Separating snow and forest temperatures with thermal infrared remote sensing

Abstract

Thermal infrared sensing from space is a well-developed field, but mixed pixels pose a problem for many applications. We present a field study in Dana Meadows, Yosemite National Park, California to scale from point (~2-m resolution) to aerial (~5-m resolution gridded, 1 km × 6 km extent) to satellite (MODIS, ~1000-m resolution, global extent) observations. We demonstrate how multiple thermal bands on MODIS can be used to separate snow and forest temperatures and determine the fractional snow-covered area (fSCA) over a 3 km × 3 km array of 9 MODIS grid cells. During the day, visible, near-infrared, and shortwave-infrared bands provide a first guess of fSCA and help to constrain the solution. This technique, which has estimated errors <2 °C and 10% fSCA for many expected conditions, enables better understanding of the snowpack energy balance, atmospheric inversions and cold air pools, and forest health.