Developing Land Surface Emissivity Product for JPSS and GOES-R Missions

Land surface emissivity (LSE) is an essential parameter for determining land surface temperature (LST) from thermal remote sensed data; it is also critical in many land surface models and weather forecasting models. A new LSE product is in development for enhancing LST production of the Joint Polar Satellite System (JPSS) and the Geostationary Operational Environmental Satellite-R Series (GOES-R) missions, as well as for supporting the forecasting models. Based on a vegetation cover method, an LSE algorithm is proposed, which combines LSE climatology and near-real-time land surface parameters for the instant LSE determination. The ASTER Global Emissivity Dataset and long-term MODIS LSE product are used to generate high quality bare ground emissivity climatology at 0.009° resolution, which is subsequently converted to the split-window (SW) bands emissivity of the Visible Infrared Imaging Radiometer Suite (VIIRS, onboard S-NPP, and future JPSS-1 and JPSS-2 satellites) and the Advanced Baseline Imager (ABI, onboard GOES-R), respectively. To generate the instant emissivity product, VIIRS green vegetation fraction and snow fraction data are applied to account the instant sub-pixel changes. Eventually, daily LSE products are produced covering VIIRS and ABI SW channels and thermal broadband. A Preliminary product evaluation is conducted at arid area of Northwest of China, which results in an average LSE error of ±0.006 for VIIRS and ABI bands and ±0.012 for broadband; while the cropland site indicates a good agreement between the product and ground LSE estimates at three different growing stages. Meanwhile, the new LSEs were employed in an emissivity-explicit SW LST algorithm for VIIRS LST derivation which then is compared to the in-situ LSTs at SURFRAD sites. The results show an improvement over current operational VIIRS LST product. The new LSEs have also been compared with the MODIS LSE product, from which it is found that the proposed LSE could depict the seasonal variation and with better accuracy at the validation sites. The validation results confirm the new product with high spatial and temporal resolution would be capable of improving LST product of JPSS and GOES-R missions.