Algorithm development for land surface temperature measurement from GOES-R satellite

摘要

The Geostationary Operational Environmental Satellite (GOES) program is developing a new generation sensor, the Advanced Baseline Imager (ABI), to be carried on the GOES-R satellite to be lunched in approximately in 2014, Compared to the current GOES imager, ABI will have significant advantages for measuring land surface temperature as well as to providing qualitative and quantitative data for a wide range of applications. Specifically, spatial resolution of the ABI sensor is 2 km, and the infrared window noise equivalent temperature is 0.1 K, which are very close to the polar-orbiting satellite sensors such as AVHRR. Most importantly, ABI observes the full disk every fi|e minutes, which not only provides more cloud-free measurements but also makes daily temperature variation analysis possible. In this study we developed split window algorithms for the LST measurement from the ABI sensor. We generated the ABI sensor data using MODTRAN radiative transfer model and NOAA88 atmospheric profiles and ran regression analyses for the LST algorithm development The algorithms are developed by optimizing existing split window LST algorithms and adding a path length correction term to minimize the retrieval errors due to difference atmospheric path absorption from nadir view to the edge-of-scan The algorithm coefficients are stratified for dry and moist atmospheric conditions, as well as for the daytime and nighttime. The algorithm sensitivity to land surface emissivity uncertainty is analyzed to ensure the algorithm performance.