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- Author or Editor: Garik Gutman x
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Abstract
The utility of the midafternoon satellite derived surface temperatures for detecting drought events is examined using the NOAA-9 AVHRR data over the U.S. Great Plains during 1986–88. The interannual differences in monthly mean clear-sky temperature and in monthly mean normalized difference vegetation index are compared to the corresponding differences in the Palmer Drought Index.
Results indicate that the thermal data from polar orbiters may be very useful in detecting the interannual changes in surface moisture when the change in vegetation index fails to produce a significant signal.
Abstract
The utility of the midafternoon satellite derived surface temperatures for detecting drought events is examined using the NOAA-9 AVHRR data over the U.S. Great Plains during 1986–88. The interannual differences in monthly mean clear-sky temperature and in monthly mean normalized difference vegetation index are compared to the corresponding differences in the Palmer Drought Index.
Results indicate that the thermal data from polar orbiters may be very useful in detecting the interannual changes in surface moisture when the change in vegetation index fails to produce a significant signal.
Abstract
This paper reviews satellite datasets from the NOAA Advanced Very High Resolution Radiometer that could be employed in support of numerical climate modeling at regional and global scales. Presently available NOAA operational and research datasets of different resolutions as well as the NASA–NOAA Pathfinder dataset, available in the near future, are briefly described. Specific problems in deriving surface characteristics in the context of their potential use for models are discussed. Possible ways of solving these problems are briefly described, based on the state-of-the-art level of understanding in this area of research. Some examples of seasonal variability of AVHRR-derived surface parameters, such as albedo, greenness, and clear-sky midafternoon temperature, for different climatic regions are presented. Validation issues and potential operational production of such land climate parameters are discussed.
Abstract
This paper reviews satellite datasets from the NOAA Advanced Very High Resolution Radiometer that could be employed in support of numerical climate modeling at regional and global scales. Presently available NOAA operational and research datasets of different resolutions as well as the NASA–NOAA Pathfinder dataset, available in the near future, are briefly described. Specific problems in deriving surface characteristics in the context of their potential use for models are discussed. Possible ways of solving these problems are briefly described, based on the state-of-the-art level of understanding in this area of research. Some examples of seasonal variability of AVHRR-derived surface parameters, such as albedo, greenness, and clear-sky midafternoon temperature, for different climatic regions are presented. Validation issues and potential operational production of such land climate parameters are discussed.
Abstract
Monthly mean diurnal cycles (MDCs) of surface temperatures over land, represented in 3-h universal time intervals, have been analyzed. Satellite near-global data from the International Satellite Cloud Climatology Project (ISCCP) with a (280 km)2 resolution (C-2 product) are available for seven individual years and as a climatology derived thereof. Surface 19-yr climatologies on ground and air temperatures, separately for all-sky and clear-sky conditions, matched with the ISCCP data, are employed to better understand satellite-derived MDCs.
The MDCs have been converted to local solar time, refined to a regular 1-h time grid using cubic splines, and subjected to principal component analysis. The first two modes approximate MDCs in air and ground–satellite temperatures with rmse’s of about σ = 0.5° and 1°C, respectively, and these accuracies are improved by 20%–35% if the third mode is added. This suggests that two to three temperature measurements during the day allow reconstruction of the full MDC. In the case of two modes, optimal observation times are close to the occurrence of minimum and maximum temperatures, T min and T max. The authors provide an empirical algorithm for reconstructing the full MDC using T min and T max, and estimate its accuracy. In the analyzed match-up dataset, the statistical structure of ground temperature for all-sky conditions most closely resembles that of the ISCCP derived temperature. The results are potentially useful for climate- and global-scale studies and applications.
Abstract
Monthly mean diurnal cycles (MDCs) of surface temperatures over land, represented in 3-h universal time intervals, have been analyzed. Satellite near-global data from the International Satellite Cloud Climatology Project (ISCCP) with a (280 km)2 resolution (C-2 product) are available for seven individual years and as a climatology derived thereof. Surface 19-yr climatologies on ground and air temperatures, separately for all-sky and clear-sky conditions, matched with the ISCCP data, are employed to better understand satellite-derived MDCs.
The MDCs have been converted to local solar time, refined to a regular 1-h time grid using cubic splines, and subjected to principal component analysis. The first two modes approximate MDCs in air and ground–satellite temperatures with rmse’s of about σ = 0.5° and 1°C, respectively, and these accuracies are improved by 20%–35% if the third mode is added. This suggests that two to three temperature measurements during the day allow reconstruction of the full MDC. In the case of two modes, optimal observation times are close to the occurrence of minimum and maximum temperatures, T min and T max. The authors provide an empirical algorithm for reconstructing the full MDC using T min and T max, and estimate its accuracy. In the analyzed match-up dataset, the statistical structure of ground temperature for all-sky conditions most closely resembles that of the ISCCP derived temperature. The results are potentially useful for climate- and global-scale studies and applications.
Abstract
Recently, a statistical procedure was proposed to analyze the angular effect in the NOAA Advanced Very High Resolution Radiometer (AVHRR) brightness temperatures. The estimated empirical angular functions (EAF) over the oceans allow one to check the algorithms for the sea surface temperature (SST) and the column water vapor content when the observation geometry is variable, as well as to test angular methods of SST retrieval. The EAF approach has been previously applied to the analysis of the AVHRR brightness temperatures in channels 3 and 4 and dual-window SST over the tropical Atlantic in June 1987 and December 1988 from NOAA-10 and NOAA-11, respectively. Here, it is extended to estimate the accuracy of the split-window sea surface temperature and atmospheric water vapor retrievals from NOAA-9 over the tropical and North Atlantic in July 1986. The authors confirm the previously drawn conclusion that in a general case no angle-independent coefficients in a linear SST retrieval algorithm can provide angle-invariant retrievals. More recent operational NOAA angle-dependent algorithms have been shown to improve retrievals in the Tropics. In high latitudes, they seem to slightly overcorrect the angular effect. Using satellite data of higher spatial resolution with better radiometric accuracy is expected to improve the accuracy of the EAFs and the reliability of the conclusions.
Abstract
Recently, a statistical procedure was proposed to analyze the angular effect in the NOAA Advanced Very High Resolution Radiometer (AVHRR) brightness temperatures. The estimated empirical angular functions (EAF) over the oceans allow one to check the algorithms for the sea surface temperature (SST) and the column water vapor content when the observation geometry is variable, as well as to test angular methods of SST retrieval. The EAF approach has been previously applied to the analysis of the AVHRR brightness temperatures in channels 3 and 4 and dual-window SST over the tropical Atlantic in June 1987 and December 1988 from NOAA-10 and NOAA-11, respectively. Here, it is extended to estimate the accuracy of the split-window sea surface temperature and atmospheric water vapor retrievals from NOAA-9 over the tropical and North Atlantic in July 1986. The authors confirm the previously drawn conclusion that in a general case no angle-independent coefficients in a linear SST retrieval algorithm can provide angle-invariant retrievals. More recent operational NOAA angle-dependent algorithms have been shown to improve retrievals in the Tropics. In high latitudes, they seem to slightly overcorrect the angular effect. Using satellite data of higher spatial resolution with better radiometric accuracy is expected to improve the accuracy of the EAFs and the reliability of the conclusions.
Abstract
Current National Oceanic and Atmospheric Administration (NOAA) operational global- and continental-scale snow cover maps are produced interactively by visual analysis of satellite imagery. This snow product is subjective, and its preparation requires a substantial daily human effort. The primary objective of the current study was to develop an automated system that could provide NOAA analysts with a first-guess snow cover map and thus to reduce the human labor in the daily snow cover analysis. The proposed system uses a combination of observations in the visible, midinfrared, and infrared made by the Imager instrument aboard Geostationary Operational Environmental Satellites (GOES) and microwave observations of the Special Sensor Microwave Imager (SSM/I) aboard the polar-orbiting Defense Meteorological Satellite Program platform. The devised technique was applied to satellite data for mapping snow cover for the North American continent during the winter season of 1998/99. To assess the system performance, the automatically produced snow maps were compared with the NOAA interactive operational product and were validated against in situ land surface observations. Validation tests revealed that in 85% of cases the automated snow maps fit exactly the ground snow cover reports. Snow identification with the combination of GOES and SSM/I observations was found to be more efficient than the one based solely on satellite microwave data. Comparisons between the automated maps and the NOAA operational product have shown their good agreement in the distribution of snow cover and its area coverage. The accuracy of the automated product was found to be similar to and sometimes higher than the accuracy of the operational snow cover maps manually produced at NOAA.
Abstract
Current National Oceanic and Atmospheric Administration (NOAA) operational global- and continental-scale snow cover maps are produced interactively by visual analysis of satellite imagery. This snow product is subjective, and its preparation requires a substantial daily human effort. The primary objective of the current study was to develop an automated system that could provide NOAA analysts with a first-guess snow cover map and thus to reduce the human labor in the daily snow cover analysis. The proposed system uses a combination of observations in the visible, midinfrared, and infrared made by the Imager instrument aboard Geostationary Operational Environmental Satellites (GOES) and microwave observations of the Special Sensor Microwave Imager (SSM/I) aboard the polar-orbiting Defense Meteorological Satellite Program platform. The devised technique was applied to satellite data for mapping snow cover for the North American continent during the winter season of 1998/99. To assess the system performance, the automatically produced snow maps were compared with the NOAA interactive operational product and were validated against in situ land surface observations. Validation tests revealed that in 85% of cases the automated snow maps fit exactly the ground snow cover reports. Snow identification with the combination of GOES and SSM/I observations was found to be more efficient than the one based solely on satellite microwave data. Comparisons between the automated maps and the NOAA operational product have shown their good agreement in the distribution of snow cover and its area coverage. The accuracy of the automated product was found to be similar to and sometimes higher than the accuracy of the operational snow cover maps manually produced at NOAA.
Global mapped data of reflected radiation in the visible (0.63 μm) and near-infrared (0.85 μm) wavebands of the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration satellites have been collected as the global vegetation index (GVI) dataset since 1982. Its primary objective has been vegetation studies (hence its title) using the normalized difference vegetation index (NDVI) calculated from the visible and near-IR data. The second-generation GVI, which started in April 1985, has also included brightness temperatures in the thermal IR (11 and 12 μm) and the associated observation–illumination geometry. This multiyear, multispectral, multisatellite dataset is a unique tool for global land studies. At the same time, it raises challenging remote sensing and data management problems with respect to uniformity in time, enhancement of signal-to-noise ratio, retrieval of geophysical parameters from satellite radiances, and large data volumes. The authors explored a four-level generic structure for processing AVHRR data—the first two levels being remote sensing oriented and the other two directed at environmental studies—and will describe the present status of each level. The uniformity of GVI data was improved by applying an updated calibration, and noise was reduced by applying a more accurate cloud-screening procedure. In addition to the enhanced weekly data (recalibrated with appended quality/cloud flags), the available land environmental products include monthly 0.15°-resolution global maps of top-of-theatmosphere visible and near-IR reflectances, NDVI, brightness temperatures, and a precipitable water index for April 1985–September 1994. For the first time, a 5-yr monthly climatology (means and standard deviations) of each quantity was produced. These products show strong potential for detecting and analyzing largescale spatial and seasonal land variability. The data can also be used for educational purposes to illustrate the annual global dynamics of vegetation cover, albedo, temperature, and water vapor. Development of the GVI data product contributes to the activities of the International Geosphere–Biosphere Programme and Global Energy and Water Cycle Experiment and, in particular, to the International Satellite Land Surface Climatology Project. Monthly standardized anomalies of the GVI variables have been calculated for April 1985–present and are routinely produced on UNIX workstations, thus providing a prototype land monitoring system. Standardized anomalies clearly indicate that strong signals at the land surface, such as droughts and floods and their teleconnections with such global environmental phenomena as El Niño–Southern Oscillation, can be detected and analyzed. The monitoring of relatively small year-to-year variability is, however, contingent on the removal of residual trends/noise in GVI data, which are of the order of the analyzed effects.
Global mapped data of reflected radiation in the visible (0.63 μm) and near-infrared (0.85 μm) wavebands of the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration satellites have been collected as the global vegetation index (GVI) dataset since 1982. Its primary objective has been vegetation studies (hence its title) using the normalized difference vegetation index (NDVI) calculated from the visible and near-IR data. The second-generation GVI, which started in April 1985, has also included brightness temperatures in the thermal IR (11 and 12 μm) and the associated observation–illumination geometry. This multiyear, multispectral, multisatellite dataset is a unique tool for global land studies. At the same time, it raises challenging remote sensing and data management problems with respect to uniformity in time, enhancement of signal-to-noise ratio, retrieval of geophysical parameters from satellite radiances, and large data volumes. The authors explored a four-level generic structure for processing AVHRR data—the first two levels being remote sensing oriented and the other two directed at environmental studies—and will describe the present status of each level. The uniformity of GVI data was improved by applying an updated calibration, and noise was reduced by applying a more accurate cloud-screening procedure. In addition to the enhanced weekly data (recalibrated with appended quality/cloud flags), the available land environmental products include monthly 0.15°-resolution global maps of top-of-theatmosphere visible and near-IR reflectances, NDVI, brightness temperatures, and a precipitable water index for April 1985–September 1994. For the first time, a 5-yr monthly climatology (means and standard deviations) of each quantity was produced. These products show strong potential for detecting and analyzing largescale spatial and seasonal land variability. The data can also be used for educational purposes to illustrate the annual global dynamics of vegetation cover, albedo, temperature, and water vapor. Development of the GVI data product contributes to the activities of the International Geosphere–Biosphere Programme and Global Energy and Water Cycle Experiment and, in particular, to the International Satellite Land Surface Climatology Project. Monthly standardized anomalies of the GVI variables have been calculated for April 1985–present and are routinely produced on UNIX workstations, thus providing a prototype land monitoring system. Standardized anomalies clearly indicate that strong signals at the land surface, such as droughts and floods and their teleconnections with such global environmental phenomena as El Niño–Southern Oscillation, can be detected and analyzed. The monitoring of relatively small year-to-year variability is, however, contingent on the removal of residual trends/noise in GVI data, which are of the order of the analyzed effects.
Abstract
A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting factor for vegetation growth (in higher latitudes and elevations, especially at the beginning of the growing season), a positive correlation exists between LST and NDVI. Multiple regression analysis revealed that during the beginning and the end of the growing season, solar radiation is the predominant factor driving the correlation between LST and NDVI, whereas other biophysical variables play a lesser role. Air temperature is the primary factor in midsummer. It is concluded that there is a need to use empirical LST–NDVI relationships with caution and to restrict their application to drought monitoring to areas and periods where negative correlations are observed, namely, to conditions when water—not energy—is the primary factor limiting vegetation growth.
Abstract
A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting factor for vegetation growth (in higher latitudes and elevations, especially at the beginning of the growing season), a positive correlation exists between LST and NDVI. Multiple regression analysis revealed that during the beginning and the end of the growing season, solar radiation is the predominant factor driving the correlation between LST and NDVI, whereas other biophysical variables play a lesser role. Air temperature is the primary factor in midsummer. It is concluded that there is a need to use empirical LST–NDVI relationships with caution and to restrict their application to drought monitoring to areas and periods where negative correlations are observed, namely, to conditions when water—not energy—is the primary factor limiting vegetation growth.
