Using paired thermal and hyperspectral aerial imagery to quantify land surface temperature variability and assess crop stress within California orchards

摘要

Remote sensing can inform agricultural knowledge of crop water use through observation of land surface temperature, which can act as an indicator of plant function and health. This study uses remotely sensed data to quantify thermal variability within fruit and nut orchards during an intense drought period in California's Central Valley (2013-2015). First, fractions of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil were derived for a variety of crop species using visible-shortwave infrared (VSWIR) spectra imaged by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Fractional estimates were then used to select thermal endmembers for each class using simultaneously collected MODIS/ASTER Airborne Simulator (MASTER) thermal imagery and a crop species map. Expected pixel temperatures of non-stressed crop fields were then modeled, and the per-pixel difference between measured and expected temperatures was calculated as a temperature residual. Crop residuals serve to capture variability in temperature that may be attributable to differences in crop health and/or management practices. We found multiple distinct thermal classes to exist across the study site. Furthermore, crop temperatures correlated to expected crop ET rates, and temperature residuals showed correlations to changes in crop yields during the study period. Further assessment of findings revealed an increase in temperature residuals during the study period that is consistent with increasing stress, likely linked to the progression of drought. The method presented here shows utility for regional agricultural analysis of crop water use and is particularly relevant for ECOSTRESS and the upcoming Surface Biology & Geology mission.