral restoration along with a higher BE risk.

Dependence of NOAA-AVHRR recorded radiance on scan angle, atmospheric turbidity and unresolved cloud

NASA Technical Reports Server (NTRS)

Piwinski, D. J.; Schoch, L. B.; Duggin, M. J.; Whitehead, V.; Ryland, E.

1984-01-01

Experimental evidence on the scan angle and sun angle dependence of radiance recorded by the Advanced Very High Resolution Radiometer (AVHRR) devices on the NOAA-6 and NOAA-7 satellites is presented. The effects of atmospheric turbidity at various scan angles is shown, and simulations of angular anisotropy and recorded radiance are compared with the recorded digital data from the AVHRR obtained over the Great Plains area of the US. Evidence is presented on the effects of unresolved cloud on the recorded radiance and vegetative indices from uniform, vegetative targets.

NOAA AVHRR Land Surface Albedo Algorithm Development

NASA Technical Reports Server (NTRS)

Toll, D. L.; Shirey, D.; Kimes, D. S.

1997-01-01

The primary objective of this research is to develop a surface albedo model for the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). The primary test site is the Konza prairie, Kansas (U.S.A.), used by the International Satellite Land Surface Climatology Project (ISLSCP) in the First ISLSCP Field Experiment (FIFE). In this research, high spectral resolution field spectrometer data was analyzed to simulate AVHRR wavebands and to derive surface albedos. Development of a surface albedo algorithm was completed by analysing a combination of satellite, field spectrometer, and ancillary data. Estimated albedos from the field spectrometer data were compared to reference albedos derived using pyranometer data. Variations from surface anisotropy of reflected solar radiation were found to be the most significant albedo-related error. Additional error or sensitivity came from estimation of a shortwave mid-IR reflectance (1.3-4.0 micro-m) using the AVHRR red and near-IR bands. Errors caused by the use of AVHRR spectral reflectance to estimate both a total visible (0.4-0.7 micro-m) and near-IR (0.7-1.3 micro-m) reflectance were small. The solar spectral integration, using the derived ultraviolet, visible, near-IR and SW mid-IR reflectivities, was not sensitive to many clear-sky changes in atmospheric properties and illumination conditions.

Sea ice motions in the Central Arctic pack ice as inferred from AVHRR imagery

NASA Technical Reports Server (NTRS)

Emery, William; Maslanik, James; Fowler, Charles

1995-01-01

Synoptic observations of ice motion in the Arctic Basin are currently limited to those acquired by drifting buoys and, more recently, radar data from ERS-1. Buoys are not uniformly distributed throughout the Arctic, and SAR coverage is currently limited regionally and temporally due to the data volume, swath width, processing requirements, and power needs of the SAR. Additional ice-motion observations that can map ice responses simultaneously over large portions of the Arctic on daily to weekly time intervals are thus needed to augment the SAR and buoys data and to provide an intermediate-scale measure of ice drift suitable for climatological analyses and ice modeling. Principal objectives of this project were to: (1) demonstrate whether sufficient ice features and ice motion existed within the consolidated ice pack to permit motion tracking using AVHRR imagery; (2) determine the limits imposed on AVHRR mapping by cloud cover; and (3) test the applicability of AVHRR-derived motions in studies of ice-atmosphere interactions. Each of these main objectives was addressed. We conclude that AVHRR data, particularly when blended with other available observations, provide a valuable data set for studying sea ice processes. In a follow-on project, we are now extending this work to cover larger areas and to address science questions in more detail.

Vegetation monitoring for Guatemala: a comparison between simulated VIIRS and MODIS satellite data

USGS Publications Warehouse

Boken, Vijendra K.; Easson, Gregory L.; Rowland, James

2010-01-01

The advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) data are being widely used for vegetation monitoring across the globe. However, sensors will discontinue collecting these data in the near future. National Aeronautics and Space Administration is planning to launch a new sensor, visible infrared imaging radiometer suite (VIIRS), to continue to provide satellite data for vegetation monitoring. This article presents a case study of Guatemala and compares the simulated VIIRS-Normalized Difference Vegetation Index (NDVI) with MODIS-NDVI for four different dates each in 2003 and 2005. The dissimilarity between VIIRS-NDVI and MODIS-NDVI was examined on the basis of the percent difference, the two-tailed student's t-test, and the coefficient of determination, R 2. The per cent difference was found to be within 3%, the p-value ranged between 0.52 and 0.99, and R 2 exceeded 0.88 for all major types of vegetation (basic grains, rubber, sugarcane, coffee and forests) found in Guatemala. It was therefore concluded that VIIRS will be almost equally capable of vegetation monitoring as MODIS.

Atmospheric effects on the NDVI - Strategies for its removal. [Normalized Difference Vegetation Index

NASA Technical Reports Server (NTRS)

Kaufman, Y. J.; Tanre, D.; Holben, B. N.; Markham, B.; Gitelson, A.

1992-01-01

The compositing technique used to derive global vegetation index (NDVI) from the NOAA AVHRR radiances reduces the residual effect of water vapor and aerosol on the NDVI. The reduction in the atmospheric effect is shown using a comprehensive measured data set for desert conditions, and a simulation for grass with continental aerosol. A statistical analaysis of the probability of occurrence of aerosol optical thickness and precipitable water vapor measured in different climatic regimes is used for this simulation. It is concluded that for a long compositing period (e.g., 27 days), the residual aerosol optical thickness and precipitable water vapor are usually too small to be corrected. For a 9-day compositing, the residual average aerosol effect may be about twice the correction uncertainty. For Landsat TM or Earth Observing System Moderate Resolution Imaging Spectrometer (EOS-MODIS) data, the newly defined atmospherically resistant vegetation index (ARVI) is more promising than possible direct atmospheric correction schemes, except for heavy desert dust conditions.

Data pre-processing: Stratospheric aerosol perturbing effect on the remote sensing of vegetation: Correction method for the composite NDVI after the Pinatubo eruption

NASA Technical Reports Server (NTRS)

Vermote, E.; Elsaleous, N.; Kaufman, Y. J.; Dutton, E.

1994-01-01

An operational stratospheric correction scheme used after the Mount Pinatubo (Phillipines) eruption (Jun. 1991) is presented. The stratospheric aerosol distribution is assumed to be only variable with latitude. Each 9 days the latitudinal distribution of the optical thickness is computed by inverting radiances observed in the NOAA AVHRR channel 1 (0.63 micrometers) and channel 2 (0.83 micrometers) over the Pacific Ocean. This radiance data set is used to check the validity of model used for inversion by checking consistency of the optical thickness deduced from each channel as well as optical thickness deduced from different scattering angles. Using the optical thickness profile previously computed and radiative transfer code assuming Lambertian boundary condition, each pixel of channel 1 and 2 are corrected prior to computation of NDVI (Normalized Difference Vegetation Index). Comparison between corrected, non corrected, and years prior to Pinatubo eruption (1989 to 1990) NDVI composite, shows the necessity and the accuracy of the operational correction scheme.

A Climate-Data Record (CDR) of the "Clear Sky" Surface Temperature of the Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

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

2011-01-01

To quantify the ice-surface temperature (IST) we are developing a climate-data record (CDR) of monthly IST of the Greenland ice sheet, from 1982 to the present using Advanced Very High Resolution Radiometer (AVHRR) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data at 5-km resolution. "Clear-sky" surface temperature increases have been measured from the early 1980s to the early 2000s in the Arctic using AVHRR data, showing increases ranging from 0.57-0.02 (Wang and Key, 2005) to 0.72 0.10 deg C per decade (Comiso, 2006). Arctic warming has implications for ice-sheet mass balance because much of the periphery of the ice sheet is near 0 deg C in the melt season and is thus vulnerable to more extensive melting (Hanna et al., 2008). The algorithm used for this work has a long history of measuring IST in the Arctic with AVHRR (Key and Haefliger, 1992). The data are currently available from 1981 to 2004 in the AVHRR Polar Pathfinder (APP) dataset (Fowler et al., 2000). J. Key1NOAA modified the AVHRR algorithm for use with MODIS (Hall et al., 2004). The MODIS algorithm is now being processed over Greenland. Issues being addressed in the production of the CDR are: time-series bias caused by cloud cover, and cross-calibration between AVHRR and MODIS instruments. Because of uncertainties, time series of satellite ISTs do not necessarily correspond with actual surface temperatures. The CDR will be validated by comparing results with in-situ (see Koenig and Hall, in press) and automatic-weather station data (e.g., Shuman et al., 2001).

Results of calibrations of the NOAA-11 AVHRR made by reference to calibrated SPOT imagery at White Sands, N.M

NASA Technical Reports Server (NTRS)

Nianzeng, Che; Grant, Barbara G.; Flittner, David E.; Slater, Philip N.; Biggar, Stuart F.; Jackson, Ray D.; Moran, M. S.

1991-01-01

The calibration method reported here makes use of the reflectances of several large, uniform areas determined from calibrated and atmospherically corrected SPOT Haute Resolution Visible (HRV) scenes of White Sands, New Mexico. These reflectances were used to predict the radiances in the first two channels of the NOAA-11 Advanced Very High Resolution Radiometer (AVHRR). The digital counts in the AVHRR image corresponding to these known reflectance areas were determined by the use of two image registration techniques. The plots of digital counts versus pixel radiance provided the calibration gains and offsets for the AVHRR. A reduction in the gains of 4 and 13 percent in channels 1 and 2 respectively was found during the period 1988-11-19 to 1990-6-21. An error budget is presented for the method and is extended to the case of cross-calibrating sensors on the same orbital platform in the Earth Observing System (EOS) era.

User's guide to image processing applications of the NOAA satellite HRPT/AVHRR data. Part 1: Introduction to the satellite system and its applications. Part 2: Processing and analysis of AVHRR imagery

NASA Technical Reports Server (NTRS)

Huh, Oscar Karl; Leibowitz, Scott G.; Dirosa, Donald; Hill, John M.

1986-01-01

The use of NOAA Advanced Very High Resolution Radar/High Resolution Picture Transmission (AVHRR/HRPT) imagery for earth resource applications is provided for the applications scientist for use within the various Earth science, resource, and agricultural disciplines. A guide to processing NOAA AVHRR data using the hardware and software systems integrated for this NASA project is provided. The processing steps from raw data on computer compatible tapes (1B data format) through usable qualitative and quantitative products for applications are given. The manual is divided into two parts. The first section describes the NOAA satellite system, its sensors, and the theoretical basis for using these data for environmental applications. Part 2 is a hands-on description of how to use a specific image processing system, the International Imaging Systems, Inc. (I2S) Model 75 Array Processor and S575 software, to process these data.

AVHRR, MODIS, and VIIRS radiometric stability and consistency in SST bands

NASA Astrophysics Data System (ADS)

Liang, XingMing; Ignatov, Alexander

2013-06-01

Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS; www.star.nesdis.noaa.gov/sod/sst/micros) is NESDIS near-real time web-based radiance monitoring system. It analyzes Model (Community Radiative Transfer Model, CRTM) minus Observation (M-O) biases in brightness temperatures (BT) in three bands centered at 3.7 (IR37), 11 (IR11), and 12 µm (IR12), for several AVHRR (NOAA-16, -17, -18, -19, Metop-A, -B), VIIRS (Suomi National Polar Partnership, S-NPP), and MODIS (Terra, Aqua) sensors. Double-differences (DD) are employed to check BTs for radiometric stability and consistency. All sensors are stable, with the exception of two AVHRRs, onboard NOAA-16 and to a lesser extent NOAA-18, and generally consistent. VIIRS onboard S-NPP, launched in October 2011, is well in-family, especially after its calibration was fine-tuned on 7 March 2012. MODIS M-O biases were initially out-of-family by up to -0.6 K, due to incorrect CRTM transmittance coefficients. Following MICROS feedback, CRTM Team updated coefficients and brought MODIS back in-family. Terra and Aqua BTs are very consistent in IR11 and IR12 but show cross-platform bias of 0.3 K in IR37, likely attributed to MODIS characterization. Work with MODIS Characterization Support Team is underway to resolve this. Initial analyses of AVHRR onboard Metop-B launched in September 2012 suggest that its BTs are offset from Metop-A by up to ˜0.3 K. Overall, MICROS DDs are well suited to evaluate the sensors stability, but dedicated effort is needed to ensure consistent radiative transfer modeling (RTM) calculations for various sensors before DDs can be used in Global Space-based Inter-Calibration System (GSICS) quantitative applications.

Practical Application and Obstacles of AVHRR Thermal Data for Estimation of Effusion Rates at Tolbachik Volcano, Kamchatka Peninsula, Russian Federation

NASA Astrophysics Data System (ADS)

McAlpin, D. B.; Meyer, F. J.; Webley, P. W.

2017-12-01

Using thermal data from Advanced Very High Resolution Radiometer (AVHRR) sensors, we investigated algorithms to estimate the effusive volume of lava flows from the 2012-13 eruption of Tolbachik Volcano with high temporal resolution. AVHRR are polar orbiting, radiation detection instruments that provide reflectance and radiance data in six spectral bands with a ground resolution of 1.1 km². During the Tolbachik eruption of 2012-13, active AVHRR instruments were available aboard four polar orbiting platforms. Although the primary purpose of the instruments is climate and ocean studies, their multiple platforms provide global coverage at least twice daily, with data for all regions of the earth no older than six hours. This frequency makes the AVHRR instruments particularly suitable for the study of volcanic activity. While methods for deriving effusion rates from thermal observations have been previously published, a number of topics complicate their practical application. In particular, these include (1) unknown material parameters used in the estimation process; (2) relatively coarse resolution of thermal sensors; (3) optimizing a model to describe the number of thermal regimes within each pixel and (4) frequent saturation issues in thermal channels. We present ongoing investigations into effusion rate estimation from AVHRR data using the 2012-13 eruption of Tolbachik Volcano as a test event. For this eruption we studied approaches for coping with issues (1) - (4) to pave the way to a more operational implementation of published techniques. To address (1), we used Monte Carlo simulations to understand the sensitivity of effusion rate estimates to changes in material parameters. To study (2) and (3) we compared typical two-component (exposed lava on ambient background) and three-component models (exposed lava, cooled crust, ambient background) for their relative performance. To study issue (4), we compared AVHRR-derived effusion rates to reference data derived from multi-temporal digital elevation models. In our workflow, we correct for scan angle of the sensor and the transmissivity of the atmosphere before including include corrected temperatures in heat equations to determine the effusion volume necessary to satisfy the equations.

Linear mixing model applied to AVHRR LAC data

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 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the mixing model results. 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 resolution data for global studies.

A remote sensing based vegetation classification logic for global land cover analysis

USGS Publications Warehouse

Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond

1995-01-01

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

An AVHRR Cloud Classification Database Typed by Experts

DTIC Science & Technology

1993-10-01

analysis. Naval Research Laboratory, Monterey, CA. 110 pp. Gallaudet , Timothy C. and James J. Simpson, 1991: Automated cloud screening of AVHRR imagery...1987) and Saunders and Kriebel (1988a,b) have used threshold techniques to classify clouds. Gallaudet and Simpson (1991) have used split-and-merge

Integrating reconstructed scatterometer and advanced very high resolution radiometer data for tropical forest inventory

NASA Astrophysics Data System (ADS)

Hardin, Perry J.; Long, David G.

1995-11-01

A scientific effort is currently underway to assess tropical forest degradation and its potential impact on Earth's climate. Because of the large continental regions involved, Advanced Very High Resolution Radiometer (AVHRR) imagery and its derivative vegetation index products with resolutions between 1 and 12 km are typically used to inventory the Earth's equatorial vegetation. Archival AVHRR imagery is also used to obtain a temporal baseline of historical forest extent. Recently however, 50-km Seasat-A Scatterometer (SASS) Ku-band imagery (acquired in 1978) has been reconstructed to approximately equals 4-km resolution, making it a supplement to AVHRR imagery for historical vegetation assessment. In order to test the utility of reconstructed Ku-band scatterometer imagery for this purpose, seasonal AVHRR vegetation index and SASS images of identical resolutions were constructed. Using the imagery, discrimination experiments involving 18 vegetation categories were conducted for a central South America study area. The results of these experiments indicate that AVHRR vegetation- index images are slightly superior to reconstructed SASS images for differentiating between equatorial vegetation classes when used alone. However, combining the scatterometer imagery with the vegetation-index images provides discrimination superior to any other combination of the data sets. Using the two data sets together, 90.3% of the test data could be correctly classified into broad classes of equatorial forest, degraded woodland/forest, woodland/savanna, and caatinga.

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products

PubMed Central

Guo, Xiaoyi; Zhang, Hongyan; Wu, Zhengfang; Zhao, Jianjun; Zhang, Zhengxiang

2017-01-01

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics. PMID:28587266

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products.

PubMed

Guo, Xiaoyi; Zhang, Hongyan; Wu, Zhengfang; Zhao, Jianjun; Zhang, Zhengxiang

2017-06-06

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics.

Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project

NASA Astrophysics Data System (ADS)

Stengel, Martin; Stapelberg, Stefan; Sus, Oliver; Schlundt, Cornelia; Poulsen, Caroline; Thomas, Gareth; Christensen, Matthew; Carbajal Henken, Cintia; Preusker, Rene; Fischer, Jürgen; Devasthale, Abhay; Willén, Ulrika; Karlsson, Karl-Göran; McGarragh, Gregory R.; Proud, Simon; Povey, Adam C.; Grainger, Roy G.; Fokke Meirink, Jan; Feofilov, Artem; Bennartz, Ralf; Bojanowski, Jedrzej S.; Hollmann, Rainer

2017-11-01

New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude-longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.

For each dataset a digital object identifier has been issued:

Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002

Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002

Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002

Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002

Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002

Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

AVHRR imagery reveals Antarctic ice dynamics

NASA Technical Reports Server (NTRS)

Bindschadler, Robert A.; Vornberger, Patricia L.

1990-01-01

A portion of AVHRR data taken on December 5, 1987 at 06:15 GMT over a part of Antarctica is used here to show that many of the most significant dynamic features of ice sheets can be identified by a careful examination of AVHRR imagery. The relatively low resolution of this instrument makes it ideal for obtaining a broad view of the ice sheets, while its wide swath allows coverage of areas beyond the reach of high-resolution imagers either currently in orbit or planned. An interpretation is given of the present data, which cover the area of ice streams that drain the interior of the West Antarctic ice sheet into the Ross Ice Shelf.

High resolution image processing on low-cost microcomputers

NASA Technical Reports Server (NTRS)

Miller, R. L.

1993-01-01

Recent advances in microcomputer technology have resulted in systems that rival the speed, storage, and display capabilities of traditionally larger machines. Low-cost microcomputers can provide a powerful environment for image processing. A new software program which offers sophisticated image display and analysis on IBM-based systems is presented. Designed specifically for a microcomputer, this program provides a wide-range of functions normally found only on dedicated graphics systems, and therefore can provide most students, universities and research groups with an affordable computer platform for processing digital images. The processing of AVHRR images within this environment is presented as an example.

Midwest agriculture and ENSO: A comparison of AVHRR NDVI3g data and crop yields in the United States Corn Belt from 1982 to 2014

NASA Astrophysics Data System (ADS)

Glennie, Erin; Anyamba, Assaf

2018-06-01

A time series of Advanced Very High Resolution Radiometer (AVHRR) derived normalized difference vegetation index (NDVI) data were compared to National Agricultural Statistics Service (NASS) corn yield data in the United States Corn Belt from 1982 to 2014. The main objectives of the comparison were to assess 1) the consistency of regional Corn Belt responses to El Niño/Southern Oscillation (ENSO) teleconnection signals, and 2) the reliability of using NDVI as an indicator of crop yield. Regional NDVI values were used to model a seasonal curve and to define the growing season - May to October. Seasonal conditions in each county were represented by NDVI and land surface temperature (LST) composites, and corn yield was represented by average annual bushels produced per acre. Correlation analysis between the NDVI, LST, corn yield, and equatorial Pacific sea surface temperature anomalies revealed patterns in land surface dynamics and corn yield, as well as typical impacts of ENSO episodes. It was observed from the study that growing seasons coincident with La Niña events were consistently warmer, but El Niño events did not consistently impact NDVI, temperature, or corn yield data. Moreover, the El Niño and La Niña composite images suggest that impacts vary spatially across the Corn Belt. While corn is the dominant crop in the region, some inconsistencies between corn yield and NDVI may be attributed to soy crops and other background interference. The overall correlation between the total growing season NDVI anomaly and detrended corn yield was 0.61(p = 0.00013), though the strength of the relationship varies across the Corn Belt.

An investigation of spectral change as influenced by irrigation and evapotranspiration volume estimation in western Nebraska

USGS Publications Warehouse

Seevers, P.M.; Sadowski, F.C.; Lauer, D.T.

1990-01-01

Retrospective satellite image data were evaluated for their ability to demonstrate the influence of center-pivot irrigation development in western Nebraska on spectral change and climate-related factors for the region. Periodic images of an albedo index and a normalized difference vegetation index (NDVI) were generated from calibrated Landsat multispectral scanner (MSS) data and used to monitor spectral changes associated with irrigation development from 1972 through 1986. The albedo index was not useful for monitoring irrigation development. For the NDVI, it was found that proportions of counties in irrigated agriculture, as discriminated by a threshold, were more highly correlated with reported ground estimates of irrigated agriculture than were county mean greenness values. A similar result was achieved when using coarse resolution Advanced Very High Resolution Radiometer (AVHRR) image data for estimating irrigated agriculture. The NDVI images were used to evaluate a procedure for making areal estimates of actual evapotranspiration (ET) volumes. Estimates of ET volumes for test counties, using reported ground acreages and corresponding standard crop coefficients, were correlated with the estimates of ET volume using crop coefficients scaled to NDVI values and pixel counts of crop areas. These county estimates were made under the assumption that soil water availability was unlimited. For nonirrigated vegetation, this may result in over-estimation of ET volumes. Ground information regarding crop types and acreages are required to derive the NDVI scaling factor. Potential ET, estimated with the Jensen-Haise model, is common to both methods. These results, achieved with both MSS and AVHRR data, show promise for providing climatologically important land surface information for regional and global climate models. ?? 1990 Kluwer Academic Publishers.

Midwest Agriculture: A comparison of AVHRR NDVI3g data and crop yields in Corn Belt region of the United States from 1982 to 2014

NASA Astrophysics Data System (ADS)

Glennie, E.; Anyamba, A.; Eastman, R.

2016-12-01

A time series of Advanced Very High Resolution Radiometer (AVHRR) derived normalized difference vegetation index (NDVI) images was compared to National Agricultural Statistics Service (NASS) corn yield data in the Corn Belt of the United States from 1982 to 2014. The relationship between NDVI and crop yields under El Nino, neutral, and La Nina conditions was used to assess 1) the reliability of using NDVI as an indicator of crop productivity, and 2) the response of the Corn Belt to El Nino/ Southern Oscillation (ENSO) teleconnection effects. First, bi-monthly NDVI data were combined into monthly data using the maximum value compositing technique to reduce cloud contamination and other effects. The most representative seasonal curve of NDVI values over the course of the study period was extracted to define the growing season in the region - May to October. Standardized NDVI anomalies were calculated and averaged to produce a growing season NDVI metrics to represent each Agricultural Statistics Division (ASD) for each year in the study period. The corn yields were detrended in order to remove effects of technological advancements on crop productivity (use of genetically modified seeds, fertilizer, herbicides). Correlation (R) values between the NDVI anomalies and detrended corn yields varied across the Corn Belt, with a maximum of 0.81 and mean of 0.49. While corn is the dominant crop in the region, some inconsistencies between corn yield and NDVI may be accounted for by an increase in soy yield for a given year due to crop rotation practices. The 10 El Nino events and 9 La Nina events that occurred between 1982 and 2014 are not reflected in a consistent manner in NDVI or corn yield data. However, composites of NDVI and crop yields for all El Nino events indicate there is a tendency for higher than normal NDVI and increased corn yields. Conversely, the composite crop yield image for La Nina events shows a slight decrease in productivity.

Introducing Real-Time AVHRR-APT Satellite Imagery in the Classroom Environment

ERIC Educational Resources Information Center

Moxey, Lucas; Tucker, Compton; Sloan, Jim; Chadwick, John

2004-01-01

A low-cost (US$350) satellite receiving station was assembled and operated within a classroom environment in Gainesville (Florida) on October 2001 for acquiring satellite data directly from the Advanced Very High Resolution Radiometer (AVHRR) satellites. The simplicity of the satellite signal makes this source of real-time satellite data readily…

Linear and nonlinear trending and prediction for AVHRR time series data

NASA Technical Reports Server (NTRS)

Smid, J.; Volf, P.; Slama, M.; Palus, M.

1995-01-01

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Snow and Ice Applications of AVHRR in Polar Regions: Report of a Workshop

NASA Technical Reports Server (NTRS)

Steffen, K.; Bindschadler, R.; Casassa, G.; Comiso, J.; Eppler, D.; Fetterer, F.; Hawkins, J.; Key, J.; Rothrock, D.; Thomas, R.;

Interpreting forest biome productivity and cover utilizing nested scales of image resolution and biogeographical analysis

NASA Technical Reports Server (NTRS)

Iverson, Louis R.; Cook, Elizabeth A.; Graham, Robin L.; Olson, Jerry S.; Frank, Thomas D.; Ying, KE

1988-01-01

The objective was to relate spectral imagery of varying resolution with ground-based data on forest productivity and cover, and to create models to predict regional estimates of forest productivity and cover with a quantifiable degree of accuracy. A three stage approach was outlined. In the first stage, a model was developed relating forest cover or productivity to TM surface reflectance values (TM/FOREST models). The TM/FOREST models were more accurate when biogeographic information regarding the landscape was either used to stratigy the landscape into more homogeneous units or incorporated directly into the TM/FOREST model. In the second stage, AVHRR/FOREST models that predicted forest cover and productivity on the basis of AVHRR band values were developed. The AVHRR/FOREST models had statistical properties similar to or better than those of the TM/FOREST models. In the third stage, the regional predictions were compared with the independent U.S. Forest Service (USFS) data. To do this regional forest cover and forest productivity maps were created using AVHRR scenes and the AVHRR/FOREST models. From the maps the county values of forest productivity and cover were calculated. It is apparent that the landscape has a strong influence on the success of the approach. An approach of using nested scales of imagery in conjunction with ground-based data can be successful in generating regional estimates of variables that are functionally related to some variable a sensor can detect.

ncreasing altitudinal spring phenology gradient of vegetation over the last decade in Qinghai-Tibetan Plateau

NASA Astrophysics Data System (ADS)

Shen, M.; Piao, S.

2013-12-01

Vegetation spring phenology in temperate and cold regions is widely expected to advance with temperature elevation and is often used as an indicator of regional climatic change. The Qinghai-Tibetan Plateau (QTP) has experienced intensive warming recently, but substantial contradictions exist about the changes of vegetation spring phenology. We investigated spatiotemporal variations in green-up dates in the QTP from 2000 to 2011 determined through five methods using four satellite-derived datasets including the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), Système Pour l'Observation de la Terre, and MODerate resolution Imaging Spectroradiometer (MODIS), and the enhanced vegetation index from MODIS. On regional scale, no significant temporal trends (all P > 0.05) were found in the green-up dates, consistently among all the vegetation indices and methods. This insignificance was resulted from the substantial spatial heterogeneity of trends in green-up date, with delay by greater than 0.5 day yr-1 in the southwest region, and extensive advance in the other areas, although the temperature elevation was region-wide. These changes doubled the altitudinal gradient of green-up date, from 0.63 day 100m-1 in the early 2000s to 1.30 days 100m-1 in the early 2010s. The delay in the southwest region and high altitudes was likely caused by the decline in spring precipitation, despite the increasing spring temperature. This study suggests that spring precipitation is an important regulator of phenological response to climatic warming in QTP, and that, even in cold region, delay of vegetation spring phenology does not necessarily indicate spring cooling. Besides, the phenological changes retrieved from the widely used AVHRR NDVI differed from those from the other 3 vegetation indices, necessitating the use of multi-datasets while monitoring vegetation dynamics from space.

AVHRR channel selection for land cover classification

USGS Publications Warehouse

Maxwell, S.K.; Hoffer, R.M.; Chapman, P.L.

2002-01-01

Mapping land cover of large regions often requires processing of satellite images collected from several time periods at many spectral wavelength channels. However, manipulating and processing large amounts of image data increases the complexity and time, and hence the cost, that it takes to produce a land cover map. Very few studies have evaluated the importance of individual Advanced Very High Resolution Radiometer (AVHRR) channels for discriminating cover types, especially the thermal channels (channels 3, 4 and 5). Studies rarely perform a multi-year analysis to determine the impact of inter-annual variability on the classification results. We evaluated 5 years of AVHRR data using combinations of the original AVHRR spectral channels (1-5) to determine which channels are most important for cover type discrimination, yet stabilize inter-annual variability. Particular attention was placed on the channels in the thermal portion of the spectrum. Fourteen cover types over the entire state of Colorado were evaluated using a supervised classification approach on all two-, three-, four- and five-channel combinations for seven AVHRR biweekly composite datasets covering the entire growing season for each of 5 years. Results show that all three of the major portions of the electromagnetic spectrum represented by the AVHRR sensor are required to discriminate cover types effectively and stabilize inter-annual variability. Of the two-channel combinations, channels 1 (red visible) and 2 (near-infrared) had, by far, the highest average overall accuracy (72.2%), yet the inter-annual classification accuracies were highly variable. Including a thermal channel (channel 4) significantly increased the average overall classification accuracy by 5.5% and stabilized interannual variability. Each of the thermal channels gave similar classification accuracies; however, because of the problems in consistently interpreting channel 3 data, either channel 4 or 5 was found to be a more appropriate choice. Substituting the thermal channel with a single elevation layer resulted in equivalent classification accuracies and inter-annual variability.

Arctic sea ice albedo from AVHRR

NASA Technical Reports Server (NTRS)

Lindsay, R. W.; Rothrock, D. A.

1994-01-01

The seasonal cycle of surface albedo of sea ice in the Arctic is estimated from measurements made with the Advanced Very High Resolution Radiometer (AVHRR) on the polar-orbiting satellites NOAA-10 and NOAA-11. The albedos of 145 200-km-square cells are analyzed. The cells are from March through September 1989 and include only those for which the sun is more than 10 deg above the horizon. Cloud masking is performed manually. Corrections are applied for instrument calibration, nonisotropic reflection, atmospheric interference, narrowband to broadband conversion, and normalization to a common solar zenith angle. The estimated albedos are relative, with the instrument gain set to give an albedo of 0.80 for ice floes in March and April. The mean values for the cloud-free portions of individual cells range from 0.18 to 0.91. Monthly averages of cells in the central Arctic range from 0.76 in April to 0.47 in August. The monthly averages of the within-cell standard deviations in the central Arctic are 0.04 in April and 0.06 in September. The surface albedo and surface temperature are correlated most strongly in March (R = -0.77) with little correlation in the summer. The monthly average lead fraction is determined from the mean potential open water, a scaled representation of the temperature or albedo between 0.0 (for ice) and 1.0 (for water); in the central Arctic it rises from an average 0.025 in the spring to 0.06 in September. Sparse data on aerosols, ozone, and water vapor in the atmospheric column contribute uncertainties to instantaneous, area-average albedos of 0.13, 0.04, and 0.08. Uncertainties in monthly average albedos are not this large. Contemporaneous estimation of these variables could reduce the uncertainty in the estimated albedo considerably. The poor calibration of AVHRR channels 1 and 2 is another large impediment to making accurate albedo estimates.

A brief comparison of radiometers at NSIDC and their potential to generate long ESDRs

NASA Astrophysics Data System (ADS)

Moth, P.; Johnston, T.; Haran, T. M.; Fowler, D. K.

2017-12-01

Radiometers have played a big part in Earth observing science. In this poster we compare three such instruments: the Advanced Very-High-resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). The NASA National Snow and Ice Distributed Active Archive Center (NSIDC DAAC) has archived cryospheric data from all three of these instruments. AVHRR was a 4-channel radiometer that was first launched in 1978 aboard the TIROS-N satellite. Subsequent missions launched improved versions of AVHRR with five and six channels, observing Earth in frequencies ranging from 0.58 μm to 12.5 μm with a resolution at nadir of 1.09 km. MODIS instruments fly onboard NASA's Earth Observing System (EOS) Terra and Aqua satellites. Launched in 1999 and 2002, respectively, they still produce much sought after data observed in 36 spectral bands ranging from 0.4 μm to 14.4 μm. Two bands image Earth at a nominal resolution of 250 m at nadir, five at 500 m, and the remaining 29 bands at 1 km. A ±55-degree scanning pattern at the sun-synchronous orbit of 705 km achieves a 2,330 km swath and provides global coverage every one to two days VIIRS, NOAA's latest radiometer, was launched aboard the Suomi National Polar-orbiting Partnership satellite on October 28, 2011. Working collaboratively, NASA and NOAA are producing data that is archived and distributed via NASA DAACs. The VIIRS radiometer comprises 22 bands; five for high-resolution imagery, 16 at moderate resolution, and one panchromatic day/night band. VIIRS is a whiskbroom scanning radiometer that covers the spectrum between 0.412 μm and 12.01 μm and acquires spatial resolutions at nadir of 750 m, 375 m, and 750 m, respectively. Although these instruments are configured with different spectral bands, each was designed with an eye to the future. MODIS can be thought of as a successor to the AVHRR mission, adding capabilities that yielded better data. Similarly, VIIRS will extend the MODIS record with new, higher quality data. Starting in the early 1980s, the AVHRR-MODIS-VIIRS timeline should span at least four decades and perhaps beyond, enabling researchers to produce and gain valuable insight from very long, high-quality Earth System Data Records (ESDRs).

Forest mapping of Central America and Mexico with AVHRR data

Treesearch

Keith B. Lannom

2001-01-01

Concerns over changes in global forest resource distributions have prompted a number of studies to examine and map forest areas at continental scales with various types of satillite data. The work described here details the use of Advanced Very High Resolution Radiometer (AVHRR) data in concert with Landsat Thematic Mapper (TM) and Systeme Probatoire d'...

An evaluation of atmospheric corrections to advanced very high resolution radiometer data

USGS Publications Warehouse

Meyer, David; Hood, Joy J.

1993-01-01

A data set compiled to analyze vegetation indices is used to evaluate the effect of atmospheric correction to AVHRR measurement in the solar spectrum. Such corrections include cloud screening and "clear sky" corrections. We used the "clouds from AVHRR" (CLAVR) method for cloud detection and evaluated its performance over vegetated targets. Clear sky corrections, designed to reduce the effects of molecular scattering and absorption due to ozone, water vapor, carbon dioxide, and molecular oxygen, were applied to data values determine to be cloud free. Generally, it was found that the screening and correction of the AVHRR data did not affect the maximum NDVI compositing process adversely, while at the same time improving estimates of the land-surface radiances over a compositing period.

Calibration of the AVHRR visible and near IR channels using radiances measured over remote ocean areas

NASA Technical Reports Server (NTRS)

Vermote, Eric F.; Vassiliou, George D.; Kaufman, Yoram J.; Holben, Brent N.

1992-01-01

An inflight absolute calibration method has been adapted and applied to channel 1 of the AVHRR. The approach is based on AVHRR observations in channels 1, 2 and 4. A rigorous cloud screening is performed, based on the homogeneity of the data in channel 1 and 2 and on the temperature in channel 4. In a combined approach, the off-nadir view satellite count in channel 2 is used to detect the aerosol optical thickness and loading and the count of channel 1 is used to calibrate this channel, based on the predictable Rayleigh scattering component. Water vapor data are used, and the channels are intercalibrated using the ratio between channels 1 and 2 over the glint region.

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.

A RESEARCH PLAN FOR THE USE OF THERMAL AVHRR IMAGERY TO STUDY ANNUAL AND SEASONAL MEAN SURFACE TEMPERATURES FOR LARGE LAKES IN NORTH AMERICA

EPA Science Inventory

Surface and vertical temperature data will be obtained from several large lakes With surface areas large enough to be effectively sampled with AVHRR imagery. Yearly and seasonal patterns of surface and whole water column thermal values will be compared to estimates of surface tem...

A Comparison of Various Estimators for Updating Forest Area Coverage Using AVHRR and Forest Inventory Data

Treesearch

Francis A. Roesch; Paul C. van Deusen; Zhiliang Zhu

1995-01-01

Various methods of adjusting low-cost and possibly biased estimates of percent forest coverage from AVHRR data with a subsample of higher-cost estimates from the USDA Forest Service's Forest Inventory and Analysis plots were investigated. Two ratio and two regression estimators were evaluated. Previous work (Zhu and Teuber, 1991) finding that the estimates from...

Recent history of large-scale ecosystem disturbances in North America derived from the AVHRR satellite record.

Treesearch

Christopher Potter; Tan Pang-Ning; Vipin Kumar; Chris Kucharik; Steven Klooster; Vanessa Genovese; Warren Cohen; Sean Healey

2005-01-01

Ecosystem structure and function are strongly affected by disturbance events, many of which in North America are associated with seasonal temperature extremes, wildfires, and tropical storms. This study was conducted to evaluate patterns in a 19-year record of global satellite observations of vegetation phenology from the advanced very high resolution radiometer (AVHRR...

Scan angle calculation and image compositing for the Mexico forest mapping project

Treesearch

Zhiliang Zhu

1994-01-01

Data from the Advanced Very High Resolution Radiometer (AVHRR) were used in a cooperative project, sponsored by the U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, and the United Nations, Food and Agriculture Organization (FAO), to map Mexicos forest cover types.To provide satisfactory AVHRR data sets for the project, the sensor scan...

On the Use of Deep Convective Clouds to Calibrate AVHRR Data

NASA Technical Reports Server (NTRS)

Doelling, David R.; Nguyen, Louis; Minnis, Patrick

2004-01-01

Remote sensing of cloud and radiation properties from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellites requires constant monitoring of the visible sensors. NOAA satellites do not have onboard visible calibration and need to be calibrated vicariously in order to determine the calibration and the degradation rate. Deep convective clouds are extremely bright and cold, are at the tropopause, have nearly a Lambertian reflectance, and provide predictable albedos. The use of deep convective clouds as calibration targets is developed into a calibration technique and applied to NOAA-16 and NOAA-17. The technique computes the relative gain drift over the life-span of the satellite. This technique is validated by comparing the gain drifts derived from inter-calibration of coincident AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) radiances. A ray-matched technique, which uses collocated, coincident, and co-angled pixel satellite radiance pairs is used to intercalibrate MODIS and AVHRR. The deep convective cloud calibration technique was found to be independent of solar zenith angle, by using well calibrated Visible Infrared Scanner (VIRS) radiances onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, which precesses through all solar zenith angles in 23 days.

AVHRR composite period selection for land cover classification

USGS Publications Warehouse

Maxwell, S.K.; Hoffer, R.M.; Chapman, P.L.

2002-01-01

Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, near-infrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite data-set as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa) of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, B.C.; Tieszen, L.T.

2003-01-01

Time integrated normalized difference vegetation index (??NDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989-1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ??NDVI and the ??NDVI coefficient of variation (CV ??NDVI) used as a proxy for interranual climate variability is analysed. Results suggest that the differences in the long-term climate control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primary C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ??NDVI values.

Radiation energy budget studies using collocated AVHRR and ERBE observations

NASA Technical Reports Server (NTRS)

Ackerman, Steven A.; Inoue, Toshiro

1994-01-01

Changes in the energy balance at the top of the atmosphere are specified as a function of atmospheric and surface properties using observations from the Advanced Very High Resolution Radiometer (AVHRR) and the Earth Radiation Budget Experiment (ERBE) scanner. By collocating the observations from the two instruments, flown on NOAA-9, the authors take advantage of the remote-sensing capabilities of each instrument. The AVHRR spectral channels were selected based on regions that are strongly transparent to clear sky conditions and are therefore useful for characterizing both surface and cloud-top conditions. The ERBE instruments make broadband observations that are important for climate studies. The approach of collocating these observations in time and space is used to study the radiative energy budget of three geographic regions: oceanic, savanna, and desert.

Automated cloud screening of AVHRR imagery using split-and-merge clustering

NASA Technical Reports Server (NTRS)

Gallaudet, Timothy C.; Simpson, James J.

1991-01-01

Previous methods to segment clouds from ocean in AVHRR imagery have shown varying degrees of success, with nighttime approaches being the most limited. An improved method of automatic image segmentation, the principal component transformation split-and-merge clustering (PCTSMC) algorithm, is presented and applied to cloud screening of both nighttime and daytime AVHRR data. The method combines spectral differencing, the principal component transformation, and split-and-merge clustering to sample objectively the natural classes in the data. This segmentation method is then augmented by supervised classification techniques to screen clouds from the imagery. Comparisons with other nighttime methods demonstrate its improved capability in this application. The sensitivity of the method to clustering parameters is presented; the results show that the method is insensitive to the split-and-merge thresholds.

Observed cloud reflectivities and liquid water paths: An update

NASA Technical Reports Server (NTRS)

Coakley, James A., Jr.; Snider, Jack B.

1990-01-01

The FIRE microwave radiometer observations of liquid water path from San Nicolas Island and simultaneous NOAA AVHRR observations of cloud reflectivity were used to test a relationship between cloud liquid water path and cloud reflectivity that is often used in general circulation climate models (Stephens, 1978). The results of attempts to improve the data analysis which was described at the previous FIRE Science Team Workshop and elsewhere (Coakley and Snider, 1989) are reported. The improvements included the analysis of additional satellite passes over San Nicolas and sensitivity studies to estimate the effects on the observed reflectivities due to: (1) nonzero surface reflectivities beneath the clouds; (2) the anisotropy of the reflected radiances observed by the AVHRR; (3) small scale spatial structure in the liquid water path; and (4) adjustments to the calibration of AVHRR.

Cloud cover determination in polar regions from satellite imagery

NASA Technical Reports Server (NTRS)

Barry, R. G.; Maslanik, J. A.; Key, J. R.

1987-01-01

A definition is undertaken of the spectral and spatial characteristics of clouds and surface conditions in the polar regions, and to the creation of calibrated, geometrically correct data sets suitable for quantitative analysis. Ways are explored in which this information can be applied to cloud classifications as new methods or as extensions to existing classification schemes. A methodology is developed that uses automated techniques to merge Advanced Very High Resolution Radiometer (AVHRR) and Scanning Multichannel Microwave Radiometer (SMMR) data, and to apply first-order calibration and zenith angle corrections to the AVHRR imagery. Cloud cover and surface types are manually interpreted, and manual methods are used to define relatively pure training areas to describe the textural and multispectral characteristics of clouds over several surface conditions. The effects of viewing angle and bidirectional reflectance differences are studied for several classes, and the effectiveness of some key components of existing classification schemes is tested.

Rural cases of equine West Nile virus encephalomyelitis and the normalized difference vegetation index

USGS Publications Warehouse

Ward, M.P.; Ramsay, B.H.; Gallo, K.

2005-01-01

Data from an outbreak (August to October, 2002) of West Nile virus (WNV) encephalomyelitis in a population of horses located in northern Indiana was scanned for clusters in time and space. One significant (p = 0.04) cluster of case premises was detected, occurring between September 4 and 10 in the south-west part of the study area (85.70??N, 45.50??W). It included 10 case premises (3.67 case premises expected) within a radius of 2264 m. Image data were acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard a National Oceanic and Atmospheric Administration polar-orbiting satellite. The Normalized Difference Vegetation Index (NDVI) was calculated from visible and near-infrared data of daily observations, which were composited to produce a weekly-1km2 resolution raster image product. During the epidemic, a significant (p<0.01) decrease (0.025 per week) in estimated NDVI was observed at all case and control premise sites. The median estimated NDVI (0.659) for case premises within the cluster identified was significantly (p<0.01) greater than the median estimated NDVI for other case (0.571) and control (0.596) premises during the same period. The difference in median estimated NDVI for case premises within this cluster, compared to cases not included in this cluster, was greatest (5.3% and 5.1%, respectively) at 1 and 5 weeks preceding occurrence of the cluster. The NDVI may be useful for identifying foci of WNV transmission. ?? Mary Ann Liebert, Inc.

Cloud classification in polar regions using AVHRR textural and spectral signatures

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Weger, R. C.; Christopher, S. A.; Kuo, K. S.; Carsey, F. D.

1990-01-01

Arctic clouds and ice-covered surfaces are classified on the basis of textural and spectral features obtained with AVHRR 1.1-km spatial resolution imagery over the Beaufort Sea during May-October, 1989. Scenes were acquired about every 5 days, for a total of 38 cases. A list comprising 20 arctic-surface and cloud classes is compiled using spectral measures defined by Garand (1988).

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.

Engima of a thermal anomaly - A TM/AVHRR study of the volcanic Arabian highlands

NASA Technical Reports Server (NTRS)

Blodget, H. W.; Andre, C. G.; Masuoka, P. M.

1987-01-01

Discovery of a large thermal anomaly in the western Arabian highlands on Landsat TM imagery is reported. The anomaly, 15 C warmer than surroundings, forms a 2-km-wide arc around the southern flank of Jebel Chada, a volcano active in 1256 AD. It is recorded by AVHRR imagery as well, despite the 1.1-km spatial resolution of this sensor. Air photos and geologic maps show no bedrock unit that corresponds to the anomaly. Digital techniques were applied to the TM and AVHRR data, including contrast enhancement, density slicing, principal components analysis, and construction of multiband composite images. It is concluded that the anomaly results from a thin cover of volcanic ash or cinder that is optically indistinguishable from underlying basalt, rather than from internal (volcanic or hydrologic) heat sources.

AVHRR for monitoring global tropical deforestation

NASA Technical Reports Server (NTRS)

Malingreau, J. P.; Laporte, N.; Tucker, C. J.

1989-01-01

Advanced Very High Resolution Radiometer (AVHRR) data have been used to assess the dynamics of forest trnsformations in three parts of the tropical belt. A large portion of the Amazon Basin has been systematically covered by Local Area Coverage (LAC) data in the 1985-1987 period. The analysis of the vegetation index and thermal data led to the identification and measurement of large areas of active deforestation. The Kalimantan/Borneo forest fires were monitored and their impact was evaluated using the Global Area Coverage (GAC) 4 km resolution data. Finally, High Resolution Picture Transmission (HRPT) data have provided preliminary information on current activities taking place at the boundary between the savanna and the forest in the Southern part of West Africa. The AVHRR approach is found to be a highly valuable means for carrying out deforestation assessments in regional and global perspectives.

Evaluating operational AVHRR sea surface temperature data at the coastline using surfers

NASA Astrophysics Data System (ADS)

Brewin, Robert J. W.; de Mora, Lee; Billson, Oliver; Jackson, Thomas; Russell, Paul; Brewin, Thomas G.; Shutler, Jamie D.; Miller, Peter I.; Taylor, Benjamin H.; Smyth, Tim J.; Fishwick, James R.

2017-09-01

Sea surface temperature (SST) is an essential climate variable that can be measured routinely from Earth Observation (EO) with high temporal and spatial coverage. To evaluate its suitability for an application, it is critical to know the accuracy and precision (performance) of the EO SST data. This requires comparisons with co-located and concomitant in situ data. Owing to a relatively large network of in situ platforms there is a good understanding of the performance of EO SST data in the open ocean. However, at the coastline this performance is not well known, impeded by a lack of in situ data. Here, we used in situ SST measurements collected by a group of surfers over a three year period in the coastal waters of the UK and Ireland, to improve our understanding of the performance of EO SST data at the coastline. At two beaches near the city of Plymouth, UK, the in situ SST measurements collected by the surfers were compared with in situ SST collected from two autonomous buoys located ∼7 km and ∼33 km from the coastline, and showed good agreement, with discrepancies consistent with the spatial separation of the sites. The in situ SST measurements collected by the surfers around the coastline, and those collected offshore by the two autonomous buoys, were used to evaluate the performance of operational Advanced Very High Resolution Radiometer (AVHRR) EO SST data. Results indicate: (i) a significant reduction in the performance of AVHRR at retrieving SST at the coastline, with root mean square errors in the range of 1.0 to 2.0 °C depending on the temporal difference between match-ups, significantly higher than those at the two offshore stations (0.4 to 0.6 °C); (ii) a systematic negative bias in the AVHRR retrievals of approximately 1 °C at the coastline, not observed at the two offshore stations; and (iii) an increase in the root mean square error at the coastline when the temporal difference between match-ups exceeded three hours. Harnessing new solutions to improve in situ sampling coverage at the coastline, such as tagging surfers with sensors, can improve our understanding of the performance of EO SST data in coastal regions, helping inform users interested in EO SST products for coastal applications. Yet, validating EO SST products using in situ SST data at the coastline is challenged by difficulties reconciling the two measurements, which are provided at different spatial scales in a dynamic and complex environment.

Evidence for Phanerozoic reactivation of the Najd Fault System in AVHRR, TM, and SPOT images of central Arabia

NASA Technical Reports Server (NTRS)

Andre, Constance G.

1989-01-01

SPOT stereoscopic and TM multispectral images support evidence in AVHRR thermal-IR images of a major unmapped shear zone in Phanerozoic cover rocks southeast of the ancient Najd Fault System in the Arabian Shield. This shear zone and faults of the Najd share a common alignment, orientation, and sinistral sense of movement. These similarities suggest a 200-km extension of the Najd Fault System and reactivation since it formed in the late Precambrian. Topographic and lithologic features in the TM and SPOT data along one of three faults inferred from the AVHRR data indicate sinistral offsets up to 2.5 km, en echelon folds and secondary faults like those predicted by models of left-lateral strike-slip faulting. The age of the affected outcrops indicates reactivation of Najd faults in the Cretaceous, judging from TM and SPOT data or in the Tertiary, based on AVHRR data. The total length of the system visible at the surface measures 1300 km. If the Najd Fault System is extrapolated beneath sands of the Empty Quarter to faults of a similar trend in South Yemen, the shear zone would span the Arabian Plate. Furthermore, if extensions into the Arabian Sea bed and into Egypt proposed by others are considered, it would exceed 3000 km.

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.

An improved RST approach for timely alert and Near Real Time monitoring of oil spill disasters by using AVHRR data

NASA Astrophysics Data System (ADS)

Grimaldi, C. S. L.; Casciello, D.; Coviello, I.; Lacava, T.; Pergola, N.; Tramutoli, V.

2011-05-01

Information acquired and provided in Near Real Time is fundamental in contributing to reduce the impact of different sea pollution sources on the maritime environment. Optical data acquired by sensors aboard meteorological satellites, thanks to their high temporal resolution as well as to their delivery policy, can be profitably used for a Near Real Time sea monitoring, provided that accurate and reliable methodologies for analysis and investigation are designed, implemented and fully assessed. In this paper, the results achieved by the application of an improved version of RST (Robust Satellite Technique) to oil spill detection and monitoring will be shown. In particular, thermal infrared data acquired by the NOAA-AVHRR (National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer) have been analyzed and a new RST-based change detection index applied to the case of the oil spills that occurred off the Kuwait and Saudi Arabian coasts in January 1991 and during the Lebanon War in July 2006. The results obtained, even in comparison with those achieved by other AVHRR-based techniques, confirm the unique performance of the proposed approach in automatically detecting the presence of oil spill with a high level of reliability and sensitivity. Moreover, the potential of the extension of the proposed technique to sensors onboard geostationary satellites will be discussed within the context of oil spill monitoring systems, integrating products generated by high temporal (optical) and high spatial (radar) resolution satellite systems.

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.

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.

Mapping Fire Scars in the Brazilian Cerrado Using AVHRR Imagery

NASA Technical Reports Server (NTRS)

Hlavka, C. A.; Ambrosia, V. G.; Brass, J. A.; Rezendez, A.; Alexander, S.; Guild, L. S.; Peterson, David L. (Technical Monitor)

1995-01-01

The Brazilian cerrado, or savanna, spans an area of 1,800,000 square kilometers on the great plateau of Central Brazil. Large fires covering hundreds of square kilometers, frequently occur in wildland areas of the cerrado, dominated by grasslands or grasslands mixed with shrubs and small trees, and also within area in the cerrado used for agricultural purposes, particularly for grazing. Smaller fires, typically extending over arm of a few square kilometers or less, are associated with the clewing of crops, such as dry land rice. A method for mapping fire scars and differentiating them from extensive areas of bare sod with AVHRR bands 1 (.55 -.68 micrometer) and 3 (3.5 - 3.9 micrometers) and measures of performance based on comparison with maps of fires with Landsat imagery will be presented. Methods of estimating total area burned from the AVHRR fire scar map will be discussed and related to land use and scar size.

Satellite Analysis of Ocean Biogeochemistry and Mesoscale Variability in the Sargasso Sea

NASA Technical Reports Server (NTRS)

Siegel, D. A.; Micheals, A. F.; Nelson, N. B.

1997-01-01

The objective of this study was to analyze the impact of spatial variability on the time-series of biogeochemical measurements made at the U.S. JGOFS Bermuda Atlantic Time-series Study (BATS) site. Originally the study was planned to use SeaWiFS as well as AVHRR high-resolution data. Despite the SeaWiFS delays we were able to make progress on the following fronts: (1) Operational acquisition, processing, and archive of HRPT data from a ground station located in Bermuda; (2) Validation of AVHRR SST data using BATS time-series and spatial validation cruise CTD data; (3) Use of AVHRR sea surface temperature imagery and ancillary data to assess the impact of mesoscale spatial variability on P(CO2) and carbon flux in the Sargasso Sea; (4) Spatial and temporal extent of tropical cyclone induced surface modifications; and (5) Assessment of eddy variability using TOPEX/Poseidon data.

Investigation of scene identification algorithms for radiation budget measurements

NASA Technical Reports Server (NTRS)

Diekmann, F. J.

1986-01-01

The computation of Earth radiation budget from satellite measurements requires the identification of the scene in order to select spectral factors and bidirectional models. A scene identification procedure is developed for AVHRR SW and LW data by using two radiative transfer models. These AVHRR GAC pixels are then attached to corresponding ERBE pixels and the results are sorted into scene identification probability matrices. These scene intercomparisons show that there generally is a higher tendency for underestimation of cloudiness over ocean at high cloud amounts, e.g., mostly cloudy instead of overcast, partly cloudy instead of mostly cloudy, for the ERBE relative to the AVHRR results. Reasons for this are explained. Preliminary estimates of the errors of exitances due to scene misidentification demonstrates the high dependency on the probability matrices. While the longwave error can generally be neglected the shortwave deviations have reached maximum values of more than 12% of the respective exitances.

Multi-frequency SAR, SSM/I and AVHRR derived geophysical information of the marginal ice zone

NASA Technical Reports Server (NTRS)

Shuchman, R. A.; Onstott, R. G.; Wackerman, C. C.; Russel, C. A.; Sutherland, L. L.; Johannessen, O. M.; Johannessen, J. A.; Sandven, S.; Gloerson, P.

1991-01-01

A description is given of the fusion of synthetic aperture radar (SAR), special sensor microwave imager (SSM/I), and NOAA Advanced Very High Resolution Radiometer (AVHRR) data to study arctic processes. These data were collected during the SIZEX/CEAREX experiments that occurred in the Greenland Sea in March of 1989. Detailed comparisons between the SAR, AVHRR, and SSM/I indicated: (1) The ice edge position was in agreement to within 25 km, (2) The SSM/I SAR total ice concentration compared favorably, however, the SSM/I significantly underpredicted the multiyear fraction, (3) Combining high resolution SAR with SSM/I can potentially map open water and new ice features in the marginal ice zone (MIZ) which cannot be mapped by the single sensors, and (4) The combination of all three sensors provides accurate ice information as well as sea surface temperature and wind speeds.

Long Term Cloud Property Datasets From MODIS and AVHRR Using the CERES Cloud Algorithm

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Bedka, Kristopher M.; Doelling, David R.; Sun-Mack, Sunny; Yost, Christopher R.; Trepte, Qing Z.; Bedka, Sarah T.; Palikonda, Rabindra; Scarino, Benjamin R.; Chen, Yan;

Estimating the effective spatial resolution of an AVHRR time series

USGS Publications Warehouse

Meyer, D.J.

1996-01-01

A method is proposed to estimate the spatial degradation of geometrically rectified AVHRR data resulting from misregistration and off-nadir viewing, and to infer the cumulative effect of these degradations over time. Misregistrations are measured using high resolution imagery as a geometric reference, and pixel sizes are computed directly from satellite zenith angles. The influence or neighbouring features on a nominal 1 km by 1 km pixel over a given site is estimated from the above information, and expressed as a spatial distribution whose spatial frequency response is used to define an effective field-of-view (EFOV) for a time series. In a demonstration of the technique applied to images from the Conterminous U.S. AVHRR data set, an EFOV of 3·1km in the east-west dimension and 19 km in the north-south dimension was estimated for a time series accumulated over a grasslands test site.

Satellite measurements of large-scale air pollution - Methods

NASA Technical Reports Server (NTRS)

Kaufman, Yoram J.; Ferrare, Richard A.; Fraser, Robert S.

1990-01-01

A technique for deriving large-scale pollution parameters from NIR and visible satellite remote-sensing images obtained over land or water is described and demonstrated on AVHRR images. The method is based on comparison of the upward radiances on clear and hazy days and permits simultaneous determination of aerosol optical thickness with error Delta tau(a) = 0.08-0.15, particle size with error + or - 100-200 nm, and single-scattering albedo with error + or - 0.03 (for albedos near 1), all assuming accurate and stable satellite calibration and stable surface reflectance between the clear and hazy days. In the analysis of AVHRR images of smoke from a forest fire, good agreement was obtained between satellite and ground-based (sun-photometer) measurements of aerosol optical thickness, but the satellite particle sizes were systematically greater than those measured from the ground. The AVHRR single-scattering albedo agreed well with a Landsat albedo for the same smoke.

Evaluating an Automated Approach for Monitoring Forest Disturbances in the Pacific Northwest from Logging, Fire and Insect Outbreaks with Landsat Time Series Data

NASA Technical Reports Server (NTRS)

R.Neigh, Christopher S.; Bolton, Douglas K.; Williams, Jennifer J.; Diabate, Mouhamad

2014-01-01

Forests are the largest aboveground sink for atmospheric carbon (C), and understanding how they change through time is critical to reduce our C-cycle uncertainties. We investigated a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 1991 in Pacific Northwest forests, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometers (AVHRRs). To understand the causal factors of this decline, we evaluated an automated classification method developed for Landsat time series stacks (LTSS) to map forest change. This method included: (1) multiple disturbance index thresholds; and (2) a spectral trajectory-based image analysis with multiple confidence thresholds. We produced 48 maps and verified their accuracy with air photos, monitoring trends in burn severity data and insect aerial detection survey data. Area-based accuracy estimates for change in forest cover resulted in producer's and user's accuracies of 0.21 +/- 0.06 to 0.38 +/- 0.05 for insect disturbance, 0.23 +/- 0.07 to 1 +/- 0 for burned area and 0.74 +/- 0.03 to 0.76 +/- 0.03 for logging. We believe that accuracy was low for insect disturbance because air photo reference data were temporally sparse, hence missing some outbreaks, and the annual anniversary time step is not dense enough to track defoliation and progressive stand mortality. Producer's and user's accuracy for burned area was low due to the temporally abrupt nature of fire and harvest with a similar response of spectral indices between the disturbance index and normalized burn ratio. We conclude that the spectral trajectory approach also captures multi-year stress that could be caused by climate, acid deposition, pathogens, partial harvest, thinning, etc. Our study focused on understanding the transferability of previously successful methods to new ecosystems and found that this automated method does not perform with the same accuracy in Pacific Northwest forests. Using a robust accuracy assessment, we demonstrate the difficulty of transferring change attribution methods to other ecosystems, which has implications for the development of automated detection/attribution approaches. Widespread disturbance was found within AVHRR-negative anomalies, but identifying causal factors in LTSS with adequate mapping accuracy for fire and insects proved to be elusive. Our results provide a background framework for future studies to improve methods for the accuracy assessment of automated LTSS classifications.

The ATOVS and AVHRR product processing facility for EPS

NASA Astrophysics Data System (ADS)

Klaes, D.; Ackermann, J.; Schraidt, R.; Patterson, T.; Schlüssel, P.; Phillips, P.; Arriaga, A.; Grandell, J.

The ATOVS/AVHRR Product Processing Facility (PPF) of the EPS (EUMETSAT Polar System) Core Ground Segment comprises the Level 1 processing of the data from the ATOVS sounding instruments AMSU-A, MHS and HIRS/4, and the imager AVHRR/3 into calibrated and navigated radiances. A second component includes the level 2 processing, which uses as input the level 1 products of the aforementioned instruments. The specification of the PPF is based on two well-known and well-established software packages, which have been used by the international community for some years: The AAPP (ATOVS and AVHRR Pre-processing Package) and ICI (Inversion Coupled with Imager). The PPF is able to process data from instruments flown on the Metop and NOAA satellites. For the level 1 processing of the sounding instruments' data (HIRS, AMSU-A and MHS), the basic functionality of AAPP has been kept; however, the individual chains for each instrument have been separated and additional functionality has been integrated. For HIRS a global calibration, as performed by NOAA/NESDIS today, has been included. For AMSU-A and MHS the moon contamination of the calibration space view can be corrected for. Additional functionality has also been included in the AVHRR processing. In particular, an enhanced navigation by landmark processing has been implemented to ensure accurate geo-location. Additionally, the PPF can digest and process the global AVHRR data either at full pixel resolution (1 km at nadir), which is the nominal mode for the Metop processing, or at the reduced resolution of the NOAA/GAC (Global Area Coverage) data (about 4 km resolution at nadir). For the level 2 processing the ICI had to be modified to include the most recent improvement in fast radiative transfer modelling as included in the RTTOV-7. As a first step towards the realisation of the PPF a prototype has been generated for the purpose to help specifying the details of the PPF, and for verification of the latter by generation of reference and test data. The prototype is able to process HRPT data, GAC data from the NOAA satellite active archive (SAA), and also Local Area Coverage (LAC) data. GAC data processing means that the processing of whole orbits is possible. Current work is aimed to assess the quality of the Level 2 retrievals and to generate reference test data for the operational PPF.

Quantification of Surface Suspended Sediments along a River Dominated Coast with NOAA AVHRR and SeaWiFS Measurements: Louisiana, USA

NASA Technical Reports Server (NTRS)

Myint, S. W.; Walker, N. D.

2002-01-01

The ability to quantify suspended sediment concentrations accurately over both time and space using satellite data has been a goal of many environmental researchers over the past few decades This study utilizes data acquired by the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the Orbview-2 Sea-viewing wide field-of-view (SeaWiFS) ocean colour sensor, coupled with field measurements to develop statistical models for the estimation of near-surface suspended sediment and suspended solids "Ground truth" water samples were obtained via helicopter, small boat and automatic water sampler within a few hours of satellite overpasses The NOAA AVHRR atmospheric correction was modified for the high levels of turbidity along the Louisiana coast. Models were developed based on the field measurements and reflectance/radiance measurements in the visible and near infrared Channels of NOAA-14 and Orbview-2 SeaWiFS. The best models for predicting surface suspended sediment concentrations were obtained with a NOAA AVHRR Channel 1 (580-680nm) cubic model, Channel 2 (725-1100 nm) linear mod$ and SeaWiFs Channel 6 (660-68Onm) power modeL The suspended sediment models developed using SeaWiFS Channel 5 (545-565 nm) were inferior, a result that we attribute mainly to the atmospheric correction technique, the shallow depth of the water samples and absorption effects from non-sediment water constituents.

Does the Madden-Julian Oscillation influence aerosol variability?

NASA Astrophysics Data System (ADS)

Tian, Baijun; Waliser, Duane E.; Kahn, Ralph A.; Li, Qinbin; Yung, Yuk L.; Tyranowski, Tomasz; Geogdzhayev, Igor V.; Mishchenko, Michael I.; Torres, Omar; Smirnov, Alexander

2008-06-01

We investigate the modulation of aerosols by the Madden-Julian Oscillation (MJO) using multiple, global satellite aerosol products: aerosol index (AI) from the Total Ozone Mapping Spectrometer (TOMS) on Nimbus-7, and aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on NOAA satellites. A composite MJO analysis indicates that large variations in the TOMS AI and MODIS/AVHRR AOT are found over the equatorial Indian and western Pacific Oceans where MJO convection is active, as well as the tropical Africa and Atlantic Ocean where MJO convection is weak but the background aerosol level is high. A strong inverse linear relationship between the TOMS AI and rainfall anomalies, but a weaker, less coherent positive correlation between the MODIS/AVHRR AOT and rainfall anomalies, were found. The MODIS/AVHRR pattern is consistent with ground-based Aerosol Robotic Network data. These results indicate that the MJO and its associated cloudiness, rainfall, and circulation variability systematically influence the variability in remote sensing aerosol retrieval results. Several physical and retrieval algorithmic factors that may contribute to the observed aerosol-rainfall relationships are discussed. Preliminary analysis indicates that cloud contamination in the aerosol retrievals is likely to be a major contributor to the observed relationships, although we cannot exclude possible contributions from other physical mechanisms. Future research is needed to fully understand these complex aerosol-rainfall relationships.

Aerosol variation over Continental Europe from 1980 to 2015 Using ALAD Aerosol Retrievals

NASA Astrophysics Data System (ADS)

Che, Yahui; Xue, Yong; Mei, Linlu; Guang, Jie; She, Lu

2017-04-01

The Advanced Very High Resolution Radiometer (AVHRR) on-board National Oceanic and Atmospheric Administration (NOAA) series satellites has been used to observe the Earth and is the only spaceborne instrument which can provide users continuous long time series global coverage for more than 35 years since 1979. The initial purpose of AVHRR is for cloud detection and monitoring thermal emission of the Earth so that it lacks visible channels (only 0.64μm) and spaceborne which is unignorably unfavourable to its applications in aerosol retrieving over bright and inhomogeneous surface. Using AVHRR data, an Algorithm for the retrieval over Land of the Aerosol optical Depth (ALAD) was developed data which has great potential to be used to retrieve long time series aerosol globally from 1979 to now. The core of ALAD is to assume that the contribution of aerosol at 3.75μm wavelength to reflectance at top of the atmosphere (TOA) is negligible. At this basis, one stable and firm relationship between surface reflectance at 0.64μm and 3.75μm will be found by regression analysis at different land types after separating reflectance from radiance at 3.75μm. Then, an atmospheric transfer model is applied to calculate AOD at 0.64μm. In this study, we recalibrate AVHRR Global Area Coverage (GAC) data and then apply ALAD to calculate AOD over continental Europe (30°N to 80°N, 170°W to 40°E) to investigate aerosol changes and possible reason in past 35 years from 1981 to 2015. The retrieved AOD has been validated with ground-based data from Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS) and AErosol RObotic NETwork (AERONET). The correlation of ALAD AOD with AERONET and ACTRIS is 0.77 and 0.66, respectively. Further, we also make long time series comparison of monthly averaged ALAD AOD with AERONET, ACTRIS and MODIS, showing that ALAD underestimate AOD a little. Finally, we find that the AOD over most areas in Continental Europe are less than 0.3, even less than 0.1, changing little without any obvious increase.

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

USGS Publications Warehouse

Loveland, Thomas R.; Reed, B.C.; Brown, Jesslyn F.; Ohlen, D.O.; Zhu, Z.; Yang, L.; Merchant, J.W.

2000-01-01

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

Two MODIS Aerosol Products over Ocean on the Terra and Aqua CERES SSF Datasets.

NASA Astrophysics Data System (ADS)

Ignatov, Alexander; Minnis, Patrick; Loeb, Norman; Wielicki, Bruce; Miller, Walter; Sun-Mack, Sunny; Tanré, Didier; Remer, Lorraine; Laszlo, Istvan; Geier, Erika

2005-04-01

Understanding the impact of aerosols on the earth's radiation budget and the long-term climate record requires consistent measurements of aerosol properties and radiative fluxes. The Clouds and the Earth's Radiant Energy System (CERES) Science Team combines satellite-based retrievals of aerosols, clouds, and radiative fluxes into Single Scanner Footprint (SSF) datasets from the Terra and Aqua satellites. Over ocean, two aerosol products are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) using different sampling and aerosol algorithms. The primary, or M, product is taken from the standard multispectral aerosol product developed by the MODIS aerosol group while a simpler, secondary [Advanced Very High Resolution Radiometer (AVHRR) like], or A, product is derived by the CERES Science Team using a different cloud clearing method and a single-channel aerosol algorithm. Two aerosol optical depths (AOD), τA1 and τA2, are derived from MODIS bands 1 (0.644 μm) and 6 (1.632 μm) resembling the AVHRR/3 channels 1 and 3A, respectively. On Aqua the retrievals are made in band 7 (2.119 μm) because of poor quality data from band 6. The respective Ångström exponents can be derived from the values of τ. The A product serves as a backup for the M product. More importantly, the overlap of these aerosol products is essential for placing the 20+ year heritage AVHRR aerosol record in the context of more advanced aerosol sensors and algorithms such as that used for the M product.This study documents the M and A products, highlighting their CERES SSF specifics. Based on 2 weeks of global Terra data, coincident M and A AODs are found to be strongly correlated in both bands. However, both domains in which the M and A aerosols are available, and the respective τ/α statistics significantly differ because of discrepancies in sampling due to differences in cloud and sun-glint screening. In both aerosol products, correlation is observed between the retrieved aerosol parameters (τ/α) and ambient cloud amount, with the dependence in the M product being more pronounced than in the A product.

Developing NOAA's Climate Data Records From AVHRR and Other Data

NASA Astrophysics Data System (ADS)

Privette, J. L.; Bates, J. J.; Kearns, E. J.

2010-12-01

As part of the provisional NOAA Climate Service, NOAA is providing leadership in the development of authoritative, measurement-based information on climate change and variability. NOAA’s National Climatic Data Center (NCDC) recently initiated a satellite Climate Data Record Program (CDRP) to provide sustained and objective climate information derived from meteorological satellite data that NOAA has collected over the past 30+ years - particularly from its Polar Orbiting Environmental Satellites (POES) program. These are the longest sustained global measurement records in the world and represent billions of dollars of investment. NOAA is now applying advanced analysis methods -- which have improved remarkably over the last decade -- to the POES AVHRR and other instrument data. Data from other satellite programs, including NASA and international research programs and the Defense Meteorological Satellite Program (DMSP), are also being used. This process will unravel the underlying climate trend and variability information and return new value from the records. In parallel, NCDC will extend these records by applying the same methods to present-day and future satellite measurements, including the Joint Polar Satellite System (JPSS) and Jason-3. In this presentation, we will describe the AVHRR-related algorithm development activities that CDRP recently selected and funded through open competitions. We will particularly discuss some of the technical challenges related to adapting and using AVHRR algorithms with the VIIRS data that should become available with the launch of the NPOESS Preparatory Project (NPP) satellite in early 2012. We will also describe IT system development activities that will provide data processing and reprocessing, storage and management. We will also outline the maturing Program framework, including the strategies for coding and development standards, community reviews, independent program oversight, and research-to-operations algorithm migration and execution. Timeline of NOAA's polar orbiters that carried AVHRR. NOAA's approach to flying the same or similar instruments sequentially is well-suited to CDR development.

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, Bradley C.; Tieszen, Larry L.

2003-01-01

Time integrated normalized difference vegetation index (ΣNDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989–1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxy for interannual climate variability is analysed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ΣNDVI values.

Advantageous GOES IR results for ash mapping at high latitudes: Cleveland eruptions 2001

USGS Publications Warehouse

Gu, Yingxin; Rose, William I.; Schneider, D.J.; Bluth, G.J.S.; Watson, I.M.

2005-01-01

The February 2001 eruption of Cleveland Volcano, Alaska allowed for comparisons of volcanic ash detection using two-band thermal infrared (10-12 ??m) remote sensing from MODIS, AVHRR, and GOES 10. Results show that high latitude GOES volcanic cloud sensing the range of about 50 to 65??N is significantly enhanced. For the Cleveland volcanic clouds the MODIS and AVHRR data have zenith angles 6-65 degrees and the GOES has zenith angles that are around 70 degrees. The enhancements are explained by distortion in the satellite view of the cloud's lateral extent because the satellite zenith angles result in a "side-looking" aspect and longer path lengths through the volcanic cloud. The shape of the cloud with respect to the GOES look angle also influences the results. The MODIS and AVHRR data give consistent retrievals of the ash cloud evolution over time and are good corrections for the GOES data. Copyright 2005 by the American Geophysical Union.

Temporal and spatial change in coastal ecosystems using remote sensing: Example with Florida Bay, USA, emphasizing AVHRR imagery

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

Stumpf, R.P.; Frayer, M.L.

1997-06-01

Florida Bay, at the southern tip of Florida, USA, has undergone dramatic changes in recent years. Following seagrass dieoffs starting in the late 1980`s, both algal blooms and high turbidity (the latter from resuspended sediments) have been reported as more common in the Bay. Remotely sensed data, particularly from the AVHRR (advanced very high resolution radiometer), can provide information on conditions prior to the start of monitoring programs as well as provide additional spatial detail on water clarity and particulate loads in this estuary . The AVHRR record currently available to us consists of over 600 usable scenes from December,more » 1989. Comparisons with field data have provided relationships with light attenuation, total suspended solids, and other turbidity measures. The imagery shows the seasonal change in turbidity resulting from high winds associated with winter cold fronts. Over the seven-year record, areas of clear water have decreased in the north-central Bay, while expanding in the southwestern Bay.« less

Unmixing AVHRR Imagery to Assess Clearcuts and Forest Regrowth in Oregon

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Spanner, Michael A.

1995-01-01

Advanced Very High Resolution Radiometer imagery provides frequent and low-cost coverage of the earth, but its coarse spatial resolution (approx. 1.1 km by 1.1 km) does not lend itself to standard techniques of automated categorization of land cover classes because the pixels are generally mixed; that is, the extent of the pixel includes several land use/cover classes. Unmixing procedures were developed to extract land use/cover class signatures from mixed pixels, using Landsat Thematic Mapper data as a source for the training set, and to estimate fractions of class coverage within pixels. Application of these unmixing procedures to mapping forest clearcuts and regrowth in Oregon indicated that unmixing is a promising approach for mapping major trends in land cover with AVHRR bands 1 and 2. Including thermal bands by unmixing AVHRR bands 1-4 did not lead to significant improvements in accuracy, but experiments with unmixing these four bands did indicate that use of weighted least squares techniques might lead to improvements in other applications of unmixing.

Early warning and crop condition assessment research

NASA Technical Reports Server (NTRS)

Boatwright, G. O.; Whitehead, V. S.

1986-01-01

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicators models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U.S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.

A satellite-based climatology (1989-2012) of lake surface water temperature from AVHRR 1-km for Central European water bodies

NASA Astrophysics Data System (ADS)

Riffler, Michael; Wunderle, Stefan

2013-04-01

The temperature of lakes is an important parameter for lake ecosystems influencing the speed of physio-chemical reactions, the concentration of dissolved gazes (e.g. oxygen), and vertical mixing. Even small temperature changes might have irreversible effects on the lacustrine system due to the high specific heat capacity of water. These effects could alter the quality of lake water depending on parameters like lake size and volume. Numerous studies mention lake water temperature as an indicator of climate change and in the Global Climate Observing System (GCOS) requirements it is listed as an essential climate variable. In contrast to in situ observations, satellite imagery offers the possibility to derive spatial patterns of lake surface water temperature (LSWT) and their variability. Moreover, although for some European lakes long in situ time series are available, the temperatures of many lakes are not measured or only on a non-regular basis making these observations insufficient for climate monitoring. However, only few satellite sensors offer the possibility to analyze time series which cover more than 20 years. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown on the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellites (POES) and on the Meteorological Operational Satellites (MetOp) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) as a heritage instrument for almost 35 years. It will be carried on for at least ten more years finally offering a unique opportunity for satellite-based climate studies. Herein we present the results from a study initiated by the Swiss GCOS office to generate a satellite-based LSWT climatology for the pre-alpine water bodies in Switzerland. It relies on the extensive AVHRR 1-km data record (1985-2012) of the Remote Sensing Research Group at the University of Bern (RSGB) and has been derived from the AVHRR/2 (NOAA-11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and Metop-A). A high accuracy is needed for climate related studies, which requires a careful pre-processing and consideration of the atmospheric state. Especially data from NOAA-16 and prior satellites were prone to unwanted noise, e.g., due to transmission errors or fluctuations in the instrument's thermal state. This has resulted in partly corrupted thermal calibration data and may cause errors of up to several Kelvin in the final brightness temperatures. Therefore, a multistage correction scheme has been applied to the data, in order to minimize these artefacts in the satellite observations. For the LSWT retrieval we have tested three different methods. First, we applied the operational NOAA National Environmental Satellite, Data, and Information Service (NESDIS) and NOAA Pathfinder global sea surface temperature (SST) algorithms to our data set. In addition, we developed an optimized simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with operational analysis and reanalysis data from the European Centre for Medium Range Weather Forecasts (ECMWF). All methods were validated extensively using in situ measurements from lakes with various sizes between 14 km2 (Lake Sempach) and 580 km2 (Lake Geneva). The simulation-based algorithm reduces the RMSE and Bias for the lakes in the study region of Switzerland compared to the global SST algorithms and even small lakes yield good results. Following these successful outcome, the model-based LSWT retrieval shall be expanded to all European lakes covered and recorded by the AVHRR data receiving station at the RSGB.

Use of descriptors of ecosystem functioning for monitoring a national park network: a remote sensing approach.

PubMed

Alcaraz-Segura, Domingo; Cabello, Javier; Paruelo, José M; Delibes, Miguel

2009-01-01

Baseline assessments and monitoring of protected areas are essential for making management decisions, evaluating the effectiveness of management practices, and tracking the effects of global changes. For these purposes, the analysis of functional attributes of ecosystems (i.e., different aspects of the exchange of matter and energy) has advantages over the traditional use of structural attributes, like a quicker response to disturbances and the fact that they are easily monitored through remote sensing. In this study, we described the spatiotemporal patterns of different aspects of the ecosystem functioning of the Spanish national parks and their response to environmental changes between 1982 and 2006. To do so, we used the NOAA/AVHRR-GIMMS dataset of the Normalized Difference Vegetation Index (NDVI), a linear estimator of the fraction of photosynthetic active radiation intercepted by vegetation, which is the main control of carbon gains. Nearly all parks have significantly changed during the last 25 years: The radiation interception has increased, the contrast between the growing and nongrowing seasons has diminished, and the dates of maximum and minimum interception have advanced. Some parks concentrated more changes than others and the degree of change varied depending on their different environmental conditions, management, and conservation histories. Our approach identified reference conditions and temporal changes for different aspects of ecosystem functioning, which can be used for management purposes of protected areas in response to global changes.

Vegetation spatial variability and its effect on vegetation indices

NASA Technical Reports Server (NTRS)

Ormsby, J. P.; Choudhury, B. J.; Owe, M.

1987-01-01

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification program. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12.7 per cent in determining these areas. NDVI values less than 0.3 represented fractional vegetated areas of 5 per cent or less, while a value of 0.7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0.89 and 0.95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.

Lag and seasonality considerations in evaluating AVHRR NDVI response to precipitation

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2005-01-01

Assessment of the relationship between the normalized difference vegetation index (NDVI) and precipitation is important in understanding vegetation and climate interaction at a large scale. NDVI response to precipitation, however, is difficult to quantify due to the lag and seasonality effects, which will vary due to vegetation cover type, soils and climate. A time series analysis was performed on biweekly NDVI and precipitation around weather stations in the northern and central U.S. Great Plains. Regression models that incorporate lag and seasonality effects were used to quantify the relationship between NDVI and lagged precipitation in grasslands and croplands. It was found that the time lag was shorter in the early growing season, but longer in the mid- to late-growing season for most locations. The regression models with seasonal adjustment indicate that the relationship between NDVI and precipitation over the entire growing season was strong, with R2 values of 0.69 and 0.72 for grasslands and croplands, respectively. We conclude that vegetation greenness can be predicted using current and antecedent precipitation, if seasonal effects are taken into account.

Satellite Observation of El Nino Effects on Amazon Forest Phenology and Productivity

NASA Technical Reports Server (NTRS)

Asner, Gregory P.; Townsend, Alan R.; Braswell, Bobby H.

2000-01-01

Climate variability may affect the functioning of Amazon moist tropical forests, and recent modeling analyses suggest that the carbon dynamics of the region vary interannually in response to precipitation and temperature anomalies. However, due to persistent orbital and atmospheric artifacts in the satellite record, remote sensing observations have not provided quantitative evidence that climate variation affects Amazon forest phenology or productivity, We developed a method to minimize and quantify non-biological artifacts in NOAA AVHRR satellite data, providing a record of estimated forest phenological variation from 1982-1993. The seasonal Normalized Difference Vegetation Index (NDVI) amplitude (a proxy for phenology) increased throughout much of the basin during El Nino periods when rainfall was anomalously low. Wetter La Nina episodes brought consistently smaller NDVI amplitudes. Using radiative transfer and terrestrial biogeochemical models driven by these satellite data, we estimate that canopy-energy absorption and net primary production of Amazon forests varied interannually by as much as 21% and 18%, respectively. These results provide large-scale observational evidence for interannual sensitivity to El Nino of plant phenology and carbon flux in Amazon forests.

Evapotranspiration from combined reflected solar and emitted terrestrial radiation - Preliminary FIFE results from AVHRR data

NASA Technical Reports Server (NTRS)

Goward, S. N.; Hope, A. S.

1989-01-01

The relation between remotely sensed spectral vegetation indices and thermal IR measurements is studied. Land surface evapotranspiration is evaluated based on this relationship. Analysis of the AVHRR data, obtained in Kansas in 1987, reveal a strong correlation between the spectral vegetation indices and surface temperature and this relation covaries with surface moisture conditions. It is noted that the relation between remotely sensed measurements of canopy green foliage and surface temperature is useful for examining variations in the interface thermal inertia and energy balance Bowen ratio.

On the combined use of high temporal resolution, optical satellite data for flood monitoring and mapping: a possible contribution from the RST approach

NASA Astrophysics Data System (ADS)

Faruolo, M.; Coviello, I.; Lacava, T.; Pergola, N.; Tramutoli, V.

2009-04-01

Among natural disasters, floods are ones of those more common and devastating, often causing high environmental, economical and social costs. When a flooding event occurs, timely information about precise location, extent, dynamic evolution, etc., is highly required in order to effectively support civil protection activities aimed at managing the emergency. Satellite remote sensing may represent a supplementary information source, providing mapping and continuous monitoring of flooding extent as well as a quick damage assessment. Such purposes need frequently updated satellite images as well as suitable image processing techniques, able to identify flooded areas with reliability and timeliness. Recently, an innovative satellite data analysis approach (named RST, Robust Satellite Technique) has been applied to NOAA-AVHRR (Advanced Very High Resolution Radiometer) satellite data in order to dynamically map flooded areas. Thanks to a multi-temporal analysis of co-located satellite records and an automatic change detection scheme, such an approach allows to overcome major drawbacks related to the previously proposed methods (mostly not automatic and based on empirically chosen thresholds, often affected by false identifications). In this paper, RST approach has been for the first time applied to both AVHRR and EOS/MODIS (Moderate Resolution Imaging Spectroradiometer) data, in order to assess its potential - in flooded area mapping and monitoring - on different satellite packages characterized by different spectral and spatial resolutions. As a study case, the flooding event which hit the Europe in August 2002 has been selected. Preliminary results shown in this study seem to confirm the potential of such an approach in providing reliable and timely information, useful for near real time flood hazard assessment and monitoring, using both MODIS and AVHRR data. Moreover, the combined use of information coming from both satellite packages (easily achievable thanks to the intrinsic RST exportability on different sensors) significantly improves (from 6 to less than 3 hours) surface sampling rate, reducing the negative impact of cloud coverage, currently one of the main limit of this kind of satellite technology.

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.;

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics

USGS Publications Warehouse

Ji, Lei; Brown, Jesslyn

2017-01-01

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.

Comparative Analysis of Daytime Fire Detection Algorithms, Using AVHRR Data for the 1995 Fire Season in Canda: Perspective for MODIS

NASA Technical Reports Server (NTRS)

Ichoku, Charles; Kaufman, Y. J.; Fraser, R. H.; Jin, J.-Z.; Park, W. M.; Lau, William K. M. (Technical Monitor)

2001-01-01

Two fixed-threshold Canada Centre for Remote Sensing and European Space Agency (CCRS and ESA) and three contextual GIGLIO, International Geosphere and Biosphere Project, and Moderate Resolution Imaging Spectroradiometer (GIGLIO, IGBP, and MODIS) algorithms were used for fire detection with Advanced Very High Resolution Radiometer (AVHRR) data acquired over Canada during the 1995 fire season. The CCRS algorithm was developed for the boreal ecosystem, while the other four are for global application. The MODIS algorithm, although developed specifically for use with the MODIS sensor data, was applied to AVHRR in this study for comparative purposes. Fire detection accuracy assessment for the algorithms was based on comparisons with available 1995 burned area ground survey maps covering five Canadian provinces. Overall accuracy estimations in terms of omission (CCRS=46%, ESA=81%, GIGLIO=75%, IGBP=51%, MODIS=81%) and commission (CCRS=0.35%, ESA=0.08%, GIGLIO=0.56%, IGBP=0.75%, MODIS=0.08%) errors over forested areas revealed large differences in performance between the algorithms, with no relevance to type (fixed-threshold or contextual). CCRS performed best in detecting real forest fires, with the least omission error, while ESA and MODIS produced the highest omission error, probably because of their relatively high threshold values designed for global application. The commission error values appear small because the area of pixels falsely identified by each algorithm was expressed as a ratio of the vast unburned forest area. More detailed study shows that most commission errors in all the algorithms were incurred in nonforest agricultural areas, especially on days with very high surface temperatures. The advantage of the high thresholds in ESA and MODIS was that they incurred the least commission errors.

Influence of resolution in irrigated area mapping and area estimation

USGS Publications Warehouse

Velpuri, N.M.; Thenkabail, P.S.; Gumma, M.K.; Biradar, C.; Dheeravath, V.; Noojipady, P.; Yuanjie, L.

2009-01-01

The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM+ 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R2 = 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that "the finer the spatial resolution of the sensor used, greater was the irrigated area derived," since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and nonirrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent). ?? 2009 American Society for Photogrammetry and Remote Sensing.

A satellite-based climatology of European alpine lake surface water temperature for the period 1989-2013

NASA Astrophysics Data System (ADS)

Riffler, M.; Lieberherr, G.; Wunderle, S.

2014-12-01

Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. At some European lakes LWT has been observed over several decades, but the majority of lakes is not monitored, or only on a non-regular basis, which is insufficient to track a climate signal. Satellite observations might be utilized to fill these gaps, however, only few satellite sensors offer the possibility to analyze time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years finally offering a unique opportunity for satellite-based climate studies. We present a satellite-based lake surface water temperature (LSWT) data set for European (pre-alpine) water bodies based on the extensive AVHRR 1 km data record (1989-2013) of the Remote Sensing Research Group at the University of Bern, Switzerland. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and Metop-A) day and night time data. We will discuss the processing steps (e.g. geolocation, calibration, LSWT algorithm, etc.) which are necessary to obtain the accuracy needed for climate related studies. The resulting climatology covers pre-alpine and alpine lakes with sizes between 14 and 580 km2. We will present and discuss the analysis of trends for some sample lakes in various regions of the Alps.

SeaShark and Starfish opertional data processing schemes for AVHRR and SeaWiFs

NASA Astrophysics Data System (ADS)

Flowerdew, R. J.; Corlyon, Anaa M.; Greer, W. A. D.; Newby, Steve J.; Winder, C. P.

1997-02-01

SeaShark is an operational software package for processing, archiving and cataloguing AVHRR and SeaWiFS data using an operator friendly GUI. Upon receipt of a customer order, it produces standard AVHRR data products, including Sea Surface Temperature (SST) and it has recently been modified to include SeaWiFS level 2 data processing. This uses an atmospheric correction scheme developed by the Plymouth Marine Laboratory, UK (PML) that builds upon the standard Gordon and Wang approach to be applicable over both case 1 and case 2 waters. Higher level products are then generated using PML algorithms, including chlorophyll a, a CZCS-type pigment, Kd, and suspended particulate matter. Outputs are in CEOS-compatible format. The software also produces fast delivery products (FDPs) of chlorophyll a and SST. These FDPs are combined in the StarFish software package to provide maps indicating potential location of phytoplankton and the preferred thermal environment of certain pelagic fish species. Fishing vessels may obtain these maps over Inmarsat, allowing them to achieve a greater efficiency hence lower cost.

Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data

USGS Publications Warehouse

Markon, Carl J.; Fleming, Michael D.; Binnian, Emily F.

1995-01-01

Advanced Very High Resolution Radiometer (AVHRR) satellite data were acquired and composited into twice-a-month periods from 1 May 1991 to 15 October 1991 in order to map vegetation characteristics of the Alaskan landscape. Unique spatial and temporal qualities of the AVHRR data provide information that leads to a better understanding of regional biophysical characteristics of vegetation communities and patterns. These data provided synoptic views of the landscape and depicted phenological diversity, temporal vegetation phenology (green-up, peak of green, and senescence), photosynthetic activity, and regional landscape patterns. Products generated from the data included a phenological class map, phenological composite maps (onset, peak, and duration), and photosynthetic activity maps (mean and maximum greenness). The time-series data provide opportunities to study phenological processes at small landscape scales over time periods of weeks, months, and years. Regional patterns identified on some of the maps are unique to specific areas; others correspond to biophysical or ecoregional boundaries. The data provide new insights to landscape processes, ecology, and landscape physiognomy that allow scientists to look at landscapes in ways that were previously difficult to achieve.

Intercomparison Between in situ and AVHRR Polar Pathfinder-Derived Surface Albedo over Greenland

NASA Technical Reports Server (NTRS)

Stroeve, Julienne C.; Box, Jason E.; Fowler, Charles; Haran, Terence; Key, Jeffery

2001-01-01

The Advanced Very High Resolution (AVHRR) Polar Pathfinder Data (APP) provides the first long time series of consistent, calibrated surface albedo and surface temperature data for the polar regions. Validations of these products have consisted of individual studies that analyzed algorithm performance for limited regions and or time periods. This paper reports on comparisons made between the APP-derived surface albedo and that measured at fourteen automatic weather stations (AWS) around the Greenland ice sheet from January 1997 to August 1998. Results show that satellite-derived surface albedo values are on average 10% less than those measured by the AWS stations. However, the station measurements tend to be biased high by about 4% and thus the differences in absolute albedo may be less (e.g. 6%). In regions of the ice sheet where the albedo variability is small, such as the dry snow facies, the APP albedo uncertainty exceeds the natural variability. Further work is needed to improve the absolute accuracy of the APP-derived surface albedo. Even so, the data provide temporally and spatially consistent estimates of the Greenland ice sheet albedo.

Satellite-based climate data records of surface solar radiation from the CM SAF

NASA Astrophysics Data System (ADS)

Trentmann, Jörg; Cremer, Roswitha; Kothe, Steffen; Müller, Richard; Pfeifroth, Uwe

2017-04-01

The incoming surface solar radiation has been defined as an essential climate variable by GCOS. Long term monitoring of this part of the earth's energy budget is required to gain insights on the state and variability of the climate system. In addition, climate data sets of surface solar radiation have received increased attention over the recent years as an important source of information for solar energy assessments, for crop modeling, and for the validation of climate and weather models. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) is deriving climate data records (CDRs) from geostationary and polar-orbiting satellite instruments. Within the CM SAF these CDRs are accompanied by operational data at a short time latency to be used for climate monitoring. All data from the CM SAF is freely available via www.cmsaf.eu. Here we present the regional and the global climate data records of surface solar radiation from the CM SAF. The regional climate data record SARAH (Surface Solar Radiation Dataset - Heliosat, doi: 10.5676/EUM_SAF_CM/SARAH/V002) is based on observations from the series of Meteosat satellites. SARAH provides 30-min, daily- and monthly-averaged data of the effective cloud albedo, the solar irradiance (incl. spectral information), the direct solar radiation (horizontal and normal), and the sunshine duration from 1983 to 2015 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° allowing for detailed regional studies. The global climate data record CLARA (CM SAF Clouds, Albedo and Radiation dataset from AVHRR data, doi: 10.5676/EUM_SAF_CM/CLARA_AVHRR/V002) is based on observations from the series of AVHRR satellite instruments. CLARA provides daily- and monthly-averaged global data of the solar irradiance (SIS) from 1982 to 2015 with a spatial resolution of 0.25°. In addition to the solar surface radiation also the longwave surface radiation as well as surface albedo and numerous cloud properties are provided in CLARA. Here we provide an overview of the climate data records of the surface solar radiation and present the results of the quality assessment of both climate data records against available surface reference observations, e.g., from the BSRN and the GEBA data archive.

Identification of cloud fields by the nonparametric algorithm of pattern recognition from normalized video data recorded with the AVHRR instrument

NASA Astrophysics Data System (ADS)

Protasov, Konstantin T.; Pushkareva, Tatyana Y.; Artamonov, Evgeny S.

2002-02-01

The problem of cloud field recognition from the NOAA satellite data is urgent for solving not only meteorological problems but also for resource-ecological monitoring of the Earth's underlying surface associated with the detection of thunderstorm clouds, estimation of the liquid water content of clouds and the moisture of the soil, the degree of fire hazard, etc. To solve these problems, we used the AVHRR/NOAA video data that regularly displayed the situation in the territory. The complexity and extremely nonstationary character of problems to be solved call for the use of information of all spectral channels, mathematical apparatus of testing statistical hypotheses, and methods of pattern recognition and identification of the informative parameters. For a class of detection and pattern recognition problems, the average risk functional is a natural criterion for the quality and the information content of the synthesized decision rules. In this case, to solve efficiently the problem of identifying cloud field types, the informative parameters must be determined by minimization of this functional. Since the conditional probability density functions, representing mathematical models of stochastic patterns, are unknown, the problem of nonparametric reconstruction of distributions from the leaning samples arises. To this end, we used nonparametric estimates of distributions with the modified Epanechnikov kernel. The unknown parameters of these distributions were determined by minimization of the risk functional, which for the learning sample was substituted by the empirical risk. After the conditional probability density functions had been reconstructed for the examined hypotheses, a cloudiness type was identified using the Bayes decision rule.

Long-term Satellite NDVI Data Sets: Evaluating Their Ability to Detect Ecosystem Functional Changes in South America.

PubMed

Baldi, Germán; Nosetto, Marcelo D; Aragón, Roxana; Aversa, Fernando; Paruelo, José M; Jobbágy, Esteban G

2008-09-03

In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR "Normalized Difference Vegetation Index" (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (≤11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the "Eastern Paraguay" and "Uruguay River margins" focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative "Land ecosystem change utility for South America", which facilitates this type of evaluations and helps to identify the most important functional changes of the continent.

Long-term Satellite NDVI Data Sets: Evaluating Their Ability to Detect Ecosystem Functional Changes in South America

PubMed Central

Baldi, Germán; Nosetto, Marcelo D.; Aragón, Roxana; Aversa, Fernando; Paruelo, José M.; Jobbágy, Esteban G.

2008-01-01

In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR “Normalized Difference Vegetation Index” (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (≤11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the “Eastern Paraguay” and “Uruguay River margins” focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative “Land ecosystem change utility for South America”, which facilitates this type of evaluations and helps to identify the most important functional changes of the continent. PMID:27873821

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Compression of the Global Land 1-km AVHRR dataset

USGS Publications Warehouse

Kess, B. L.; Steinwand, D.R.; Reichenbach, S.E.

1996-01-01

Large datasets, such as the Global Land 1-km Advanced Very High Resolution Radiometer (AVHRR) Data Set (Eidenshink and Faundeen 1994), require compression methods that provide efficient storage and quick access to portions of the data. A method of lossless compression is described that provides multiresolution decompression within geographic subwindows of multi-spectral, global, 1-km, AVHRR images. The compression algorithm segments each image into blocks and compresses each block in a hierarchical format. Users can access the data by specifying either a geographic subwindow or the whole image and a resolution (1,2,4, 8, or 16 km). The Global Land 1-km AVHRR data are presented in the Interrupted Goode's Homolosine map projection. These images contain masked regions for non-land areas which comprise 80 per cent of the image. A quadtree algorithm is used to compress the masked regions. The compressed region data are stored separately from the compressed land data. Results show that the masked regions compress to 0·143 per cent of the bytes they occupy in the test image and the land areas are compressed to 33·2 per cent of their original size. The entire image is compressed hierarchically to 6·72 per cent of the original image size, reducing the data from 9·05 gigabytes to 623 megabytes. These results are compared to the first order entropy of the residual image produced with lossless Joint Photographic Experts Group predictors. Compression results are also given for Lempel-Ziv-Welch (LZW) and LZ77, the algorithms used by UNIX compress and GZIP respectively. In addition to providing multiresolution decompression of geographic subwindows of the data, the hierarchical approach and the use of quadtrees for storing the masked regions gives a marked improvement over these popular methods.

Vegetation Response to Climate Change in the Southern Part of Qinghai-Tibet Plateau at Basinal Scale

NASA Astrophysics Data System (ADS)

Liu, X.; Liu, C.; Kang, Q.; Yin, B.

2018-04-01

Global climate change has significantly affected vegetation variation in the third-polar region of the world - the Qinghai-Tibet Plateau. As one of the most important indicators of vegetation variation (growth, coverage and tempo-spatial change), the Normalized Difference Vegetation Index (NDVI) is widely employed to study the response of vegetation to climate change. However, a long-term series analysis cannot be achieved because a single data source is constrained by time sequence. Therefore, a new framework was presented in this paper to extend the product series of monthly NDVI, taking as an example the Yarlung Zangbo River Basin, one of the most important river basins in the Qinghai-Tibet Plateau. NDVI products were acquired from two public sources: Global Inventory Modeling and Mapping Studies (GIMMS) Advanced Very High Resolution Radiometer (AVHRR) and Moderate-Resolution Imaging spectroradiometer (MODIS). After having been extended using the new framework, the new time series of NDVI covers a 384 months period (1982-2013), 84 months longer than previous time series of NDVI product, greatly facilitating NDVI related scientific research. In the new framework, the Gauss Filtering Method was employed to filter out noise in the NDVI product. Next, the standard method was introduced to enhance the comparability of the two data sources, and a pixel-based regression method was used to construct NDVI-extending models with one pixel after another. The extended series of NDVI fit well with original AVHRR-NDVI. With the extended time-series, temporal trends and spatial heterogeneity of NDVI in the study area were studied. Principal influencing factors on NDVI were further determined. The monthly NDVI is highly correlated with air temperature and precipitation in terms of climatic change wherein the spatially averaged NDVI slightly increases in the summer and has increased in temperature and decreased in precipitation in the 32 years period. The spatial heterogeneity of NDVI is in accordance with the seasonal variation of the two climate-change factors. All of these findings can provide valuable scientific support for water-land resources exploration in the third-polar region of the world.

Mapping Landscape Phenology Preference of Yellow-billed Cuckoo with AVHRR data

NASA Astrophysics Data System (ADS)

Wallace, C.; Villarreal, M. L.; Van Riper, C., III

2011-12-01

The yellow-billed cuckoo (Coccycus americanus occidentalis) is a neo-tropical migrant bird that travels north from South America into the southwestern United States during the summer to nest. In Arizona, favored riparian forest and woodland nesting habitat has declined in recent decades, due primarily to human activities and the prolonged drought conditions. As a result, western yellow-billed cuckoos have been petitioned for possible listing under the Endangered Species Act. In this study, we map yellow-billed cuckoo habitat 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) satellite Normalized Difference Vegetation Index (NDVI) composite data using Fourier harmonic analysis. Applying Fourier analysis to the waveform composed of the 26 annual composite NDVI values produces phenometrics related to the overall vegetation amount, variability and timing. Field data on Cuckoo presence were obtained from 1998 surveys conducted by Northern Arizona University (NAU), the Arizona Game and Fish Department (AGFD) and the U.S. Geological Survey (USGS). To focus the research within probable landscapes, an AGFD vegetation map (derived from the Arizona GAP program) was used to extract polygons of riparian vegetation and cottonwood-willow riparian vegetation. To create the models, we coupled the satellite phenometrics with field data of cuckoo presence or absence and with points sampling the entirety of mapped riparian and cottonwood-willow vegetation types. Statistical tests reveal that locations with cuckoos present are landscapes with greenness that is significantly more variable and that peaks significantly later than locations in average riparian vegetation, average cottonwood-willow vegetation, or with cuckoos absent. Interestingly, the mean peak greenness date of July 3 for survey locations with cuckoos present coincides with the first day of the 1998 monsoon season recorded for Tucson in southern Arizona. Models developed from the 1998 parameters and applied to 1999 data were effective at predicting cuckoo presence for survey locations visited in 1999, with up to 64 percent of cuckoos located in the highest preference class.

A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Jedlovec, Gary J.

2011-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and show sample output from summer 2010, compare the MODIS GVF data to the AVHRR monthly climatology, and illustrate the sensitivity of the Noah LSM within LIS and/or the coupled LIS/WRF system to the new MODIS GVF dataset.

Climate Sensitivity Studies of the Greenland Ice Sheet Using Satellite AVHRR, SMMR, SSM/I and in Situ Data

NASA Technical Reports Server (NTRS)

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

1993-01-01

The feasibility of using satellite data for climate research over the Greenland ice sheet is discussed. In particular, we demonstrate the usefulness of Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) and Global Area Coverage (GAC) data for narrow-band albedo retrieval. Our study supports the use of lower resolution AVHRR (GAC) data for process studies over most of the Greenland ice sheet. Based on LAC data time series analysis, we can resolve relative albedo changes on the order of 2-5%. In addition, we examine Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) passive microwave data for snow typing and other signals of climatological significance. Based on relationships between in situ measurements and horizontally polarized 19 and 37 GHz observations, wet snow regions are identified. The wet snow regions increase in aerial percentage from 9% of the total ice surface in June to a maximum of 26% in August 1990. Furthermore, the relationship between brightness temperatures and accumulation rates in the northeastern part of Greenland is described. We found a consistent increase in accumulation rate for the northeastern part of the ice sheet from 1981 to 1986.

Clouds as calibration targets for AVHRR reflected-solar channels - Results from a two-year study at NOAA/NESDIS

NASA Technical Reports Server (NTRS)

Abel, Peter

1991-01-01

NOAA-11 Advanced Very High Resolution Radiometer (AVHRR) and associated ground-based data have been collected at NOAA/NESDIS, on a daily basis and for 600 days, using five stations within the continental United States in the NOAA solar radiation (SOLRAD) monitoring network. The data have been filtered to include only uniformly overcast conditions and analyzed along the lines described by Paris and Justus (1988). Results from this first long-term pilot operational application of the method are presented. The method is potentially useful for establishing yearly-averaged trends in the radiometric gain of AVHRR Channels. The relatively small data base examined here suggests a precision in the 600 day mean gain of 5 percent or worse, with a significant part of this uncertainty being driven by poor knowlege of the bidirectional reflectance properties of clouds. The results suggest that the method in its present formulation has insufficient precision to be used as a primary method for the measurement of in-orbit gains of reflected-solar radiometers aboard polar orbiting satellites. Intrinsic limitations to the precision and time resolution of the method are discussed, and suggestions are offered for improving the precision of future results.

Historic AVHRR Processing in the Eumetsat Climate Monitoring Satellite Application Facility (cmsaf) (Invited)

NASA Astrophysics Data System (ADS)

Karlsson, K.

2010-12-01

The EUMETSAT CMSAF project (www.cmsaf.eu) compiles climatological datasets from various satellite sources with emphasis on the use of EUMETSAT-operated satellites. However, since climate monitoring primarily has a global scope, also datasets merging data from various satellites and satellite operators are prepared. One such dataset is the CMSAF historic GAC (Global Area Coverage) dataset which is based on AVHRR data from the full historic series of NOAA-satellites and the European METOP satellite in mid-morning orbit launched in October 2006. The CMSAF GAC dataset consists of three groups of products: Macroscopical cloud products (cloud amount, cloud type and cloud top), cloud physical products (cloud phase, cloud optical thickness and cloud liquid water path) and surface radiation products (including surface albedo). Results will be presented and discussed for all product groups, including some preliminary inter-comparisons with other datasets (e.g., PATMOS-X, MODIS and CloudSat/CALIPSO datasets). A background will also be given describing the basic methodology behind the derivation of all products. This will include a short historical review of AVHRR cloud processing and resulting AVHRR applications at SMHI. Historic GAC processing is one of five pilot projects selected by the SCOPE-CM (Sustained Co-Ordinated Processing of Environmental Satellite data for Climate Monitoring) project organised by the WMO Space programme. The pilot project is carried out jointly between CMSAF and NOAA with the purpose of finding an optimal GAC processing approach. The initial activity is to inter-compare results of the CMSAF GAC dataset and the NOAA PATMOS-X dataset for the case when both datasets have been derived using the same inter-calibrated AVHRR radiance dataset. The aim is to get further knowledge of e.g. most useful multispectral methods and the impact of ancillary datasets (for example from meteorological reanalysis datasets from NCEP and ECMWF). The CMSAF project is currently defining plans for another five years (2012-2017) of operations and development. New GAC reprocessing efforts are planned and new methodologies will be tested. Central questions here will be how to increase the quantitative use of the products through improving error and uncertainty estimates and how to compile the information in a way to allow meaningful and efficient ways of using the data for e.g. validation of climate model information.

Developing the Framework for an Early Warning System for Ebola based on Environmental Conditions

NASA Astrophysics Data System (ADS)

Dartevelle, Sebastien; Nguy-Robertson, Anthony; Bell, Jesse; Chretien, Jean-Paul

2017-04-01

The 2014-2016 Ebola outbreak in West Africa indicated that this lethal disease can become a National Security issue as it crossed boarders and taxed regional health care systems. Ebola symptoms are also similar to other endemic diseases. Thus, forewarning of its possible presence can alert local public health facilities and populations, and may thereby reduce response time. Early work by our group has identified local climate (e.g. temperature, precipitation) and vegetation health (e.g. remote sensing using normalized difference vegetation index, NDVI) variables as leading indicators to known historical Ebola outbreaks. The environmental stress placed on the system as it reaches a climatic tipping point provides optimal conditions for spillover of Ebola virus from the reservoir host (which is unknown but suspected to be bats) to humans. This work outlines a framework for an approach to provide early warning maps based on the present state of the environment. Time series data from Climate Forecast System ver. 2 and AVHRR and MODIS satellite sensors are the basis for the early warning models used. These maps can provide policy makers and local health care professionals timely information for disease surveillance and preparation for future Ebola outbreaks.

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.

Lake Surface Water Temperature of European Lakes retrieved from AVHRR Data - Time Series and Quality Assessment

NASA Astrophysics Data System (ADS)

Wunderle, S.; Lieberherr, G.; Riffler, M.

2016-12-01

Data analysis of the recent years showed an increase of lake surface water temperature for many lakes around the world. But due to sparse in-situ measurements, which are often not well documented, only satellite data can provide the needed information of the last decades. The importance of lakes for climate research was also highlighted by the Global Climate Observing System (GCOS) defining lakes as Essential Climate Variables (ECVs). Within the frame of a research project funded by the Swiss National Science Foundation a procedure was developed to retrieve lake surface water temperature with high accuracy based on our archived AVHRR data at the University of Bern, Switzerland. The data archive starts in 1985 and is continuously filled with NOAA-/MetOp-AVHRR data received by our antenna resulting in a time series of more than 30 years (WMO definition of a climate period). The data set covering Europe is also used by other teams for climate related studies resulting in improved pre-processing to guarantee precise calibration and geocoding. The first part of our presentation will be dedicated to the quality of the LSWT retrieval comparing various in-situ measurements from lakes in Switzerland with varying sizes (150km2 - 9km2). The quality of the used split-window approach is sensitive to the derived split-window coefficients. The influence of water vapor, view angle, temporal and spatial validity and day vs. night data will be shown. In addition, some information will be presented about the influence of topography and climatic regions (e.g. Scandinavia vs. Greece) on the quality of the LSWT product. Based on these findings compiling time series for different lakes in Europe will be the focus of the second part of our presentation with details of the applied quality assessment to avoid erroneous signals. Hence, some information is given about hierarchical quality checks which are needed to guarantee a dataset without artefacts. Finally, some results of time series are presented to show the reaction of different lakes (size, depth) on climate forcing. The lakes are selected to be representative for different climatic regions in Europe (northern - southern Europe, etc.). At the end of the project the data set will be accessible for the public.

Predicting eruptions from precursory activity using remote sensing data hybridization

NASA Astrophysics Data System (ADS)

Reath, K. A.; Ramsey, M. S.; Dehn, J.; Webley, P. W.

2016-07-01

Many volcanoes produce some level of precursory activity prior to an eruption. This activity may or may not be detected depending on the available monitoring technology. In certain cases, precursors such as thermal output can be interpreted to make forecasts about the time and magnitude of the impending eruption. Kamchatka (Russia) provides an ideal natural laboratory to study a wide variety of eruption styles and precursory activity prior to an eruption. At Bezymianny volcano for example, a clear increase in thermal activity commonly occurs before an eruption, which has allowed predictions to be made months ahead of time. Conversely, the eruption of Tolbachik volcano in 2012 produced no discernable thermal precursors before the large scale effusive eruption. However, most volcanoes fall between the extremes of consistently behaved and completely undetectable, which is the case with neighboring Kliuchevskoi volcano. This study tests the effectiveness of using thermal infrared (TIR) remote sensing to track volcanic thermal precursors using data from both the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) sensors. It focuses on three large eruptions that produced different levels and durations of effusive and explosive behavior at Kliuchevskoi. Before each of these eruptions, TIR spaceborne sensors detected thermal anomalies (i.e., pixels with brightness temperatures > 2 °C above the background temperature). High-temporal, low-spatial resolution (i.e., hours and 1 km) AVHRR data are ideal for detecting large thermal events occurring over shorter time scales, such as the hot material ejected following strombolian eruptions. In contrast, high-spatial, low-temporal resolution (i.e., days to weeks and 90 m) ASTER data enables the detection of much lower thermal activity; however, activity with a shorter duration will commonly be missed. ASTER and AVHRR data are combined to track low-level anomalies months prior to an eruption and higher-energy events prior to large eruptions to develop a monitoring approach for this eruption style. Results show that strombolian eruptions produce enough energy in the pre-eruptive phase to trigger an AVHRR detection. Paired with ASTER data, the results can be extended back in time to develop a precursory timeline, which captures subtle changes in volcanic activity that would commonly go unnoticed in a single data set. Although these precursors may be volcano and eruption specific, the now sixteen-year-old database from ASTER allows this methodology to be repeatable at other volcanoes to establish a quantitative precursory baseline, which would be an improvement over current eruption classifications.

Teleconnection of the 1997 El Nino Observed by Spaceborne Sensors and the Dacadal Anomalies in the Northeast Pacific

NASA Technical Reports Server (NTRS)

Liu, W.; Hu, H.; Xie, X.

1999-01-01

Liu et al.[1998] (hereafter referred as LTH), superimposed wind velocity anomalies observed by the NASA Scatterometer (NSCAT) on the map of sea surface temperature (SST) anomalies observed by the Advanced Very High Resolution Radiometer (AVHRR) in the Pacific at the end of May 1997, and illustrated that the three regions of anomalous warming in the North Pacific Ocean are related to wind anomalies through different mechanisms.

The correlated k-distribution technique as applied to the AVHRR channels

NASA Technical Reports Server (NTRS)

Kratz, David P.

1995-01-01

Correlated k-distributions have been created to account for the molecular absorption found in the spectral ranges of the five Advanced Very High Resolution Radiometer (AVHRR) satellite channels. The production of the k-distributions was based upon an exponential-sum fitting of transmissions (ESFT) technique which was applied to reference line-by-line absorptance calculations. To account for the overlap of spectral features from different molecular species, the present routines made use of the multiplication transmissivity property which allows for considerable flexibility, especially when altering relative mixing ratios of the various molecular species. To determine the accuracy of the correlated k-distribution technique as compared to the line-by-line procedure, atmospheric flux and heating rate calculations were run for a wide variety of atmospheric conditions. For the atmospheric conditions taken into consideration, the correlated k-distribution technique has yielded results within about 0.5% for both the cases where the satellite spectral response functions were applied and where they were not. The correlated k-distribution's principal advantages is that it can be incorporated directly into multiple scattering routines that consider scattering as well as absorption by clouds and aerosol particles.

Characterization of Land Surfaces with Satellite-borne Sensor

NASA Astrophysics Data System (ADS)

Qiao, Y.

Hot groundwater is a kind of valuable natural resources to be explored utilized. Shanxi Province, located in the eastern Loess Plateau of China, is rich in geothermal resources, most of which was found in irrigation well drilling or geological survey. Basic study is weak. Now new developed Remote Sensing technique provides geothermal study with an advanced way. Air-RS information of thermal infrared and dada from thermal channel of Meteorological Landset AVHRR has been used widely. A thermal infrared channel (TM6) was installed in the U.S. second Landset, Its resolving power of space is as high as 120m, 10 times more than one of AVHRR. A Landset earth recourses launched by China and Brazil (CBERS-1) in 1999, including a spectrum of thermal infrared. It is paid a great interested and attention to survey geothermal resources using thermal infrared. This article is a brief introduction of finding hot groundwater with on the bases of differences of thermal radiation of objects reflected by thermal infrared in the Landset, and treated with HIS colors changes. This study provides an advanced way widely used to exploit hot groundwater and to promote the development of tourism and geothermal medical in China.

A primary study on finding hot groundwater using infrared remote sensing

NASA Astrophysics Data System (ADS)

Qiao, Y.; Wu, Q.

Hot groundwater is a kind of valuable natural resources to be explored utilized. Shanxi Province, located in the eastern Loess Plateau of China, is rich in geothermal resources, most of which was found in irrigation well drilling or geological survey. Basic study is weak. Now new developed Remote Sensing technique provides geothermal study with an advanced way. Air-RS information of thermal infrared and dada from thermal channel of Meteorological Landset AVHRR has been used widely. A thermal infrared channel (TM6) was installed in the U. S. second Landset, Its resolving power of space is as high as 120 m, 10 times more t an one ofh AVHRR. A Landset earth recourses launched by China and Brazil (CBERS-1) in 1999, including a spectrum of thermal infrared. It is paid a great interested and attention to survey geothermal resources using thermal infrared. This article is a brief introduction of finding hot groundwater with on the bases of differences of thermal radiation of objects reflected by thermal infrared in the Landset, and treated with HIS colors changes. This study provides an advanced way widely used to exploit hot groundwater and to promote the development of tourism and geothermal medical in China.

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

Streets, D. G.; Yarber, K. F.; Woo, J.-H.

Estimates of biomass burning in Asia are developed to facilitate the modeling of Asian and global air quality. A survey of national, regional, and international publications on biomass burning is conducted to yield consensus estimates of 'typical' (i.e., non-year-specific) estimates of open burning (excluding biofuels). We conclude that 730 Tg of biomass are burned in a typical year from both anthropogenic and natural causes. Forest burning comprises 45% of the total, the burning of crop residues in the field comprises 34%, and 20% comes from the burning of grassland and savanna. China contributes 25% of the total, India 18%, Indonesiamore » 13%, and Myanmar 8%. Regionally, forest burning in Southeast Asia dominates. National, annual totals are converted to daily and monthly estimates at 1{sup o} x 1{sup o} spatial resolution using distributions based on AVHRR fire counts for 1999--2000. Several adjustment schemes are applied to correct for the deficiencies of AVHRR data, including the use of moving averages, normalization, TOMS Aerosol Index, and masks for dust, clouds, landcover, and other fire sources. Good agreement between the national estimates of biomass burning and adjusted fire counts is obtained (R{sup 2} = 0.71--0.78). Biomass burning amounts are converted to atmospheric emissions, yielding the following estimates: 0.37 Tg of SO{sub 2}, 2.8 Tg of NO{sub x}, 1100 Tg of CO{sub 2}, 67 Tg of CO, 3.1 Tg of CH{sub 4}, 12 Tg of NMVOC, 0.45 Tg of BC, 3.3 Tg of OC, and 0.92 Tg of NH{sub 3}. Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of {+-}65% for CO{sub 2} emissions in Japan to a high of {+-}700% for BC emissions in India.« less

Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

USGS Publications Warehouse

Steyaert, Louis T.; Loveland, Thomas R.; Brown, Jesslyn F.; Reed, Bradley C.

1993-01-01

Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues involving regional extrapolations and parameterizations of dynamic land processes within simulation models.

Snow cover monitoring model and change over both time and space in pastoral area of northern China

NASA Astrophysics Data System (ADS)

Cui, Yan; Li, Suju; Wang, Ping; Zhang, Wei; Nie, Juan; Wen, Qi

2014-11-01

Snow disaster is a natural phenomenon owning to widespread snowfall for a long time and usually affect people's life, property and economic. During the whole disaster management circle, snow disaster in pastoral area of northern china which including Xinjiang, Inner Mongolia, Qinghai, Tibet has been paid more attention. Thus do a good job in snow cover monitoring then found snow disaster in time can help the people in disaster area to take effective rescue measures, which always been the central and local government great important work. Remote sensing has been used widely in snow cover monitoring for its wide range, high efficiency, less conditions, more methods and large information. NOAA/AVHRR data has been used for wide range, plenty bands information and timely acquired and act as an import data of Snow Cover Monitoring Model (SCMM). SCMM including functions list below: First after NOAA/AVHRR data has been acquired, geometric calibration, radiometric calibration and other pre-processing work has been operated. Second after band operation, four threshold conditions are used to extract snow spectrum information among water, cloud and other features in NOAA/AVHRR image. Third snow cover information has been analyzed one by one and the maximum snow cover from about twenty images in a week has been selected. Then selected image has been mosaic which covered the pastoral area of China. At last both time and space analysis has been carried out through this operational model ,such as analysis on the difference between this week and the same period of last year , this week and last week in three level regional. SCMM have been run successfully for three years, and the results have been take into account as one of the three factors which led to risk warning of snow disaster and analysis results from it always play an important role in disaster reduction and relief.

How consistent are global long-term satellite LAI products in terms of interannual variability and trend?

NASA Astrophysics Data System (ADS)

Jiang, C.; Ryu, Y.; Fang, H.

2016-12-01

Proper usage of global satellite LAI products requires comprehensive evaluation. To address this issue, the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup proposed a four-stage validation hierarchy. During the past decade, great efforts have been made following this validation framework, mainly focused on absolute magnitude, seasonal trajectory, and spatial pattern of those global satellite LAI products. However, interannual variability and trends of global satellite LAI products have been investigated marginally. Targeting on this gap, we made an intercomparison between seven global satellite LAI datasets, including four short-term ones: MODIS C5, MODIS C6, GEOV1, MERIS, and three long-term products ones: LAI3g, GLASS, and GLOBMAP. We calculated global annual LAI time series for each dataset, among which we found substantial differences. During the overlapped period (2003 - 2011), MODIS C5, GLASS and GLOBMAP have positive correlation (r > 0.6) between each other, while MODIS C6, GEOV1, MERIS, and LAI3g are highly consistent (r > 0.7) in interannual variations. However, the previous three datasets show negative trends, all of which use MODIS C5 reflectance data, whereas the latter four show positive trends, using MODIS C6, SPOT/VGT, ENVISAT/MERIS, and NOAA/AVHRR, respectively. During the pre-MODIS era (1982 - 1999), the three AVHRR-based datasets (LAI3g, GLASS and GLOBMAP) agree well (r > 0.7), yet all of them show oscillation related with NOAA platform changes. In addition, both GLASS and GLOBMAP show clear cut-points around 2000 when they move from AVHRR to MODIS. Such inconsistency is also visible for GEOV1, which uses SPOT-4 and SPOT-5 before and after 2002. We further investigate the map-to-map deviations among these products. This study highlights that continuous sensor calibration and cross calibration are essential to obtain reliable global LAI time series.

The effect of surface anisotropy and viewing geometry on the estimation of NDVI from AVHRR

USGS Publications Warehouse

Meyer, David; Verstraete, M.; Pinty, B.

1995-01-01

Since terrestrial surfaces are anisotropic, all spectral reflectance measurements obtained with a small instantaneous field of view instrument are specific to these angular conditions, and the value of the corresponding NDVI, computed from these bidirectional reflectances, is relative to the particular geometry of illumination and viewing at the time of the measurement. This paper documents the importance of these geometric effects through simulations of the AVHRR data acquisition process, and investigates the systematic biases that result from the combination of ecosystem-specific anisotropies with instrument-specific sampling capabilities. Typical errors in the value of NDVI are estimated, and strategies to reduce these effects are explored. -from Authors

Some practical aspects of lossless and nearly-lossless compression of AVHRR imagery

NASA Technical Reports Server (NTRS)

Hogan, David B.; Miller, Chris X.; Christensen, Than Lee; Moorti, Raj

1994-01-01

Compression of Advanced Very high Resolution Radiometers (AVHRR) imagery operating in a lossless or nearly-lossless mode is evaluated. Several practical issues are analyzed including: variability of compression over time and among channels, rate-smoothing buffer size, multi-spectral preprocessing of data, day/night handling, and impact on key operational data applications. This analysis is based on a DPCM algorithm employing the Universal Noiseless Coder, which is a candidate for inclusion in many future remote sensing systems. It is shown that compression rates of about 2:1 (daytime) can be achieved with modest buffer sizes (less than or equal to 2.5 Mbytes) and a relatively simple multi-spectral preprocessing step.

Passive Microwave Algorithms for Sea Ice Concentration: A Comparison of Two Techniques

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.; Cavalieri, Donald J.; Parkinson, Claire L.; Gloersen, Per

1997-01-01

The most comprehensive large-scale characterization of the global sea ice cover so far has been provided by satellite passive microwave data. Accurate retrieval of ice concentrations from these data is important because of the sensitivity of surface flux(e.g. heat, salt, and water) calculations to small change in the amount of open water (leads and polynyas) within the polar ice packs. Two algorithms that have been used for deriving ice concentrations from multichannel data are compared. One is the NASA Team algorithm and the other is the Bootstrap algorithm, both of which were developed at NASA's Goddard Space Flight Center. The two algorithms use different channel combinations, reference brightness temperatures, weather filters, and techniques. Analyses are made to evaluate the sensitivity of algorithm results to variations of emissivity and temperature with space and time. To assess the difference in the performance of the two algorithms, analyses were performed with data from both hemispheres and for all seasons. The results show only small differences in the central Arctic in but larger disagreements in the seasonal regions and in summer. In some ares in the Antarctic, the Bootstrap technique show ice concentrations higher than those of the Team algorithm by as much as 25%; whereas, in other areas, it shows ice concentrations lower by as much as 30%. The The differences in the results are caused by temperature effects, emissivity effects, and tie point differences. The Team and the Bootstrap results were compared with available Landsat, advanced very high resolution radiometer (AVHRR) and synthetic aperture radar (SAR) data. AVHRR, Landsat, and SAR data sets all yield higher concentrations than the passive microwave algorithms. Inconsistencies among results suggest the need for further validation studies.

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.

Integration of remote sensing and geographic information systems for Great Lakes water quality monitoring

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

Lathrop, R.G. Jr.

1988-01-01

The utility of three operational satellite remote sensing systems, namely, the Landsat Thematic Mapper (TM), the SPOT High Resolution Visible (HRV) sensors and the NOAA Advanced Very High Resolution Radiometer (AVHRR), were evaluated as a means of estimating water quality and surface temperature. Empirical calibration through linear regression techniques was used to relate near-simultaneously acquired satellite radiance/reflectance data and water quality observations obtained in Green Bay and the nearshore waters of Lake Michigan. Four dates of TM and one date each of SPOT and AVHRR imagery/surface reference data were acquired and analyzed. Highly significant relationships were identified between the TMmore » and SPOT data and secchi disk depth, nephelometric turbidity, chlorophyll a, total suspended solids (TSS), absorbance, and surface temperature (TM only). The AVHRR data were not analyzed independently but were used for comparison with the TM data. Calibrated water quality image maps were input to a PC-based raster GIS package, EPPL7. Pattern interpretation and spatial analysis techniques were used to document the circulation dynamics and model mixing processes in Green Bay. A GIS facilitates the retrieval, query and spatial analysis of mapped information and provides the framework for an integrated operational monitoring system for the Great Lakes.« less

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.

Wind and tidal forcing of a buoyant plume, Mobile Bay, Alabama

USGS Publications Warehouse

Stumpf, R.P.; Gelfenbaum, G.; Pennock, J.R.

1993-01-01

AVHRR satellite imagery and in situ observations were combined to study the motion of a buoyant plume at the mouth of Mobile Bay, Alabama. The plume extended up to 30 km from shore, with a thickness of about 1 m. The inner plume, which was 3-8 m thick, moved between the Bay and inner shelf in response to tidal forcing. The tidal prism could be identified through the movement of plume waters between satellite images. The plume responded rapidly to alongshore wind, with sections of the plume moving at speeds of more than 70 cm s-1, about 11% of the wind speed. The plume moved predominantly in the direction of the wind with a weak Ekman drift. The enhanced speed of the plume relative to normal surface drift is probably due to the strong stratification in the plume, which limits the transfer of momentum into the underlying ambient waters. ?? 1993.

Monitoring land surface change over semi-arid regions using multispectral satellite data

NASA Technical Reports Server (NTRS)

Choudhury, B. J.

1990-01-01

Visible reflectance and surface temperature are derived from observations by the AVHRR on the NOAA-7 and NOAA-9 satellites and microwave emission at 37-GHz by the SMMR on Nimbus-7 satellite over the Sahel and Sudan zones. The AVHRR data is for the period January 1982 to December 1986, while the SMMR data is for the period January 1979 to December 1986. Rainfall data show that both the Sahel and Sudan zones experienced a particularly severe drought during 1984, and thus the present analysis shows the patterns leading to and recovering from the 1984 drought. Interrelationships among these multispectral data and the ways these relationships change in response to drought are evaluated in relation to field observations and heat balance models.

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

NASA Astrophysics Data System (ADS)

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

2018-07-01

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

Thirty Years of Cloud Cover Patterns from Satellite Data: Fog in California's Central Valley and Coast

NASA Astrophysics Data System (ADS)

Waller, E.; Baldocchi, D. D.

2012-12-01

In an effort to assess long term trends in winter fog in the Central Valley of California, custom maps of daily cloud cover from an approximately 30 year record of AVHRR (1981-1999) and MODIS (2000-2012) satellite data were generated. Spatial rules were then used to differentiate between fog and general cloud cover. Differences among the sensors (e.g., spectral content, spatial resolution, overpass time) presented problems of consistency, but concurrent climate station data were used to resolve systematic differences in products, and to confirm long term trends. The frequency and extent of Central Valley ("Tule") fog appear to have some periodic oscillation, but also appear to be on the decline, especially in the Sacramento Valley and in the "shoulder" months of November and February. These results may have strong implications for growers of fruit and nut trees in the Central Valley dependent on winter chill hours that are augmented by the foggy daytime conditions. Conclusions about long term trends in fog are limited to daytime patterns, as results are primarily derived from reflectance-based products. Similar analyses of daytime cloud cover are performed on other areas of concern, such as the coastal fog belt of California. Large area and long term patterns here appear to have periodic oscillation similar to that for the Central Valley. However, the relatively coarse spatial resolution of the AVHRR LTDR (Long Term Data Record) data (~5-km) may be limiting for fine-scale analysis of trends.

Assessment of the Collection 6 Terra and Aqua MODIS bands 1 and 2 calibration performance

NASA Astrophysics Data System (ADS)

Wu, A.; Chen, X.; Angal, A.; Li, Y.; Xiong, X.

2015-09-01

MODIS (Moderate Resolution Imaging Spectroradiometer) is a key sensor aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. MODIS collects data in 36 spectral bands and generates over 40 data products for land, atmosphere, cryosphere and oceans. MODIS bands 1 and 2 have nadir spatial resolution of 250 m, compared with 500 m for bands 3 to 7 and 1000 m for all the remaining bands, and their measurements are crucial to derive key land surface products. This study evaluates the calibration performance of the Collection-6 L1B for both Terra and Aqua MODIS bands 1 and 2 using three vicarious approaches. The first and second approaches focus on stability assessment using data collected from two pseudo-invariant sites, Libya 4 desert and Antarctic Dome C snow surface. The third approach examines the relative stability between Terra and Aqua in reference to a third sensor from a series of NOAA 15-19 Advanced Very High Resolution Radiometer (AVHRR). The comparison is based on measurements from MODIS and AVHRR Simultaneous Nadir Overpasses (SNO) over a thirteen-year period from 2002 to 2015. Results from this study provide a quantitative assessment of Terra and Aqua MODIS bands 1 and 2 calibration stability and the relative calibration differences between the two sensors.

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.

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

History of greenness mapping at the EROS data center

USGS Publications Warehouse

Van Beek, Carolyn; Vandersnick, Richard

1993-01-01

In 1987, the U.S. Geological Survey's EROS Data Center (EDC)installed a system to acquire, process, and distribute advanced very high resolution radiometer (AVHRR) satellite image data collected over North America. Using this system, the EDC began an experimental greenness mapping program as part of the U.S. Agency for the International Development Famine Early Warning System. The program used the greenness information derived from AVHRR data to identify potential outbreaks of locusts and grasshoppers in the Sahelian region of Africa. In 1988, the EDC began greenness mapping projects in Africa and the northern Great Plains of the United States. In 1989, the system was augmented to acquire AVHRR information for the rest of the world. As a result, the greenness mapping program was able to collect data for fire danger assessment, agricultural assessment, and land characterization. Illustrations of each of the mapping projects trace the chronology of the greenness mapping program at the EDC. Displays represent the initial activity in Africa and the transition of the north Great Plains project to the current conterminous U.S. project. The program's expansion to include Alaska, Eurasia, a prototype North America data set, and ultimately, an experimental global land 1-km product is also shown. The poster describes major technical advances in data processing, the development of derivative products, the magnitude of the data volume of each level, and major applications.

A CERES-like Cloud Property Climatology Using AVHRR Data

NASA Astrophysics Data System (ADS)

Minnis, P.; Bedka, K. M.; Yost, C. R.; Trepte, Q.; Bedka, S. T.; Sun-Mack, S.; Doelling, D.

2015-12-01

Clouds affect the climate system by modulating the radiation budget and distributing precipitation. Variations in cloud patterns and properties are expected to accompany changes in climate. The NASA Clouds and the Earth's Radiant Energy System (CERES) Project developed an end-to-end analysis system to measure broadband radiances from a radiometer and retrieve cloud properties from collocated high-resolution MODerate-resolution Imaging Spectroradiometer (MODIS) data to generate a long-term climate data record of clouds and clear-sky properties and top-of-atmosphere radiation budget. The first MODIS was not launched until 2000, so the current CERES record is only 15 years long at this point. The core of the algorithms used to retrieve the cloud properties from MODIS is based on the spectral complement of the Advanced Very High Resolution Radiometer (AVHRR), which has been aboard a string of satellites since 1978. The CERES cloud algorithms were adapted for application to AVHRR data and have been used to produce an ongoing CERES-like cloud property and surface temperature product that includes an initial narrowband-based radiation budget. This presentation will summarize this new product, which covers nearly 37 years, and its comparability with cloud parameters from CERES, CALIPSO, and other satellites. Examples of some applications of this dataset are given and the potential for generating a long-term radiation budget CDR is also discussed.

Observational Evidence Against Mountain-Wave Generation of Ice Nuclei as a Prerequisite for the Formation of Three Solid Nitric Acid Polar Stratospheric Clouds Observed in the Arctic in Early December 1999

NASA Technical Reports Server (NTRS)

Pagan, Kathy L.; Tabazadeh, Azadeh; Drdla, Katja; Hervig, Mark E.; Eckermann, Stephen D.; Browell, Edward V.; Legg, Marion J.; Foschi, Patricia G.

2004-01-01

A number of recently published papers suggest that mountain-wave activity in the stratosphere, producing ice particles when temperatures drop below the ice frost point, may be the primary source of large NAT particles. In this paper we use measurements from the Advanced Very High Resolution Radiometer (AVHRR) instruments on board the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites to map out regions of ice clouds produced by stratospheric mountain-wave activity inside the Arctic vortex. Lidar observations from three DC-8 flights in early December 1999 show the presence of solid nitric acid (Type Ia or NAT) polar stratospheric clouds (PSCs). By using back trajectories and superimposing the position maps on the AVHRR cloud imagery products, we show that these observed NAT clouds could not have originated at locations of high-amplitude mountain-wave activity. We also show that mountain-wave PSC climatology data and Mountain Wave Forecast Model 2.0 (MWFM-2) raw hemispheric ray and grid box averaged hemispheric wave temperature amplitude hindcast data from the same time period are in agreement with the AVHRR data. Our results show that ice cloud formation in mountain waves cannot explain how at least three large scale NAT clouds were formed in the stratosphere in early December 1999.

Initial Validation of NDVI time seriesfrom AVHRR, VEGETATION, and MODIS

NASA Technical Reports Server (NTRS)

Morisette, Jeffrey T.; Pinzon, Jorge E.; Brown, Molly E.; Tucker, Jim; Justice, Christopher O.

2004-01-01

The paper will address Theme 7: Multi-sensor opportunities for VEGETATION. We present analysis of a long-term vegetation record derived from three moderate resolution sensors: AVHRR, VEGETATION, and MODIS. While empirically based manipulation can ensure agreement between the three data sets, there is a need to validate the series. This paper uses atmospherically corrected ETM+ data available over the EOS Land Validation Core Sites as an independent data set with which to compare the time series. We use ETM+ data from 15 globally distributed sites, 7 of which contain repeat coverage in time. These high-resolution data are compared to the values of each sensor by spatially aggregating the ETM+ to each specific sensors' spatial coverage. The aggregated ETM+ value provides a point estimate for a specific site on a specific date. The standard deviation of that point estimate is used to construct a confidence interval for that point estimate. The values from each moderate resolution sensor are then evaluated with respect to that confident interval. Result show that AVHRR, VEGETATION, and MODIS data can be combined to assess temporal uncertainties and address data continuity issues and that the atmospherically corrected ETM+ data provide an independent source with which to compare that record. The final product is a consistent time series climate record that links historical observations to current and future measurements.

Automated detection of jet contrails using the AVHRR split window

NASA Technical Reports Server (NTRS)

Engelstad, M.; Sengupta, S. K.; Lee, T.; Welch, R. M.

1992-01-01

This paper investigates the automated detection of jet contrails using data from the Advanced Very High Resolution Radiometer. A preliminary algorithm subtracts the 11.8-micron image from the 10.8-micron image, creating a difference image on which contrails are enhanced. Then a three-stage algorithm searches the difference image for the nearly-straight line segments which characterize contrails. First, the algorithm searches for elevated, linear patterns called 'ridges'. Second, it applies a Hough transform to the detected ridges to locate nearly-straight lines. Third, the algorithm determines which of the nearly-straight lines are likely to be contrails. The paper applies this technique to several test scenes.

Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia

USGS Publications Warehouse

Suzuki, Rikie; Masuda, Kooiti; Dye, Dennis G.

2007-01-01

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over northern Asia from 1982 to 2000. Interannual changes over 19 years in the PAL-NDVI and GIMMS-NDVI were compared with interannual changes in ET estimated from model-assimilated atmospheric data and gridded precipitation data. For both NDVI datasets, the annual maximum correlation with ET occurred in June, which is the beginning of the vegetation growing season. The PAL and GIMMS datasets showed a significant, positive correlation between interannual changes in the NDVI and ET over most of the vegetated land area in June. These results suggest that interannual changes in vegetation activity predominantly control interannual changes in ET in June. Based on analyses of interannual changes in temperature, precipitation, and the NDVI in June, the study area can be roughly divided into two regions, the warmth-dominated northernmost region and the wetness-dominated southern region, indicating that interannual changes in vegetation and the resultant interannual changes in ET are controlled by warmth and wetness in these two regions, respectively.

Measurement of evapotranspiration with combined reflective and thermal infrared radiance observations

NASA Technical Reports Server (NTRS)

Hope, Allen S.

1993-01-01

The broad goal of the research summarized in this report was 'To facilitate the evaluation of regional evapotranspiration (ET) through the combined use of solar reflective and thermal infrared radiance observations.' The specific objectives stated by Goward and Hope (1986) were to: (1) investigate the nature of the relationship between surface temperature (T(sub S)) and the normalized difference vegetation index (NDVI) and develop an understanding of this relationship in terms of energy exchange processes, particularly latent flux heat (LE); (2) develop procedures to estimate large area LE using combined T(sub S) and NDVI observations obtained from AVHRR data; and (3) determine whether measurements derived from satellite observations relate directly to measurements made at the surface or from aircraft platforms. Both empirical and modeling studies were used to develop an understanding of the T(sub S)-NDVI relationship. Most of the modeling was based on the Tergra model as originally proposed by Goward. This model, and modified versions developed in this project, simulates the flows of water and energy in the soil-plant-atmosphere system using meteorological, soil and vegetation inputs. Model outputs are the diurnal course of soil moisture, T(sub S), LE and the other individual components of the surface energy balance.

Satellite-Derived Sea Surface Temperature: Workshop-2

NASA Technical Reports Server (NTRS)

Njoku, E. G.

1984-01-01

Global accuracies and error characteristics of presently orbiting satellite sensors are examined. The workshops are intended to lead to a better understanding of present capabilities for sea surface temperature measurement and to improve measurement concepts for the future. Data from the Advanced Very High Resolution Radiometer AVHRR and Scanning Multichannel Microwave Radiometer is emphasized. Some data from the High Resolution Infrared Sounder HIRS and AVHRR are also examined. Comparisons of satellite data with ship and eXpendable BathyThermograph XBT measurement show standard deviations in the range 0.5 to 1.3 C with biases of less than 0.4 C, depending on the sensor, ocean region, and spatial/temporal averaging. The Sea Surface Temperature SST anomaly maps show good agreement in some cases, but a number of sensor related problems are identified.

BOREAS Level 3-b AVHRR-LAC Imagery: Scaled At-sensor Radiance in LGSOWG Format

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime; Newcomer, Jeffrey A.; Cihlar, Josef

2000-01-01

The BOREAS Staff Science Satellite Data Acquisition Program focused on providing the research teams with the remotely sensed satellite data products they needed to compare and spatially extend point results. Data acquired from the AVHRR instrument on the NOAA-9, -11, -12, and -14 satellites were processed and archived for the BOREAS region by the MRSC and BORIS. The data were acquired by CCRS and were provided for use by BOREAS researchers. A few winter acquisitions are available, but the archive contains primarily growing season imagery. These gridded, at-sensor radiance image data cover the period of 30-Jan-1994 to 18-Sep-1996. Geographically, the data cover the entire 1,000-km x 1,000-km BOREAS region. The data are stored in binary image format files.

Life test results for the advanced very high resolution radiometer scanner

NASA Technical Reports Server (NTRS)

Lenz, James

1996-01-01

The following paper reports the results obtained during a 3.33-year life test on the TIROS Advanced Very High Resolution Radiometer/3 (AVHRR/3) Scanner. The bearing drag torque and lubricant loss over life will be compared to predicted values developed through modeling. The condition of the lubricant at the end of the test will be described and a theory presented to explain the results obtained. The differences (if any) in the predicted and measured values of drag torque and lubricant loss will be discussed and possible reasons for these examined.

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.

Global Sea Surface Temperature: A Harmonized Multi-sensor Time-series from Satellite Observations

NASA Astrophysics Data System (ADS)

Merchant, C. J.

2017-12-01

This paper presents the methods used to obtain a new global sea surface temperature (SST) dataset spanning the early 1980s to the present, intended for use as a climate data record (CDR). The dataset provides skin SST (the fundamental measurement) and an estimate of the daily mean SST at depths compatible with drifting buoys (adjusting for skin and diurnal variability). The depth SST provided enables the CDR to be used with in situ records and centennial-scale SST reconstructions. The new SST timeseries is as independent as possible from in situ observations, and from 1995 onwards is harmonized to an independent satellite reference (namely, SSTs from the Advanced Along Track Scanning Radiometer (Advanced ATSR)). This maximizes the utility of our new estimates of variability and long-term trends in interrogating previous datasets tied to in situ observations. The new SSTs include full resolution (swath, level 2) data, single-sensor gridded data (level 3, 0.05 degree latitude-longitude grid) and a multi-sensor optimal analysis (level 4, same grid). All product levels are consistent. All SSTs have validated uncertainty estimates attached. The sensors used include all Advanced Very High Resolution Radiometers from NOAA-6 onwards and the ATSR series. AVHRR brightness temperatures (BTs) are calculated from counts using a new in-flight re-calibration for each sensor, ultimately linked through to the AATSR BT calibration by a new harmonization technique. Artefacts in AVHRR BTs linked to varying instrument temperature, orbital regime and solar contamination are significantly reduced. These improvements in the AVHRR BTs (level 1) translate into improved cloud detection and SST (level 2). For cloud detection, we use a Bayesian approach for all sensors. For the ATSRs, SSTs are derived with sufficient accuracy and sensitivity using dual-view coefficients. This is not the case for single-view AVHRR observations, for which a physically based retrieval is employed, using a hybrid maximum a posteriori / maximum likelihood retrieval, which optimises retrieval uncertainty and SST sensitivity for climate applications. Validation results will be presented along with examples of the variability and trends in SST evident in the dataset.

Changes in the NDVI of Boreal Forests over the period 1984 to 2003 measured using time series of Landsat TM/ETM+ surface reflectance and the GIMMS AVHRR NDVI record.

NASA Astrophysics Data System (ADS)

McMillan, A. M.; Rocha, A. V.; Goulden, M. L.

2006-12-01

There is a prevailing opinion that the boreal landscape is undergoing change as a result of warming temperatures leading to earlier springs, greater forest fire frequency and possibly CO2 fertilization. One widely- used line of evidence is the GIMMS AVHRR NDVI record. Several studies suggest increasing rates of photosynthesis in boreal forests from 1982 to 1991 (based on NDVI increases) while others suggest declining photosynthesis from 1996 to 2003. We suspect that a portion of these changes are due to the successional stage of the forests. We compiled a time-series of atmospherically-corrected Landsat TM/ETM+ images spanning the period 1984 to 2003 over the BOREAS Northern Study Area and compared spatial and temporal patterns of NDVI between the two records. The Landsat time series is higher resolution and, together with the Canadian Fire Service Large Fire Database, provides stand-age information. We then (1) analyzed the agreement between the Landsat and GIMMS AVHRR time series; (2) determined how the stage of forest succession affected NDVI; (3) assessed how the calculation method of annual averages of NDVI affects decadal-scale trends. The agreement between the Landsat and the AVHRR was reasonable although the depression of NDVI associated with the aerosols from the Pinatubo volcano was greater in the GIMMS time series. Pixels containing high proportions of stands burned within a decade of the observation period showed very high gains in NDVI while the more mature stands were constant. While NDVI appears to exhibit a large sensitivity to the presence of snow, the choice of a May to September averaging period for NDVI over a June to August averaging period did not affect the interannual patterns in NDVI at this location because the snow pack was seldom present in either of these periods. Knowledge of the spatial and temporal patterns of wild fire will prove useful in interpreting trends of remotely-sensed proxies of photosynthesis.

Time scales of pattern evolution from cross-spectrum analysis of advanced very high resolution radiometer and coastal zone color scanner imagery

NASA Technical Reports Server (NTRS)

Denman, Kenneth L.; Abbott, Mark R.

1994-01-01

We have selected square subareas (110 km on a side) from coastal zone color scanner (CZCS) and advanced very high resolution radiometer (AVHRR) images for 1981 in the California Current region off northern California for which we could identify sequences of cloud-free data over periods of days to weeks. We applied a two-dimensional fast Fourier transformation to images after median filtering, (x, y) plane removal, and cosine tapering. We formed autospectra and coherence spectra as functions of a scalar wavenumber. Coherence estimates between pairs of images were plotted against time separation between images for several wide wavenumber bands to provide a temporal lagged coherence function. The temporal rate of loss of correlation (decorrelation time scale) in surface patterns provides a measure of the rate of pattern change or evolution as a function of spatial dimension. We found that patterns evolved (or lost correlation) approximately twice as rapidly in upwelling jets as in the 'quieter' regions between jets. The rapid evolution of pigment patterns (lifetime of about 1 week or less for scales of 50-100 km) ought to hinder biomass transfer to zooplankton predators compared with phytoplankton patches that persist for longer times. We found no significant differences between the statistics of CZCS and AVHRR images (spectral shape or rate of decorrelation). In addition, in two of the three areas studied, the peak correlation between AVHRR and CZCS images from the same area occurred at zero lag, indicating that the patterns evolved simutaneously. In the third area, maximum coherence between thermal and pigment patterns occurred when pigment images lagged thermal images by 1-2 days, mirroring the expected lag of high pigment behind low temperatures (and high nutrients) in recently upwelled water. We conclude that in dynamic areas such as coastal upwelling systems, the phytoplankton cells (identified by pigment color patterns) behave largely as passive scalars at the mesoscale and that growth, death, and sinking of phytoplankton collectively play at most a mariginal role in determining the spectral statistics of the pigment patterns.

GLCF: Gallery

Science.gov Websites

UMD Land Cover Classification Product External Galleries * ASTER at JPL * AVHRR at JHU * Earth Observatory at NASA * Landsat 7 at USGS * MODIS at NASA * Visible Earth at NASA e-link 4321 Hartwick Building

Application of Risk Analysis Based On Remote Sensing and Geographic Information System Technologies To Control of Malaria In Nigeria

NASA Astrophysics Data System (ADS)

Njemanze, Philip; Njemanze, Philip; Peters, Constance; Uwaeziozi, Amarachukwu

More than 1 million Africans die from malaria each year. Remote sensing (RS) and geographic information system (GIS) technologies could be applied to study the risk of malaria epidemic. The patient population included 45,140 of persons aged 0-85 years seen at primary health centers in 18 different local government areas (LGAs) of Imo State. Maps of old Imo State were converted to digital form using ARC/INFO GIS software, and the resulting coverages included hydrology, towns, and villages. Remote sensing images from Advanced Very High Resolution Radiometer (AVHRR) data set were used to obtain color-coded monthly normalized-difference vegetation index or NDVI. Three groups were distinguished as: group A LGAs using water from natural hydrology and bore-holes, group B - using rain water harvesting from roof tops into surface water reservoirs, and group C - using ground surface catchment of rain water with ground ponds. These stagnant ponds were Anopheles mosquito breeding sites. The NDVI values were used to determine water availability, and were least in January/February each year, and highest in April/May. Probabilistic layer analysis (PLA) was used to determine the Odds Ratio (OR), Relative Risk (RR) and Attributable Risk (AR) for malaria in groups A, B, C. Significant risk for malaria was associated with local water conservation methods in group C, compared to A, (OR = 4.55; RR = 4.46, AR = 77.6

An atlas of monthly mean distributions of SSMI surface wind speed, ARGOS buoy drift, AVHRR/2 sea surface temperature, and ECMWF surface wind components during 1990

NASA Technical Reports Server (NTRS)

Halpern, D.; Knauss, W.; Brown, O.; Wentz, F.

1993-01-01

The following monthly mean global distributions for 1990 are proposed with a common color scale and geographical map: 10-m height wind speed estimated from the Special Sensor Microwave Imager (SSMI) on a United States (US) Air Force Defense Meteorological Satellite Program (DMSP) spacecraft; sea surface temperature estimated from the advanced very high resolution radiometer (AVHRR/2) on a U.S. National Oceanic and Atmospheric Administration (NOAA) spacecraft; Cartesian components of free drifting buoys which are tracked by the ARGOS navigation system on NOAA satellites; and Cartesian components on the 10-m height wind vector computed by the European Center for Medium-Range Weather Forecasting (ECMWF). Charts of monthly mean value, sampling distribution, and standard deviation values are displayed. Annual mean distributions are displayed.

An atlas of monthly mean distributions of SSMI surface wind speed, ARGOS buoy drift, AVHRR/2 sea surface temperature, and ECMWF surface wind components during 1991

NASA Technical Reports Server (NTRS)

Halpern, D.; Knauss, W.; Brown, O.; Wentz, F.

1993-01-01

The following monthly mean global distributions for 1991 are presented with a common color scale and geographical map: 10-m height wind speed estimated from the Special Sensor Microwave Imager (SSMI) on a United States Air Force Defense Meteorological Satellite Program (DMSP) spacecraft; sea surface temperature estimated from the advanced very high resolution radiometer (AVHRR/2) on a U.S. National Oceanic and Atmospheric Administration (NOAA) spacecraft; Cartesian components of free-drifting buoys which are tracked by the ARGOS navigation system on NOAA satellites; and Cartesian components of the 10-m height wind vector computed by the European Center for Medium-Range Weather Forecasting (ECMWF). Charts of monthly mean value, sampling distribution, and standard deviation value are displayed. Annual mean distributions are displayed.

Advanced very high resolution radiometer

NASA Technical Reports Server (NTRS)

1978-01-01

The program covered the design, construction, and test of a Breadboard Model, Engineering Model, Protoflight Model, Mechanical/Structural Model, and a Life Test Model. Special bench test and calibration equipment was also developed for use on the program. Initially, the instrument was to operate from a 906 n.mi. orbit and be thermally isolated from the spacecraft. The Breadboard Model and the Mechanical/Structural Model were designed and built to these requirements. The spacecraft altitude was changed to 450 n.mi., IFOVs and spectral characteristics were modified, and spacecraft interfaces were changed. The final spacecraft design provided a temperature-controlled Instrument Mounting Platform (IMP) to carry the AVHRR and other instruments. The design of the AVHRR was modified to these new requirements and the modifications were incorporated in the Engineering Model. The Protoflight Model and the Flight Models conform to this design.

The extent of burning in African savanna

NASA Technical Reports Server (NTRS)

Cahoon, D. R. JR.; Levine, J. S.; Cofer, W. R. Iii; Stocks, B. J.

1994-01-01

The temporal and spatial distribution of African savanna grassland fires has been examined, and the areal extent of these fires has been estimated for the subequatorial African continent. African savanna fires have been investigated using remote sensing techniques and imagery collected by low-light sensors on Defense Meteorological Satellite Program (DMSP) satellites and by the Advanced Very High Resolution Radiometer (AVHRR) which is aboard polar orbiting National Oceanic and Atmospheric Administration (NOAA) satellites. DMSP imagery has been used to map the evolution of savanna burning over all of the African continent and the analysis of AVHRR imagery has been used to estimate the areal extent of the burning in the southern hemispheric African savannas. The work presented primarily reflects the analysiscompleted for the year 1987. However, comparisons have been made with other years and the representativeness of the 1987 analysis is discussed.

Improvements in Near-Terminator and Nocturnal Cloud Masks using Satellite Imager Data over the Atmospheric Radiation Measurement Sites

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

Trepte, Q.Z.; Minnis, P.; Heck, P.W.

2005-03-18

Cloud detection using satellite measurements presents a big challenge near the terminator where the visible (VIS; 0.65 {micro}m) channel becomes less reliable and the reflected solar component of the solar infrared 3.9-{micro}m channel reaches very low signal-to-noise ratio levels. As a result, clouds are underestimated near the terminator and at night over land and ocean in previous Atmospheric Radiation Measurement (ARM) Program cloud retrievals using Geostationary Operational Environmental Satellite (GOES) imager data. Cloud detection near the terminator has always been a challenge. For example, comparisons between the CLAVR-x (Clouds from Advanced Very High Resolution Radiometer [AVHRR]) cloud coverage and Geosciencemore » Laser Altimeter System (GLAS) measurements north of 60{sup o}N indicate significant amounts of missing clouds from AVHRR because this part of the world was near the day/night terminator viewed by AVHRR. Comparisons between MODIS cloud products and GLAS at the same regions also shows the same difficulty in the MODIS cloud retrieval (Pavolonis and Heidinger 2005). Consistent detection of clouds at all times of day is needed to provide reliable cloud and radiation products for ARM and other research efforts involving the modeling of clouds and their interaction with the radiation budget. To minimize inconsistencies between daytime and nighttime retrievals, this paper develops an improved twilight and nighttime cloud mask using GOES-9, 10, and 12 imager data over the ARM sites and the continental United States (CONUS).« less

Satellite Remote Sensing of Aerosol Forcing

NASA Technical Reports Server (NTRS)

Remer, Lorraine; Kaufman, Yoram; Ramaprasad, Jaya; Procopio, Aline; Levin, Zev

1999-01-01

The role of aerosol forcing remains one of the largest uncertainties in estimating man's impact on the global climate system. One school of thought suggests that remote sensing by satellite sensors will provide the data necessary to narrow these uncertainties. While satellite measurements of direct aerosol forcing appear to be straightforward, satellite measurements of aerosol indirect forcing will be more complicated. Pioneering studies identified indirect aerosol forcing using AVHRR data in the biomass burning regions of Brazil. We have expanded this analysis with AVHRR to include an additional year of data and assimilated water vapor fields. The results show similar latitudinal dependence as reported by Kaufman and Fraser, but by using water vapor observations we conclude that latitude is not a proxy for water vapor and the strength of the indirect effect is not correlated to water vapor amounts. In addition to the AVHRR study we have identified indirect aerosol forcing in Brazil at much smaller spatial scales using the MODIS Airborne Simulator. The strength of the indirect effect appears to be related to cloud type and cloud dynamics. There is a suggestion that some of the cloud dynamics may be influenced by smoke destabilization of the atmospheric column. Finally, this study attempts to quantify remote sensing limitations due to the accuracy limits of the retrieval algorithms. We use a combination of numerical aerosol transport models, ground-based AERONET data and ISCCP cloud climatology to determine how much of the forcing occurs in regions too clean to determine from satellite retrievals.

The CACAO Method for Smoothing, Gap Filling, and Characterizing Seasonal Anomalies in Satellite Time Series

NASA Technical Reports Server (NTRS)

Verger, Aleixandre; Baret, F.; Weiss, M.; Kandasamy, S.; Vermote, E.

2013-01-01

Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981-2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods-the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT-revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50-60% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction.

A Combined Surface Temperature Dataset for the Arctic from MODIS and AVHRR

NASA Astrophysics Data System (ADS)

Dodd, E.; Veal, K. L.; Ghent, D.; Corlett, G. K.; Remedios, J. J.

2017-12-01

Surface Temperature (ST) changes in the Polar Regions are predicted to be more rapid than either global averages or responses in lower latitudes. Observations of STs and other changes associated with climate change increasingly confirm these predictions in the Arctic. Furthermore, recent high profile events of anomalously warm temperatures have increased interest in Arctic surface temperatures. It is, therefore, particularly important to monitor Arctic climate change. Satellites are particularly relevant to observations of Polar Regions as they are well-served by low-Earth orbiting satellites. Whilst clouds often cause problems for satellite observations of the surface, in situ observations of STs are much sparser. Previous work at the University of Leicester has produced a combined land, ocean and ice ST dataset for the Arctic using ATSR data (AAST) which covers the period 1995 to 2012. In order to facilitate investigation of more recent changes in the Arctic (2010 to 2016) we have produced another combined surface temperature dataset using MODIS and AVHRR; the Metop-A AVHRR and MODIS Arctic Surface Temperature dataset (AMAST). The method of cloud-clearing, use of auxiliary data for ice classification and the ST retrievals used for each surface-type in AMAST will be described. AAST and AMAST were compared in the time period common to both datasets. We will provide results from this intercomparison, as well as an assessment of the impact of utilising data from wide and narrow swath sensors. Time series of ST anomalies over the Arctic region produced from AMAST will be presented.

Revisiting AVHRR Tropospheric Aerosol Trends Using Principal Component Analysis

NASA Technical Reports Server (NTRS)

Li, Jing; Carlson, Barbara E.; Lacis, Andrew A.

2014-01-01

The advanced very high resolution radiometer (AVHRR) satellite instruments provide a nearly 25 year continuous record of global aerosol properties over the ocean. It offers valuable insights into the long-term change in global aerosol loading. However, the AVHRR data record is heavily influenced by two volcanic eruptions, El Chichon on March 1982 and Mount Pinatubo on June 1991. The gradual decay of volcanic aerosols may last years after the eruption, which potentially masks the estimation of aerosol trends in the lower troposphere, especially those of anthropogenic origin. In this study, we show that a principal component analysis approach effectively captures the bulk of the spatial and temporal variability of volcanic aerosols into a single mode. The spatial pattern and time series of this mode provide a good match to the global distribution and decay of volcanic aerosols. We further reconstruct the data set by removing the volcanic aerosol component and reestimate the global and regional aerosol trends. Globally, the reconstructed data set reveals an increase of aerosol optical depth from 1985 to 1990 and decreasing trend from 1994 to 2006. Regionally, in the 1980s, positive trends are observed over the North Atlantic and North Arabian Sea, while negative tendencies are present off the West African coast and North Pacific. During the 1994 to 2006 period, the Gulf of Mexico, North Atlantic close to Europe, and North Africa exhibit negative trends, while the coastal regions of East and South Asia, the Sahel region, and South America show positive trends.

Improvements in Near-Terminator and Nocturnal Cloud Masks using Satellite Image Data over the Atmospheric Radiation Measurement Sites

NASA Technical Reports Server (NTRS)

Trepte, Q. Z.; Minnis, P.; Heck, R. W.; Palikonda, R.

2005-01-01

Cloud detection using satellite measurements presents a big challenge near the terminator where the visible (VIS; 0.65 (micro)m) channel becomes less reliable and the reflected solar component of the solar infrared 3.9-(micro)m channel reaches very low signal-to-noise ratio levels. As a result, clouds are underestimated near the terminator and at night over land and ocean in previous Atmospheric Radiation Measurement (ARM) Program cloud retrievals using Geostationary Operational Environmental Satellite (GOES) imager data. Cloud detection near the terminator has always been a challenge. For example, comparisons between the CLAVR-x (Clouds from Advanced Very High Resolution Radiometer (AVHRR)) cloud coverage and Geoscience Laser Altimeter System (GLAS) measurements north of 60 degrees N indicate significant amounts of missing clouds from AVHRR because this part of the world was near the day/night terminator viewed by AVHRR. Comparisons between MODIS cloud products and GLAS at the same regions also shows the same difficulty in the MODIS cloud retrieval (Pavolonis and Heidinger 2005). Consistent detection of clouds at all times of day is needed to provide reliable cloud and radiation products for ARM and other research efforts involving the modeling of clouds and their interaction with the radiation budget. To minimize inconsistencies between daytime and nighttime retrievals, this paper develops an improved twilight and nighttime cloud mask using GOES-9, 10, and 12 imager data over the ARM sites and the continental United States (CONUS).

Automatic AVHRR image navigation software

NASA Technical Reports Server (NTRS)

Baldwin, Dan; Emery, William

1992-01-01

This is the final report describing the work done on the project entitled Automatic AVHRR Image Navigation Software funded through NASA-Washington, award NAGW-3224, Account 153-7529. At the onset of this project, we had developed image navigation software capable of producing geo-registered images from AVHRR data. The registrations were highly accurate but required a priori knowledge of the spacecraft's axes alignment deviations, commonly known as attitude. The three angles needed to describe the attitude are called roll, pitch, and yaw, and are the components of the deviations in the along scan, along track and about center directions. The inclusion of the attitude corrections in the navigation software results in highly accurate georegistrations, however, the computation of the angles is very tedious and involves human interpretation for several steps. The technique also requires easily identifiable ground features which may not be available due to cloud cover or for ocean data. The current project was motivated by the need for a navigation system which was automatic and did not require human intervention or ground control points. The first step in creating such a system must be the ability to parameterize the spacecraft's attitude. The immediate goal of this project was to study the attitude fluctuations and determine if they displayed any systematic behavior which could be modeled or parameterized. We chose a period in 1991-1992 to study the attitude of the NOAA 11 spacecraft using data from the Tiros receiving station at the Colorado Center for Astrodynamic Research (CCAR) at the University of Colorado.

Earth Radiation Budget Experiment (ERBE) Data Sets for Global Environment and Climate Change Studies

NASA Technical Reports Server (NTRS)

Bess, T. Dale; Carlson, Ann B.; Denn, Fredrick M.

1997-01-01

For a number of years there has been considerable interest in the earth's radiation budget (ERB) or energy balance, and entails making the best measurements possible of absorbed solar radiation, reflected shortwave radiation (RSW), thermal outgoing longwave radiation (OLR), and net radiation. ERB data are fundamental to the development of realistic climate models and studying natural and anthropogenic perturbations of the climate. Much of the interest and investigations in the earth's energy balance predated the age of earth-orbiting satellites (Hunt et al., 1986). Beginning in the mid 1960's earth-orbiting satellites began to play an important role in making measurements of the earth's radiation flux although much effort had gone into measuring ERB parameters prior to 1960 (House et al., 1986). Beginning in 1974 and extending until the present time, three different satellite experiments (not all operating at the same time) have been making radiation budget measurements almost continually in time. Two of the experiments were totally dedicated to making radiation budget measurements of the earth, and the other experiment flown on NOAA sun-synchronous AVHRR weather satellites produced radiation budget parameters as a by-product. The heat budget data from the AVHRR satellites began collecting data in June 1974 and have operated almost continuously for 23 years producing valuable data for long term climate monitoring.

Bark Beetles Modify the Impact of Snow Drought on Remotely Sensed Forest Productivity in the Southern Rocky Mountains, USA

NASA Astrophysics Data System (ADS)

Knowles, J. F.; Lestak, L.; Molotch, N. P.

2016-12-01

We evaluated the long term (1989-2012) relationship between the satellite-observed Normalized Difference Vegetation Index (NDVI), snowpack accumulation, and atmospheric demand throughout the Southern Rocky Mountain Ecoregion, USA. Deviations from this relationship were further explored during pre- and post-disturbance conditions associated with bark beetles and drought. Over the entire study area, both the snow water equivalent (SWE) and a snow aridity index (SAI), which used the SWE to normalize potential evapotranspiration (PET), were significant predictors of the long-term AVHRR NDVI, but the SAI was a better predictor of NDVI relative to SWE regardless of disturbance. Since these relationships were weaker in disturbed areas, we also introduced a metric of tree mortality, and subsequent multiple linear regression of SAI and cumulative mortality best predicted the NDVI from a pair of heavily impacted focus areas within the larger study area. The post-disturbance NDVI was systematically reduced per unit SAI in these areas, and the difference between the observed and predicted (from pre-disturbance regressions) post-disturbance NDVI was significantly correlated with the cumulative forest mortality. At the Ecoregion scale, these disturbance effects were not clearly evident, and we attribute this to spatial variability of both SAI and NDVI throughout the large study area as evidenced by spatial analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived data. These results constrain the expected reduction in forest productivity due to disturbance and demonstrate that this reduction can be particularly evident during drought conditions resultant from low snow accumulation during the winter. Hence, terrestrial carbon uptake may decrease non-linearly post disturbance. This work has implications for predicting the ecohydrological response to climate change in the southern Rocky Mountains, as reductions in SWE and increases in PET are predicted for this area in the future, and therefore changes in the terrestrial carbon, water, and energy cycles should be expected.

Characterization of drought patterns through remote sensing over The Chihuahua Desert, Mexico"

NASA Astrophysics Data System (ADS)

Madrigal, J. M.; Lopez, A.; Garatuza, J.

2013-12-01

Drought is a phenomenon that has intensified during the last few decades in the arid and semi-arid zones of northern Mexico. In the Chihuahua desert, across Chihuahua, Durango and Coahuila states has caused loss of food sustainability (agriculture, livestock), an increase in human health problems, and detriment of ecosystem services as well as important economic losses. In order to understand this phenomenon, it is necessary to create tools that allow monitoring the territory's spatial heterogeneity and multi-temporality. With this purpose we propose the implementation of a drought model which includes the traditional indexes of climatic drought, such as the Palmer Drought Severity Index PDSI, the Standardized Index of Rainfall SPI, data from meteorological stations and biophysical variations obtained from the MODIS sensors product MOD13 NDVI from 2001 to 2010, as well as biophysical variables characteristic of the environment, such as land use and vegetation coverage, Eco-regions, soil moisture, digital elevation model and irrigate agriculture districts. With the MODIS images, a spatially coherent time series was created analyzing the study area's phenology (TIMESAT) created the Seasonal Greenness (SG) and Start of Season Anomaly (SOSA) for the mentioned nine years. Through this, the annual cycles were established. With a decision tree model, all the previously mentioned proposed variables were integrated. The proposed model produces a general map which characterizes the vegetation condition (extreme drought, severe drought, moderate drought, near normal). Even though different techniques have been proposed on the monitoring of droughts, most of them generate drought indexes with a spatial resolution of 1km (Wardlow, B. et. al 2008; Levent T. et al. 2013). One of the main concerns of researchers on the matter is on improving the spatial information content and on having a better representation of the phenomenon. We use the normalized difference vegetation index (NDVI) data acquired by MODIS instead of the Advanced Very High Resolution Radiometer (AVHRR). The results show a better drought pattern characterization over The Chihuahua Desert, Mexico". The future work will consist of making a sensibility and optimization study of the variables used in the CART model, including others such as evapotranspiration and rainfall. Additionally, this work will research on the potential of using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI).

Remote sensing advances in agricultural inventories

NASA Technical Reports Server (NTRS)

Dragg, J. L.; Bizzell, R. M.; Trichel, M. C.; Hatch, R. E.; Phinney, D. E.; Baker, T. C.

1984-01-01

As the complexity of the world's agricultural industry increases, more timely and more accurate world-wide agricultural information is required to support production and marketing decisions, policy formulation, and technology development. The Inventory Technology Development Project of the AgRISTARS Program has developed new automated technology that uses data sets acquired by spaceborne remote sensors. Research has emphasized the development of multistage, multisensor sampling and estimation techniques for use in global environments where reliable ground observations are not available. This paper presents research results obtained from data sets acquired by four different sensors: Landsat MSS, Landsat TM, Shuttle-Imaging Radar and environmental satellite (AVHRR).

Lake surface water temperatures of European Alpine lakes (1989-2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set

NASA Astrophysics Data System (ADS)

Riffler, M.; Wunderle, S.

2014-05-01

Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Thus, the Global Climate Observing System (GCOS) lists LWT as an Essential Climate Variable (ECV). Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years finally offering a unique opportunity for satellite-based climate studies. Herein we present a satellite-based lake surface water temperature (LSWT) data set for European (pre-alpine) water bodies based on the extensive AVHRR 1 km data record (1989-2013) of the Remote Sensing Research Group at the University of Bern. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and Metop-A) data. The high accuracy needed for climate related studies requires careful pre-processing and consideration of the atmospheric state. Especially data from NOAA-16 and prior satellites were prone to noise, e.g., due to transmission errors or fluctuations in the instrument's thermal state. This has resulted in partly corrupted thermal calibration data and may cause errors of up to several Kelvin in the final resulting LSWT. Thus, a multi-stage correction scheme has been applied to the data to minimize these artefacts. The LSWT retrieval is based on a simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with operational analysis and reanalysis data from the European Centre for Medium Range Weather Forecasts. The resulting LSWTs were extensively validated using in situ measurements from lakes with various sizes between 14 and 580 km2 and the resulting biases and RMSEs were found to be within the range of -0.4-0.6 K and 1.0-1.9 K, respectively. The upper limits of the reported errors could be rather attributed to uncertainties in the data comparison between in situ and satellite observations than inaccuracies of the satellite retrieval. The cross-platform consistency of the retrieval was found to be within ~0.2 K. A comparison with LSWT derived through global sea surface temperature (SST) algorithms shows lower RMSEs and biases for the simulation-based approach. A running project will apply the developed method to retrieve LSWT from the northern part of Finland to southern Italy to derive the climate signal of the last 30 years. The data are available at doi:10.1594/PANGAEA.831007.

CCRS Landcover Maps From Satellite Data

DOE Data Explorer

Trishchenko, Alexander

2008-01-15

The Canadian Centre for Remote Sensing (CCRS) presents several landcover maps over the SGP CART site area (32-40N, 92-102W) derived from satellite data including AVHRR, MODIS, SPOT vegetation data, and Landsat satellite TM imagery.

First Estimates of the Radiative Forcing of Aerosols Generated from Biomass Burning using Satellite Data

NASA Technical Reports Server (NTRS)

Chistopher, Sundar A.; Kliche, Donna V.; Chou, Joyce; Welch, Ronald M.

1996-01-01

Collocated measurements from the Advanced Very High Resolution Radiometer (AVHRR) and the Earth Radiation Budget Experiment (ERBE) scanner are used to examine the radiative forcing of atmospheric aerosols generated from biomass burning for 13 images in South America. Using the AVHRR, Local Area Coverage (LAC) data, a new technique based on a combination of spectral and textural measures is developed for detecting these aerosols. Then, the instantaneous shortwave, longwave, and net radiative forcing values are computed from the ERBE instantaneous scanner data. Results for the selected samples from 13 images show that the mean instantaneous net radiative forcing for areas with heavy aerosol loading is about -36 W/sq m and that for the optically thin aerosols are about -16 W/sq m. These results, although preliminary, provide the first estimates of radiative forcing of atmospheric aerosols from biomass burning using satellite data.

An analysis of IGBP global land-cover characterization process

USGS Publications Warehouse

Loveland, Thomas R.; Zhu, Zhiliang; Ohlen, Donald O.; Brown, Jesslyn F.; Reed, Bradley C.; Yang, Limin

1999-01-01

The international Geosphere Biosphere Programme (IGBP) has called for the development of improved global land-cover data for use in increasingly sophisticated global environmental models. To meet this need, the staff of the U.S. Geological Survey and the University of Nebraska-Lincoln developed and applied a global land-cover characterization methodology using 1992-1993 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) and other spatial data. The methodology, based on unsupervised classification with extensive postclassification refinement, yielded a multi-layer database consisting of eight land-cover data sets, descriptive attributes, and source data. An independent IGBP accuracy assessment reports a global accuracy of 73.5 percent, and continental results vary from 63 percent to 83 percent. Although data quality, methodology, interpreter performance, and logistics affected the results, significant problems were associated with the relationship between AVHRR data and fine-scale, spectrally similar land-cover patterns in complex natural or disturbed landscapes.

A multisensor approach to sea ice classification for the validation of DMSP-SSM/I passive microwave derived sea ice products

NASA Technical Reports Server (NTRS)

Steffen, K.; Schweiger, A. J.

1990-01-01

The validation of sea ice products derived from the Special Sensor Microwave Imager (SSM/I) on board a DMSP platform is examined using data from the Landsat MSS and NOAA-AVHRR sensors. Image processing techniques for retrieving ice concentrations from each type of imagery are developed and results are intercompared to determine the ice parameter retrieval accuracy of the SSM/I NASA-Team algorithm. For case studies in the Beaufort Sea and East Greenland Sea, average retrieval errors of the SSM/I algorithm are between 1.7 percent for spring conditions and 4.3 percent during freeze up in comparison with Landsat derived ice concentrations. For a case study in the East Greenland Sea, SSM/I derived ice concentration in comparison with AVHRR imagery display a mean error of 9.6 percent.

The investigation of identifying method on grass fire by FY-3 VIRR images

NASA Astrophysics Data System (ADS)

Jiang, Youyan; Han, Tao; Wang, Dawei

2018-03-01

Grassland fire has the characteristics of fierce fire and rapid spreading, and many fires occur in sparsely populated places. Satellite remote sensing has the characteristics of fast imaging period and wide coverage, and plays an important role in the rapid monitoring and evaluation of grassland fire. FY-3 satellite has been widely used since its launch in September 2008, and this paper uses the fire information of Gansu grassland from 2011 to 2016, based on the more mature MODIS and NOAA-AVHRR fire identification method. The results show that the accuracy of FY-3/VIRR satellite data fire detection are higher than that of NOAA-AVHRR satellite, and the accuracy of FY-3/VIRR satellite data is described. There is a greater improvement, the ability to identify slightly worse than the MODIS satellite, the region is relatively large fire detection accuracy is higher.

First Estimates of the Radiative Forcing of Aerosols Generated from Biomass Burning Using Satellite Data

NASA Technical Reports Server (NTRS)

Christopher, Sundar A.; Kliche, Donna A.; Chou, Joyce; Welch, Ronald M.

1996-01-01

Collocated measurements from the Advanced Very High Resolution Radiometer (AVHRR) and the Earth Radiation Budget Experiment (ERBE) scanner are used to examine the radiative forcing of atmospheric aerosols generated from biomass burning for 13 images in South America. Using the AVHRR, Local Area Coverage (LAC) data, a new technique based on a combination of spectral and textural measures is developed for detecting these aerosols. Then, the instantaneous shortwave, longwave, and net radiative forcing values are computed from the ERBE instantaneous scanner data. Results for the selected samples from 13 images show that the mean instantaneous net radiative forcing for areas with heavy aerosol loading is about -36 W/sq m and that for the optically thin aerosols are about -16 W/sq m. These results, although preliminary, provide the first estimates of radiative forcing of atmospheric aerosols from biomass burning using satellite data.

Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

PubMed Central

Zhang, Jia-Hua; Yao, Feng-Mei; Liu, Cheng; Yang, Li-Min; Boken, Vijendra K.

2011-01-01

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status. PMID:21909297

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

Detection, emission estimation and risk prediction of forest fires in China using satellite sensors and simulation models in the past three decades--an overview.

PubMed

Zhang, Jia-Hua; Yao, Feng-Mei; Liu, Cheng; Yang, Li-Min; Boken, Vijendra K

2011-08-01

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.

The Potential for Collocated AGLP and ERBE data for Fire, Smoke, and Radiation Budget Studies

NASA Technical Reports Server (NTRS)

Christropher, S. A.; Chou, J.

1997-01-01

One month of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Land Pathfinder (AGLP) data from September 1985 are used to examine the spatial and temporal distribution of fires over four major ecosystems in South America. The Earth Radiation Budget Experiment (ERBE) scanner data are used to examine the top of atmosphere (TOA) shortwave and longwave fluxes over smoke generated from biomass burn- ing. The relationship between the AGLP-derived Normalized Difference Vegetation Index (NDVI) and the ERBE-estimated clear sky albedos are also examined as a function of the four ecosystems. This study shows that the grassland areas in South America have the highest number of fires for September 1985, and their corresponding NDVI values are smaller than the tropical rainforest region where the number of fires were comparatively small. Clear sky statistics accumulated during the days when smoke was not present show that clear sky albedos derived from ERBE are higher for grassland areas when compared to the tropical rainforest. The results show that the AGLP can be used to determine the spatial and temporal distribution of fires along with vegetation characteristics, while ERBE data can provide necessary information on broadband albedos and regional top of atmosphere radiative impacts of biomass burning aerosols. Since the AGLP data are available from 1981 to the present day, several climate-related issues can be addressed.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

The Potential for Collocated AGLP and ERBE Data for Fire, Smoke, and Radiation Budget Studies

NASA Technical Reports Server (NTRS)

Christopher, S. A.; Chou, J.

1997-01-01

One month of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Land Pathfinder (AGLP) data from September 1985 are used to examine the spatial and temporal distribution of fires over four major ecosystems in South America. The Earth Radiation Budget Experiment (ERBE) scanner data are used to examine the top of atmosphere (TOA) shortwave and longwave fluxes over smoke generated from biomass burning. The relationship between the AGLP-derived Normalized Difference Vegetation Index (NDVI) and the ERBE-estimated clear sky albedos are also examined as a function of the four ecosystems. This study shows that the grassland areas in South America have the highest number of tires for September 1985, and their corresponding NDVI values are smaller than the tropical rainforest region where the number of fires were comparatively small. Clear sky statistics accumulated during the days when smoke was not present show that clear sky albedos derived from ERBE are higher for grassland areas when compared to the tropical rainforest. The results show that the AGLP can be used to determine the spatial and temporal distribution of fires along with vegetation characteristics, while ERBE data can provide necessary information on broadband albedos and regional top of atmosphere radiative impacts of biomass burning aerosols. Since the AGLP data are available from 1981 to the present day, several climate-related issues can be addressed.

The Potential for Collocated AGLP and ERBE Data for Fire, Smoke, and Radiation Budget Studies

NASA Technical Reports Server (NTRS)

Christopher, S. A.; Chou, J.

1997-01-01

One month of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Land Pathfinder (AGLP) data from September 1985 are used to examine the spatial and temporal distribution of fires over four major ecosystems in South America. The Earth Radiation Budget Experiment (ERBE) scanner data are used to examine the top of atmosphere (TOA) shortwave and longwave fluxes over smoke generated from biomass burning. The relationship between the AGLP-derived Normalized Difference Vegetation Index (NDVI) and the ERBE-estimated clear sky albedos are also examined as a function of the four ecosystems. This study shows that the grassland areas in South America have the highest number of fires for September 1985. and their corresponding NDVI values are smaller than the tropical rainforest region where the number of fires were comparatively small. Clear sky statistics accumulated during the days when smoke was not present show that clear sky albedos derived from ERBE are higher for grassland areas when compared to the tropical rainforest. The results show that the AGLP can be used to determine the spatial and temporal distribution of fires along with vegetation characteristics, while ERBE data can provide necessary information on broadband albedos and regional top of atmosphere radiative impacts of biomass burning aerosols. Since the AGLP data are available from 1981 to the present day, several climate-related issues can be addressed,

Operational atmospheric correction of AVHRR visible and infrared data

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

Vermote, E.; El Saleous, N.; Roger, J.C.

1995-12-31

The satellite level radiance is affected by the presence of the atmosphere between the sensor and the target. The ozone and water vapor absorption bands affect the signal recorded by the AVHRR visible and near infrared channels respectively. The Rayleigh scattering mainly affects the visible channel and is more pronounced when dealing with small sun elevations and large view angles. The aerosol scattering affects both channels and is certainly the most challenging term for atmospheric correction because of the spatial and temporal variability of both the type and amount of particles in the atmosphere. This paper presents the equation ofmore » the satellite signal, the scheme to retrieve atmospheric properties and corrections applied to AVHRR observations. The operational process uses TOMS data and a digital elevation model to correct for ozone absorption and rayleigh scattering. The water vapor content is evaluated using the split-window technique that is validated over ocean using 1988 SSM/I data. The aerosol amount retrieval over Ocean is achieved in channels 1 and 2 and compared to sun photometer observations to check consistency of the radiative transfer model and the sensor calibration. Over land, the method developed uses reflectance at 3.75 microns to deduce target reflectance in channel 1 and retrieve aerosol optical thickness that can be extrapolated in channel 2. The method to invert the reflectance at 3.75 microns is based on MODTRAN simulations and is validated by comparison to measurements performed during FIFE 87. Finally, aerosol optical thickness retrieved over Brazil and Eastern US is compared to sun photometer measurements.« less

Moss and lichen cover mapping at local and regional scales in the boreal forest ecosystem of central Canada

USGS Publications Warehouse

Rapalee, G.; Steyaert, L.T.; Hall, F.G.

2001-01-01

Mosses and lichens are important components of boreal landscapes [Vitt et al., 1994; Bubier et al., 1997]. They affect plant productivity and belowground carbon sequestration and alter the surface runoff and energy balance. We report the use of multiresolution satellite data to map moss and lichens over the BOREAS region at a 10 m, 30 m, and 1 km scales. Our moss and lichen classification at the 10 m scale is based on ground observations of associations among soil drainage classes, overstory composition, and cover type among four broad classes of ground cover (feather, sphagnum, and brown mosses and lichens). For our 30 m map, we used field observations of ground cover-overstory associations to map mosses and lichens in the BOREAS southern study area (SSA). To scale up to a 1 km (AVHRR) moss map of the BOREAS region, we used the TM SSA mosaics plus regional field data to identify AVHRR overstory-ground cover associations. We found that: 1) ground cover, overstory composition and density are highly correlated, permitting inference of moss and lichen cover from satellite-based land cover classifications; 2) our 1 km moss map reveals that mosses dominate the boreal landscape of central Canada, thereby a significant factor for water, energy, and carbon modeling; 3) TM and AVHRR moss cover maps are comparable; 4) satellite data resolution is important; particularly in detecting the smaller wetland features, lakes, and upland jack pine sites; and 5) distinct regional patterns of moss and lichen cover correspond to latitudinal and elevational gradients. Copyright 2001 by the American Geophysical Union.

Dependence of marine stratocumulus reflectivities on liquid water paths

NASA Technical Reports Server (NTRS)

Coakley, James A., Jr.; Snider, Jack B.

1990-01-01

Simple parameterizations that relate cloud liquid water content to cloud reflectivity are often used in general circulation climate models to calculate the effect of clouds in the earth's energy budget. Such parameterizations have been developed by Stephens (1978) and by Slingo and Schrecker (1982) and others. Here researchers seek to verify the parametric relationship through the use of simultaneous observations of cloud liquid water content and cloud reflectivity. The column amount of cloud liquid was measured using a microwave radiometer on San Nicolas Island following techniques described by Hogg et al., (1983). Cloud reflectivity was obtained through spatial coherence analysis of Advanced Very High Resolution Radiometer (AVHRR) imagery data (Coakley and Beckner, 1988). They present the dependence of the observed reflectivity on the observed liquid water path. They also compare this empirical relationship with that proposed by Stephens (1978). Researchers found that by taking clouds to be isotropic reflectors, the observed reflectivities and observed column amounts of cloud liquid water are related in a manner that is consistent with simple parameterizations often used in general circulation climate models to determine the effect of clouds on the earth's radiation budget. Attempts to use the results of radiative transfer calculations to correct for the anisotropy of the AVHRR derived reflectivities resulted in a greater scatter of the points about the relationship expected between liquid water path and reflectivity. The anisotropy of the observed reflectivities proved to be small, much smaller than indicated by theory. To critically assess parameterizations, more simultaneous observations of cloud liquid water and cloud reflectivities and better calibration of the AVHRR sensors are needed.

Prototype global burnt area algorithm using the AVHRR-LTDR time series

NASA Astrophysics Data System (ADS)

López-Saldaña, Gerardo; Pereira, José Miguel; Aires, Filipe

2013-04-01

One of the main limitations of products derived from remotely-sensed data is the length of the data records available for climate studies. The Advanced Very High Resolution Radiometer (AVHRR) long-term data record (LTDR) comprises a daily global atmospherically-corrected surface reflectance dataset at 0.05° spatial resolution and is available for the 1981-1999 time period. Fire is strong cause of land surface change and emissions of greenhouse gases around the globe. A global long-term identification of areas affected by fire is needed to analyze trends and fire-clime relationships. A burnt area algorithm can be seen as a change point detection problem where there is an abrupt change in the surface reflectance due to the biomass burning. Using the AVHRR-LTDR dataset, a time series of bidirectional reflectance distribution function (BRDF) corrected surface reflectance was generated using the daily observations and constraining the BRDF model inversion using a climatology of BRDF parameters derived from 12 years of MODIS data. The identification of the burnt area was performed using a t-test in the pre- and post-fire reflectance values and a change point detection algorithm, then spectral constraints were applied to flag changes caused by natural land processes like vegetation seasonality or flooding. Additional temporal constraints are applied focusing in the persistence of the affected areas. Initial results for year 1998, which was selected because of a positive fire anomaly, show spatio-temporal coherence but further analysis is required and a formal rigorous validation will be applied using burn scars identified from high-resolution datasets.

Albedo climatology for European land surfaces retrieved from AVHRR data (1990-2014) and its spatial and temporal analysis from green-up to vegetation senescence

NASA Astrophysics Data System (ADS)

Sütterlin, M.; Stöckli, R.; Schaaf, C. B.; Wunderle, S.

2016-07-01

Satellite-based, long-term records of surface albedo characterization that accurately capture spatial and temporal patterns are essential to develop climate models and to monitor the impact of land use changes on the terrestrial energy and water balance. This study presents the first Bidirectional Reflectance Distribution Function (BRDF) and albedo data set derived from the Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage reflectance data acquired on board National Oceanic and Atmospheric Administration and Meteorological Operational platforms from 1990 to 2014 over Europe. The objectives of this paper are to describe the data set's surface albedo climatology and anomalies in the visible, near-infrared, and shortwave broadbands for the growing season months of May to September in order to facilitate utilization of the data by the climate modeling communities. The results demonstrate that the AVHRR BRDF and albedo data have temporal and spatial patterns that are appropriate for the underlying predominant land cover type and accurately reflect the associated climate variation. Visible and near-infrared broadband albedo anomalies are found to be contrasting in most years, and their spatial distributions depict responses of vegetation to climate events (e.g., heat waves). Visible albedo of crops and near-infrared albedo of pastures show a higher interannual variation than respective albedos of other snow-free land covers, while the interannual standard deviations are found to be lower than 0.015. Our findings indicate the importance of taking into account the spectrally distinct variability of surface albedo when analyzing its complex spatiotemporal dynamics in climate-related research.

Satellite infrared imagery for thermal plume contamination monitoring in coastal ecosystem of Cernavoda NPP

NASA Astrophysics Data System (ADS)

Zoran, M. A.; Zoran, Liviu Florin V.; Dida, Adrian I.

2017-10-01

Satellite remote sensing is an important tool for spatio-temporal analysis and surveillance of NPP environment, thermal heat waste of waters being a major concern in many coastal ecosystems involving nuclear power plants. As a test case the adopted methodology was applied for 700x2 MW Cernavoda nuclear power plant (NPP) located in the South-Eastern part of Romania, which discharges warm water affecting coastal ecology. The thermal plume signatures in the NPP hydrological system have been investigated based on TIR (Thermal Infrared) spectral bands of NOAA AVHRR, Landsat TM/ETM+/OLI, and MODIS Terra/Aqua time series satellite data during 1990-2016 period. If NOAA AVHRR data proved the general pattern and extension of the thermal plume signature in Danube river and Black Sea coastal areas, Landsat TM/ETM and MODIS data used for WST (Water Surface Temperature) change detection, mapping and monitoring provided enhanced information about the plume shape, dimension and direction of dispersion in these waters. Thermal discharge from two nuclear reactors cooling is dissipated as waste heat in Danube-Black -Sea Channel and Danube River. From time-series analysis of satellite data during period 1990-2016 was found that during the winter season thermal plume was localized to an area of a few km of NPP, and the mean temperature difference between the plume and non-plume areas was about 1.7 oC. During summer and fall, derived mean temperature difference between the plume and non-plume areas was of about 1.3°C and thermal plume area was extended up to 5- 10 km far along Danube Black Sea Channel.

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

Jiang, Nan; Zhu, Wenquan; Zheng, Zhoutao

The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this research made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses tomore » climate change in the middle and high latitudes of the Northern Hemisphere during 1982–2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. Finally, these results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.« less

MODIS and GIMMS Inferred Northern Hemisphere Spring Greenup in Responses to Preseason Climate

NASA Astrophysics Data System (ADS)

Xu, X.; Riley, W. J.; Koven, C.; Jia, G.

2017-12-01

We compare the discrepancies in Normalized Difference Vegetation Index (NDVI) inferred spring greenup (SG) between Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) instruments carried by the Global Inventory Monitoring and Modeling Studies (GIMMS) in North Hemisphere. The interannual variation of SG inferred by MODIS and GIMMS NDVI is well correlated in the mid to high latitudes. However, the presence of NDVI discrepancies leads to discrepancies in SG with remarkable latitudinal characteristics. MODIS NDVI inferred later SG in the high latitude while earlier SG in the mid to low latitudes, in comparison to GIMMS NDVI inferred SG. MODIS NDVI inferred SG is better correlated to preseason climate. Interannual variation of SG is only sensitive to preseason temperature. The GIMMS SG to temperature sensitivity over two periods implied that the inter-biome SG to temperature sensitivity is relatively stable, but SG to temperature sensitivity decreased over time. Over the same period, MODIS SG to temperature sensitivity is much higher than GIMMS. This decreased sensitivity demonstrated the findings from previous studies with continuous GIMMS NDVI analysis that vegetation growth (indicated by growing season NDVI) to temperature sensitivity is reduced over time and SG advance trend ceased after 2000s. Our results also explained the contradictive findings that SG advance accelerated after 2000s according to the merged GIMMS and MODIS NDVI time series. Despite the found discrepancies, without ground data support, the quality of NDVI and its inferred SG cannot be effectively evaluated. The discrepancies and uncertainties in different NDVI products and its inferred SG may bias the scientific significance of climate-vegetation relationship. The different NDVI products when used together should be first evaluated and harmonized.

The Vegetation Greenness Trend in Canada and US Alaska from 1984-2012 Landsat Data

NASA Technical Reports Server (NTRS)

Ju, Junchang; Masek, Jeffrey G.

2016-01-01

To assess the North American high-latitude vegetation response to the rising temperature, we derived NDVI end for 91.2% of the non-water, non-snow land area of Canada and Alaska using the peak-summer Landsat surface reflectance data of 19842012. Our analysis indicated that 29.4% and 2.9 of the land area of Canada and Alaska showed statistically significant positive (greening) and negative (browning) trends respectively, at significance level p b 0.01, after burned forest areas were masked out. The area with greening trend dominated over that with browning trend for all land cover types. The greening occurred primarily in the tundra of western Alaska, along the north coast of Canada and in northeastern Canada; the most intensive and extensive greening occurred in Quebec and Labrador. The browning occurred mostly in the boreal forests of eastern Alaska. The Landsat-based greenness trend is broadly similar to the 8-km GIMMS AVHRR-based trend for all vegetation zones. However, for tundra, the Landsat data indicated much less extensive greening in Alaska North Slope and much more extensive greening in Quebec and Labrador, and substantially less extensive browning trend in the boreal forests that were free of fire disturbances. These differences call for further validation of the Landsat reflectance and the AVHRR NDVI datasets. Correlation study with local environmental factors, such as topography, glacial history and soil condition, will be needed to understand the heterogeneous greenness change at the Landsat scale.

Land Surface Albedo From EPS/AVHRR : Method For Retrieval and Validation

NASA Astrophysics Data System (ADS)

Jacob, G.

2015-12-01

The scope of Land Surface Analysis Satellite Applications Facility (LSA-SAF) is to increase benefit from EUMETSAT Satellites (MSG and EPS) data by providing added value products for the meteorological and environmental science communities with main applications in the fields of climate modelling, environmental management, natural hazards management, and climate change detection. The MSG/SEVIRI daily albedo product is disseminated operationally by the LSA-SAF processing centre based in Portugal since 2009. This product so-called MDAL covers Europe and Africa includes in the visible, near infrared and shortwave bands at a resolution of 3km at the equator. Recently, an albedo product at 1km so-called ETAL has been built from EPS/AVHRR observations in order to primarily MDAL product outside the MSG disk, while ensuring a global coverage. The methodology is common to MSG and EPS data and relies on the inversion of the BRDF (Bidirectional Reflectance Distribution Function) model of Roujean et al. On a given target, ETAL products exploits the variability of viewing angles whereas MDAL looks at the variations of solar illumination. The comparison of ETAL albedo product against MODIS and MSG/SEVIRI products over the year 2015 is instructive in many ways and shows in general a good agreement between them. The dispersion may be accounted by different factors that will be explained The additional information provided by EPS appears to be particularly beneficial for high latitudes during winter and for snow albedo.

Comparison of Monthly Mean Cloud Fraction and Cloud Optical depth Determined from Surface Cloud Radar, TOVS, AVHRR, and MODIS over Barrow, Alaska

NASA Technical Reports Server (NTRS)

Uttal, Taneil; Frisch, Shelby; Wang, Xuan-Ji; Key, Jeff; Schweiger, Axel; Sun-Mack, Sunny; Minnis, Patrick

2005-01-01

A one year comparison is made of mean monthly values of cloud fraction and cloud optical depth over Barrow, Alaska (71 deg 19.378 min North, 156 deg 36.934 min West) between 35 GHz radar-based retrievals, the TOVS Pathfinder Path-P product, the AVHRR APP-X product, and a MODIS based cloud retrieval product from the CERES-Team. The data sets represent largely disparate spatial and temporal scales, however, in this paper, the focus is to provide a preliminary analysis of how the mean monthly values derived from these different data sets compare, and determine how they can best be used separately, and in combination to provide reliable estimates of long-term trends of changing cloud properties. The radar and satellite data sets described here incorporate Arctic specific modifications that account for cloud detection challenges specific to the Arctic environment. The year 2000 was chosen for this initial comparison because the cloud radar data was particularly continuous and reliable that year, and all of the satellite retrievals of interest were also available for the year 2000. Cloud fraction was chosen as a comparison variable as accurate detection of cloud is the primary product that is necessary for any other cloud property retrievals. Cloud optical depth was additionally selected as it is likely the single cloud property that is most closely correlated to cloud influences on surface radiation budgets.

Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Potter, Christopher; Klooster, Steven; de Carvalho, Claudio Reis; Genovese, Vanessa Brooks; Torregrosa, Alicia; Dungan, Jennifer; Bobo, Matthew; Coughlan, Joseph

2001-05-01

Previous field measurements have implied that undisturbed Amazon forests may represent a substantial terrestrial sink for atmospheric carbon dioxide. We investigated this hypothesis using a regional ecosystem model for net primary production (NPP) and soil biogeochemical cycling. Seasonal and interannual controls on net ecosystem production (NEP) were studied with integration of high-resolution (8-km) multiyear satellite data to characterize Amazon land surface properties over time. Background analysis of temporal and spatial relationships between regional rainfall patterns and satellite observations (for vegetation land cover, fire counts, and smoke aerosol effects) reveals several notable patterns in the model driver data. Autocorrelation analysis for monthly vegetation "greenness" index (normalized difference vegetation index, NDVI) from the advanced very high resolution radiometer (AVHRR) and monthly rainfall indicates a significant lag time correlation of up to 12 months. At lag times approaching 36 months, autocorrelation function (ACF) values did not exceed the 95% confidence interval at locations west of about 47°W, which is near the transition zone of seasonal tropical forest and other (nonforest) vegetation types. Even at lag times of 12 months or less, the location near Manaus (approximately 60°W) represents the farthest western point in the Amazon region where seasonality of rainfall accounts significantly for monthly variations in forest phenology, as observed using NDVI. Comparisons of NDVI seasonal profiles in areas of the eastern Amazon widely affected by fires (as observed from satellite) suggest that our adjusted AVHRR-NDVI captures year-to-year variation in land cover greenness with minimal interference from small fires and smoke aerosols. Ecosystem model results using this newly generated combination of regional forcing data from satellite suggest that undisturbed Amazon forests can be strong net sinks for atmospheric carbon dioxide, particularly during wet (non El Niño) years. However, drought effects during El Niño years can reduce NPP in primary forests of the eastern Amazon by 10-20%, compared to long-term average estimates of regional productivity. Annual NEP for the region is predicted to range from -0.4 Pg C yr-1 (net CO2 source) to 0.5 Pg C yr-1 (net CO2 sink), with large interannual variability over the states of Pará, Maranhao, and Amazonas. As in the case of predicted NPP, it appears that periods of relatively high solar surface irradiance combined with several months of adequate rainfall are required to sustain the forest carbon sink for positive yearly NEP estimates.

Mapping of West Nile Virus Risk in the Northeast United States Using Multi-temporal Meteorological Satellite Data

NASA Astrophysics Data System (ADS)

Backenson, P.; White, D. J.; Eidson, M.; Smith, P. F.; Kramer, L. D.; Morse, D. L.; Tucker, C. J.; Myers, M. F.; Hay, S. I.; Rogers, D. J.

2002-05-01

West Nile Virus (WNV) was first discovered in the United States in September of 1999, after a cluster of cases of human neurological illness was identified in the borough of Queens in New York City. Eventually, that outbreak led to 62 human cases of WNV, including seven deaths. Multiple researchers identified and isolated the virus in several bird and mosquito species in New York. In 2000, an elaborate surveillance system was developed to detect the presence of WNV before human cases occur. This system was largely successful, as the number of WNV detections in birds and mosquitoes increased tremendously, while the number of human cases dropped to 14. In 2001, this surveillance system, and those like it in other states, detected the spread of WNV to over 25 states, with over 50 human cases. Detecting WNV in both birds and mosquitoes, however, is a time and labor intensive task, requiring dedicated staff and resources. In New York it has required hundreds of staff, and millions of dollars. It often takes at least 10 days from the time of specimen collection to the time when results are available. To improve efficiency and cost-effectiveness, proxies are sought to estimate the risk of WNV infection in a given area, preferably on a real-time basis. The project discussed here utilizes remotely sensed meteorological data to accomplish that goal. Data from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of meteorological satellites provided the Normalized Difference Vegetation Index (NDVI) and land surface temperature proxies, as well as elevation, and were temporal Fourier processed. Bird and mosquito data (both infected and uninfected) were added to these images to suggest conditions favoring disease transmission. AVHRR data were also used to analyze changes over time that might be associated with the arrival of WNV in the United States, and with its potential spread over time. Maximum likelihood methods applied to these satellite data allowed production of a series of risk maps that measured the similarity of satellite conditions in a given area to the bird and mosquito data collected on the ground. Both bird and mosquito risk maps showed high kappa indices of agreement. As surveillance teams collect more field data on the ground, these risk maps should become more accurate. These risk maps can then be used by state and local authorities to better direct public health staff and resources, and hopefully prevent large-scale outbreaks of West Nile Virus in the future.

Online Access to Weather Satellite Imagery Through the World Wide Web

NASA Technical Reports Server (NTRS)

Emery, W.; Baldwin, D.

1998-01-01

Both global area coverage (GAC) and high-resolution picture transmission (HRTP) data from the Advanced Very High Resolution Radiometer (AVHRR) are made available to laternet users through an online data access system. Older GOES-7 data am also available. Created as a "testbed" data system for NASA's future Earth Observing System Data and Information System (EOSDIS), this testbed provides an opportunity to test both the technical requirements of an onune'd;ta system and the different ways in which the -general user, community would employ such a system. Initiated in December 1991, the basic data system experienced five major evolutionary changes In response to user requests and requirements. Features added with these changes were the addition of online browse, user subsetting, dynamic image Processing/navigation, a stand-alone data storage system, and movement,from an X-windows graphical user Interface (GUI) to a World Wide Web (WWW) interface. Over Its lifetime, the system has had as many as 2500 registered users. The system on the WWW has had over 2500 hits since October 1995. Many of these hits are by casual users that only take the GIF images directly from the interface screens and do not specifically order digital data. Still, there b a consistent stream of users ordering the navigated image data and related products (maps and so forth). We have recently added a real-time, seven- day, northwestern United States normalized difference vegetation index (NDVI) composite that has generated considerable Interest. Index Terms-Data system, earth science, online access, satellite data.

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.

Effects of spatial resolution and landscape structure on land cover characterization

NASA Astrophysics Data System (ADS)

Yang, Wenli

This dissertation addressed problems in scaling, problems that are among the main challenges in remote sensing. The principal objective of the research was to investigate the effects of changing spatial scale on the representation of land cover. A second objective was to determine the relationship between such effects, characteristics of landscape structure and scaling procedures. Four research issues related to spatial scaling were examined. They included: (1) the upscaling of Normalized Difference Vegetation Index (NDVI); (2) the effects of spatial scale on indices of landscape structure; (3) the representation of land cover databases at different spatial scales; and (4) the relationships between landscape indices and land cover area estimations. The overall bias resulting from non-linearity of NDVI in relation to spatial resolution is generally insignificant as compared to other factors such as influences of aerosols and water vapor. The bias is, however, related to land surface characteristics. Significant errors may be introduced in heterogeneous areas where different land cover types exhibit strong spectral contrast. Spatially upscaled SPOT and TM NDVIs have information content comparable with the AVHRR-derived NDVI. Indices of landscape structure and spatial resolution are generally related, but the exact forms of the relationships are subject to changes in other factors including the basic patch unit constituting a landscape and the proportional area of foreground land cover under consideration. The extent of agreement between spatially aggregated coarse resolution land cover datasets and full resolution datasets changes with the properties of the original datasets, including the pixel size and class definition. There are close relationships between landscape structure and class areas estimated from spatially aggregated land cover databases. The relationships, however, do not permit extension from one area to another. Inversion calibration across different geographic/ecological areas is, therefore, not feasible. Different rules govern the land cover area changes across resolutions when different upscaling methods are used. Special attention should be given to comparison between land cover maps derived using different methods.

Multiplatform observations enabling albedo retrievals with high temporal resolution

NASA Astrophysics Data System (ADS)

Riihelä, Aku; Manninen, Terhikki; Key, Jeffrey; Sun, Qingsong; Sütterlin, Melanie; Lattanzio, Alessio; Schaaf, Crystal

2017-04-01

In this paper we show that combining observations from different polar orbiting satellite families (such as AVHRR and MODIS) is physically justifiable and technically feasible. Our proposed approach will lead to surface albedo retrievals at higher temporal resolution than the state of the art, with comparable or better accuracy. This study is carried out in the World Meteorological Organization (WMO) Sustained and coordinated processing of Environmental Satellite data for Climate Monitoring (SCOPE-CM) project SCM-02 (http://www.scope-cm.org/projects/scm-02/). Following a spectral homogenization of the Top-of-Atmosphere reflectances of bands 1 & 2 from AVHRR and MODIS, both observation datasets are atmospherically corrected with a coherent atmospheric profile and algorithm. The resulting surface reflectances are then fed into an inversion of the RossThick-LiSparse-Reciprocal surface bidirectional reflectance distribution function (BRDF) model. The results of the inversion (BRDF kernels) may then be integrated to estimate various surface albedo quantities. A key principle here is that the larger number of valid surface observations with multiple satellites allows us to invert the BRDF coefficients within a shorter time span, enabling the monitoring of relatively rapid surface phenomena such as snowmelt. The proposed multiplatform approach is expected to bring benefits in particular to the observation of the albedo of the polar regions, where persistent cloudiness and long atmospheric path lengths present challenges to satellite-based retrievals. Following a similar logic, the retrievals over tropical regions with high cloudiness should also benefit from the method. We present results from a demonstrator dataset of a global combined AVHRR-GAC and MODIS dataset covering the year 2010. The retrieved surface albedo is compared against quality-monitored in situ albedo observations from the Baseline Surface Radiation Network (BSRN). Additionally, the combined retrieval dataset is compared against MODIS C6 albedo/BRDF datasets to assess the quality of the multiplatform approach against current state of the art. This approach is not limited to AHVRR and MODIS observations. Provided that the spectral homogenization produces an acceptably good match, any instrument observing the Earth's surface in the visible and near-infrared wavelengths could, in principal, be included to further enhance the temporal resolution and accuracy of the retrievals. The SCOPE-CM initiative provides a potential framework for such expansion in the future.

Lake surface water temperatures of European Alpine lakes (1989-2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set

NASA Astrophysics Data System (ADS)

Riffler, M.; Lieberherr, G.; Wunderle, S.

2015-02-01

Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Consequently, the Global Climate Observing System (GCOS) lists LWT as an essential climate variable. Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years, offering a unique opportunity for satellite-based climate studies. Herein we present a satellite-based lake surface water temperature (LSWT) data set for European water bodies in or near the Alps based on the extensive AVHRR 1 km data record (1989-2013) of the Remote Sensing Research Group at the University of Bern. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and MetOp-A) data. The high accuracy needed for climate related studies requires careful pre-processing and consideration of the atmospheric state. The LSWT retrieval is based on a simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with ERA-interim reanalysis data from the European Centre for Medium-range Weather Forecasts. The resulting LSWTs were extensively compared with in situ measurements from lakes with various sizes between 14 and 580 km2 and the resulting biases and RMSEs were found to be within the range of -0.5 to 0.6 K and 1.0 to 1.6 K, respectively. The upper limits of the reported errors could be rather attributed to uncertainties in the data comparison between in situ and satellite observations than inaccuracies of the satellite retrieval. An inter-comparison with the standard Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature product exhibits RMSEs and biases in the range of 0.6 to 0.9 and -0.5 to 0.2 K, respectively. The cross-platform consistency of the retrieval was found to be within ~ 0.3 K. For one lake, the satellite-derived trend was compared with the trend of in situ measurements and both were found to be similar. Thus, orbital drift is not causing artificial temperature trends in the data set. A comparison with LSWT derived through global sea surface temperature (SST) algorithms shows lower RMSEs and biases for the simulation-based approach. A running project will apply the developed method to retrieve LSWT for all of Europe to derive the climate signal of the last 30 years. The data are available at doi:10.1594/PANGAEA.831007.

Synergistic use of multispectral satellite data for monitoring land surface change

NASA Technical Reports Server (NTRS)

Choudhury, Bhaskar J.

1991-01-01

Observations by the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites were used to compute visible and near infrared reflectances and surface temperature, while passive microwave observations at 37 GHz frequency by the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) on board, respectively, the Nimbus-7 and DMSP-F8 satellites were used to compute polarization difference. These observations were analyzed along transects from rainforest to desert over northern Africa for the period 1979-1987, which included an unprecedented drought during 1984 over the Sahel zone. Model simulations were made to understand the interrelationship among multispectral data.

Evaluating WRF-Chem multi-scale model in simulating aerosol radiative properties over the tropics – A case study over India

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

Seethala, C.; Pandithurai, G.; Fast, Jerome D.

We utilized WRF-Chem multi-scale model to simulate the regional distribution of aerosols, optical properties and its effect on radiation over India for a winter month. The model is evaluated using measurements obtained from upper-air soundings, AERONET sun photometers, various satellite instruments, and pyranometers operated by the Indian Meteorological Department. The simulated downward shortwave flux was overestimated when the effect of aerosols on radiation and clouds was neglected. Downward shortwave radiation from a simulation that included aerosol-radiation interaction processes was 5 to 25 Wm{sup -2} closer to the observations, while a simulation that included aerosol-cloud interaction processes were another 1 tomore » 20 Wm{sup -2} closer to the observations. For the few observations available, the model usually underestimated particulate concentration. This is likely due to turbulent mixing, transport errors and the lack of secondary organic aerosol treatment in the model. The model efficiently captured the broad regional hotspots such as high aerosol optical depth over Indo-Gangetic basin as well as the northwestern and southern part of India. The regional distribution of aerosol optical depth compares well with AVHRR aerosol optical depth and the TOMS aerosol index. The magnitude and wavelength-dependence of simulated aerosol optical depth was also similar to the AERONET observations across India. Differences in surface shortwave radiation between simulations that included and neglected aerosol-radiation interactions were as high as -25 Wm{sup -2}, while differences in surface shortwave radiation between simulations that included and neglect aerosol-radiation-cloud interactions were as high as -30 Wm{sup -2}. The spatial variations of these differences were also compared with AVHRR observation. This study suggests that the model is able to qualitatively simulate the impact of aerosols on radiation over India; however, additional measurements of particulate mass and composition are needed to fully evaluate whether the aerosol precursor emissions are adequate when simulating radiative forcing in the region.« less

On the characterization of vegetation recovery after fire disturbance using Fisher-Shannon analysis and SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series

NASA Astrophysics Data System (ADS)

Lasaponara, Rosa; Lanorte, Antonio; Lovallo, Michele; Telesca, Luciano

2015-04-01

Time series can fruitfully support fire monitoring and management from statistical analysis of fire occurrence (Tuia et al. 2008) to danger estimation (lasaponara 2005), damage evaluation (Lanorte et al 2014) and post fire recovery (Lanorte et al. 2014). In this paper, the time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher-Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher-Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers. Reference Lanorte A, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbanceInternational Journal of Applied Earth Observation and Geoinformation 26 441-446 Lanorte A, M Danese, R Lasaponara, B Murgante 2014 Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis International Journal of Applied Earth Observation and Geoinformation 20, 42-51 Tuia D, F Ratle, R Lasaponara, L Telesca, M Kanevski 2008 Scan statistics analysis of forest fire clusters Communications in Nonlinear Science and Numerical Simulation 13 (8), 1689-1694 Telesca L, R Lasaponara 2006 Pre and post fire behavioral trends revealed in satellite NDVI time series Geophysical Research Letters 33 (14) Lasaponara R 2005 Intercomparison of AVHRR based fire susceptibility indicators for the Mediterranean ecosystems of southern Italy International Journal of Remote Sensing 26 (5), 853-870

Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Zhang, Qingyuan

2016-04-01

Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05° (∼5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers.

Quantifying spatial and temporal variabilities of microwave brightness temperature over the U.S. Southern Great Plains

NASA Technical Reports Server (NTRS)

Choudhury, B. J.; Owe, M.; Ormsby, J. P.; Chang, A. T. C.; Wang, J. R.; Goward, S. N.; Golus, R. E.

1987-01-01

Spatial and temporal variabilities of microwave brightness temperature over the U.S. Southern Great Plains are quantified in terms of vegetation and soil wetness. The brightness temperatures (TB) are the daytime observations from April to October for five years (1979 to 1983) obtained by the Nimbus-7 Scanning Multichannel Microwave Radiometer at 6.6 GHz frequency, horizontal polarization. The spatial and temporal variabilities of vegetation are assessed using visible and near-infrared observations by the NOAA-7 Advanced Very High Resolution Radiometer (AVHRR), while an Antecedent Precipitation Index (API) model is used for soil wetness. The API model was able to account for more than 50 percent of the observed variability in TB, although linear correlations between TB and API were generally significant at the 1 percent level. The slope of the linear regression between TB and API is found to correlate linearly with an index for vegetation density derived from AVHRR data.

The plume of the Yukon River in relation to the oceanography of the Bering Sea

NASA Technical Reports Server (NTRS)

Dean, Kenneson G.; Mcroy, C. Peter; Ahlnas, Kristina; Springer, Alan

1989-01-01

The ecosystem of the northern Bering-Sea shelf was studied using data from the NOAA Very High Resolution Radiometer and AVHRR and the Landsat MSS and Thematic Mapper (TM) in conjunction with shipboard measurements. Emphasis was placed on the influence of the Yukon River on this inner shelf environment and on the evaluation of the utility of the new Landsat TM data for oceanography. It was found that the patterns of water mass distribution obtained from satellite images agreed reasonably well with the areal patterns of temperature, salinity, and phytoplankton distributions. The AVHRR, MSS, and TM data show that the Yukon-River discharge is warmer and more turbid than the surrounding coastal water that originates to the south; thus, the Yukon water contributes to the higher temperatures and lower transmissivity of the coastal water. The high resolution of the TM thermal IR band made it possible to observe complex patterns and structures in the surface water that could not be resolved on previous data sets.

The Van Sant AVHRR image projected onto a rhombicosidodecahedron

NASA Astrophysics Data System (ADS)

Baron, Michael; Morain, Stan

1996-03-01

IDEATION, a design and development corporation, Santa Fe, New Mexico, has modeled Tom Van Sant's ``The Earth From Space'' image to a rhombicosidodecahedron. ``The Earth from Space'' image, produced by the Geosphere® Project in Santa Monica, California, was developed from hundreds of AVHRR pictures and published as a Mercator projection. IDEATION, utilizing a digitized Robinson Projection, fitted the image to foldable, paper components which, when interconnected by means of a unique tabular system, results in a rhombicosidodecahedron representation of the Earth exposing 30 square, 20 triangular, and 12 pentagonal faces. Because the resulting model is not spherical, the borders of the represented features were rectified to match the intersecting planes of the model's faces. The resulting product will be licensed and commercially produced for use by elementary and secondary students. Market research indicates the model will be used in both the demonstration of geometric principles and the teaching of fundamental spatial relations of the Earth's lands and oceans.

An atlas of monthly mean distributions of SSMI surface wind speed, AVHRR/2 sea surface temperature, AMI surface wind velocity, TOPEX/POSEIDON sea surface height, and ECMWF surface wind velocity during 1993

NASA Technical Reports Server (NTRS)

Halpern, D.; Fu, L.; Knauss, W.; Pihos, G.; Brown, O.; Freilich, M.; Wentz, F.

1995-01-01

The following monthly mean global distributions for 1993 are presented with a common color scale and geographical map: 10-m height wind speed estimated from the Special Sensor Microwave Imager (SSMI) on a United States (U.S.) Air Force Defense Meteorological Satellite Program (DMSP) spacecraft; sea surface temperature estimated from the Advanced Very High Resolution Radiometer (AVHRR/2) on a U.S. National Oceanic and Atmospheric Administration (NOAA) satellite; 10-m height wind speed and direction estimated from the Active Microwave Instrument (AMI) on the European Space Agency (ESA) European Remote Sensing (ERS-1) satellite; sea surface height estimated from the joint U.S.-France Topography Experiment (TOPEX)/POSEIDON spacecraft; and 10-m height wind speed and direction produced by the European Center for Medium-Range Weather Forecasting (ECMWF). Charts of annual mean, monthly mean, and sampling distributions are displayed.

Methodology for interpretation of SST retrievals using the AVHRR split window algorithm

NASA Technical Reports Server (NTRS)

Barbieri, R. W.; Mcclain, C. R.; Endres, D. L.

1983-01-01

Intercomparisons of sea surface temperature (SST) products derived from the operational NOAA-7 AVHRR-II algorithm and in situ observations are made. The 1982 data sets consist of ship survey data during the winter from the Mid-Atlantic Bight (MAB), ship and buoy measurements during April and September in the Gulf of Mexico and shipboard observations during April off the N.W. Spanish coast. The analyses included single pixel comparisons and the warmest pixel technique for 2 x 2 pixel and 10 x 10 pixel areas. The reason for using multi-pixel areas was for avoiding cloud contaminated pixels in the vicinity of the field measurements. Care must be taken when applying the warmest pixel technique near oceanic fronts. The Gulf of Mexico results clearly indicate a persistent degradation in algorithm accuracy due to El Chichon aerosols. The MAB and Spanish data sets indicate that very accurate estimates can be achieved if care is taken to avoid clouds and oceanic fronts.

Distributed Active Archive Center

NASA Technical Reports Server (NTRS)

Bodden, Lee; Pease, Phil; Bedet, Jean-Jacques; Rosen, Wayne

1993-01-01

The Goddard Space Flight Center Version 0 Distributed Active Archive Center (GSFC V0 DAAC) is being developed to enhance and improve scientific research and productivity by consolidating access to remote sensor earth science data in the pre-EOS time frame. In cooperation with scientists from the science labs at GSFC, other NASA facilities, universities, and other government agencies, the DAAC will support data acquisition, validation, archive and distribution. The DAAC is being developed in response to EOSDIS Project Functional Requirements as well as from requirements originating from individual science projects such as SeaWiFS, Meteor3/TOMS2, AVHRR Pathfinder, TOVS Pathfinder, and UARS. The GSFC V0 DAAC has begun operational support for the AVHRR Pathfinder (as of April, 1993), TOVS Pathfinder (as of July, 1993) and the UARS (September, 1993) Projects, and is preparing to provide operational support for SeaWiFS (August, 1994) data. The GSFC V0 DAAC has also incorporated the existing data, services, and functionality of the DAAC/Climate, DAAC/Land, and the Coastal Zone Color Scanner (CZCS) Systems.

A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description, Validation, and Case Study

NASA Technical Reports Server (NTRS)

Claverie, Martin; Matthews, Jessica L.; Vermote, Eric F.; Justice, Christopher O.

2016-01-01

In- land surface models, which are used to evaluate the role of vegetation in the context ofglobal climate change and variability, LAI and FAPAR play a key role, specifically with respect to thecarbon and water cycles. The AVHRR-based LAIFAPAR dataset offers daily temporal resolution,an improvement over previous products. This climate data record is based on a carefully calibratedand corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitablefor climate studies. It spans from mid-1981 to the present. Further, this operational dataset is availablein near real-time allowing use for monitoring purposes. The algorithm relies on artificial neuralnetworks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparisonwith MODIS products and in situ data show the dataset is consistent and reliable with overalluncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect isobserved in the broadleaf forest biomes with high LAI (greater than 4.5) and FAPAR (greater than 0.8) values.

PC-SEAPAK user's guide, version 4.0

NASA Technical Reports Server (NTRS)

Mcclain, Charles R.; Fu, Gary; Darzi, Michael; Firestone, James K.

1992-01-01

PC-SEAPAK is designed to provide a complete and affordable capability for processing and analysis of NOAA Advanced Very High Resolution Radiometer (AVHRR) and Nimbus-7 Coastal Zone Color Scanner (CZCS) data. Since the release of version 3.0 over a year ago, significant revisions were made to the AVHRR and CZCS programs and to the statistical data analysis module, and a number of new programs were added. This new version has 114 procedures listed in its menus. The package continues to emphasize user-friendliness and interactive data analysis. Additionally, because the scientific goals of the ocean color research being conducted have shifted to larger space and time scales, batch processing capabilities were enhanced, allowing large quantities of data to be easily ingested and analyzed. The development of PC-SEAPAK was paralled by two other activities that were influential and assistive: the global CZCS processing effort at GSFC and the continued development of VAX-SEAPAK. SEAPAK incorporates the instrument calibration and support all levels of data available from the CZCS archive.

Satellite observation of lake ice as a climate indicator - Initial results from statewide monitoring in Wisconsin

NASA Technical Reports Server (NTRS)

Wynne, Randolph H.; Lillesand, Thomas M.

1993-01-01

The research reported herein focused on the general hypothesis that satellite remote sensing of large-area, long-term trends in lake ice phenology (formation and breakup) is a robust, integrated measure of regional and global climate change. To validate this hypothesis, we explored the use of data from the Advanced Very High Resolution Radiometer (AVHRR) to discriminate the presence and extent of lake ice during the winter of 1990-1991 on the 45 lakes and reservoirs in Wisconsin with a surface area greater than 1,000 hectares. Our results suggest both the feasibility of using the AVHRR to determine the date of lake ice breakup as well as the strong correlation (R= -0.87) of the date so derived with local surface-based temperature measurements. These results suggest the potential of using current and archival satellite data to monitor changes in the date of lake ice breakup as a means of detecting regional 'signals' of greenhouse warming.

Satellite measurements of large-scale air pollution - Measurements of forest fire smoke

NASA Technical Reports Server (NTRS)

Ferrare, Richard A.; Kaufman, Yoram J.; Fraser, Robert S.

1990-01-01

The transport, optical properties, total mass, and removal of smoke produced by forest fires in western Canada during late July and early August 1982 are studied using NOAA 7 AVHRR data. Color composite imagery is produced to track the movement of the smoke over Canada and the U.S. as the smoke traveled thousands of km from the source region. Smoke optical thickness, particle size, and single scattering albedo are computed using radiances measured by AVHRR bands 1 and 2. Results show that smoke optical thickness ranged from less that 0.1 to greater than 3.7 and the geometric mean mass radii ranged from 300 to 900 nm. The smoke single scattering albedo ranged from 0.9 to nearly 1.0. The total smoke mass over the eastern U.S. ranged from 0.1 to 0.5 Tg, which is close to the 0.5 Tg estimated from the forest fuel content. The smoke lifetime is estimated to be between 15 and 20 days.

Global Single and Multiple Cloud Classification with a Fuzzy Logic Expert System

NASA Technical Reports Server (NTRS)

Welch, Ronald M.; Tovinkere, Vasanth; Titlow, James; Baum, Bryan A.

1996-01-01

An unresolved problem in remote sensing concerns the analysis of satellite imagery containing both single and multiple cloud layers. While cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget, most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. Coakley (1983) used a spatial coherence method to determine whether a region contained more than one cloud layer. Baum et al. (1995) developed a scheme for detection and analysis of daytime multiple cloud layers using merged AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High-resolution Infrared Radiometer Sounder) data collected during the First ISCCP Regional Experiment (FIRE) Cirrus 2 field campaign. Baum et al. (1995) explored the use of a cloud classification technique based on AVHRR data. This study examines the feasibility of applying the cloud classifier to global satellite imagery.

Cloud cover analysis with Arctic Advanced Very High Resolution Radiometer data. II - Classification with spectral and textural measures

NASA Technical Reports Server (NTRS)

Key, J.

1990-01-01

The spectral and textural characteristics of polar clouds and surfaces for a 7-day summer series of AVHRR data in two Arctic locations are examined, and the results used in the development of a cloud classification procedure for polar satellite data. Since spatial coherence and texture sensitivity tests indicate that a joint spectral-textural analysis based on the same cell size is inappropriate, cloud detection with AVHRR data and surface identification with passive microwave data are first done on the pixel level as described by Key and Barry (1989). Next, cloud patterns within 250-sq-km regions are described, then the spectral and local textural characteristics of cloud patterns in the image are determined and each cloud pixel is classified by statistical methods. Results indicate that both spectral and textural features can be utilized in the classification of cloudy pixels, although spectral features are most useful for the discrimination between cloud classes.

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

NASA Astrophysics Data System (ADS)

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

2010-05-01

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

Diversity of ecological communities of the United States

Treesearch

J.D. Wickham; T.G. Wade; K.B. Jones; Kurt H. Riitters; R.V. O' Neill

1995-01-01

Biodiversity, although recognized as encompassing several levels of biological organization, is often thought of as species diversity. Three diversity estimates were calculated for the conterminous United States using satellite data acquired from the Advanced Very High Resolution Radiometer (AVHRR): land cover richness, vegetation richness, and vegetation clustering....

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

Tan, Jianguang; Piao, Shilong; Chen, Anping

Over the last century the Northern Hemisphere has experienced rapid climate warming, but this warming has not been evenly distributed seasonally, as well as diurnally. The implications of such seasonal and diurnal heterogeneous warming on regional and global vegetation photosynthetic activity, however, are still poorly understood. Here, we investigated for different seasons how photosynthetic activity of vegetation correlates with changes in seasonal daytime and night-time temperature across the Northern Hemisphere (>30°N), using Normalized Difference Vegetation Index (NDVI) data from 1982 to 2011 obtained from the Advanced Very High Resolution Radiometer (AVHRR). Our analysis revealed some striking seasonal differences in themore » response of NDVI to changes in day- versus night-time temperatures. For instance, while higher daytime temperature (T max) is generally associated with higher NDVI values across the boreal zone, the area exhibiting a statistically significant positive correlation between T max and NDVI is much larger in spring (41% of area in boreal zone – total area 12.6 × 10 6 km 2) than in summer and autumn (14% and 9%, respectively). In contrast to the predominantly positive response of boreal ecosystems to changes in T max, increases in T max tended to negatively influence vegetation growth in temperate dry regions, particularly during summer. Changes in night-time temperature (T min) correlated negatively with autumnal NDVI in most of the Northern Hemisphere, but had a positive effect on spring and summer NDVI in most temperate regions (e.g., Central North America and Central Asia). Such divergent covariance between the photosynthetic activity of Northern Hemispheric vegetation and day- and night-time temperature changes among different seasons and climate zones suggests a changing dominance of ecophysiological processes across time and space. Lastly, understanding the seasonally different responses of vegetation photosynthetic activity to diurnal temperature changes, which have not been captured by current land surface models, is important for improving the performance of next generation regional and global coupled vegetation-climate models« less

Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2003-01-01

The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989–2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI–SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.

The Impact of Livestock Grazing on US Rangeland Productive Capacity from 1981 to 2009

NASA Astrophysics Data System (ADS)

Washington-Allen, R. A.; Kulawardhana, R. W.; Reeves, M. C.; Mitchell, J. E.

2010-12-01

Humans have appropriated an estimated 20% of global net primary productivity (NPP) and 38% of this population is dependant on the $900 billion yr-1 in ecosystem services from drylands that cover 41% of the terrestrial surface. Commercial and subsistence livestock (cattle, sheep, and goats) grazing is embedded in this appropriation and has been implicated in dryland degradation. However, the extent of dryland degradation is unknown with estimates ranging from 10 - 80%. As a solution to this problem, we derived rangeland above-ground biomass or the forage available for grazing from a 1981 - 2009 time-series of 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) NPP and 8-km Advanced Very High Resolution Radiometer Global Inventory Modeling and Mapping Studies (AVHRR-GIMMS) annual summed normalized difference vegetation index (NDVI) data. We derived the forage required by livestock (cattle, sheep, and goats) at the county and state spatial scales from annual agricultural census records that were collected by the United States Department of Agriculture’s National Agricultural Statistics Service (USDA-NASS) from 1981 - 2009. We found that in 2002 US rangelands covered some 257 million ha and that grazing livestock reached 216 million tons of biomass appropriated though only an estimated 149 million tons were available in the US. Consequently, the percentage US rangeland impacted by livestock appropriation of NPP (distributed at the state spatial scale) was 19%. This hotspot was primarily located in southwestern Arizona.

Assessing the Influence of Precipitation Variability on the Vegetation Dynamics of the Mediterranean Rangelands using NDVI and Machine Learning

NASA Astrophysics Data System (ADS)

Daliakopoulos, Ioannis; Tsanis, Ioannis

2017-04-01

Mitigating the vulnerability of Mediterranean rangelands against degradation is limited by our ability to understand and accurately characterize those impacts in space and time. The Normalized Difference Vegetation Index (NDVI) is a radiometric measure of the photosynthetically active radiation absorbed by green vegetation canopy chlorophyll and is therefore a good surrogate measure of vegetation dynamics. On the other hand, meteorological indices such as the drought assessing Standardised Precipitation Index (SPI) are can be easily estimated from historical and projected datasets at the global scale. This work investigates the potential of driving Random Forest (RF) models with meteorological indices to approximate NDVI-based vegetation dynamics. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The updated E-OBS-v13.1 dataset of the ENSEMBLES EU FP6 program provides observed monthly meteorological input to estimate SPI over the Mediterranean rangelands. RF models are trained to depict vegetation dynamics using the latest version (3g.v1) of the third generation GIMMS NDVI generated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensors. Analysis is conducted for the period 1981-2015 at a gridded spatial resolution of 25 km. Preliminary results demonstrate the potential of machine learning algorithms to effectively mimic the underlying physical relationship of drought and Earth Observation vegetation indices to provide estimates based on precipitation variability.

Estimating release of carbon from 1990 and 1991 forest fires in Alaska

NASA Technical Reports Server (NTRS)

Kaisischke, Eric S.; French, Nancy H. F.; Bourgeau-Chavez, Laura L.; Christensen, N. L., Jr.

1995-01-01

An improved method to estimate the amounts of carbon released during fires in the boreal forest zone of Alaska in 1990 and 1991 is described. This method divides the state into 64 distinct physiographic regions and estimates areal extent of five different land covers: two forest types, peat land, tundra, and nonvegetated. The areal extent of each cover type was estimated from a review of topographic maps of each region and observations on the distribution of foreat types within the state. Using previous observations and theoretical models for the two forest types found in interior Alaska, models of biomass accumulation as a function of stand age were developed. Stand age distributions for each region were determined using a statistical distribution based on fire frequency, which was from available long-term historical records. Estimates of the degree of biomass combusted were based on recent field observations as well as research reported in the literature. The location and areal extent of fires in this region for 1990 and 1991 were based on both field observations and analysis of satellite (advanced very high resolution radiometer (AVHRR)) data sets. Estimates of average carbon release for the two study years ranged between 2.54 and 3.00 kg/sq m, which are 2.2 to 2.6 times greater than estimates used in other studies of carbon release through biomass burning in boreal forests. Total average annual carbon release for the two years ranged between 0.012 and 0.018 Pg C/yr, with the lower value resulting from the AVHRR estimates of fire location and area.

Classification methods for monitoring Arctic sea ice using OKEAN passive/active two-channel microwave data

USGS Publications Warehouse

Belchansky, Gennady I.; Douglas, David C.

2000-01-01

This paper presents methods for classifying Arctic sea ice using both passive and active (2-channel) microwave imagery acquired by the Russian OKEAN 01 polar-orbiting satellite series. Methods and results are compared to sea ice classifications derived from nearly coincident Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) image data of the Barents, Kara, and Laptev Seas. The Russian OKEAN 01 satellite data were collected over weekly intervals during October 1995 through December 1997. Methods are presented for calibrating, georeferencing and classifying the raw active radar and passive microwave OKEAN 01 data, and for correcting the OKEAN 01 microwave radiometer calibration wedge based on concurrent 37 GHz horizontal polarization SSM/I brightness temperature data. Sea ice type and ice concentration algorithms utilized OKEAN's two-channel radar and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter, together with a priori knowledge about the scattering parameters and natural emissivities of basic sea ice types. OKEAN 01 data and algorithms tended to classify lower concentrations of young or first-year sea ice when concentrations were less than 60%, and to produce higher concentrations of multi-year sea ice when concentrations were greater than 40%, when compared to estimates produced from SSM/I data. Overall, total sea ice concentration maps derived independently from OKEAN 01, SSM/I, and AVHRR satellite imagery were all highly correlated, with uniform biases, and mean differences in total ice concentration of less than four percent (sd<15%).

An overview of sensor calibration inter-comparison and applications

USGS Publications Warehouse

Xiong, Xiaoxiong; Cao, Changyong; Chander, Gyanesh

2010-01-01

Long-term climate data records (CDR) are often constructed using observations made by multiple Earth observing sensors over a broad range of spectra and a large scale in both time and space. These sensors can be of the same or different types operated on the same or different platforms. They can be developed and built with different technologies and are likely operated over different time spans. It has been known that the uncertainty of climate models and data records depends not only on the calibration quality (accuracy and stability) of individual sensors, but also on their calibration consistency across instruments and platforms. Therefore, sensor calibration inter-comparison and validation have become increasingly demanding and will continue to play an important role for a better understanding of the science product quality. This paper provides an overview of different methodologies, which have been successfully applied for sensor calibration inter-comparison. Specific examples using different sensors, including MODIS, AVHRR, and ETM+, are presented to illustrate the implementation of these methodologies.

Biological-Physical Coupling in the Gulf of Maine: Satellite and Model Studies of Phytoplankton Variability

NASA Technical Reports Server (NTRS)

Thomas, Andrew C.; Chai, F.; Townsend, D. W.; Xue, H.

2002-01-01

The goals of this project were to acquire, process, QC, archive and analyze SeaWiFS chlorophyll fields over the Gulf of Maine and Scotia Shelf region. The focus of the analysis effort was to calculate and quantify seasonality and interannual. variability of SeaWiFS-measured phytoplankton biomass in the study area and compare these to physical forcing and hydrography. An additional focus within this effort was on regional differences within the heterogeneous biophysical regions of the Gulf of Maine / Scotia Shelf. Overall goals were approached through the combined use of SeaWiFS and AVHRR data and the development of a coupled biology-physical numerical model.

Optimal directional view angles for remote-sensing missions

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Holben, B. N.; Tucker, C. J.; Newcomb, W. W.

1984-01-01

The present investigation is concerned with the directional, off-nadir viewing of terrestrial scenes using remote-sensing systems from aircraft and satellite platforms, taking into account advantages of such an approach over strictly nadir viewing systems. Directional reflectance data collected for bare soil and several different vegetation canopies in NOAA-7 AVHRR bands 1 and 2 were analyzed. Optimum view angles were recommended for two strategies. The first strategy views the utility of off-nadir measurements as extending spatial and temporal coverage of the target area. The second strategy views the utility of off-nadir measurements as providing additional information about the physical characteristics of the target. Conclusions regarding the two strategies are discussed.

The Landsat Phenology Study (LaPS): Preliminary CONUS Results for 2008

NASA Astrophysics Data System (ADS)

Henebry, Geoffrey M.; Roy, David P.; Ju, Junchang; Kovalskyy, Valeriy

2010-05-01

Most studies of land surface phenology (LSP) have used time series derived from moderate spatial resolution satellite sensor data (e.g., AVHRR, MODIS, VEGETATION) because these data are freely available and because they provide an acceptable trade-off between higher, near daily, temporal frequency of observation needed to reduce cloud contamination against lower (500m-5km) spatial resolution. The recent opening of the USGS Landsat archive to web-enabled access presents the opportunity to explore how well Landsat time series can portray LSPs at high spatial resolution. The NASA Web-enabled Landsat data (WELD) project (http://landsat.usgs.gov/WELD.php) has produced 30m composited mosaics for all the conterminous US (CONUS) from Landsat 7 ETM+ data. The composited mosaics are generated on monthly, seasonal, and annual basis and include spectral reflectance, normalized difference vegetation index (NDVI), and the acquisition date of each composited pixel. The WELD compositing approach is designed to select valid land surface observations with minimal cloud, snow, and atmospheric contamination. We extracted 30m pixel time series from the twelve monthly WELD composited mosaics for 2008 at 320 locations across the CONUS where we have ground phenological observations that are heterogeneous with respect to the types of plants observed, the phenophases recorded (predominantly spring green-up) and the ground sampling protocols used. The ground data came from several sources, including the cloned lilac/honeysuckle network, the Phenocam network, five LTER sites (H.J. Andrews, Harvard Forest, Jornada, Konza Prairie, and Sevilleta), and a private woodlot in Maine. Temporal profiles of the 30m WELD Landsat NDVI, the green NDVI (GNDVI), the normalized difference infrared index (NDII) derived from the composited reflectances, are compared to the ground observations. Results show that (i) inclusion of the Landsat acquisition date for each pixel improves the characterization of the LSP, (ii) the use of the NDII and GNDVI in conjunction with the NDVI improves identification of the onset both of canopy development and senescence, and (iii) the WELD compositing approach and the resulting mosaics provide a rich new data source for phenological investigations.

The Landsat Phenology Study (LaPS): Preliminary CONUS Results for 2008

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Roy, D.; Ju, J.; Kovalskyy, V.

2009-12-01

Most studies of land surface phenology (LSP) have used time series derived from moderate spatial resolution satellite sensor data (e.g., AVHRR, MODIS, VEGETATION) because these data are freely available and because they provide an acceptable trade-off between higher, near daily, temporal frequency of observation needed to reduce cloud contamination against lower (500m-5km) spatial resolution. The recent opening of the USGS Landsat archive to web-enabled access presents the opportunity to explore how well Landsat time series can portray LSPs at high spatial resolution. The NASA Web-enabled Landsat data (WELD) project has produced 30m composited mosaics for all the conterminous US (CONUS) from Landsat 7 ETM+ data. The composited mosaics are generated on monthly, seasonal, and annual basis and include spectral reflectance, normalized difference vegetation index (NDVI), and the acquisition date of each composited pixel. The WELD compositing approach is designed to select valid land surface observations with minimal cloud, snow, and atmospheric contamination. We extracted 30m pixel time series from the twelve monthly WELD composited mosaics for 2008 at 320 locations across the CONUS where we have ground phenological observations that are heterogeneous with respect to the types of plants observed, the phenophases recorded (predominantly spring green-up) and the ground sampling protocols used. The ground data came from several sources, including the cloned lilac/honeysuckle network, the Phenocam network, five LTER sites (H.J. Andrews, Harvard Forest, Jornada, Konza Prairie, and Sevilleta), and a private woodlot in New Hampshire. Temporal profiles of the 30m WELD Landsat NDVI and the normalized difference infrared index (NDII) derived from the composited reflectances, are compared to the ground observations. Results show that (i) inclusion of the Landsat acquisition date for each pixel improves the characterization of the LSP, (ii) the use of the NDII in conjunction with the NDVI improves identification of the onset both of canopy development and senescence, and (iii) the WELD compositing approach and the resulting mosaics provide a rich new data source for phenological investigations.

Variation and Trends of Landscape Dynamics, Land Surface Phenology and Net Primary Production of the Appalachian Mountains

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

Wang, Yeqiao; Zhao, Jianjun; Zhou, Yuyu

2012-12-15

The gradients of the Appalachian Mountains in elevations and latitudes provide a unique regional perspective of landscape variations in the eastern United States and a section of the southeastern Canada. This study reveals patterns and trends of landscape dynamics, land surface phenology and ecosystem production along the Appalachian Mountains using time series data from Global Inventory Modeling and Mapping Studies (GIMMS) and AVHRR Global Production Efficiency Model (GloPEM) datasets. We analyzed the spatial and temporal patterns of Normalized Difference Vegetation Index (NDVI), length of growing season (LOS) and net primary production (NPP) of selected ecoregions along the Appalachian Mountains regions.more » We compared the results out of the Appalachian Mountains regions in different spatial contexts including the North America and the Appalachian Trail corridor area. To reveal latitudinal variations we analyzed data and compared the results between 30°N-40°N and 40°N-50°N latitudes. The result revealed significant decreases in annual peak NDVI in the Appalachian Mountains regions. The trend for the Appalachian Mountains regions was -0.0018 (R2=0.55, P<0.0001) NDVI unit decrease per year during 25 years between 1982 and 2006. The LOS had prolonged 0.3 day yr-1 during 25 years over the Appalachian Mountains regions. The NPP increased by 2.68 gC m-2yr-2 in Appalachian Mountains regions from 1981 to 2000. The comparison with the North America reveals the effects of topography and ecosystem compositions of the Appalachian Mountains. The comparison with the Appalachian Trail corridor area provides a regional mega-transect view of the measured variables.« less

RST (Robust Satellite Techniques) analysis for monitoring earth emitted radiation in seismically active area of California (US): a long term (2006-2011) analysis of GOES-W/IMAGER thermal data

NASA Astrophysics Data System (ADS)

Tramutoli, V.; Armandi, B.; Filizzola, C.; Genzano, N.; Lisi, M.; Paciello, R.; Pergola, N.

2014-12-01

More than ten years of applications of the RST (Robust Satellite Techniques) methodology for monitoring earthquake prone area by using satellite TIR(Thermal InfraRed) data, have shown the ability of this approach to discern anomalous TIR signals possibly associated to seismic activity from normal fluctuations of Earth's thermal emission related to other causes independent on the earthquake occurrence. The RST approach was already tested in the case of tens of earthquakes occurred in different continents (Europe, Asia, America and Africa), in various geo-tectonic settings (compressive, extensional and transcurrent) and with a wide range of magnitudes (from 4.0 to 7.9), by analyzing time series of TIR images acquired by sensors on board of polar (like NOAA/AVHRR, EOS/MODIS) and geostationary satellites (like MFG/MVIRI, MSG/SEVIRI, GOES/IMAGER). In addition RST method has been independently tested by several researchers around the world as well as in the framework of several projects funded by different national space agencies (like the Italian ASI, the U.S. NASA and the German DLR) and recently during the EC-FP7 projectPRE-EARTHQUAKES (www.pre-earthquakes.org),which was devoted to study the earthquake precursors using satellite techniques. This paper will show the results of RST analysis on 6 years (2006-2011)of TIR satellite record collected by GOES-W/IMAGER over Southern part United State (California).Results will be discussed particularly in the prospective of an integrated approach devoted to systematically collectand analyze in real-time, independent observations for a time-Dependent Assessment of Seismic Hazard (t-DASH).

Complex responses of spring alpine vegetation phenology to snow cover dynamics over the Tibetan Plateau, China.

PubMed

Wang, Siyuan; Wang, Xiaoyue; Chen, Guangsheng; Yang, Qichun; Wang, Bin; Ma, Yuanxu; Shen, Ming

2017-09-01

Snow cover dynamics are considered to play a key role on spring phenological shifts in the high-latitude, so investigating responses of spring phenology to snow cover dynamics is becoming an increasingly important way to identify and predict global ecosystem dynamics. In this study, we quantified the temporal trends and spatial variations of spring phenology and snow cover across the Tibetan Plateau by calibrating and analyzing time series of the NOAA AVHRR-derived normalized difference vegetation index (NDVI) during 1983-2012. We also examined how snow cover dynamics affect the spatio-temporal pattern of spring alpine vegetation phenology over the plateau. Our results indicated that 52.21% of the plateau experienced a significant advancing trend in the beginning of vegetation growing season (BGS) and 34.30% exhibited a delaying trend. Accordingly, the snow cover duration days (SCD) and snow cover melt date (SCM) showed similar patterns with a decreasing trend in the west and an increasing trend in the southeast, but the start date of snow cover (SCS) showed an opposite pattern. Meanwhile, the spatial patterns of the BGS, SCD, SCS and SCM varied in accordance with the gradients of temperature, precipitation and topography across the plateau. The response relationship of spring phenology to snow cover dynamics varied within different climate, terrain and alpine plant community zones, and the spatio-temporal response patterns were primarily controlled by the long-term local heat-water conditions and topographic conditions. Moreover, temperature and precipitation played a profound impact on diverse responses of spring phenology to snow cover dynamics. Copyright © 2017 Elsevier B.V. All rights reserved.

Global Analysis of Empirical Relationships Between Annual Climate and Seasonality of NDVI

NASA Technical Reports Server (NTRS)

Potter, C. S.; Brooks, V.

1997-01-01

This paper describes the use of satellite data to calibrate a new climate-vegetation greenness relationship for global change studies. We examined statistical relationships between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes If the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980's in order to refine our understanding of intra-annual patterns and global abiotic controls on natural vegetation dynamics. Multiple linear regression results using global 1o gridded data sets suggest that three climate indexes: degree days (growing/chilling), annual precipitation total, and an annual moisture index together can account to 70-80 percent of the geographic variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same annual climate index values from the previous year explains no substantial additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes is closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from lo grid cells mapped as greater than 25 percent inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI for several different years at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes are not accurately predicted are mainly high latitude zones, mixed and disturbed vegetation types, and other remote locations where climate station data are sparse.

Accuracy of sea ice temperature derived from the advanced very high resolution radiometer

NASA Technical Reports Server (NTRS)

Yu, Y.; Rothrock, D. A.; Lindsay, R. W.

1995-01-01

The accuracy of Arctic sea ice surface temperatures T(sub s) dericed from advanced very high resolution radiometer (AVHRR) thermal channels is evaluated in the cold seasons by comparing them with surface air temperatures T(sub air) from drifting buoys and ice stations. We use three different estimates of satellite surface temperatures, a direct estimate from AVHRR channel 4 with only correction for the snow surface emissivity but not for the atmosphere, a single-channel regression of T(sub s) with T(sub air), and Key and Haefliger's (1992) polar multichannel algorithm. We find no measurable bias in any of these estimates and few differences in their statistics. The similar performance of all three methods indicates that an atmospheric water vapor correction is not important for the dry winter atmosphere in the central Arctic, given the other sources of error that remain in both the satellite and the comparison data. A record of drifting station data shows winter air temperature to be 1.4 C warmer than the snow surface temperature. `Correcting' air temperatures to skin temperature by subtracting this amount implies that satellite T(sub s) estimates are biased warm with respect to skin temperature by about this amount. A case study with low-flying aircraft data suggests that ice crystal precipitation can cause satellite estimates of T(sub s) to be several degrees warmer than radiometric measurements taken close to the surface, presumably below the ice crystal precipitation layer. An analysis in which errors are assumed to exist in all measurements, not just the satellite measurements, gives a standard deviation in the satellite estimates of 0.9 C, about half the standard deviation of 1.7 C estimated by assigning all the variation between T(sub s) and T(sub air) to errors in T(sub s).

On the potential of RST approach for a continuous monitoring of flooded areas

NASA Astrophysics Data System (ADS)

Faruolo, Mariapia; Coviello, Irina; Lacava, Teodosio; Pergola, Nicola; Tramutoli, Valerio

2010-05-01

In recent decades many efforts have been made in the field of remote sensing for the management of flood risk. In fact, among all natural disasters floods are probably the most frequent, causing high human suffering and large losses. All activities designed to mitigate and manage flood risk, in order to be effective and to help civil protection agencies in limiting losses of life, human suffering and damages, need of timely information about the onset of floods, their extent, intensity and duration. At present, sensors aboard meteorological satellites, mainly thanks to their high temporal resolution, may furnish frequent and updated images, ensuring a continuous monitoring of areas involved by a flood. In particular, optical instruments on board polar satellites, like NOAA-AVHRR (National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer) and more recently EOS-MODIS (Earth Observing System-Moderate Resolution Imaging Spectroradiometer) have been used for dynamic flood monitoring. A robust methodology for satellite based flood monitoring and detection, named RST (Robust Satellite Technique), has been recently developed and implemented using data acquired by AVHRR and MODIS to identify flooded areas with reliability and timeliness. Such an approach, based on a multi-temporal analysis of co-located satellite records and an automatic change detection scheme, has been used to analyze floods occurred in different geographic areas and observational conditions. In detail, in order to identify flooded areas within the region of interest, the spectral behavior of water in the visible (VIS) and near infrared (NIR) bands of such satellite systems has been successfully exploited. Starting from these satisfactory results, the main purpose of this paper is to show, in the case of several flooding events occurred recently in different parts of the world, the achievements arising from the use of such methodology also to data acquired in the thermal infrared (TIR) region in order to guarantee a continuous monitoring of flooded areas both during night and day.

Automated cloud classification with a fuzzy logic expert system

NASA Technical Reports Server (NTRS)

Tovinkere, Vasanth; Baum, Bryan A.

1993-01-01

An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

Optical remote sensing for forest area estimation

Treesearch

Randolph H. Wynne; Richard G. Oderwald; Gregory A. Reams; John A. Scrivani

2000-01-01

The air photo dot-count method is now widely and successfully used for estimating operational forest area in the USDA Forest Inventory and Analysis (FIA) program. Possible alternatives that would provide for more frequent updates, spectral change detection, and maps of forest area include the AVHRR calibration center technique and various Landsat TM classification...

NASA Cold Land Processes Experiment (CLPX 2002/03): Spaceborne remote sensing

Treesearch

Robert E. Davis; Thomas H. Painter; Don Cline; Richard Armstrong; Terry Haran; Kyle McDonald; Rick Forster; Kelly Elder

2008-01-01

This paper describes satellite data collected as part of the 2002/03 Cold Land Processes Experiment (CLPX). These data include multispectral and hyperspectral optical imaging, and passive and active microwave observations of the test areas. The CLPX multispectral optical data include the Advanced Very High Resolution Radiometer (AVHRR), the Landsat Thematic Mapper/...

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

Analysis of AVHRR, CZCS and historical in situ data off the Oregon Coast

NASA Technical Reports Server (NTRS)

Strub, P. Ted; Chelton, Dudley B.

1990-01-01

The original scientific objectives of this grant were to: (1) characterize the seasonal cycles and interannual variability for phytoplankton concentrations and sea surface temperature (SST) in the California Current using satellite data; and (2) to explore the spatial and temporal relationship between these variables and surface wind forcing. An additional methodological objective was to develop statistical methods for forming mean fields, which minimize the effects of random data gaps and errors in the irregularly sampled CZCS (Coastal Zone Color Scanner) and AVHRR (Advanced Very High Resolution Radiometer) satellite data. A final task was to evaluate the level of uncertainty in the wind fields used for the statistical analysis. Funding in the first year included part of the cost of an image processing system to enable this and other projects to process and analyze satellite data. This report consists of summaries of the major projects carried out with all or partial support from this grant. The appendices include a list of papers and professional presentations supported by the grant, as well as reprints of the major papers and reports.

Influence of the Yukon River on the Bering Sea

NASA Technical Reports Server (NTRS)

Dean, K.; Mcroy, C. P.

1986-01-01

The relationships between the discharge of the Yukon River to the currents and biological productivity in the northern Bering Sea were studied. Specific objectives were: to develop thermal, sediment, and chlorophyll surface maps using Thematic Mapper (TM) data of the discharge of the Yukon River and the Alaskan Coastal Current during the ice free season; to develop a historical model of the distribution of the Yukon River discharge and the Alaskan Coastal Current using LANDSAT Multispectral band scanner (MSS) and NOAA satellite imagery; and to use high resolution TM data to define the surface dynamics of the front between the Alaskan Coastal Current and the Bering Shelf/Anadyr Current. LANDSAT MSS, TM, and Advanced Very High Resolution Radiometer (AVHRR) data were recorded during the 1985 ice free period. The data coincided with shipboard measurements acquired by Inner Shelf Transfer and Recycling (ISTAR) project scientists. An integrated model of the distribution of turbid water discharged from the Yukon River was compiled. A similar model is also being compiled for the Alaskan Coastal and Bering Shelf/Anadyr water masses based on their thermal expressions seen on AVHRR imagery.

Changes in cloud properties over East Asia deduced from the CLARA-A2 satellite data record

NASA Astrophysics Data System (ADS)

Benas, Nikos; Fokke Meirink, Jan; Hollmann, Rainer; Karlsson, Karl-Göran; Stengel, Martin

2017-04-01

Studies on cloud properties and processes, and their role in the Earth's changing climate, have advanced during the past decades. A significant part of this advance was enabled by satellite measurements, which offer global and continuous monitoring. Lately, a new satellite-based cloud data record was released: the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - second edition (CLARA-A2) includes high resolution cloud macro- and micro-physical properties derived from the AVHRR instruments on board NOAA and MetOp polar orbiters. Based on this data record, an analysis of cloud property changes over East Asia during the 12-year period 2004-2015 was performed. Significant changes were found in both optical and geometric cloud properties, including increases in cloud liquid water path and top height. The Cloud Droplet Number Concentration (CDNC) was specifically studied in order to gain further insight into possible connections between aerosol and cloud processes. To this end, aerosol and cloud observations from MODIS, covering the same area and period, were included in the analysis.

A source-specific model for lossless compression of global Earth data

NASA Astrophysics Data System (ADS)

Kess, Barbara Lynne

A Source Specific Model for Global Earth Data (SSM-GED) is a lossless compression method for large images that captures global redundancy in the data and achieves a significant improvement over CALIC and DCXT-BT/CARP, two leading lossless compression schemes. The Global Land 1-Km Advanced Very High Resolution Radiometer (AVHRR) data, which contains 662 Megabytes (MB) per band, is an example of a large data set that requires decompression of regions of the data. For this reason, SSM-GED compresses the AVHRR data as a collection of subwindows. This approach defines the statistical parameters for the model prior to compression. Unlike universal models that assume no a priori knowledge of the data, SSM-GED captures global redundancy that exists among all of the subwindows of data. The overlap in parameters among subwindows of data enables SSM-GED to improve the compression rate by increasing the number of parameters and maintaining a small model cost for each subwindow of data. This lossless compression method is applicable to other large volumes of image data such as video.

The relationship between sea surface temperature and chlorophyll concentration of phytoplanktons in the Black Sea using remote sensing techniques.

PubMed

Kavak, Mehmet Tahir; Karadogan, Sabri

2012-04-01

Present work investigated the relationship between Chlorophyll (Chl), of phytoplankton biomass, and sea surface temperature (SST) of the Black Sea, using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Advanced Very High Resolution Radiometer (AVHRR) satellite imagery. Satellite derived data could provide information on the amount of sea life present (Brown algae, called kelp, proliferate, supporting new species of sea life, including otters, fish, and various invertebrates) in a given area throughout the world. SST from AVHRR from 1993 to 2008 showed seasonal, annual and interannual variability of temperature, monthly variability Chl from SeaWiFS from 1997 to 2009 has also been investigated. Chl showed two high peaks for the year 1999 and 2008. The correlation between SST and Chl for the same time has been found to be 60%. Correlation was significant at p<0.05. The information could also be useful in connection with studies of global changes in temperature and what effect they could have on the total abundance of marine life.

APOLLO_NG - a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

NASA Astrophysics Data System (ADS)

Klüser, L.; Killius, N.; Gesell, G.

2015-10-01

The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented.

APOLLO_NG - a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

NASA Astrophysics Data System (ADS)

Klüser, L.; Killius, N.; Gesell, G.

2015-04-01

The cloud processing scheme APOLLO (Avhrr Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. While building upon the physical principles having served well in the original APOLLO a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is not performed as a binary yes/no decision based on these physical principals but is expressed as cloud probability for each satellite pixel. Consequently the outcome of the algorithm can be tuned from clear confident to cloud confident depending on the purpose. The probabilistic approach allows to retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for the application with large amounts of historical satellite data. Thus the radiative transfer solution is approximated by the same two stream approach which also had been used for the original APOLLO. This allows the algorithm to be robust enough for being applied to a wide range of sensors without the necessity of sensor-specific tuning. Moreover it allows for online calculation of the radiative transfer (i.e. within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from on NOAA-18 are presented.

Characterization of the Cloud-Topped Boundary Layer at the Synoptic Scale Using AVHRR Observations during the SEMAPHORE Experiment.

NASA Astrophysics Data System (ADS)

Mathieu, A.; Sèze, G.; Lahellec, A.; Guerin, C.; Weill, A.

2003-12-01

Satellite platforms NOAA-11 and -12 Advanced Very High Resolution Radiometer (AVHRR) data are used during the daytime to study large sheets of stratocumulus over the North Atlantic Ocean. The application concerns an anticyclonic period of the Structure des Echanges Mer Atmosphère, Propriétés des Hétérogénéités Océaniques: Recherché Expérimentale (SEMAPHORE) campaign (10 17 November 1993). In the region of interest, the satellite images are recorded under large solar zenith angles. Extending the SEMAPHORE area, a region of about 3000 × 3000 km2 is studied to characterize the atmospheric boundary layer. A statistical cloud classification method is applied to discriminate for low-level and optically thick clouds. For AVHRR pixels covered with thick clouds, brightness temperatures are used to evaluate the boundary layer cloud-top temperature (CTT). The objective is to obtain accurate CTT maps for evaluation of a global model. In this application, the full-resolution fields are reduced to match model grid size. An estimate of overall temperature uncertainty associated with each grid point is also derived, which incorporates subgrid variability of the fields and quality of the temperature retrieval. Results are compared with the SEMAPHORE campaign measurements. A comparison with “DX” products obtained with the same dataset, but at lower resolution, is also presented. The authors claim that such instantaneous CTT maps could be as intensively used as classical SST maps, and both could be efficiently complemented with gridpoint error-bar maps. They may be used for multiple applications: (i) to provide a means to improve numerical weather prediction and climatological reanalyses, (ii) to represent a boundary layer global characterization to analyze the synoptic situation of field experiments, and (iii) to allow validation and to test development of large-scale and mesoscale models.

Space-borne Observations of Aerosols

NASA Astrophysics Data System (ADS)

Kaufman, Y. J.; Tanre, D.; Coakley, J. A.; Fraser, R. S.

2005-12-01

As early as 1963, photographs of the twilight horizon from the Vostok-6 spaceship were used by G. V. Rozenberg and V. V. Nikolaeva-Tereshkova to derive profiles of stratospheric aerosols. The launch of the ATS III satellite in 1967 sparked interest in using satellites to observe aerosol emission, transport, and their effects on climate, precipitation and health. The first use of autonomous satellites in aerosol research appears to be by Toby Carlson and Joe Prospero who tracked dust from the Sahara to the Americas in the early `70s using ATS III images. The launch of the calibrated Landsat instrument in 1972 allowed Bob Fraser to perform quantitative analyses of dust column concentrations for individual scenes. GOES launched in 1975 provided hourly data that allowed Walter Lyons and J.C. Dooley in the late 70's to report on the transport of sulfate air pollution which was later followed by estimates of the export of sulfate aerosol from the US to the Atlantic Ocean. With the launch of SAGE in 1979, Pat McCormick and co-workers began long term observations of statospheric aerosols. The launch of TIROS(N) and the AVHRR in 1979 marked the start of concerted efforts by Larry Stowe and his colleagues to produce operationally an aerosol product over oceans from the NOAA polar orbiting satellite. With the launch of the Earth Radiation Budget Experiment scanners in the late 1980's, Sundar Christopher and his colleagues began linking AVHRR-derived aerosol burdens to their effects on the Earth's radiation budget. A remarkable aspect of this early work is that instruments like the AVHRR, Landsat, and GOES imager were not originally designed to perform quantitative estimates of aerosol properties. In fact, corrections for the effects of aerosols in determining ocean reflectances implemented primarily through the work of Howard Gordon, facilitated much improved pictures of chlorophyll in the upper oceans than had been hoped for from CZCS data collected in the late 70's. This evolution of instrumentation, understanding, and interest paralleled a remarkable growth in the number of papers concerning space-borne observations of aerosols, ranging from an average of 3 papers a year in the 1980s to 60 papers in the 1990s and 150 papers after 2000. The last growth came with the availability of data from satellite instruments designed for aerosol measurements, such as, for example, ATSR, POLDER, MODIS, MISR and GLI launched between 1991 and 2004. Nonetheless, the information potential of the reflected sunlight, not to mention the active sensing of aerosols from space-borne lidars, has yet to be realized. Different instruments specialize in different aspects: wide spectral and high spatial resolution (MODIS, GLI), polarization (POLDER), or angular (MISR) properties of the reflected light. Future missions that include full spectral-angular polarimeteric measurements coupled with active sensing from space will continue to provide a wealth of new insights on aerosol properties, transport, and evolution.

Firn Thickness Changes (1982-2015) Driven by SMB from MERRA-2, RACMO2.3, ERA-Int and AVHRR Surface Temperature and the Impacts to Greenland Ice Sheet Mass Balance

NASA Astrophysics Data System (ADS)

Li, J.; Medley, B.; Neumann, T.; Smith, B. E.; Luthcke, S. B.; Zwally, H. J.

2016-12-01

Surface mass balance (SMB) data are essential in the derivation of ice sheet mass balance. This is because ice sheet mass change consists of short-term and long-term variations. The short-term variations are directly given by the SMB data. For altimetry based ice sheet mass balance studies, these short-term mass changes are converted to firn thickness changes by using a firn densification-elevation model, and then the variations are subtracted from the altimetry measurements to give the long-term ice thickness changes that are associated with the density of ice. So far various SMB data sets such as ERA-Interim, RACMO and MERRA are available and some have been widely used in large number of ice sheet mass balance studies. However theses data sets exhibit the clear discrepancies in both random and systematic manner. In this study, we use our time dependent firn densification- elevation model, driven by the SMB data from MERRA-2, RACMO2.3 and ERA-Int for the period of 1982-2015 and the temperature variations from AVHRR for the same period to examine the corresponding firn thickness variations and the impacts to the mass changes over the Greenland ice sheet. The model was initialized with the1980's climate. Our results show that the relative smaller (centimeter level) differences in the firn thickness driven by the different data set occur at the early stage (1980's) of the model run. As the time progressing, the discrepancies between the SMB data sets accumulate, and the corresponding firn thickness differences quickly become larger with the value > 2m at the end of the period. Although the overall rates for the whole period driven by each of the three data sets are small ranging -0.2 - 0.2 cm a-1 (-3.0-2.7 Gt a-1), the decadal rates can vary greatly with magnitude > 3 cm a-1 and the impact to the Greenland mass change exceeds 30 Gt a-1.

Space measurements of tropospheric aerosols

NASA Technical Reports Server (NTRS)

Griggs, M.

1981-01-01

A global-scale ground-truth experiment was conducted in the summer of 1980 with the AVHRR sensor on NOAA-6 to investigate the relationship between the upwelling visible radiance and the aerosol optical thickness over oceans at different sites around the globe. The possibility of using inland bodies of water such as rivers, lakes and reservoirs has been recently investigated using the Landsat MSS7 (approximately 0.9 micron) channel. This upwelling near-infrared radiance is less influenced than the visible radiance by the suspended matter generally found in the inland bodies of water, and by the adjacency effect of the surrounding higher albedo land. It is found that the water turbidity has more influence than the adjacency effect and reduces the effectiveness of the technique for inland observations.

Reflectivities of uniform and broken marine stratiform clouds

NASA Technical Reports Server (NTRS)

Coakley, James A., Jr.

1990-01-01

Plane-parallel radiative transfer models are often used to estimate the effects of clouds on the earth's energy budget and to retrieve cloud properties from satellite observations. An attempt is made to assess the performance of such models by using AVHRR data collected during the FIRE MARINE Stratus IFO to determine the reflectivities and, in particular, the anisotropy of the reflected radiances for the clouds observed during the field experiment. The intent is to determine the anisotropy for conditions that are overcast and to compare this anisotropy with that produced by the same cloud when broken. The observations are used to quantify aspects of the differences between reflection by plane-parallel clouds and non-planar clouds expected on the basis of theoretical studies.

An intercomparison of artificial intelligence approaches for polar scene identification

NASA Technical Reports Server (NTRS)

Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

1993-01-01

The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

Thermal signatures of urban land cover types: High-resolution thermal infrared remote sensing of urban heat island in Huntsville, AL

NASA Technical Reports Server (NTRS)

Lo, Chor Pang

1996-01-01

The main objective of this research is to apply airborne high-resolution thermal infrared imagery for urban heat island studies, using Huntsville, AL, a medium-sized American city, as the study area. The occurrence of urban heat islands represents human-induced urban/rural contrast, which is caused by deforestation and the replacement of the land surface by non-evaporating and non-porous materials such as asphalt and concrete. The result is reduced evapotranspiration and more rapid runoff of rain water. The urban landscape forms a canopy acting as a transitional zone between the atmosphere and the land surface. The composition and structure of this canopy have a significant impact on the thermal behavior of the urban environment. Research on the trends of surface temperature at rapidly growing urban sites in the United States during the last 30 to 50 years suggests that significant urban heat island effects have caused the temperatures at these sites to rise by 1 to 2 C. Urban heat islands have caused changes in urban precipitation and temperature that are at least similar to, if not greater than, those predicted to develop over the next 100 years by global change models. Satellite remote sensing, particularly NOAA AVHRR thermal data, has been used in the study of urban heat islands. Because of the low spatial resolution (1.1 km at nadir) of the AVHRR data, these studies can only examine and map the phenomenon at the macro-level. The present research provides the rare opportunity to utilize 5-meter thermal infrared data acquired from an airplane to characterize more accurately the thermal responses of different land cover types in the urban landscape as input to urban heat island studies.

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

USGS Publications Warehouse

Hansen, M.C.; Reed, B.

2000-01-01

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

Development of a Methodology for Predicting Forest Area for Large-Area Resource Monitoring

Treesearch

William H. Cooke

2001-01-01

The U.S. Department of Agriculture, Forest Service, Southcm Research Station, appointed a remote-sensing team to develop an image-processing methodology for mapping forest lands over large geographic areds. The team has presented a repeatable methodology, which is based on regression modeling of Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic...

Distribution and Causes of Global Forest Fragmentation

Treesearch

Timothy G. Wade; Kurt H. Riitters; James D. Wickham; K. Bruce Jones

2003-01-01

Abstract Because human land uses tend to expand over time, forests that share a high proportion of their borders with anthropogenic uses are at higher risk of further degradation than forests that share a high proportion of their borders with non-forest, natural land cover (e.g., wetland). Using 1-km advanced very high resolution radiometer (AVHRR)...

DOES ANTHROPOGENIC ACTIVITIES OR NATURE DOMINATE THE SHAPING OF THE LANDSCAPE IN THE OREGON PILOT STUDY AREA FOR 1990-1997?

EPA Science Inventory

We developed a simple method to locate changes in vegetation greenness, which can be used to identify areas under stress. The method only requires inexpensive NDVI data, which can be derived from many sources, and basic statistical and mapping software. AVHRR data are useful fo...

Radiometric sensitivity comparisons of multispectral imaging systems

NASA Technical Reports Server (NTRS)

Lu, Nadine C.; Slater, Philip N.

1989-01-01

Multispectral imaging systems provide much of the basic data used by the land and ocean civilian remote-sensing community. There are numerous multispectral imaging systems which have been and are being developed. A common way to compare the radiometric performance of these systems is to examine their noise-equivalent change in reflectance, NE Delta-rho. The NE Delta-rho of a system is the reflectance difference that is equal to the noise in the recorded signal. A comparison is made of the noise equivalent change in reflectance of seven different multispectral imaging systems (AVHRR, AVIRIS, ETM, HIRIS, MODIS-N, SPOT-1, HRV, and TM) for a set of three atmospheric conditions (continental aerosol with 23-km visibility, continental aerosol with 5-km visibility, and a Rayleigh atmosphere), five values of ground reflectance (0.01, 0.10, 0.25, 0.50, and 1.00), a nadir viewing angle, and a solar zenith angle of 45 deg.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

PubMed

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-06-14

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

A Comparative Analysis between GIMSS NDVIg and NDVI3g for Monitoring Vegetation Activity Change in the Northern Hemisphere during 1982-2008

DOE PAGES

Jiang, Nan; Zhu, Wenquan; Zheng, Zhoutao; ...

2013-08-12

The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this research made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses tomore » climate change in the middle and high latitudes of the Northern Hemisphere during 1982–2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. Finally, these results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades.« less

Robust Satellite Techniques for monitoring earth emitted radiation in the Japanese seismic area by using MTSAT observations in the TIR spectral range

NASA Astrophysics Data System (ADS)

Genzano, Nicola; Filizzola, Carolina; Hattori, Katsumi; Lisi, Mariano; Paciello, Rossana; Pergola, Nicola; Tramutoli, Valerio

2016-04-01

Since eighties, the fluctuations of Earth's thermally emitted radiation, measured by satellite sensors operating in the thermal infrared (TIR) spectral range, have been associated with the complex process of preparation for major earthquakes. But, like other claimed earthquake precursors (seismological, physical, chemical, biological, etc.) they have been for long-time considered with some caution by scientific community. The lack of a rigorous definition of anomalous TIR signal fluctuations and the scarce attention paid to the possibility that other causes (e.g. meteorological) different from seismic activity could be responsible for the observed TIR variations were the main causes of such skepticism. Compared with previously proposed approaches the general change detection approach, named Robust Satellite Techniques (RST), showed good ability to discriminate anomalous TIR signals possibly associated to seismic activity, from the normal variability of TIR signal due to other causes. Thanks to its full exportability on different satellite packages, since 2001 RST has been implemented on TIR images acquired by polar (e.g. NOAA-AVHRR, EOS -MODIS) and geostationary (e.g. MSG-SEVIRI, NOAA-GOES/W, GMS-5/VISSR) satellite sensors, in order to verify the presence (or absence) of TIR anomalies in presence (absence) of earthquakes (with M>4) in different seismogenic areas around the world (e.g. Italy, Greece, Turkey, India, Taiwan, etc.). In this paper, the RST data analysis approach has been implemented on TIR satellite records collected over Japan by the geostationary satellite sensor MTSAT (Multifunctional Transport SATellites) and RETIRA (Robust Estimator of TIR Anomalies) index was used to identify Significant Sequences of TIR Anomalies (SSTAs) in a possible space-time relations with seismic events. Achieved results will be discussed in the perspective of a multi-parametric approach for a time-Dependent Assessment of Seismic Hazard (t-DASH).

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

PubMed Central

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-01-01

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably. PMID:27314369

Urban green land cover changes and their relation to climatic variables in an anthropogenically impacted area

NASA Astrophysics Data System (ADS)

Zoran, Maria A.; Dida, Adrian I.

2017-10-01

Urban green areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. Vegetation index time series provide a useful way to monitor urban vegetation phenological variations. This study quantitatively describes Normalized Difference Vegetation Index NDVI) /Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI) temporal changes for Bucharest metropolitan region land cover in Romania from the perspective of vegetation phenology and its relation with climate changes and extreme climate events. The time series from 2000 to 2016 of the NOAA AVHRR and MODIS Terra/Aqua satellite data were analyzed to extract anomalies. Time series of climatic variables were also analyzed through anomaly detection techniques and the Fourier Transform. Correlations between NDVI/EVI time series and climatic variables were computed. Temperature, rainfall and radiation were significantly correlated with almost all land-cover classes for the harmonic analysis amplitude term. However, vegetation phenology was not correlated with climatic variables for the harmonic analysis phase term suggesting a delay between climatic variations and vegetation response. Training and validation were based on a reference dataset collected from IKONOS high resolution remote sensing data. The mean detection accuracy for period 2000- 2016 was assessed to be of 87%, with a reasonable balance between change commission errors (19.3%), change omission errors (24.7%), and Kappa coefficient of 0.73. This paper demonstrates the potential of moderate - and high resolution, multispectral imagery to map and monitor the evolution of the physical urban green land cover under climate and anthropogenic pressure.

Cloud Computing-based Platform for Drought Decision-Making using Remote Sensing and Modeling Products: Preliminary Results for Brazil

NASA Astrophysics Data System (ADS)

Vivoni, E.; Mascaro, G.; Shupe, J. W.; Hiatt, C.; Potter, C. S.; Miller, R. L.; Stanley, J.; Abraham, T.; Castilla-Rubio, J.

2012-12-01

Droughts and their hydrological consequences are a major threat to food security throughout the world. In arid and semiarid regions dependent on irrigated agriculture, prolonged droughts lead to significant and recurring economic and social losses. In this contribution, we present preliminary results on integrating a set of multi-resolution drought indices into a cloud computing-based visualization platform. We focused our initial efforts on Brazil due to a severe, on-going drought in a large agricultural area in the northeastern part of the country. The online platform includes drought products developed from: (1) a MODIS-based water stress index (WSI) based on inferences from normalized difference vegetation index and land surface temperature fields, (2) a volumetric water content (VWC) index obtained from application of the NASA CASA model, and (3) a set of AVHRR-based vegetation health indices obtained from NOAA/NESDIS. The drought indices are also presented in terms of anomalies with respect to a baseline period. Since our main objective is to engage stakeholders and decision-makers in Brazil, we incorporated other relevant geospatial data into the platform, including irrigation areas, dams and reservoirs, administrative units and annual climate information. We will also present a set of use cases developed to help stakeholders explore, query and provide feedback that allowed fine-tuning of the drought product delivery, presentation and analysis tools. Finally, we discuss potential next steps in development of the online platform, including applications at finer resolutions in specific basins and at a coarser global scale.

Surface temperature-modulating factors in the Sonoran Desert, Mexico

NASA Astrophysics Data System (ADS)

Tereshchenko, I.; Zolotokryin, A.; Titkova, T.; Brito-Castillo, L.; Monzon, C.

2013-05-01

This study is focused on seasonal cycle of parameters, which modulate surface temperature in the Sonora desert (North-West Mexico). The understanding of this process is important for monitoring of desertification. In this paper, a new approach to the monitoring of desertification based on the use of the albedo mechanism is proposed. It is known that the positive albedo-precipitation feedback plays a significant role in the desertification process. The originality of the work rest on considering the albedo mechanism not in isolation but as a joint effect of two temperature-modulating factors: radiation and evapotranspiration. It is assumed that the prevalence of the radiation factor is a manifestation of the albedo mechanism. One indirect characteristic of prevalence of the radiation factor is Normalized Difference Vegetation Index (NDVI), which is an indicator of green phytomass. We define and substantiate the criterion of predominance of the radiation factor by using the threshold value of NDVI AVHRR. The area, within which the threshold value is achieved, is a key factor; the data on the variability of this area becomes useful and essential in the process of monitoring of desertification. This is true because in a certain year, the time span of the period, during which the radiation factor is predominant, is an important factor in the desertification process. The main features of the ratio between albedo and surface temperature are discussed in terms of analysis of monthly means (albedo, temperature, NDVI) in the state of Sonora (29-32N, 111-115W), in particular, within the box 30-31N, 112-113W.

Spatio-Temporal Dynamics of Vegetation and Their Relationships with Climate in Southeast Asia Based on Three Satellite NDVI Products

NASA Astrophysics Data System (ADS)

Zhang, Y.; Zeng, Z.; Piao, S.

2014-12-01

Tropical vegetation plays an essential role for global biogeochemical cycles. An abundant literature focused on the vegetation dynamics in Amazon. It is shown that the Amazonian rainforest is strongly controlled by radiation, even during dry season. However, only few researches deal with tropical rainforest in Southeast Asia; the vegetation dynamics in Southeast Asia remain poorly understood. In this study, we investigated the spatio-temporal dynamics of vegetation in Southeast Asia with three independent satellite derived Normalized Difference Vegetation Index (NDVI) products (GIMMS AVHRR NDVI3g, SPOT, and MODIS) as well as the recently developed Sun Induced chlorophyll Fluorescence (SIF). We furthermore examined how climate drivers (precipitation, temperature and radiation) exert influences on the vegetation dynamics. We find that the three NDVI datasets are generally consistent with each other. At seasonal scale, NDVI decreases from the beginning to the end of the dry season; at interannual scale, dry season NDVI is positively correlated to precipitation but negatively correlated to radiation, while wet season NDVI is positively correlated to radiation. Compared to evergreen forests, deciduous forests have a larger NDVI decrease rate and more extended area with positive relationships between NDVI and precipitation during the dry season. SIF is lower during dry season than during wet season. Our results indicate that most forests in Southeast Asia, unlike in the Amazonian basin, are water-limited in the dry season but radiation-limited in the wet season. These results imply that droughts may have a stronger impact on forests in Southeast Asia than in Amazon.

Analysis of spatial distribution and transmission characters for highly pathogenic avian influenza in Chinese mainland in 2004

NASA Astrophysics Data System (ADS)

Liu, Y. L.; Wei, C. J.; Yan, L.; Chi, T. H.; Wu, X. B.; Xiao, C. S.

2006-03-01

After the outbreak of highly pathogenic Avian Influenza (HPAI) in South Korea in the end of year 2003, estimates of the impact of HPAI in affected countries vary greatly, the total direct losses are about 3 billion US dollars, and it caused 15 million birds and poultry flocks death. It is significant to understand the spatial distribution and transmission characters of HPAI for its prevention and control. According to 50 outbreak cases for HPAI in Chinese mainland during 2004, this paper introduces the approach of spatial distribution and transmission characters for HPAI and its results. Its approach is based on remote sensing and GIS techniques. Its supporting data set involves normalized difference vegetation index (NDVI) and land surface temperature (Ts) derived from a time-series of remote sensing data of 1 kilometer-resolution NOAA/AVHRR, birds' migration routes, topology geographic map, lake and wetland maps, and meteorological observation data. In order to analyze synthetically using these data, a supporting platform for analysis Avian Influenza epidemic situation (SPAS/AI) was developed. Supporting by SPAS/AI, the integrated information from multi-sources can be easily used to the analysis of the spatial distribution and transmission character of HPAI. The results show that the range of spatial distribution and transmission of HPAI in China during 2004 connected to environment factors NDVI, Ts and the distributions of lake and wetland, and especially to bird migration routes. To some extent, the results provide some suggestions for the macro-decision making for the prevention and control of HPAI in the areas of potential risk and reoccurrence.

Climatic driving forces in inter-annual variation of global FPAR

NASA Astrophysics Data System (ADS)

Peng, Dailiang; Liu, Liangyun; Yang, Xiaohua; Zhou, Bin

2012-09-01

Fraction of Absorbed Photosynthetically Active Radiation (FPAR) characterizes vegetation canopy functioning and its energy absorption capacity. In this paper, we focus on climatic driving forces in inter-annual variation of global FPAR from 1982 to 2006 by Global Historical Climatology Network (GHCN-Monthly) data. Using FPAR-Simple Ratio Vegetation Index (SR) relationship, Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) was used to estimate FPAR at the global scale. The correlation between inter-annual variation of FPAR and temperature, precipitation derived from GHCN-Monthly was examined, during the periods of March-May (MAM), June-August (JJA), September-November (SON), and December-February (DJF) over from 1982 to 2006. The analysis of climatic influence on global FPAR revealed the significant correlation with temperature and precipitation in some meteorological stations area, and a more significant correlation with precipitation was found than which with temperature. Some stations in the regions between 30° N and 60° N and around 30° S in South America, where the annual FPAR variation showed a significant positive correlation with temperature (P < 0.01 or P < 0.05) during MAM, SON, and DJF, as well as in Europe during MAM and SON period. A negative correlation for more stations was observed during JJA. For precipitation, there were many stations showed a significant positive correlation with inter-annual variation of global FPAR (P < 0.01 or P < 0.05), especially for the tropical rainfall forest of Africa and Amazon during the dry season of JJA and SON.

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.

Global Analysis of Empirical Relationships Between Annual Climate and Seasonality of NDVI

NASA Technical Reports Server (NTRS)

Potter, C. S.

1997-01-01

This study describes the use of satellite data to calibrate a new climate-vegetation greenness function for global change studies. We examined statistical relationships between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our empirical understanding of intraannual patterns and global abiotic controls on natural vegetation dynamics. Multiple linear regression results using global l(sup o) gridded data sets suggest that three climate indexes: growing degree days, annual precipitation total, and an annual moisture index together can account to 70-80 percent of the variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same climate index values from the previous year explained no significant additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes was closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from l(sup o) grid cells mapped as greater than 25 percent inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes were not accurately predicted are mainly high latitude ecosystems and other remote locations where climate station data are sparse.

Validation of a mapping and prediction model for human fasciolosis transmission in Andean very high altitude endemic areas using remote sensing data.

PubMed

Fuentes, M V; Malone, J B; Mas-Coma, S

2001-04-27

The present paper aims to validate the usefulness of the Normalized Difference Vegetation Index (NDVI) obtained by satellite remote sensing for the development of local maps of risk and for prediction of human fasciolosis in the Northern Bolivian Altiplano. The endemic area, which is located at very high altitudes (3800-4100 m) between Lake Titicaca and the valley of the city of La Paz, presents the highest prevalences and intensities of fasciolosis known in humans. NDVI images of 1.1 km resolution from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the National Oceanic and Atmospheric Administration (NOAA) series of environmental satellites appear to provide adequate information for a study area such as that of the Northern Bolivian Altiplano. The predictive value of the remotely sensed map based on NDVI data appears to be better than that from forecast indices based only on climatic data. A close correspondence was observed between real ranges of human fasciolosis prevalence at 13 localities of known prevalence rates and the predicted ranges of fasciolosis prevalence using NDVI maps. However, results based on NDVI map data predicted zones as risk areas where, in fact, field studies have demonstrated the absence of lymnaeid populations during snail surveys, corroborated by the absence of the parasite in humans and livestock. NDVI data maps represent a useful data component in long-term efforts to develop a comprehensive geographical information system control program model that accurately fits real epidemiological and transmission situations of human fasciolosis in high altitude endemic areas in Andean countries.

Seasonally different response of photosynthetic activity to daytime and night-time warming in the Northern Hemisphere

DOE PAGES

Tan, Jianguang; Piao, Shilong; Chen, Anping; ...

2014-08-27

Over the last century the Northern Hemisphere has experienced rapid climate warming, but this warming has not been evenly distributed seasonally, as well as diurnally. The implications of such seasonal and diurnal heterogeneous warming on regional and global vegetation photosynthetic activity, however, are still poorly understood. Here, we investigated for different seasons how photosynthetic activity of vegetation correlates with changes in seasonal daytime and night-time temperature across the Northern Hemisphere (>30°N), using Normalized Difference Vegetation Index (NDVI) data from 1982 to 2011 obtained from the Advanced Very High Resolution Radiometer (AVHRR). Our analysis revealed some striking seasonal differences in themore » response of NDVI to changes in day- versus night-time temperatures. For instance, while higher daytime temperature (T max) is generally associated with higher NDVI values across the boreal zone, the area exhibiting a statistically significant positive correlation between T max and NDVI is much larger in spring (41% of area in boreal zone – total area 12.6 × 10 6 km 2) than in summer and autumn (14% and 9%, respectively). In contrast to the predominantly positive response of boreal ecosystems to changes in T max, increases in T max tended to negatively influence vegetation growth in temperate dry regions, particularly during summer. Changes in night-time temperature (T min) correlated negatively with autumnal NDVI in most of the Northern Hemisphere, but had a positive effect on spring and summer NDVI in most temperate regions (e.g., Central North America and Central Asia). Such divergent covariance between the photosynthetic activity of Northern Hemispheric vegetation and day- and night-time temperature changes among different seasons and climate zones suggests a changing dominance of ecophysiological processes across time and space. Lastly, understanding the seasonally different responses of vegetation photosynthetic activity to diurnal temperature changes, which have not been captured by current land surface models, is important for improving the performance of next generation regional and global coupled vegetation-climate models« less

Mapping Mexico's Forest Lands with Advanced Very High Resolution Radiometer

Treesearch

David J. Evans; Zhiliang Zhu; Susan Eggen-McIntosh; Pedro García Mayoral; Jose Luis Ornelas de Anda

1992-01-01

Data from the Advanced Very High Resolution Radiometer (AVHRR) were used in a program sponsored by the U.S. Department of Agriculture, Forest Service, and the United Nations Food and Agriculture Organization to help scientists from Mexico generate forest-cover maps of that country. Two near-cloud-free composite images were generated for December and March 1990 from...

Regional forest cover estimation via remote sensing: the calibration center concept

Treesearch

Louis R. Iverson; Elizabeth A. Cook; Robin L. Graham; Robin L. Graham

1994-01-01

A method for combining Landsat Thematic Mapper (TM), Advanced Very High Resolution Radiometer (AVHRR) imagery, and other biogeographic data to estimate forest cover over large regions is applied and evaluated at two locations. In this method, TM data are used to classify a small area (calibration center) into forest/nonforest; the resulting forest cover map is then...

The Validation of Cloud Retrieval Algorithms Using Synthetic Datasets

NASA Astrophysics Data System (ADS)

Kokhanovsky, Alexander; Fischer, Jurgen; Linstrot, Rasmus; Meirink, Jan Fokke; Poulsen, Caroline; Preusker, Rene; Siddans, Richard; Thomas, Gareth; Arnold, Chris; Grainger, Roy; Lilli, Luca; Rozanov, Vladimir

2012-11-01

We have performed the inter-comparison study of cloud property retrievals using algorithms initially developed for AATSR (ORAC, RAL-Oxford University), AVHRR and SEVIRI (CPP, KNMI), SCIAMACHY/GOME (SACURA, University of Bremen), and MERIS (ANNA, Free University of Berlin). The accuracy of retrievals of cloud optical thickness (COT), effective radius (ER) of droplets, and cloud top height (CTH) is discussed.

Simulation of meteorological satellite (METSAT) data using LANDSAT data

NASA Technical Reports Server (NTRS)

Austin, W. W.; Ryland, W. E.

1983-01-01

The information content which can be expected from the advanced very high resolution radiometer system, AVHRR, on the NOAA-6 satellite was assessed, and systematic techniques of data interpretation for use with meteorological satellite data were defined. In-house data from LANDSAT 2 and 3 were used to simulate the spatial, spectral, and sampling methods of the NOAA-6 satellite data.

Remote sensing monitoring the spatio-temporal changes of aridification in the Mongolian Plateau based on the general Ts-NDVI space, 1981-2012

NASA Astrophysics Data System (ADS)

Cao, Xiaoming; Feng, Yiming; Wang, Juanle

2017-06-01

This paper has developed a general Ts-NDVI triangle space with vegetation index time-series data from AVHRR and MODIS to monitor soil moisture in the Mongolian Plateau during 1981-2012, and studied the spatio-temporal variations of drought based on the temperature vegetation dryness index (TVDI). The results indicated that (1) the developed general Ts-NDVI space extracted from the AVHRR and MODIS remote sensing data would be an effective method to monitor regional drought, moreover, it would be more meaningful if the single time Ts-NDVI space showed an unstable condition; (2) the inverted TVDI was expected to reflect the water deficit in the study area. It was found to be in close negative agreement with precipitation and 10 cm soil moisture; (3) in the Mongolian Plateau, TVDI presented a zonal distribution with changes in land use/land cover types, vegetation cover and latitude. The soil moisture is low in bare land, construction land and grassland. During 1981-2012, drought was widely spread throughout the plateau, and aridification was obvious in the study period. Vegetation degradation, overgrazing, and climate warming could be considered as the main reasons.

Comparison of North and South American biomes from AVHRR observations

NASA Technical Reports Server (NTRS)

Goward, Samuel N.; Dye, Dennis; Kerber, Arlene; Kalb, Virginia

1987-01-01

Previous analysis of the North American continent with AVHRR-derived vegetation index measurements showed a strong relation between known patterns of vegetation seasonality, productivity and the spectral vegetation index measurements. This study extends that analysis to South America to evaluate the degree to which these findings extend to tropical regions. The results show that the spectral vegetation index measurements provide a general indicator of vegetation activity across the major biomes of the Western Hemisphere of the earth, including tropical regions. The satellite-observed patterns are strongly related to the known climatology of the continents and may offer a means to improve understanding of global bioclimatology. For example, South America is shown to have a longer growing season with much earlier spring green-up than North America. The time integral of the measurements, computed from 12 composited monthly values, produces a value that is related to published net primary productivity data. However, limited net primary production data does not allow complete evaluation of satellite-observed contrasts between North and South American biomes. These results suggest that satellite-derived spectral vegetation index measurements are of great potential value in improving knowledge of the earth's biosphere.

NASA's Earth Observations Program: Past, Present and Future

NASA Technical Reports Server (NTRS)

King, Michael D.

1999-01-01

A presentation will be given at the Annual National Awards and President's Invited Lecture. The event is sponsored by the Associated Scientific and Technical Societies, an organization which serves the interests of 40,000 scientists and engineers all over South Africa. A general presentation will be given on the topic of NASA's Earth Observation Program and will be supplemented with visualizations using the NASA/NOAA Earth Science Electronic theater. Included will be space observations with an eye on southern Africa, including Etosha National Park, Namibia, Okavanga Delta, Botswana, Victoria Falls, Zimbabwe, and Cape Town, the Highveld around Johannesburg, Blyde River Canyon, and the Lowveld of Kruger National Park in South Africa; also included will be some AVHRR imagery of fire occurrence during the dry season, mostly the Miombo woodland of Zambia, Angola, Malawi, and northern Mozambique, supplemented with SeaWiFS imagery for VI, aerosols, clouds, AVHRR fire time series, Landsat TM (and possibly ETM+, if available), and other global data sets. Would also like to include some Terra animations from SVS, including perhaps the launch sequence. The presentation would conclude with some of the ER-2 MAS imagery from Brazil that highlights the capability that we plan to bring to Africa in August 2000.

Analyzing the Effects of Climate Change on Sea Surface Temperature in Monitoring Coral Reef Health in the Florida Keys Using Sea Surface Temperature Data

NASA Technical Reports Server (NTRS)

Jones, Jason; Burbank, Renane; Billiot, Amanda; Schultz, Logan

2011-01-01

This presentation discusses use of 4 kilometer satellite-based sea surface temperature (SST) data to monitor and assess coral reef areas of the Florida Keys. There are growing concerns about the impacts of climate change on coral reef systems throughout the world. Satellite remote sensing technology is being used for monitoring coral reef areas with the goal of understanding the climatic and oceanic changes that can lead to coral bleaching events. Elevated SST is a well-documented cause of coral bleaching events. Some coral monitoring studies have used 50 km data from the Advanced Very High Resolution Radiometer (AVHRR) to study the relationships of sea surface temperature anomalies to bleaching events. In partnership with NOAA's Office of National Marine Sanctuaries and the University of South Florida's Institute for Marine Remote Sensing, this project utilized higher resolution SST data from the Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) and AVHRR. SST data for 2000-2010 was employed to compute sea surface temperature anomalies within the study area. The 4 km SST anomaly products enabled visualization of SST levels for known coral bleaching events from 2000-2010.

The ESA Cloud CCI project: Generation of Multi Sensor consistent Cloud Properties with an Optimal Estimation Based Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Jerg, M.; Stengel, M.; Hollmann, R.; Poulsen, C.

2012-04-01

The ultimate objective of the ESA Climate Change Initiative (CCI) Cloud project is to provide long-term coherent cloud property data sets exploiting and improving on the synergetic capabilities of past, existing, and upcoming European and American satellite missions. The synergetic approach allows not only for improved accuracy and extended temporal and spatial sampling of retrieved cloud properties better than those provided by single instruments alone but potentially also for improved (inter-)calibration and enhanced homogeneity and stability of the derived time series. Such advances are required by the scientific community to facilitate further progress in satellite-based climate monitoring, which leads to a better understanding of climate. Some of the primary objectives of ESA Cloud CCI Cloud are (1) the development of inter-calibrated radiance data sets, so called Fundamental Climate Data Records - for ESA and non ESA instruments through an international collaboration, (2) the development of an optimal estimation based retrieval framework for cloud related essential climate variables like cloud cover, cloud top height and temperature, liquid and ice water path, and (3) the development of two multi-annual global data sets for the mentioned cloud properties including uncertainty estimates. These two data sets are characterized by different combinations of satellite systems: the AVHRR heritage product comprising (A)ATSR, AVHRR and MODIS and the novel (A)ATSR - MERIS product which is based on a synergetic retrieval using both instruments. Both datasets cover the years 2007-2009 in the first project phase. ESA Cloud CCI will also carry out a comprehensive validation of the cloud property products and provide a common data base as in the framework of the Global Energy and Water Cycle Experiment (GEWEX). The presentation will give an overview of the ESA Cloud CCI project and its goals and approaches and then continue with results from the Round Robin algorithm comparison exercise carried out at the beginning of the project which included three algorithms. The purpose of the exercise was to assess and compare existing cloud retrieval algorithms in order to chose one of them as backbone of the retrieval system and also identify areas of potential improvement and general strengths and weaknesses of the algorithm. Furthermore the presentation will elaborate on the optimal estimation algorithm subsequently chosen to derive the heritage product and which is presently further developed and will be employed for the AVHRR heritage product. The algorithm's capabilities to coherently and simultaneously process all radiative input and yield retrieval parameters together with associated uncertainty estimates will be presented together with first results for the heritage product. In the course of the project the algorithm is being developed into a freely and publicly available community retrieval system for interested scientists.

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Coastal warming and wind-driven upwelling: A global analysis.

PubMed

Varela, Rubén; Lima, Fernando P; Seabra, Rui; Meneghesso, Claudia; Gómez-Gesteira, Moncho

2018-10-15

Long-term sea surface temperature (SST) warming trends are far from being homogeneous, especially when coastal and ocean locations are compared. Using data from NOAA's AVHRR OISST, we have analyzed sea surface temperature trends over the period 1982-2015 at around 3500 worldwide coastal points and their oceanic counterparts with a spatial resolution of 0.25 arc-degrees. Significant warming was observed at most locations although with important differences between oceanic and coastal points. This is especially patent for upwelling regions, where 92% of the coastal locations showed lower warming trends than at neighboring ocean locations. This result strongly suggests that upwelling has the potential to buffer the effects of global warming nearshore, with wide oceanographic, climatic, and biogeographic implications. Copyright © 2018 Elsevier B.V. All rights reserved.

Cloud, Aerosol, and Volcanic Ash Retrievals Using ASTR and SLSTR with ORAC

NASA Astrophysics Data System (ADS)

McGarragh, Gregory; Poulsen, Caroline; Povey, Adam; Thomas, Gareth; Christensen, Matt; Sus, Oliver; Schlundt, Cornelia; Stapelberg, Stefan; Stengel, Martin; Grainger, Don

2015-12-01

The Optimal Retrieval of Aerosol and Cloud (ORAC) is a generalized optimal estimation system that retrieves cloud, aerosol and volcanic ash parameters using satellite imager measurements in the visible to infrared. Use of the same algorithm for different sensors and parameters leads to consistency that facilitates inter-comparison and interaction studies. ORAC currently supports ATSR, AVHRR, MODIS and SEVIRI. In this proceeding we discuss the ORAC retrieval algorithm applied to ATSR data including the retrieval methodology, the forward model, uncertainty characterization and discrimination/classification techniques. Application of ORAC to SLSTR data is discussed including the additional features that SLSTR provides relative to the ATSR heritage. The ORAC level 2 and level 3 results are discussed and an application of level 3 results to the study of cloud/aerosol interactions is presented.

Change detection with heterogeneous data using ecoregional stratification, statistical summaries and a land allocation algorithm

Treesearch

Kathleen M. Bergen; Daniel G. Brown; James F. Rutherford; Eric J. Gustafson

2005-01-01

A ca. 1980 national-scale land-cover classification based on aerial photo interpretation was combined with 2000 AVHRR satellite imagery to derive land cover and land-cover change information for forest, urban, and agriculture categories over a seven-state region in the U.S. To derive useful land-cover change data using a heterogeneous dataset and to validate our...

A technique for extrapolating and validating forest cover across large regions. Calibrating AVHRR data with TM data

Treesearch

L.R. Iverson; E.A. Cook; R.L. Graham

1989-01-01

An approach to extending high-resolution forest cover information across large regions is presented and validated. Landsat Thematic Mapper (TM) data were classified into forest and nonforest for a portion of Jackson County, Illinois. The classified TM image was then used to determine the relationship between forest cover and the spectral signature of Advanced Very High...

Smart Voyage Planning Model Sensitivity Analysis Using Ocean and Atmospheric Models Including Ensemble Methods

DTIC Science & Technology

2012-09-01

LEO Low Earth...vapor, infrared, and visible satellite imagery (Geostationary, MODIS, AVHRR, and LEO /GEO) (UCAR 2012). 20 The physics package includes: • Bulk...8217] legend([artist[color] for color in ’r’,’b’,’g’,’c’,’m’,’teal’], label, loc =’best’, shadow=True, fancybox=True)

Arctic Ice Studies

DTIC Science & Technology

1993-02-01

Aeronautics and Space Administration; DMSP: Defense Meteorological Satellit= innovative helicopter survey for ambient Program; AVHRR: Advanced Very High...a" aA z’•Lae Na g 419" .aso ." -I Nas"o Te a ’ b 0 York A4CJ ’ crk A19. YCrk t•nrtid 0 .436 b = -. 49cr9k Trott *._-. 3 - -. Now A19. No~v, AtC. Nowv

The 1997 eruption of Okmok Volcano, Alaska: A synthesis of remotely sensed imagery

USGS Publications Warehouse

Patrick, M.R.; Dehn, J.; Papp, K.R.; Lu, Z.; Dean, K.; Moxey, L.; Izbekov, P.; Guritz, R.

2003-01-01

Okmok Volcano, in the eastern Aleutian Islands, erupted in February and March of 1997 producing a 6-km-long lava flow and low-level ash plumes. This caldera is one of the most active in the Aleutian Arc, and is now the focus of international multidisciplinary studies. A synthesis of remotely sensed data (AirSAR, derived DEMs, Landsat MSS and ETM+ data, AVHRR, ERS, JERS, Radarsat) has given a sequence of events for the virtually unobserved 1997 eruption. Elevation data from the AirSAR sensor acquired in October 2000 over Okmok were used to create a 5-m resolution DEM mosaic of Okmok Volcano. AVHRR nighttime imagery has been analyzed between February 13 and April 11, 1997. Landsat imagery and SAR data recorded prior to and after the eruption allowed us to accurately determine the extent of the new flow. The flow was first observed on February 13 without precursory thermal anomalies. At this time, the flow was a large single lobe flowing north. According to AVHRR Band 3 and 4 radiance data and ground observations, the first lobe continued growing until mid to late March, while a second, smaller lobe began to form sometime between March 11 and 12. This is based on a jump in the thermal and volumetric flux determined from the imagery, and the physical size of the thermal anomalies. Total radiance values waned after March 26, indicating lava effusion had ended and a cooling crust was growing. The total area (8.9 km2), thickness (up to 50 m) and volume (1.54×108 m3) of the new lava flow were determined by combining observations from SAR, Landsat ETM+, and AirSAR DEM data. While the first lobe of the flow ponded in a pre-eruption depression, our data suggest the second lobe was volume-limited. Remote sensing has become an integral part of the Alaska Volcano Observatory’s monitoring and hazard mitigation efforts. Studies like this allow access to remote volcanoes, and provide methods to monitor potentially dangerous ones.

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

NASA Astrophysics Data System (ADS)

Chybicki, Andrzej; Łubniewski, Zbigniew

2017-09-01

Satellite imaging systems have known limitations regarding their spatial and temporal resolution. The approaches based on subpixel mapping of the Earth's environment, which rely on combining the data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution, are of considerable interest. The paper presents the downscaling process of the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR), using the inverse technique. The effective emissivity derived from another data source is used as a quantity describing thermal properties of the terrain in higher resolution, and allows the downsampling of low spatial resolution LST images. The authors propose an optimized downscaling method formulated as the inverse problem and show that the proposed approach yields better results than the use of other downsampling methods. The proposed method aims to find estimation of high spatial resolution LST data by minimizing the global error of the downscaling. In particular, for the investigated region of the Gulf of Gdansk, the RMSE between the AVHRR image downscaled by the proposed method and the Landsat 8 LST reference image was 2.255°C with correlation coefficient R equal to 0.828 and Bias = 0.557°C. For comparison, using the PBIM method, it was obtained RMSE = 2.832°C, R = 0.775 and Bias = 0.997°C for the same satellite scene. It also has been shown that the obtained results are also good in local scale and can be used for areas much smaller than the entire satellite imagery scene, depicting diverse biophysical conditions. Specifically, for the analyzed set of small sub-datasets of the whole scene, the obtained RSME between the downscaled and reference image was smaller, by approx. 0.53°C on average, in the case of applying the proposed method than in the case of using the PBIM method.

Monitoring volcanic thermal activity by Robust Satellite Techniques: achievements and perspectives

NASA Astrophysics Data System (ADS)

Tramutoli, V.; Marchese, F.; Mazzeo, G.; Pergola, N.

2009-12-01

Satellite data have been increasingly used in last decades to study active volcanoes and to monitor thermal activity variation in space-time domain. Several satellite techniques and original methods have been developed and tested, devoted to hotspot detection and thermal monitoring. Among them, a multi-temporal approach, named RST (Robust Satellite Techniques), has shown high performances in detecting hotspots, with a low false positive rate under different observational and atmospheric conditions, providing also a potential toward low-level thermal anomalies which may announce incoming eruptions. As the RST scheme is intrinsically exportable on different geographic areas and satellite sensors, it has been applied and tested on a number of volcanoes and in different environmental conditions. This work presents major results and outcomes of studies carried out on Etna and Stromboli (Italy), Merapi (Java Indonesia), Asamayama (Japan), Jebel Al Tair (Yemen) by using different satellite systems and sensors (e.g. NOAA-AVHRR, EOS-MODIS, MSG-SEVIRI). Performances on hotspot detection, early warning and real-time monitoring, together with capabilities in possible thermal precursor identification, will be presented and discussed.

Asia-MIP: Multi Model-data Synthesis of Terrestrial Carbon Cycles in Asia

NASA Astrophysics Data System (ADS)

Ichii, K.; Kondo, M.; Ito, A.; Kang, M.; Sasai, T.; SATO, H.; Ueyama, M.; Kobayashi, H.; Saigusa, N.; Kim, J.

2013-12-01

Asia, which is characterized by monsoon climate and intense human activities, is one of the prominent understudied regions in terms of terrestrial carbon budgets and mechanisms of carbon exchange. To better understand terrestrial carbon cycle in Asia, we initiated multi-model and data intercomparison project in Asia (Asia-MIP). We analyzed outputs from multiple approaches: satellite-based observations (AVHRR and MODIS) and related products, empirically upscaled estimations (Support Vector Regression) using eddy-covariance observation network in Asia (AsiaFlux, CarboEastAsia, FLUXNET), ~10 terrestrial biosphere models (e.g. BEAMS, Biome-BGC, LPJ, SEIB-DGVM, TRIFFID, VISIT models), and atmospheric inversion analysis (e.g. TransCom models). We focused on the two difference temporal coverage: long-term (30 years; 1982-2011) and decadal (10 years; 2001-2010; data intensive period) scales. The regions of covering Siberia, Far East Asia, East Asia, Southeast Asia and South Asia (60-80E, 10S-80N), was analyzed in this study for assessing the magnitudes, interannual variability, and key driving factors of carbon cycles. We will report the progress of synthesis effort to quantify terrestrial carbon budget in Asia. First, we analyzed the recent trends in Gross Primary Productivities (GPP) using satellite-based observation (AVHRR) and multiple terrestrial biosphere models. We found both model outputs and satellite-based observation consistently show an increasing trend in GPP in most of the regions in Asia. Mechanisms of the GPP increase were analyzed using models, and changes in temperature and precipitation play dominant roles in GPP increase in boreal and temperate regions, whereas changes in atmospheric CO2 and precipitation are important in tropical regions. However, their relative contributions were different. Second, in the decadal analysis (2001-2010), we found that the negative GPP and carbon uptake anomalies in 2003 summer in Far East Asia is one of the largest anomalies with high consistency among methods from 2001 to 2010 period. The model analysis showed that these anomalies were produced by different climate factors among the models. Therefore, we conclude that inconsistency of model sensitivity to meteorological anomalies is an important issue to be improved in future. Acknowledgement The study is financially supported by the Environment Research and Technology Development Fund (RFa-1201) of the Ministry of the Environment of Japan and JSPS KAKENHI Grant Number 25281003.

Neural network cloud top pressure and height for MODIS

NASA Astrophysics Data System (ADS)

Håkansson, Nina; Adok, Claudia; Thoss, Anke; Scheirer, Ronald; Hörnquist, Sara

2018-06-01

Cloud top height retrieval from imager instruments is important for nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS (Moderate Resolution Imaging Spectroradiometer) is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) dataset. Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 Level 2 height product and the cloud top temperature and height algorithm in the 2014 version of the NWC SAF (EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting) PPS (Polar Platform System). All three techniques are evaluated using both CALIOP and CPR (Cloud Profiling Radar for CloudSat (CLOUD SATellite)) height. Instruments like AVHRR (Advanced Very High Resolution Radiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance. Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighboring pixels are very important. The error distributions of the involved cloud top height algorithms are found to be non-Gaussian. Different descriptive statistic measures are presented and it is exemplified that bias and SD (standard deviation) can be misleading for non-Gaussian distributions. The median and mode are found to better describe the tendency of the error distributions and IQR (interquartile range) and MAE (mean absolute error) are found to give the most useful information of the spread of the errors. For all descriptive statistics presented MAE, IQR, RMSE (root mean square error), SD, mode, median, bias and percentage of absolute errors above 0.25, 0.5, 1 and 2 km the neural network perform better than the reference algorithms both validated with CALIOP and CPR (CloudSat). The neural networks using the brightness temperatures at 11 and 12 µm show at least 32 % (or 623 m) lower MAE compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25 % (or 430 m) reduction of MAE.

Specification of parameters for development of a spatial database for drought monitoring and famine early warning in the African Sahel

NASA Technical Reports Server (NTRS)

Rochon, Gilbert L.

1989-01-01

Parameters were described for spatial database to facilitate drought monitoring and famine early warning in the African Sahel. The proposed system, referred to as the African Drought and Famine Information System (ADFIS) is ultimately recommended for implementation with the NASA/FEMA Spatial Analysis and Modeling System (SAMS), a GIS/Dymanic Modeling software package, currently under development. SAMS is derived from FEMA'S Integration Emergency Management Information System (IEMIS) and the Pacific Northwest Laborotory's/Engineering Topographic Laboratory's Airland Battlefield Environment (ALBE) GIS. SAMS is primarily intended for disaster planning and resource management applications with the developing countries. Sources of data for the system would include the Developing Economics Branch of the U.S. Dept. of Agriculture, the World Bank, Tulane University School of Public Health and Tropical Medicine's Famine Early Warning Systems (FEWS) Project, the USAID's Foreign Disaster Assistance Section, the World Resources Institute, the World Meterological Institute, the USGS, the UNFAO, UNICEF, and the United Nations Disaster Relief Organization (UNDRO). Satellite imagery would include decadal AVHRR imagery and Normalized Difference Vegetation Index (NDVI) values from 1981 to the present for the African continent and selected Landsat scenes for the Sudan pilot study. The system is initially conceived for the MicroVAX 2/GPX, running VMS. To facilitate comparative analysis, a global time-series database (1950 to 1987) is included for a basic set of 125 socio-economic variables per country per year. A more detailed database for the Sahelian countries includes soil type, water resources, agricultural production, agricultural import and export, food aid, and consumption. A pilot dataset for the Sudan with over 2,500 variables from the World Bank's ANDREX system, also includes epidemiological data on incidence of kwashiorkor, marasmus, other nutritional deficiencies, and synergistically-related infectious diseases.

Development and analysis of a 12-year daily 1-km forest fire dataset across North America from NOAA/AVHRR

Treesearch

Ruiliang Pu; Zhanqing Li; Peng Gong; Ivan Csiszar; Robert Fraser; Wei-Min Hao; Shobha Kondragunta; Fuzhong Weng

2007-01-01

Fires in boreal and temperate forests play a significant role in the global carbon cycle. While forest fires in North America (NA) have been surveyed extensively by U.S. and Canadian forest services, most fire records are limited to seasonal statistics without information on temporal evolution and spatial expansion. Such dynamic information is crucial for modeling fire...

AVHRR/1-FM Advanced Very High Resolution Radiometer

NASA Technical Reports Server (NTRS)

1979-01-01

The advanced very high resolution radiometer is discussed. The program covers design, construction, and test of a breadboard model, engineering model, protoflight model, mechanical/structural model, and a life test model. Special bench test and calibration equipment was developed for use on the program. The flight model program objectives were to fabricate, assemble and test four of the advanced very high resolution radiometers along with a bench cooler and collimator.

Difficulties with estimating city-wide urban forest cover change from national, remotely-sensed tree canopy maps

Treesearch

Jeffrey T. Walton

2008-01-01

Two datasets of percent urban tree canopy cover were compared. The first dataset was based on a 1991 AVHRR forest density map. The second was the US Geological Survey's National Land Cover Database (NLCD) 2001 sub-pixel tree canopy. A comparison of these two tree canopy layers was conducted in 36 census designated places of western New York State. Reference data...

Organizations challenged by global database development

USGS Publications Warehouse

Sturdevant, J.A.; Eidenshink, J.C.; Loveland, Thomas R.

1991-01-01

Several international programs have identified the need for a global 1-kilometer spatial database for land cover and land characterization studies. In 1992, the US Geological Survey (USGS) EROS Data Center (EDC), the European Space Agency (ESA), the National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) will collect and archive all 1-kilometer Advanced Very High Resolution Radiometer (AVHRR) data acquired during afternoon orbital passes over land.

Assimilation for skin SST in the NASA GEOS atmospheric data assimilation system.

PubMed

Akella, Santha; Todling, Ricardo; Suarez, Max

2017-01-01

The present article describes the sea surface temperature (SST) developments implemented in the Goddard Earth Observing System, Version 5 (GEOS-5) Atmospheric Data Assimilation System (ADAS). These are enhancements that contribute to the development of an atmosphere-ocean coupled data assimilation system using GEOS. In the current quasi-operational GEOS-ADAS, the SST is a boundary condition prescribed based on the OSTIA product, therefore SST and skin SST (Ts) are identical. This work modifies the GEOS-ADAS Ts by modeling and assimilating near sea surface sensitive satellite infrared (IR) observations. The atmosphere-ocean interface layer of the GEOS atmospheric general circulation model (AGCM) is updated to include near surface diurnal warming and cool-skin effects. The GEOS analysis system is also updated to directly assimilate SST-relevant Advanced Very High Resolution Radiometer (AVHRR) radiance observations. Data assimilation experiments designed to evaluate the Ts modification in GEOS-ADAS show improvements in the assimilation of radiance observations that extends beyond the thermal IR bands of AVHRR. In particular, many channels of hyperspectral sensors, such as those of the Atmospheric Infrared Sounder (AIRS), and Infrared Atmospheric Sounding Interferometer (IASI) are also better assimilated. We also obtained improved fit to withheld, in-situ buoy measurement of near-surface SST. Evaluation of forecast skill scores show marginal to neutral benefit from the modified Ts.

Directional analysis and filtering for dust storm detection in NOAA-AVHRR imagery

NASA Astrophysics Data System (ADS)

Janugani, S.; Jayaram, V.; Cabrera, S. D.; Rosiles, J. G.; Gill, T. E.; Rivera Rivera, N.

2009-05-01

In this paper, we propose spatio-spectral processing techniques for the detection of dust storms and automatically finding its transport direction in 5-band NOAA-AVHRR imagery. Previous methods that use simple band math analysis have produced promising results but have drawbacks in producing consistent results when low signal to noise ratio (SNR) images are used. Moreover, in seeking to automate the dust storm detection, the presence of clouds in the vicinity of the dust storm creates a challenge in being able to distinguish these two types of image texture. This paper not only addresses the detection of the dust storm in the imagery, it also attempts to find the transport direction and the location of the sources of the dust storm. We propose a spatio-spectral processing approach with two components: visualization and automation. Both approaches are based on digital image processing techniques including directional analysis and filtering. The visualization technique is intended to enhance the image in order to locate the dust sources. The automation technique is proposed to detect the transport direction of the dust storm. These techniques can be used in a system to provide timely warnings of dust storms or hazard assessments for transportation, aviation, environmental safety, and public health.

A study of the Alboran sea mesoscale system by means of empirical orthogonal function decomposition of satellite data

NASA Astrophysics Data System (ADS)

Baldacci, A.; Corsini, G.; Grasso, R.; Manzella, G.; Allen, J. T.; Cipollini, P.; Guymer, T. H.; Snaith, H. M.

2001-05-01

This paper presents the results of a combined empirical orthogonal function (EOF) analysis of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) data and sea-viewing wide field-of-view sensor (SeaWiFS) chlorophyll concentration data over the Alboran Sea (Western Mediterranean), covering a period of 1 year (November 1997-October 1998). The aim of this study is to go beyond the limited temporal extent of available in situ measurements by inferring the temporal and spatial variability of the Alboran Gyre system from long temporal series of satellite observations, in order to gain insight on the interactions between the circulation and the biological activity in the system. In this context, EOF decomposition permits concise and synoptic representation of the effects of physical and biological phenomena traced by SST and chlorophyll concentration. Thus, it is possible to focus the analysis on the most significant phenomena and to understand better the complex interactions between physics and biology at the mesoscale. The results of the EOF analysis of AVHRR-SST and SeaWiFS-chlorophyll concentration data are presented and discussed in detail. These improve and complement the knowledge acquired during the in situ observational campaigns of the MAST-III Observations and Modelling of Eddy scale Geostrophic and Ageostrophic motion (OMEGA) Project.

Using NOAA/AVHRR based remote sensing data and PCR method for estimation of Aus rice yield in Bangladesh

NASA Astrophysics Data System (ADS)

Nizamuddin, Mohammad; Akhand, Kawsar; Roytman, Leonid; Kogan, Felix; Goldberg, Mitch

2015-06-01

Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world's fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remote sensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.

Cloud-property retrieval using merged HIRS and AVHRR data

NASA Technical Reports Server (NTRS)

Baum, Bryan A.; Wielicki, Bruce A.; Minnis, Patrick; Parker, Lindsay

1992-01-01

A technique is developed that uses a multispectral, multiresolution method to improve the overall retrieval of mid- to high-level cloud properties by combining HIRS sounding channel data with higher spatial resolution AVHRR radiometric data collocated with the HIRS footprint. Cirrus cloud radiative and physical properties are determined using satellite data, surface-based measurements provided by rawinsondes and lidar, and aircraft-based lidar data collected during the First International Satellite Cloud Climatology Program Regional Experiment in Wisconsin during the months of October and November 1986. HIRS cloud-height retrievals are compared to ground-based lidar and aircraft lidar when possible. Retrieved cloud heights are found to have close agreement with lidar for thin cloud, but are higher than lidar for optically thick cloud. The results of the reflectance-emittance relationships derived are compared to theoretical scattering model results for both water-droplet spheres and randomly oriented hexagonal ice crystals. It is found that the assumption of 10-micron water droplets is inadequate to describe the reflectance-emittance relationship for the ice clouds seen here. Use of this assumption would lead to lower cloud heights using the ISCCP approach. The theoretical results show that use of hexagonal ice crystal phase functions could lead to much improved results for cloud retrieval algorithms using a bispectral approach.

Geostatistics and remote sensing using NOAA-AVHRR satellite imagery as predictive tools in tick distribution and habitat suitability estimations for Boophilus microplus (Acari: Ixodidae) in South America. National Oceanographic and Atmosphere Administration-Advanced Very High Resolution Radiometer.

PubMed

Estrada-Peña, A

1999-02-01

Remote sensing based on NOAA (National Oceanographic and Atmosphere Administration) satellite imagery was used, together with geostatistics (cokriging) to model the correlation between the temperature and vegetation variables and the distribution of the cattle tick, Boophilus microplus (Canestrini), in the Neotropical region. The results were used to map the B. microplus habitat suitability on a continental scale. A database of B. microplus capture localities was used, which was tabulated with the AVHRR (Advanced Very High Resolution Radiometer) images from the NOAA satellite series. They were obtained at 10 days intervals between 1983 and 1994, with an 8 km resolution. A cokriging system was generated to extrapolate the results. The data for habitat suitability obtained through two vegetation and four temperature variables were strongly correlated with the known distribution of B. microplus (sensitivity 0.91; specificity 0.88) and provide a good estimation of the tick habitat suitability. This model could be used as a guide to the correct interpretation of the distribution limits of B. microplus. It can be also used to prepare eradication campaigns or to make predictions about the effects of global change on the distribution of the parasite.

Assimilation for Skin SST in the NASA GEOS Atmospheric Data Assimilation System

NASA Technical Reports Server (NTRS)

Akella, Santha; Todling, Ricardo; Suarez, Max

2017-01-01

The present article describes the sea surface temperature (SST) developments implemented in the Goddard Earth Observing System, Version 5 (GEOS) Atmospheric Data Assimilation System (ADAS). These are enhancements that contribute to the development of an atmosphere-ocean coupled data assimilation system using GEOS. In the current quasi-operational GEOS-ADAS, the SST is a boundary condition prescribed based on the OSTIA product, therefore SST and skin SST (Ts) are identical. This work modifies the GEOS-ADAS Ts by modelling and assimilating near sea surface sensitive satellite infrared (IR) observations. The atmosphere-ocean interface layer of the GEOS atmospheric general circulation model (AGCM) is updated to include near-surface diurnal warming and cool-skin effects. The GEOS analysis system is also updated to directly assimilate SST-relevant Advanced Very High Resolution Radiometer (AVHRR) radiance observations. Data assimilation experiments designed to evaluate the Ts modification in GEOS-ADAS show improvements in the assimilation of radiance observations that extend beyond the thermal infrared bands of AVHRR. In particular, many channels of hyperspectral sensors, such as those of the Atmospheric Infrared Sounder (AIRS), and Infrared Atmospheric Sounding Interferometer (IASI) are also better assimilated. We also obtained improved fit to withheld insitu buoy measurement of near-surface SST. Evaluation of forecast skill scores show neutral to marginal benefit from the modified Ts.

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 1; Theory

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Schull, Mitchell A.; Samanta, Arindam; Shabanov, Nikolay V.; Milesi, Cristina; Nemani, Ramakrishna R.; Knyazikhin, Yuri; Myneni, Ranga B.

2008-01-01

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRRmode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.

Combined Ozone Retrieval From METOP Sensors Using META-Training Of Deep Neural Networks

NASA Astrophysics Data System (ADS)

Felder, Martin; Sehnke, Frank; Kaifel, Anton

2013-12-01

The newest installment of our well-proven Neural Net- work Ozone Retrieval System (NNORSY) combines the METOP sensors GOME-2 and IASI with cloud information from AVHRR. Through the use of advanced meta- learning techniques like automatic feature selection and automatic architecture search applied to a set of deep neural networks, having at least two or three hidden layers, we have been able to avoid many technical issues normally encountered during the construction of such a joint retrieval system. This has been made possible by harnessing the processing power of modern consumer graphics cards with high performance graphic processors (GPU), which decreases training times by about two orders of magnitude. The system was trained on data from 2009 and 2010, including target ozone profiles from ozone sondes, ACE- FTS and MLS-AURA. To make maximum use of tropospheric information in the spectra, the data were partitioned into several sets of different cloud fraction ranges with the GOME-2 FOV, on which specialized retrieval networks are being trained. For the final ozone retrieval processing the different specialized networks are combined. The resulting retrieval system is very stable and does not show any systematic dependence on solar zenith angle, scan angle or sensor degradation. We present several sensitivity studies with regard to cloud fraction and target sensor type, as well as the performance in several latitude bands and with respect to independent validation stations. A visual cross-comparison against high-resolution ozone profiles from the KNMI EUMETSAT Ozone SAF product has also been performed and shows some distinctive features which we will briefly discuss. Overall, we demonstrate that a complex retrieval system can now be constructed with a minimum of ma- chine learning knowledge, using automated algorithms for many design decisions previously requiring expert knowledge. Provided sufficient training data and computation power of GPUs is available, the method can be applied to almost any kind of retrieval or, more generally, regression problem.

Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: A cross-border study on the Mongolian Plateau.

PubMed

Zhou, Xiang; Yamaguchi, Yasushi; Arjasakusuma, Sanjiwana

2018-03-01

Distinguishing the vegetation dynamics induced by anthropogenic factors and identifying the major drivers can provide crucial information for designing actionable and practical countermeasures to restore degraded grassland ecosystems. Based on the residual trend (RESTREND) method, this study distinguished the vegetation dynamics induced by anthropogenic factors from the effects of climate variability on the Mongolian Plateau during 1993-2012 using vegetation optical depth (VOD) and normalized difference vegetation index (NDVI), which measure vegetation water content in aboveground biomass and chlorophyll abundance in canopy cover respectively; afterwards, the major drivers within different agricultural zones and socio-institutional periods were identified by integrating agricultural statistics with statistical analysis techniques. The results showed that grasslands in Mongolia and the grazing zone of Inner Mongolia Autonomous Region (IMAR), China underwent a significant human-induced decrease in aboveground biomass during 1993-2012 and 1993-2000 respectively, which was attributable to the rapid growth of livestock densities stimulated by livestock privatization and market factors; by contrast, grasslands in these two regions did not experience a concurrent human-induced reduction in canopy greenness. Besides, the results indicated that grasslands in the grazing zone of IMAR underwent a significant human-induced increase in aboveground biomass since 2000, which was attributable to the reduced grazing pressure induced by China's ecological restoration programs; concurrently, grasslands in this region also experienced a remarkable increase in canopy greenness, however, this increase was found not directly caused by the decreased stocking densities. Furthermore, the results revealed that the farming and semi-grazing/farming zone of IMAR underwent a significant human-induced increase in both aboveground biomass and canopy greenness since 2000, which was attributable to the intensified grain production stimulated by market factors, open grazing regulation and confined feeding popularization. These findings suggest that China's grassland restoration practice has important implications for Mongolia to reverse the severe and continuous grassland degradation in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

Parameterization and scaling of Arctic ice conditions in the context of ice-atmosphere processes

NASA Technical Reports Server (NTRS)

Barry, R. G.; Heinrichs, J.; Steffen, K.; Maslanik, J. A.; Key, J.; Serreze, M. C.; Weaver, R. W.

1994-01-01

This report summarizes achievements during year three of our project to investigate the use of ERS-1 SAR data to study Arctic ice and ice/atmosphere processes. The project was granted a one year extension, and goals for the final year are outlined. The specific objects of the project are to determine how the development and evolution of open water/thin ice areas within the interior ice pack vary under different atmospheric synoptic regimes; compare how open water/thin ice fractions estimated from large-area divergence measurements differ from fractions determined by summing localized openings in the pack; relate these questions of scale and process to methods of observation, modeling, and averaging over time and space; determine whether SAR data might be used to calibrate ice concentration estimates from medium and low-rate bit sensors (AVHRR and DMSP-OLS) and the special sensor microwave imager (SSM/I); and investigate methods to integrate SAR data for turbulent heat flux parametrization at the atmosphere interface with other satellite data.

Early evolution of a stratospheric volcanic eruption cloud as observed with TOMS and AVHRR

USGS Publications Warehouse

Schneider, D.J.; Rose, William I.; Coke, L.R.; Bluth, G.J.S.; Sprod, I.E.; Krueger, A.J.

1999-01-01

This paper is a detailed study of remote sensing data from the total ozone mapping spectrometer (TOMS) and the advanced very high resolution radiometer (AVHRR) satellite detectors, of the 1982 eruption of El Chichón, Mexico. The volcanic cloud/atmosphere interactions in the first four days of this eruption were investigated by combining ultraviolet retrievals to estimate the mass of sulfur dioxide in the volcanic cloud [Krueger et al., 1995] with thermal infrared retrievals of the size, optical depth, and mass of fine-grained (1–10 μm radius) volcanic ash [Wen and Rose, 1994]. Our study provides the first direct evidence of gravitational separation of ash from a stratospheric, gas-rich, plinian eruption column and documents the marked differences in residence times of volcanic ash and sulfur dioxide in volcanic clouds. The eruption column reached as high as 32 km [Carey and Sigurdsson, 1986] and was injected into an atmosphere with a strong wind shear, which allowed for an observation of the separation of sulfur dioxide and volcanic ash. The upper, more sulfur dioxide-rich part of the cloud was transported to the west in the stratosphere, while the fine-grained ash traveled to the south in the troposphere. The mass of sulfur dioxide released was estimated at 7.1 × 109 kg with the mass decreasing by approximately 4% 1 day after the peak. The mass of fine-grained volcanic ash detected was estimated at 6.5 × 109 kg, amounting to about 0.7% of the estimated mass of the ash which fell out in the mapped ash blanket close to the volcano. Over the following days, 98% of this remaining fine ash was removed from the volcanic cloud, and the effective radius of ash in the volcanic cloud decreased from about 8 μm to about 4 μm.

Estimating optically-thin cirrus cloud induced cold bias on infrared radiometric satellite sea surface temperature retrieval in the tropics

NASA Astrophysics Data System (ADS)

Marquis, Jared Wayne

Passive longwave infrared radiometric satellite-based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically-thin cirrus (OTC) clouds (cloud optical depth ≤ 0.3; COD). Level 2 split-window SST retrievals over tropical oceans (30° S - 30° N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, mounted on the independent NASA CALIPSO satellite. OTC are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level-2 data, representing over 99% of all contaminating cirrus found. This results in cold-biased SST retrievals using either split- (MODIS, AVHRR and VIIRS) or triple-window (AVHRR and VIIRS only) retrieval methods. SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5 km thick OTC cloud placed incrementally from 10.0 - 18.0 km above mean sea level for cloud optical depths (COD) between 0.0 - 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud top height and COD (assuming them consistent across each platform) integrated within each corresponding modeled cold bias matrix. Split-window relative OTC cold biases, for any single observation, range from 0.40° - 0.49° C for the three sensors, with an absolute (bulk mean) bias between 0.10° - 0.13° C. Triple-window retrievals are more resilient, ranging from 0.03° - 0.04° C relative and 0.11° - 0.16° C absolute. Cold biases are constant across the Pacific and Indian Ocean domains. Absolute bias is smaller over the Atlantic, but relative bias is larger due to different cloud properties indicating that this issue persists globally.

Climate Trend Detection using Sea-Surface Temperature Data-sets from the (A)ATSR and AVHRR Space Sensors.

NASA Astrophysics Data System (ADS)

Llewellyn-Jones, D. T.; Corlett, G. K.; Remedios, J. J.; Noyes, E. J.; Good, S. A.

2007-05-01

Sea-Surface Temperature (SST) is an important indicator of global change, designated by GCOS as an essential Climate Variable (ECV). The detection of trends in Global SST requires rigorous measurements that are not only global, but also highly accurate and consistent. Space instruments can provide the means to achieve these required attributes in SST data. This paper presents an analysis of 15 years of SST data from two independent data sets, generated from the (A)ATSR and AVHRR series of sensors respectively. The analyses reveal trends of increasing global temperature between 0.13°C to 0.18 °C, per decade, closely matching that expected from some current predictions. A high level of consistency in the results from the two independent observing systems is seen, which gives increased confidence in data from both systems and also enables comparative analyses of the accuracy and stability of both data sets to be carried out. The conclusion is that these satellite SST data-sets provide important means to quantify and explore the processes of climate change. An analysis based upon singular value decomposition, allowing the removal of gross transitory disturbances, notably the El Niño, in order to examine regional areas of change other than the tropical Pacific, is also presented. Interestingly, although El Niño events clearly affect SST globally, they are found to have a non- significant (within error) effect on the calculated trends, which changed by only 0.01 K/decade when the pattern of El Niño and the associated variations was removed from the SST record. Although similar global trends were calculated for these two independent data sets, larger regional differences are noted. Evidence of decreased temperatures after the eruption of Mount Pinatubo in 1991 was also observed. The methodology demonstrated here can be applied to other data-sets, which cover long time-series observations of geophysical observations in order to characterise long-term change.

VIP Data Explorer: A Tool for Exploring 30 years of Vegetation Index and Phenology Observations

NASA Astrophysics Data System (ADS)

Barreto-munoz, A.; Didan, K.; Rivera-Camacho, J.; Yitayew, M.; Miura, T.; Tsend-Ayush, J.

2011-12-01

Continuous acquisition of global satellite imagery over the years has contributed to the creation of long term data records from AVHRR, MODIS, TM, SPOT-VGT and other sensors. These records account for 30+ years, as these archives grow, they become invaluable tools for environmental, resources management, and climate studies dealing with trends and changes from local, regional to global scale. In this project, the Vegetation Index and Phenology Lab (VIPLab) is processing 30 years of daily global surface reflectance data into an Earth Science Data Record of Vegetation Index and Phenology metrics. Data from AVHRR (N07,N09,N11 and N14) and MODIS (AQUA and TERRA collection 5) for the periods 1981-1999 and 2000-2010, at CMG resolution were processed into one seamless and sensor independent data record using various filtering, continuity and gap filling techniques (Tsend-Ayush et al., AGU 2011, Rivera-Camacho et al, AGU 2011). An interactive online tool (VIP Data Explorer) was developed to support the visualization, qualitative and quantitative exploration, distribution, and documentation of these records using a simple web 2.0 interface. The VIP Data explorer (http://vip.arizona.edu/viplab_data_explorer) can display any combination of multi temporal and multi source data, enable the quickly exploration and cross comparison of the various levels of processing of this data. It uses the Google Earth (GE) model and was developed using the GE API for images rendering, manipulation and geolocation. These ESDRs records can be quickly animated in this environment and explored for visual trends and anomalies detection. Additionally the tool enables extracting and visualizing any land pixel time series while showing the different levels of processing it went through. User can explore this ESDR database within this data explorer GUI environment, and any desired data can be placed into a dynamic "cart" to be ordered and downloaded later. More functionalities are planned and will be added to this data explorer tool as the project progresses.

Early evolution of a stratospheric volcanic eruption cloud as observed with TOMS and AVHRR

NASA Astrophysics Data System (ADS)

Schneider, David J.; Rose, William I.; Coke, Larry R.; Bluth, Gregg J. S.; Sprod, Ian E.; Krueger, Arlin J.

1999-02-01

This paper is a detailed study of remote sensing data from the total ozone mapping spectrometer (TOMS) and the advanced very high resolution radiometer (AVHRR) satellite detectors, of the 1982 eruption of El Chichón, Mexico. The volcanic cloud/atmosphere interactions in the first four days of this eruption were investigated by combining ultraviolet retrievals to estimate the mass of sulfur dioxide in the volcanic cloud [Krueger et al., 1995] with thermal infrared retrievals of the size, optical depth, and mass of fine-grained (1-10 μm radius) volcanic ash [Wen and Rose, 1994]. Our study provides the first direct evidence of gravitational separation of ash from a stratospheric, gas-rich, plinian eruption column and documents the marked differences in residence times of volcanic ash and sulfur dioxide in volcanic clouds. The eruption column reached as high as 32 km [Carey and Sigurdsson, 1986] and was injected into an atmosphere with a strong wind shear, which allowed for an observation of the separation of sulfur dioxide and volcanic ash. The upper, more sulfur dioxide-rich part of the cloud was transported to the west in the stratosphere, while the fine-grained ash traveled to the south in the troposphere. The mass of sulfur dioxide released was estimated at 7.1 × 109 kg with the mass decreasing by approximately 4% 1 day after the peak. The mass of fine-grained volcanic ash detected was estimated at 6.5 × 109 kg, amounting to about 0.7% of the estimated mass of the ash which fell out in the mapped ash blanket close to the volcano. Over the following days, 98% of this remaining fine ash was removed from the volcanic cloud, and the effective radius of ash in the volcanic cloud decreased from about 8 μm to about 4 μm.

Integrating remote sensing, geographic information system and modeling for estimating crop yield

NASA Astrophysics Data System (ADS)

Salazar, Luis Alonso

This thesis explores various aspects of the use of remote sensing, geographic information system and digital signal processing technologies for broad-scale estimation of crop yield in Kansas. Recent dry and drought years in the Great Plains have emphasized the need for new sources of timely, objective and quantitative information on crop conditions. Crop growth monitoring and yield estimation can provide important information for government agencies, commodity traders and producers in planning harvest, storage, transportation and marketing activities. The sooner this information is available the lower the economic risk translating into greater efficiency and increased return on investments. Weather data is normally used when crop yield is forecasted. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop assessment systems to be economical, more efficient methods for data collection and analysis are necessary. The purpose of this research is to use satellite data which provides 50 times more spatial information about the environment than the weather station network in a short amount of time at a relatively low cost. Specifically, we are going to use Advanced Very High Resolution Radiometer (AVHRR) based vegetation health (VH) indices as proxies for characterization of weather conditions.

Characterizing Mediterranean Land Surfaces as Component of the Regional Climate System by Remote Sensing

NASA Technical Reports Server (NTRS)

Bolle, H.-J.; Koslowsky, D.; Menenti, M.; Nerry, F.; Otterman, Joseph; Starr, D.

1998-01-01

Extensive areas in the Mediterranean region are subject to land degradation and desertification. The high variability of the coupling between the surface and the atmosphere affects the regional climate. Relevant surface characteristics, such as spectral reflectance, surface emissivity in the thermal-infrared region, and vegetation indices, serve as "primary" level indicators for the state of the surface. Their spatial, seasonal and interannual variability can be monitored from satellites. Using relationships between these primary data and combining them with prior information about the land surfaces (such as topography, dominant soil type, land use, collateral ground measurements and models), a second layer of information is built up which specifies the land surfaces as a component of the regional climate system. To this category of parameters which are directly involved in the exchange of energy, momentum and mass between the surface and the atmosphere, belong broadband albedo, thermodynamic surface temperature, vegetation types, vegetation cover density, soil top moisture, and soil heat flux. Information about these parameters finally leads to the computation of sensible and latent heat fluxes. The methodology was tested with pilot data sets. Full resolution, properly calibrated and normalized NOAA-AVHRR multi-annual primary data sets are presently compiled for the whole Mediterranean area, to study interannual variability and longer term trends.

On the impact of different volcanic hot spot detection methods on eruption energy quantification

NASA Astrophysics Data System (ADS)

Pergola, Nicola; Coviello, Irina; Falconieri, Alfredo; Lacava, Teodosio; Marchese, Francesco; Tramutoli, Valerio

2016-04-01

Several studies have shown that sensors like the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) may be effectively used to identify volcanic hotspots. These sensors offer in fact some spectral channels in the Medium Infrared (MIR) and Thermal Infrared (TIR) bands together with a good compromise between spatial and temporal resolution suited to study and monitor thermal volcanic activity. Many algorithms were developed to identify volcanic thermal anomalies from space with some of them that were extensively tested in very different geographich areas. In this work, we analyze the volcanic radiative power (VRP) representing one of parameters of major interest for volcanologists that may be estimated by satellite. In particular, we compare the radiative power estimations driven by some well-established state of the art hotspot detection methods (e.g. RSTVOLC, MODVOLC, HOTSAT). Differences in terms of radiative power estimations achieved during recent Mt. Etna (Italy) eruptions will be evaluated, assessing how much the VRP retrieved during effusive eruptions is affected by the sensitivity of hotspot detection methods.

[Comparisons and analysis of the spectral response functions' difference between FY-2E's and FY2C's split window channels].

PubMed

Zhang, Yong; Li, Yuan; Rong, Zhi-Guo

2010-06-01

Remote sensors' channel spectral response function (SRF) was one of the key factors to influence the quantitative products' inversion algorithm, accuracy and the geophysical characteristics. Aiming at the adjustments of FY-2E's split window channels' SRF, detailed comparisons between the FY-2E and FY-2C corresponding channels' SRF differences were carried out based on three data collections: the NOAA AVHRR corresponding channels' calibration look up tables, field measured water surface radiance and atmospheric profiles at Lake Qinghai and radiance calculated from the PLANK function within all dynamic range of FY-2E/C. The results showed that the adjustments of FY-2E's split window channels' SRF would result in the spectral range's movements and influence the inversion algorithms of some ground quantitative products. On the other hand, these adjustments of FY-2E SRFs would increase the brightness temperature differences between FY-2E's two split window channels within all dynamic range relative to FY-2C's. This would improve the inversion ability of FY-2E's split window channels.

[Variability of vegetation growth season in different latitudinal zones of North China: a monitoring by NOAA NDVI and MSAVI].

PubMed

Wang, Hong; Li, Xiaobing; Han, Ruibo; Ge, Yongqin

2006-12-01

In this study, North China was latitudinally divided into five zones, i.e., 32 degrees - 36 degrees N (Zone I), 36 degrees - 40 degrees N (Zone II), 40 degrees - 44 degrees N (Zone III), 44 degrees - 48 degrees N (Zone IV) and 48 degrees - 52 degrees N (Zone V), and the NOAA/ AVHRR NDVI and MSAVI time-series images from 1982 to 1999 were smoothed with Savitzky-Golay filter algorithm. Based on the EOF analysis, the principal components of NDVI and MSAVI for the vegetations in different latitudinal zones of North China were extracted, the annual beginning and ending dates and the length of growth season in 1982 - 1999 were estimated, and the related parameters were linearly fitted, aimed to analyze the variability of vegetation growth season. The results showed that the beginning date of the growth season in different zones tended to be advanced, while the ending date tended to be postponed with increasing latitude. The length of the growth season was also prolonged, with the prolonging time exceeded 10 days.

Boundary current instabilities, upwelling, shelf mixing and eutrophication processes in the Black Sea

NASA Astrophysics Data System (ADS)

Sur, Hali˙l. İ.; Özsoy, Emi˙n.; Ünlüata, Ümi˙t.

Satellite and in situ data are utilized to investigate the mesoscale dynamics of the Black Sea boundary current system with special emphasis on aspects of transport and productivity. The satellite data are especially helpful in capturing rapid sub-mesoscale motions insufficiently resolved by the in situ measurements. Various forms of isolated features, including dipole eddies and river plumes, are identified in the satellite images. Unstable flow structures at these sites appear to transport materials and momentum across the continental shelf. Species differentiation and competition are evident along the boundary current system and at the frontal regions during the development of early summer productivity. A time series of Coastal Zone Colour Scanner (CZCS) images indicate dynamical modulation of the springtime surface productivity in the southern Black Sea. Unstable meandering motions generated at Sakarya Canyon propagate east with speeds of ∼10-15 km d -1. Within weeks, a turbulent jet is created which separates from the coast, covering the entire southwestern sector. The nutrients driving the phytoplankton production (mainly Emiliana huxleyi) of the current system evidently originate from fluvial discharge entering from the northwestern region including the Danube river. The productivity pattern develops in early summer when the Danube inflow is at its peak, and through meandering motions spreads into an area several times wider than the continental shelf. In 1980, the CZCS data, and in 1991 and 1992, the Advanced Very High Resolution Radiometer (AVHRR) data indicate patches of upwelling along the west Anatolian coastline between Sakarya Canyon and Cape İnce ( Ince Burun) in summer. The upwelling phenomenon is outstanding because it occurs on a coast where normally the surface convergence near the coast implies downwelling, and under conditions of unfavorable winds. In 1992, the hydrographic data indicated the upwelling to be the result of a surface divergence of the boundary current, and sequences of satellite data indicate the role of transient dynamics. The in situ data showed the upwelling centres to be devoid of phytoplankton as well as fish eggs and larvae. The AVHRR and in situ hydrographic data in winter 1990 indicate cold water is formed over the entire western Black Sea continental shelf. The band of cold water decreases in width as it moves south and impinges on the headland at Baba Burnu, where it undergoes a sudden expansion. The maximum winter phytoplankton bloom sampled during the same period indicates explosive populations of diatoms following the band of cold water.

Improving the RST Approach for Earthquake Prone Areas Monitoring: Results of Correlation Analysis among Significant Sequences of TIR Anomalies and Earthquakes (M>4) occurred in Italy during 2004-2014

NASA Astrophysics Data System (ADS)

Tramutoli, V.; Coviello, I.; Filizzola, C.; Genzano, N.; Lisi, M.; Paciello, R.; Pergola, N.

2015-12-01

Looking toward the assessment of a multi-parametric system for dynamically updating seismic hazard estimates and earthquake short term (from days to weeks) forecast, a preliminary step is to identify those parameters (chemical, physical, biological, etc.) whose anomalous variations can be, to some extent, associated to the complex process of preparation of a big earthquake. Among the different parameters, the fluctuations of Earth's thermally emitted radiation, as measured by sensors on board of satellite system operating in the Thermal Infra-Red (TIR) spectral range, have been proposed since long time as potential earthquake precursors. Since 2001, a general approach called Robust Satellite Techniques (RST) has been used to discriminate anomalous thermal signals, possibly associated to seismic activity from normal fluctuations of Earth's thermal emission related to other causes (e.g. meteorological) independent on the earthquake occurrence. Thanks to its full exportability on different satellite packages, RST has been implemented on TIR images acquired by polar (e.g. NOAA-AVHRR, EOS-MODIS) and geostationary (e.g. MSG-SEVIRI, NOAA-GOES/W, GMS-5/VISSR) satellite sensors, in order to verify the presence (or absence) of TIR anomalies in presence (absence) of earthquakes (with M>4) in different seismogenic areas around the world (e.g. Italy, Turkey, Greece, California, Taiwan, etc.).In this paper, a refined RST (Robust Satellite Techniques) data analysis approach and RETIRA (Robust Estimator of TIR Anomalies) index were used to identify Significant Sequences of TIR Anomalies (SSTAs) during eleven years (from May 2004 to December 2014) of TIR satellite records, collected over Italy by the geostationary satellite sensor MSG-SEVIRI. On the basis of specific validation rules (mainly based on physical models and results obtained by applying RST approach to several earthquakes all around the world) the level of space-time correlation among SSTAs and earthquakes (with M≥4) occurrence has been evaluated. Achieved results will be discussed, also in the framework of a multi-parametric approach to time-Dependent Assessment of Seismic Hazard (t-DASH).

Multi-model perspectives and inter-comparison of soil moisture and evapotranspiration in East Africa—an application of Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS)

NASA Astrophysics Data System (ADS)

Pervez, M. S.; McNally, A.; Arsenault, K. R.

2017-12-01

Convergence of evidence from different agro-hydrologic sources is particularly important for drought monitoring in data sparse regions. In Africa, a combination of remote sensing and land surface modeling experiments are used to evaluate past, present and future drought conditions. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) routinely simulates daily soil moisture, evapotranspiration (ET) and other variables over Africa using multiple models and inputs. We found that Noah 3.3, Variable Infiltration Capacity (VIC) 4.1.2, and Catchment Land Surface Model based FLDAS simulations of monthly soil moisture percentile maps captured concurrent drought and water surplus episodes effectively over East Africa. However, the results are sensitive to selection of land surface model and hydrometeorological forcings. We seek to identify sources of uncertainty (input, model, parameter) to eventually improve the accuracy of FLDAS outputs. In absence of in situ data, previous work used European Space Agency Climate Change Initiative Soil Moisture (CCI-SM) data measured from merged active-passive microwave remote sensing to evaluate FLDAS soil moisture, and found that during the high rainfall months of April-May and November-December Noah-based soil moisture correlate well with CCI-SM over the Greater Horn of Africa region. We have found good correlations (r>0.6) for FLDAS Noah 3.3 ET anomalies and Operational Simplified Surface Energy Balance (SSEBop) ET over East Africa. Recently, SSEBop ET estimates (version 4) were improved by implementing a land surface temperature correction factor. We re-evaluate the correlations between FLDAS ET and version 4 SSEBop ET. To further investigate the reasons for differences between models we evaluate FLDAS soil moisture with Advanced Scatterometer and SMAP soil moisture and FLDAS outputs with MODIS and AVHRR normalized difference vegetation index. By exploring longer historic time series and near-real time products we will be aiding convergence of evidence for better understanding of historic drought, improved monitoring and forecasting, and better understanding of uncertainties of water availability estimation over Africa

Detection of Wildfires with Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Umphlett, B.; Leeman, J.; Morrissey, M. L.

2011-12-01

Currently fire detection for the National Oceanic and Atmospheric Administration (NOAA) using satellite data is accomplished with algorithms and error checking human analysts. Artificial neural networks (ANNs) have been shown to be more accurate than algorithms or statistical methods for applications dealing with multiple datasets of complex observed data in the natural sciences. ANNs also deal well with multiple data sources that are not all equally reliable or equally informative to the problem. An ANN was tested to evaluate its accuracy in detecting wildfires utilizing polar orbiter numerical data from the Advanced Very High Resolution Radiometer (AVHRR). Datasets containing locations of known fires were gathered from the NOAA's polar orbiting satellites via the Comprehensive Large Array-data Stewardship System (CLASS). The data was then calibrated and navigation corrected using the Environment for Visualizing Images (ENVI). Fires were located with the aid of shapefiles generated via ArcGIS. Afterwards, several smaller ten pixel by ten pixel datasets were created for each fire (using the ENVI corrected data). Several datasets were created for each fire in order to vary fire position and avoid training the ANN to look only at fires in the center of an image. Datasets containing no fires were also created. A basic pattern recognition neural network was established with the MATLAB neural network toolbox. The datasets were then randomly separated into categories used to train, validate, and test the ANN. To prevent over fitting of the data, the mean squared error (MSE) of the network was monitored and training was stopped when the MSE began to rise. Networks were tested using each channel of the AVHRR data independently, channels 3a and 3b combined, and all six channels. The number of hidden neurons for each input set was also varied between 5-350 in steps of 5 neurons. Each configuration was run 10 times, totaling about 4,200 individual network evaluations. Thirty network parameters were recorded to characterize performance. These parameters were plotted with various data display techniques to determine which network configuration was not only most accurate in fire classification, but also the most computationally efficient. The most accurate fire classification network used all six channels of AVHRR data to achieve an accuracy ranging from 73-90%.

EUMETCast: The Meteorological Data Dissemination Service

NASA Astrophysics Data System (ADS)

Gaertner, V. K.; Koenig, M.

2006-05-01

EUMETCast is EUMETSAT's broadcast system for environmental data. It utilises telecommunications satellites and the services of telecommunication providers to distribute data files using Digital Video Broadcast (DVB) standards to a wide audience located within the combined geographical coverage zones of the individual telecommunication satellites used to transmit the data. The telecommunication zones are now covering Europe, Africa, South America and parts of Asia and North America. This service has been established to provide the meteorological communities with satellite data and other meteorological products in near real-time for operational, but also research, education and training purposes. The following EUMETSAT services are currently available via EUMETCast: - Second Generation Meteosat - High Rate SEVIRI Image Data (every 15 minutes) - First Generation Meteosat - Indian Ocean Data Coverage (IODC) (every 30 minutes) - Other Geostationary Data from NOAA (GOES E/W) and JMA (MTSAT), (every 3 hours) - Data Collection and Retransmission (DCP) and Meteorological Data Dissemination (MDD) - Basic Meteorological Data (BMD) (Ku-band Europe only) - Meteorological Products (including some Satellite Application Facility products) - EUMETSAT Advanced Retransmission Service (EARS) (Ku-band Europe only) - DWDSAT (Ku-band Europe only) - VEGETATION data (C-band Africa only) Progressively during 2006 users will find an increasing amount of polar satellite data and products available on EUMETCast. As part of the extension of the EUMETCast Advanced Retransmission Service (EARS), ERS scatterometer data and NOAA satellite AVHRR data have already been introduced in early 2006. The ERS- SCAT demonstration service is a forerunner for the future pilot EARS-ASCAT service and the pilot EARS- AVHRR service will continue to expand during 2006 with the inclusion of data from additional AVHRR stations in the EARS network. The EUMETCast System will be also be used to provide dissemination of EPS (EUMETSAT Polar System) global products in Ku-band, for a European audience. The EUMETCast South America service commenced dissemination trials on 1 January 2006 with the formal start of service on 1 April 2006. The service comprising of MSG SEVIRI High Rate Image data disseminated every 15 minutes, will ensure continuity in the provision of Meteosat data to this region when the first generation Meteosat services from 0o will terminate. EUMETSAT Data Policy principles apply to some of the above services. Access to DWDSAT is in accordance with the data policy of Deutscher Wetterdienst.

Correcting Satellite Image Derived Surface Model for Atmospheric Effects

NASA Technical Reports Server (NTRS)

Emery, William; Baldwin, Daniel

1998-01-01

This project was a continuation of the project entitled "Resolution Earth Surface Features from Repeat Moderate Resolution Satellite Imagery". In the previous study, a Bayesian Maximum Posterior Estimate (BMPE) algorithm was used to obtain a composite series of repeat imagery from the Advanced Very High Resolution Radiometer (AVHRR). The spatial resolution of the resulting composite was significantly greater than the 1 km resolution of the individual AVHRR images. The BMPE algorithm utilized a simple, no-atmosphere geometrical model for the short-wave radiation budget at the Earth's surface. A necessary assumption of the algorithm is that all non geometrical parameters remain static over the compositing period. This assumption is of course violated by temporal variations in both the surface albedo and the atmospheric medium. The effect of the albedo variations is expected to be minimal since the variations are on a fairly long time scale compared to the compositing period, however, the atmospheric variability occurs on a relatively short time scale and can be expected to cause significant errors in the surface reconstruction. The current project proposed to incorporate an atmospheric correction into the BMPE algorithm for the purpose of investigating the effects of a variable atmosphere on the surface reconstructions. Once the atmospheric effects were determined, the investigation could be extended to include corrections various cloud effects, including short wave radiation through thin cirrus clouds. The original proposal was written for a three year project, funded one year at a time. The first year of the project focused on developing an understanding of atmospheric corrections and choosing an appropriate correction model. Several models were considered and the list was narrowed to the two best suited. These were the 5S and 6S shortwave radiation models developed at NASA/GODDARD and tested extensively with data from the AVHRR instrument. Although the 6S model was a successor to the 5S and slightly more advanced, the 5S was selected because outputs from the individual components comprising the short-wave radiation budget were more easily separated. The separation was necessary since both the 5S and 6S did not include geometrical corrections for terrain, a fundamental constituent of the BMPE algorithm. The 5S correction code was incorporated into the BMPE algorithm and many sensitivity studies were performed.

Regional fuel load modeled for two contrasting years in central and southern Africa

NASA Astrophysics Data System (ADS)

Hely, C.; Dowty, P. R.; Alleaume, S.; Caylor, K. K.; Shugart, H. H.

2001-12-01

Fuel load has been modeled for southern hemisphere Africa for the 1991-92 and 1999-2000 growing seasons. The 1991-92 year was generally dry due to a strong El Nino event in contrast to the particularly wet year of 1999-2000. The method integrates site-level process modeling with 15 day AVHRR NDVI data. The site model was used to simulate landscape light-use efficiency (LUE) at a series of sites in the Kalahari region ranging from evergreen woodland to arid shrubland. This site-level LUE is extrapolated over the southern African region with gridded tree cover data and gridded rainfall. The predicted net primary production (NPP) is allocated into the different fuel types (grass, litter, twigs) using empirical based relationships. The model results are compared with field measurements of fuel load at a number of sites. The results will be used for modeling of biomass burning emissions.

Validation of Cloud Properties From Multiple Satellites Using CALIOP Data

NASA Technical Reports Server (NTRS)

Yost, Christopher R.; Minnis, Patrick; Bedka, Kristopher M.; Heck, Patrick W.; Palikonda, Rabindra; Sun-Mack, Sunny; Trepte, Qing

2016-01-01

The NASA Langley Satellite ClOud and Radiative Property retrieval System (SatCORPS) is routinely applied to multispectral imagery from several geostationary and polar-orbiting imagers to retrieve cloud properties for weather and climate applications. Validation of the retrievals with independent datasets is continuously ongoing in order to understand differences caused by calibration, spatial resolution, viewing geometry, and other factors. The CALIOP instrument provides a decade of detailed cloud observations which can be used to evaluate passive imager retrievals of cloud boundaries, thermodynamic phase, cloud optical depth, and water path on a global scale. This paper focuses on comparisons of CALIOP retrievals to retrievals from MODIS, VIIRS, AVHRR, GOES, SEVIRI, and MTSAT. CALIOP is particularly skilled at detecting weakly-scattering cirrus clouds with optical depths less than approx. 0.5. These clouds are often undetected by passive imagers and the effect this has on the property retrievals is discussed.

Ku band airborne radar altimeter observations of marginal sea ice during the 1984 Marginal Ice Zone Experiment

NASA Technical Reports Server (NTRS)

Drinkwater, Mark R.

1991-01-01

Pulse-limited, airborne radar data taken in June and July 1984 with a 13.8-GHz altimeter over the Fram Strait marginal ice zone are analyzed with the aid of large-format aerial photography, airborne synthetic aperture radar data, and surface observations. Variations in the radar return pulse waveforms are quantified and correlated with ice properties recorded during the Marginal Ice Zone Experiment. Results indicate that the wide-beam altimeter is a flexible instrument, capable of identifying the ice edge with a high degree of accuracy, calculating the ice concentration, and discriminating a number of different ice classes. This suggests that microwave radar altimeters have a sensitivity to sea ice which has not yet been fully exploited. When fused with SSM/I, AVHRR and ERS-1 synthetic aperture radar imagery, future ERS-1 altimeter data are expected to provide some missing pieces to the sea ice geophysics puzzle.

A physically-based retrieval of cloud liquid water from SSM/I measurements

NASA Technical Reports Server (NTRS)

Greenwald, Thomas J.; Stephens, Graeme L.; Vonder Haar, Thomas H.

1992-01-01

A simple physical scheme is proposed for retrieving cloud liquid water over the ice-free global oceans from Special Sensor Microwave/Imager (SSM/I) observations. Details of the microwave retrieval scheme are discussed, and the microwave-derived liquid water amounts are compared with the ground radiometer and AVHRR-derived liquid water for stratocumulus clouds off the coast of California. Global distributions of the liquid water path derived by the method proposed here are presented.

Application of remote sensing in tropical forests

NASA Technical Reports Server (NTRS)

Joyce, Armond T.; Luvall, J. C.; Sever, T.

1990-01-01

Cloud cover in tropical humid forests can pose serious operational constraints on Landsat TM and SPOT HRV instrumentation, given their respective orbital frequencies of 16 and 26 days. SAR data intrinsically precludes such problems; the increase of data acquisition frequency to daily rates, as with the NOAA AVHRR instrument, also bears consideration. It is deemed essential that SAR data-related research be expedited, in order to ascertain inherent SAR information for tropical forests in a timely and cost-effective manner.

Earth Explorer

USGS Publications Warehouse

,

2000-01-01

The U.S. Geological Survey's (USGS) Earth Explorer Web site provides access to millions of land-related products, including the following: Satellite images from Landsat, advanced very high resolution radiometer (AVHRR), and Corona data sets. Aerial photographs from the National Aerial Photography Program, NASA, and USGS data sets.  Digital cartographic data from digital elevation models, digital line graphs, digital raster graphics, and digital orthophoto quadrangles. USGS paper maps Digital, film, and paper products are available, and many products can be previewed before ordering.

Validation of EOS Aqua AMSR Sea Ice Products for East Antarctica

NASA Technical Reports Server (NTRS)

Massom, Rob; Lytle, Vicky; Allison, Ian; Worby, Tony; Markus, Thorsten; Scambos, Ted; Haran, Terry; Enomoto, Hiro; Tateyama, Kazu; Pfaffling, Andi

2004-01-01

This paper presents results from AMSR-E validation activities during a collaborative international cruise onboard the RV Aurora Australis to the East Antarctic sea ice zone (64-65 deg.S, 110-120 deg.E) in the early Austral spring of 2003. The validation strategy entailed an IS-day survey of the statistical characteristics of sea ice and snowcover over a Lagrangian grid 100 x 50 km in size (demarcated by 9 drifting ice beacons) i.e. at a scale representative of Ah4SR pixels. Ice conditions ranged h m consolidated first-year ice to a large polynya offshore from Casey Base. Data sets collected include: snow depth and snow-ice interface temperatures on 24 (?) randomly-selected floes in grid cells within a 10 x 50 km area (using helicopters); detailed snow and ice measurements at 13 dedicated ice stations, one of which lasted for 4 days; time-series measurements of snow temperature and thickness at selected sites; 8 aerial photography and thermal-IR radiometer flights; other satellite products (SAR, AVHRR, MODIS, MISR, ASTER and Envisat MERIS); ice drift data; and ancillary meteorological (ship-based, meteorological buoys, twice-daily radiosondes). These data are applied to a validation of standard AMSR-E ice concentration, snowcover thickness and ice-temperature products. In addition, a validation is carried out of ice-surface skin temperature products h m the NOAA AVHRR and EOS MODIS datasets.

Assimilation for skin SST in the NASA GEOS atmospheric data assimilation system

PubMed Central

Akella, Santha; Todling, Ricardo; Suarez, Max

2018-01-01

The present article describes the sea surface temperature (SST) developments implemented in the Goddard Earth Observing System, Version 5 (GEOS-5) Atmospheric Data Assimilation System (ADAS). These are enhancements that contribute to the development of an atmosphere-ocean coupled data assimilation system using GEOS. In the current quasi-operational GEOS-ADAS, the SST is a boundary condition prescribed based on the OSTIA product, therefore SST and skin SST (Ts) are identical. This work modifies the GEOS-ADAS Ts by modeling and assimilating near sea surface sensitive satellite infrared (IR) observations. The atmosphere-ocean interface layer of the GEOS atmospheric general circulation model (AGCM) is updated to include near surface diurnal warming and cool-skin effects. The GEOS analysis system is also updated to directly assimilate SST-relevant Advanced Very High Resolution Radiometer (AVHRR) radiance observations. Data assimilation experiments designed to evaluate the Ts modification in GEOS-ADAS show improvements in the assimilation of radiance observations that extends beyond the thermal IR bands of AVHRR. In particular, many channels of hyperspectral sensors, such as those of the Atmospheric Infrared Sounder (AIRS), and Infrared Atmospheric Sounding Interferometer (IASI) are also better assimilated. We also obtained improved fit to withheld, in-situ buoy measurement of near-surface SST. Evaluation of forecast skill scores show marginal to neutral benefit from the modified Ts. PMID:29628531

Validation of the Version 1 NOAA/NASA Pathfinder Sea Surface Temperature Data Set

NASA Technical Reports Server (NTRS)

Smith, Elizabeth A.

1998-01-01

A high-resolution, global satellite-derived sea surface temperature (SST) data set called Pathfinder, from the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA Polar Orbiters, is available from the Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center (JPL PO.DAAC). Suitable for research as well as education, the Pathfinder SST data set is a result of a collaboration between the National Oceanographic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA) and investigators at several universities. NOAA and NASA are the sponsors of the Pathfinder Program, which takes advantage of currently archived Earth science data from satellites. Where necessary, satellite sensors have been intercalibrated, algorithms improved and processing procedures revised, in order to produce long time-series, global measurements of ocean, land and atmospheric properties necessary for climate research. Many Pathfinder data sets are available to researchers now, nearly a decade before the first launch of NASA's Earth Observing System (EOS). The lessons learned from the Pathfinder programs will facilitate the processing and management of terabytes of data from EOS. The Oceans component of Pathfinder has undertaken to reprocess all Global Area Coverage (GAC) data acquired by the 5-channel AVHRRs since 1981. The resultant data products are consistent and stably calibrated [Rao, 1993a, Rao, 1993b, Brown et al., 1993], Earth-gridded SST fields at a variety of spatial and temporal resolutions.

Effects of Telecoupling on Global Vegetation Dynamics

NASA Astrophysics Data System (ADS)

Viña, A.; Liu, J.

2016-12-01

With the ever increasing trend in telecoupling processes, such as international trade, all countries around the world are becoming more interdependent. However, the effects of this growing interdependence on vegetation (e.g., shifts in the geographic extent and distribution) remain unknown even though vegetation dynamics are crucially important for food production, carbon sequestration, provision of other ecosystem services, and biodiversity conservation. In this study we evaluate the effects of international trade on the spatio-temporal trajectories of vegetation at national and global scales, using vegetation index imagery collected over more than three decades by the Advanced Very High Resolution Radiometer (AVHRR) satellite sensor series together with concurrent national and international data on international trade (and its associated movement of people, goods, services and information). The spatio-temporal trajectories of vegetation are obtained using the scale of fluctuation technique, which is based on the decomposition of the AVHRR image time series to obtain information on its spatial dependence structure over time. Similar to the correlation length, the scale of fluctuation corresponds to the range over which fluctuations in the vegetation index are spatially correlated. Results indicate that global vegetation has changed drastically over the last three decades. These changes are not uniform across space, with hotspots in active trading countries. This study not only has direct implications for understanding global vegetation dynamics, but also sheds important insights on the complexity of human-nature interactions across telecoupled systems.

The use of satellite data for monitoring temporal and spatial patterns of fire: a comprehensive review

NASA Astrophysics Data System (ADS)

Lasaponara, R.

2009-04-01

Remotely sensed (RS) data can fruitfully support both research activities and operative monitoring of fire at different temporal and spatial scales with a synoptic view and cost effective technologies. "The contribution of remote sensing (RS) to forest fires may be grouped in three categories, according to the three phases of fire management: (i) risk estimation (before fire), (ii) detection (during fire) and (iii) assessment (after fire)" Chuvieco (2006). Relating each phase, wide research activities have been conducted over the years. (i) Risk estimation (before fire) has been mainly based on the use of RS data for (i) monitoring vegetation stress and assessing variations in vegetation moisture content, (ii) fuel type mapping, at different temporal and spatial scales from global, regional down to a local scale (using AVHRR, MODIS, TM, ASTER, Quickbird images and airborne hyperspectral and LIDAR data). Danger estimation has been mainly based on the use of AVHRR (onborad NOAA), MODIS (onboard TERRA and AQUA), VEGETATION (onboard SPOT) due to the technical characteristics (i.e. spectral, spatial and temporal resolution). Nevertheless microwave data have been also used for vegetation monitoring. (ii) Detection: identification of active fires, estimation of fire radiative energy and fire emission. AVHRR was one of the first satellite sensors used for setting up fire detection algorithms. The availbility of MODIS allowed us to obtain global fire products free downloaded from NASA web site. Sensors onboard geostationary satellite platforms, such as GOES, SEVIRI, have been used for fire detection, to obtain a high temporal resolution (at around 15 minutes) monitoring of active fires. (iii) Post fire damage assessment includes: burnt area mapping, fire emission, fire severity, vegetation recovery, fire resilience estimation, and, more recently, fire regime characterization. Chuvieco E. L. Giglio, C. Justice, 2008 Global charactrerization of fire activity: toward defining fire regimes from Earth observation data Global Change Biology vo. 14. doi: 10.1111/j.1365-2486.2008.01585.x 1-15, Chuvieco E., P. Englefield, Alexander P. Trishchenko, Yi Luo Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2381-2396 Chuvieco Emilio 2006, Remote Sensing of Forest Fires: Current limitations and future prospects in Observing Land from Space: Science, Customers and Technology, Advances in Global Change Research Vol. 4 pp 47-51 De Santis A., E. Chuvieco Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models, Remote Sensing of Environment, Volume 108, Issue 4, 29 June 2007, Pages 422-435. De Santis A., E. Chuvieco, Patrick J. Vaughan, Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models, Remote Sensing of Environment, Volume 113, Issue 1, 15 January 2009, Pages 126-136 García M., E. Chuvieco, H. Nieto, I. Aguado Combining AVHRR and meteorological data for estimating live fuel moisture content Remote Sensing of Environment, Volume 112, Issue 9, 15 September 2008, Pages 3618-3627 Ichoku C., L. Giglio, M. J. Wooster, L. A. Remer Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy. Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2950-2962. Lasaponara R. and Lanorte, On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape Ecological Modelling Volume 204, Issues 1-2, 24 May 2007, Pages 79-84 Lasaponara R., A. Lanorte, S. Pignatti,2006 Multiscale fuel type mapping in fragmented ecosystems: preliminary results from Hyperspectral MIVIS and Multispectral Landsat TM data, Int. J. Remote Sens., vol. 27 (3) pp. 587-593. Lasaponara R., V. Cuomo, M. F. Macchiato, and T. Simoniello, 2003 .A self-adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection.n International Journal of Remote Sensing, vol. 24, No 8, 1723-1749. Minchella A., F. Del Frate, F. Capogna, S. Anselmi, F. Manes Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas Remote Sensing of Environment, In Press Næsset E., T. Gobakken Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 3079-3090 Peterson S. H, Dar A. Roberts, Philip E. Dennison Mapping live fuel moisture with MODIS data: A multiple regression approach, Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4272-4284. Schroeder Wilfrid, Elaine Prins, Louis Giglio, Ivan Csiszar, Christopher Schmidt, Jeffrey Morisette, Douglas Morton Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2711-2726 Shi J., T. Jackson, J. Tao, J. Du, R. Bindlish, L. Lu, K.S. Chen Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4285-4300 Tansey, K., Grégoire, J-M., Defourny, P., Leigh, R., Pekel, J-F., van Bogaert, E. and Bartholomé, E., 2008 A New, Global, Multi-Annual (2000-2007) Burnt Area Product at 1 km Resolution and Daily Intervals Geophysical Research Letters, VOL. 35, L01401, doi:10.1029/2007GL031567, 2008. Telesca L. and Lasaponara R., 2006; "Pre-and Post- fire Behaviural trends revealed in satellite NDVI time series" Geophysical Research Letters,., 33, L14401, doi:10.1029/2006GL026630 Telesca L. and Lasaponara R 2005 Discriminating Dynamical Patterns in Burned and Unburned Vegetational Covers by Using SPOT-VGT NDVI Data. Geophysical Research Letters,, 32, L21401, doi:10.1029/2005GL024391. Telesca L. and Lasaponara R. Investigating fire-induced behavioural trends in vegetation covers , Communications in Nonlinear Science and Numerical Simulation, 13, 2018-2023, 2008 Telesca L., A. Lanorte and R. Lasaponara, 2007. Investigating dynamical trends in burned and unburned vegetation covers by using SPOT-VGT NDVI data. Journal of Geophysics and Engineering, Vol. 4, pp. 128-138, 2007 Telesca L., R. Lasaponara, and A. Lanorte, Intra-annual dynamical persistent mechanisms in Mediterranean ecosystems revealed SPOT-VEGETATION Time Series, Ecological Complexity, 5, 151-156, 2008 Verbesselt, J., Somers, B., Lhermitte, S., Jonckheere, I., van Aardt, J., and Coppin, P. (2007) Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sensing of Environment 108: 357-368. Zhang X., S. Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897 Zhang X., Shobha Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897

PC-SEAPAK - ANALYSIS OF COASTAL ZONE COLOR SCANNER AND ADVANCED VERY HIGH RESOLUTION RADIOMETER DATA

NASA Technical Reports Server (NTRS)

Mcclain, C. R.

1994-01-01

PC-SEAPAK is a user-interactive satellite data analysis software package specifically developed for oceanographic research. The program is used to process and interpret data obtained from the Nimbus-7/Coastal Zone Color Scanner (CZCS), and the NOAA Advanced Very High Resolution Radiometer (AVHRR). PC-SEAPAK is a set of independent microcomputer-based image analysis programs that provide the user with a flexible, user-friendly, standardized interface, and facilitates relatively low-cost analysis of oceanographic satellite data. Version 4.0 includes 114 programs. PC-SEAPAK programs are organized into categories which include CZCS and AVHRR level-1 ingest, level-2 analyses, statistical analyses, data extraction, remapping to standard projections, graphics manipulation, image board memory manipulation, hardcopy output support and general utilities. Most programs allow user interaction through menu and command modes and also by the use of a mouse. Most programs also provide for ASCII file generation for further analysis in spreadsheets, graphics packages, etc. The CZCS scanning radiometer aboard the NIMBUS-7 satellite was designed to measure the concentration of photosynthetic pigments and their degradation products in the ocean. AVHRR data is used to compute sea surface temperatures and is supported for the NOAA 6, 7, 8, 9, 10, 11, and 12 satellites. The CZCS operated from November 1978 to June 1986. CZCS data may be obtained free of charge from the CZCS archive at NASA/Goddard Space Flight Center. AVHRR data may be purchased through NOAA's Satellite Data Service Division. Ordering information is included in the PC-SEAPAK documentation. Although PC-SEAPAK was developed on a COMPAQ Deskpro 386/20, it can be run on most 386-compatible computers with an AT bus, EGA controller, Intel 80387 coprocessor, and MS-DOS 3.3 or higher. A Matrox MVP-AT image board with appropriate monitor and cables is also required. Note that the authors have received some reports of incompatibilities between the MVP-AT image board and ZENITH computers. Also, the MVP-AT image board is not necessarily compatible with 486-based systems; users of 486-based systems should consult with Matrox about compatibility concerns. Other PC-SEAPAK requirements include a Microsoft mouse (serial version), 2Mb RAM, and 100Mb hard disk space. For data ingest and backup, 9-track tape, 8mm tape and optical disks are supported and recommended. PC-SEAPAK has been under development since 1988. Version 4.0 was updated in 1992, and is distributed without source code. It is available only as a set of 36 1.2Mb 5.25 inch IBM MS-DOS format diskettes. PC-SEAPAK is a copyrighted product with all copyright vested in the National Aeronautics and Space Administration. Phar Lap's DOS_Extender run-time version is integrated into several of the programs; therefore, the PC-SEAPAK programs may not be duplicated. Three of the distribution diskettes contain DOS_Extender files. One of the distribution diskettes contains Media Cybernetics' HALO88 font files, also licensed by NASA for dissemination but not duplication. IBM is a registered trademark of International Business Machines. MS-DOS is a registered trademark of Microsoft Corporation. HALO88 is a registered trademark of Media Cybernetics, but the product was discontinued in 1991.

Remote Sensing of Volcanic Clouds: Sulfur Gases and Plume Top Topography

NASA Technical Reports Server (NTRS)

Crisp, Joy A.

1999-01-01

New absorption line parameters for H2S were published and submitted to the Gestion et Etude des Informations Spectroscopiques Atmospheriques (GEISA) and high resolution transmission molecular absorption (HITRAN) databases. These new absorption line parameters will make it possible to use observations from the future Tropospheric Emission Spectrometer (TES) instrument [Earth Observing System (EOS) Chemistry Mission (CHEM) platform] to make more accurate H2S measurements if it observes an H2S-rich volcanic cloud. H2S is the second most abundant volcanic sulfur gas, and like SO2, it also converts to H2SO4 aerosols and can have a climate impact. A paper on the Moderate-resolution Imaging-Spectroradiometer (MODIS) SO2 alert is being revised. New aspects in the revision include verification of the SO2 alert during the EOS mission; factors affecting SO2 detection at thermal infrared, ultraviolet, and microwave wavelengths; radiative transfer tests; more description of satellite instruments; and thermal surface alert installed for MODIS. Her research involves the use of remote sensing to generate maps of plume top altitude. This parameter is important for models of volcanic eruption, aircraft hazards, and climate impact. The topographic shape of the top surface of a volcanic plume can provide information necessary to understand the physics controlling the injection and dispersal of a volcanic plume in the atmosphere. Glaze et al. describe the application of a photoclinometric technique to volcanic plumes. The software algorithm has been improved to account for more general plume and illumination geometries and for easily extracting position information directly from Advanced Very High-Resolution Radiometer (AVHRR) level 1B data. Testing of the algorithm has focused on acquiring AVHRR data for a variety of volcanic plumes in an effort to identify problems with the software as well as model sensitivities. The plumes chosen were erupted from volcanoes at a variety of latitudes, at different times of day, and in different seasons. Another important issue that is being investigated is the importance of plume opacity. A diagram illustrates the method for deriving phi, the angle a wind-blown plume makes with the horizontal, in the direction perpendicular to the remote sensing scan. A figure shows a sample output of the algorithm, a wire mesh map of plume top topography. Additional information is contained in the original.

Image masking using polygon fills and morphological transformations

NASA Technical Reports Server (NTRS)

Simpson, James J.

1992-01-01

Polygon-fill operations and morphological transformations are effective computational tools for the land-masking and coastline-correction preprocessing operations often applied to AVHRR data prior to oceanographic applications. These masking operations, in conjunction with cloud-screening techniques, can be used on such other oceanographically significant remote-sensing data as those of the Coastal Zone Color Scanner, GOES, and Landsat. The sensitivity of the methods to regional variations in atmospheric conditions and land-ocean temperature gradients is assessed for tropical, midlatitude, and high latitude regions.