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Remote Sens. 2017, 9(3), 296;

A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring

Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
NOAA Center for Satellite Applications and Research, College Park, MD 20746, USA
Goddard Earth Science Data and Information Services Center (GES DISC), NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
INRA, Unité Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (UMR1114), Domaine St Paul, Site Agroparc, 84914 Avignon CEDEX 09, France
Science Systems and Applications Inc., Lanham, MD 20706, USA
Author to whom correspondence should be addressed.
Academic Editors: Jose Moreno, Clement Atzberger and Prasad S. Thenkabail
Received: 27 May 2016 / Revised: 14 March 2017 / Accepted: 15 March 2017 / Published: 21 March 2017
Full-Text   |   PDF [4893 KB, uploaded 21 March 2017]   |  


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. View Full-Text
Keywords: AVHRR; LCDR; MODIS; surface reflectance; yield monitoring AVHRR; LCDR; MODIS; surface reflectance; yield monitoring

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Franch, B.; Vermote, E.F.; Roger, J.-C.; Murphy, E.; Becker-Reshef, I.; Justice, C.; Claverie, M.; Nagol, J.; Csiszar, I.; Meyer, D.; Baret, F.; Masuoka, E.; Wolfe, R.; Devadiga, S. A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring. Remote Sens. 2017, 9, 296.

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