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G. Garik Gutman

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.

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G. Garik Gutman

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.

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Aleksandr M. Ignatov and G. Garik Gutman

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.

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Arnon Karnieli, Nurit Agam, Rachel T. Pinker, Martha Anderson, Marc L. Imhoff, Garik G. Gutman, Natalya Panov, and Alexander Goldberg

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.

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