GC51D-0450:
Evaluating Thermal Infrared Remote Sensing of Evapotranspiration over Cotton with Two Surface Energy Balance Models

Friday, 19 December 2014
Andrew N French, Douglas Hunsaker and Kelly Thorp, USDA/ARS, U.S. ALARC, Maricopa, AZ, United States
Abstract:
Thermal infrared remote sensing can be used to map evapotranspiration (ET) over irrigated crops, which provides a way to estimate plant water use, detect water stress, and improve water management decision support systems. Multiple thermal infrared surface energy balance models that estimate ET have been developed and refined over recent years and are actively being used at local to continental scales. However, relatively few intensive, field-based studies have been conducted to evaluate model estimates and their relative merits. To help resolve ET estimation accuracy with differing remote sensing models, a study was conducted over an irrigated crop in Central Arizona in 2009 and 2011. Using extensive ground moisture measurements over a 4.9 ha cotton field and seven airborne remote sensing flights, this study evaluated ET provided by two prominent approaches: the two-source energy balance model (TSEB) and the ‘Satellite-based energy balance for mapping evapotranspiration with internalized calibration’ model (METRIC). Both use thermal infrared data as essential inputs. However, TSEB is characterized by strong linkage to biophysics, while METRIC is distinguished by its use of contextual information. Based on soil moisture profile observations at 112 locations, and the same input remote sensing data, METRIC was found accurate to 2 mm/day in a majority of cases, while TSEB was similarly accurate at a 1.5 mm/day threshold. These accuracies were representative for emergent, full canopy, and late season cotton growth phases. TSEB and METRIC were similarly biased, ~ -0.7 mm/day. Considering similarity of results at field scale, model complexity, input data requirements, and ease of implementation, TSEB would be preferred for well-instrumented sites. In the case of data sparse sites, METRIC would be recommended as a robust ET approach. The role of land surface temperature uncertainty for modeling ET will be discussed.