A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring
2017
Franch, Belen | Vermote, Eric | Roger, Jean-Claude | Murphy, Emilie | Becker-Reshef, Inbal | Justice, Chris | Claverie, Martin | Nagol, Jyoteshwar | Csiszar, Ivan | Meyer, Dave | Baret, Frédéric | Masuoka, Edward | Wolfe, Robert | Devadiga, Sadashiva | Department of Geographical Sciences [College Park] ; University of Maryland [College Park] (UMD) ; University System of Maryland-University System of Maryland | NASA Goddard Space Flight Center (GSFC) | NOAA Center for Satellite Applications and Research (STAR) ; NOAA National Environmental Satellite, Data, and Information Service (NESDIS) ; National Oceanic and Atmospheric Administration (NOAA)-National Oceanic and Atmospheric Administration (NOAA) | Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH) ; Institut National de la Recherche Agronomique (INRA)-Avignon Université (AU) | Science Systems and Applications, Inc. [Lanham] (SSAI) | The authors would like to acknowledge NOAA NCEI for supporting the development of the surface reflectance/NDVI CDR product through the Cooperative Institute for Climate and Satellites-North Carolina under Cooperative Agreement NA14NES432003.
International audience
Afficher plus [+] Moins [-]anglais. The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.
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