Classification-based emissivity for land surface temperature measurement from space

TLDR

Classification-based global emissivity is required for NASA EOS/MODIS land surface temperature algorithms and is also applicable to Landsat, AVHRR, and other thermal infrared instruments. The authors classify each pixel into one of fourteen emissivity classes using land‑cover type, snow cover, vegetation index, and other dynamic factors, and generate class‑specific emissivity ranges by combining spectral measurements with structural parameters. Emissivity statistics for MODIS channels 31 and 32, the channels used in the LST split‑window algorithm, are presented.

Abstract

Classification-based global emissivity is needed for the National Aeronautics and Space Administration Earth Observing System Moderate Resolution Imaging Spectrometer (NASA EOS/MODIS) satellite instrument land surface temperature (LST) algorithm. It is also useful for Landsat, the Advanced Very High Resolution Radiometer (AVHRR) and other thermal infrared instruments and studies. For our approach, a pixel is classified as one of fourteen 'emissivity classes' based on the conventional land cover classification and dynamic and seasonal factors, such as snow cover and vegetation index. The emissivity models we present provide a range of values for each emissivity class by combining various spectral component measurements with structural factors. Emissivity statistics are reported for the EOS/MODIS channels 31 and 32, which are the channels that will be used in the LST split-window algorithm.