Surface reflectance is one of the key products used in developing several higher-order land products, such as vegetation indices, albedo and LAI/fpar, IT is therefore seminal to detecting trends in…

This federal contract award is a $2,400,000.00 Cooperative Agreement between the National Aeronautics and Space Administration (NASA) and the University of Maryland, College Park (UMD) to develop long-term surface reflectance data records and associated applications. The project aims to establish the consistency and accuracy of surface reflectance products derived from multiple satellite sensors, including AVHRR, MODIS, VIIRS, Landsat, and Sentinel-2. It will produce a 35+ year surface reflectance and vegetation indices dataset spanning 1981 to the present. The contract also includes developing algorithms for vegetation indices, leaf area index, and other land surface parameters, as well as applications for agriculture monitoring, such as wheat yield prediction. There is no set-aside designation for this award. The University of Maryland, College Park is the prime contractor, and the project is supported by the University System of Maryland as the parent organization.

Surface reflectance is one of the key products used in developing several higher-order land products, 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 implies combining different instruments, sensors and satellites, accounting for different spatial resolutions and spectral characteristics, assuring consistent calibration, and correcting for atmospheric and directional effects. As the spatial resolution issue is addressed by aggregating the original data to a resolution still suitable for climate studies (e.g. 0.05 degree latitude, longitude), the instrument calibration becomes the first major hurdle one has to go through before being able to proceed any further. In this proposal, we will 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 interconsistency of the reflectance products from the avhrr sensors on-board NOAA 7, 9, 11, 14, 16, 17 and 18, the modis sensors on-board aqua and terra, the viirs sensors onboard suomi-NPP and JPSS1 USGS-landsat 5, 7 & 8 and ESA-sentinel-2. The resulting dataset being the 35+ years surface reflectance product and vegetation indices from 1981 to present. There is a need to validate, monitor and improve those products that is addressed by this proposal. This project will support an evaluation of the accuracy of the operational algorithms for land surface reflectance products and associated inputs such as top of the atmosphere reflectance (SDR), spectral adjustments, aerosol over land, cloud mask, fusion and data-sets and CO-registration when needed (specifically for landsat-8 and sentinel-2). Another aspect of the project is the applications we want to develop using surface reflectance products. First, we propose to define algorithm to determine vegetation indices (VI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fpar), albedo, and land cover. Second we will develop applications related to agriculture monitoring. Specifically, we propose to evaluate the wheat yield prediction and wheat condition monitoring over the entire dataset (from 1981 to present) for each of the sensors investigated in the study (avhrr, modis, viirs, landsat-8 and sentinel-2). We also plan to test the approach with the SOY and the maize.