Abstract
The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980’s 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 geo-location, improvement of the 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 [1] and [2] 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 1980’s, the results have errors equivalent to those derived from MODIS.
Highlights
The surface reflectance product is a critical input for generating downstream products, such as vegetation indices (VI), leaf area index (LAI), fraction of absorbed photosynthetically-active radiationRemote Sens. 2017, 9, 296; doi:10.3390/rs9030296 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 296(FAPAR), bidirectional reflectance distribution function (BRDF), albedo, and land cover
We present the latest improvements of the Advanced Very High Resolution Radiometer (AVHRR) BRDF corrected surface reflectance and NDVI Version 4 products by assessing the accuracy of geolocation (Section 3.1), calibration (Section 3.2), cloud mask (Section 3.3), and the final surface reflectance product using
In addition to the geolocation and cloud mask evaluations, the assessment is done through four different exercises: first, we compare the product with the surface reflectance derived using AERONET atmospheric data (Section 3.4); second, we intercompare the AVHRR with the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products; third, we evaluate the LAI and FAPAR
Summary
The surface reflectance product is a critical input for generating downstream products, such as vegetation indices (VI), leaf area index (LAI), fraction of absorbed photosynthetically-active radiationRemote Sens. 2017, 9, 296; doi:10.3390/rs9030296 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 296(FAPAR), bidirectional reflectance distribution function (BRDF), albedo, and land cover. A surface reflectance land climate data record (LCDR) needs to be of the highest possible quality so that minimal uncertainties propagate in the dependent/downstream products. The generation of such a record necessitates the use of multi-instrument/multi-sensor science-quality data record and a strong emphasis on data consistency, which, in this study, is achieved by careful characterization and processing of the original data, rather than degrading and smoothing the dataset. The LCDR needs to be derived from accurately calibrated top of the atmosphere reflectance values that are precisely geo-located, carefully screened for clouds and cloud shadows, corrected for atmospheric effects using a radiative transfer model-based approach and, corrected for directional effects All of these steps are necessary, as spurious trends will appear in the data record if the above effects are not corrected.
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