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

Abstract

Abstract: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.

Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications

“…2018, 10, x FOR PEER REVIEW 9 of 22 surface albedo. The daily polar orbiting (BRFs) were generated within the AVHRR long-term data record (LTDR) V5 developed by [19]. The geostationary BRFs were generated using the standard SCOPE-CM (Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring) processing scheme [20].…”

“…The methodology itself is based on previous JRC-FAPAR algorithms, such as the ones developed for the medium-resolution instrument sensor (MERIS) and the ocean land colour instrument (OLCI) [36,37], except surface reflectances in Band 1 and Band 2 were used as inputs data instead of top-of-atmosphere ones [19]. The retrieval method assumes that the leaves are alive and photosynthesising, hence the 'green' FAPAR is assumed.…”

“…These products are unique, as they are the only ones containing three types of uncertainties and can be used together with SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), MERIS, and Sentinel-3 FAPAR products using the same retrieval algorithm and definition. However, despite the recent calibration and atmospheric correction performances made by [19], the products still contain, at the end of a few NOAA satellites, some artefacts that must be corrected for global changes studies [38]. This will be conducted in the new version.…”

A Simple and Universal Aerosol Retrieval Algorithm for Landsat Series Images Over Complex Surfaces

“…It is clear that the LSR images show overall high data quality with little cloud contamination and can better reflect the LSR variations over different underlying surfaces. It is apparent that LSR is overall higher in winter (January) than in summer (July), which is mainly due to the influence of decreasing vegetation and soil moisture as well as increasing snow cover during the winter (Franch et al, ; Wei & Sun, ; Zhu et al, ). LSR is obviously higher over the center of urban areas than over the surrounding areas that are covered by vegetation.…”

Detecting Historical Vegetation Changes in the Dunhuang Oasis Protected Area Using Landsat Images

“…Landsat's advanced CDR products provides SR images from the TM sensor [22]. The provisional Landsat 8 Surface Reflectance Code (LaSRC), which employs Moderate Resolution Imaging Spectroradiometer (MODIS) data, is distinctly different from the algorithm used by the USGS to process TM L1T products to obtain SR values [23,24].…”