Surface reflectance is one of the key products used in developing several land Essential Climate Variables, such as Vegetation Indices, Albedo and LAI/FPAR, it is therefore seminal to detecting trends in the biosphere and land surface and has been classed by NOAA as a “Fundamental Climate Data Record (FDCR) for Land”. Building a long-term surface reflectance data record of climate quality requires combining data from different instruments, sensors and satellites, accounting for different spatial resolutions and spectral characteristics, assuring consistent calibration, and correcting for atmospheric and directional effects.
In this work, we use robust reflectance data records and inter-comparison methods that we have developed over the past several years (consisting of atmospheric correction, directional effect correction and spectral normalization) to establish and verify the inter-consistency of the reflectance products from the AVHRR sensors on-board NOAA 7, 9, 11, 14, 16, 17,18, 19 and METOPB, the MODIS sensors on-board Aqua and Terra, the VIIRS sensors on-board Suomi-NPP and JPSS1, the TM/ETM+ on Landsat 5/7, the OLI on Landsat 8 and the MSI on Sentinels 2A,B. The resulting dataset being the 40 years surface reflectance product and vegetation indices from 1981 to present of increasing spatial, spectral and accuracy performance, which provides a solid basis for deriving a number of Terrestrial Essential Climate Variables. We will conclude by presenting applications of this dataset to long term monitoring of agriculture and human earth interactions.