Use of Aggieair UAS Remote Sensing Data to Estimate Crop ET at High Spatial Resolution
Synergy in Science: Partnering for Solutions 2015 Annual Meeting
Entomological Society of America
Estimation of the spatial distribution of evapotranspiration (ET) based on remotely sensed imagery has become useful for managing water in irrigated agricultural at various spatial scales. However, data acquired by conventional satellites (Landsat, ASTER, etc.) lack the spatial resolution to capture variability of interest to support many types of precision farming applications. In this study, an unmanned aerial system (UAS), or “drone”, called AggieAirTM, was used to acquire high-resolution imagery in the visual, near infrared and thermal infrared spectra (0.15m resolution for visual and near infrared and 0.6m resolution for thermal infrared) over a vineyard study site being monitored as part of the Grape Remote sensing Atmospheric Profiling and Evapotranspiration eXperiment (GRAPEX)near Lodi, California. The imagery was used as input to (1) a surface energy balance model based on the Mapping Evapotranspiration with Internalized Calibration (METRIC) modeling approach, which was originally developed to use Landsat data and (2) the Two-Source Energy Balance (TSEB) model to produce ET maps at high spatial resolutions. Data from flux towers located at the site were used to evaluate the performance of these two models applied to the high resolution remote sensing data in estimating ET. A comparison of the spatial distribution in METRIC and TSEB model output over the vineyards will be discussed and will highlight the similarities and differences in ET estimation from the two methodologies and the utility of the two approaches using high resolution imagery for mapping ET in vineyards.
Elarab, Manal; Torres-Rua, Alfonso F.; Kustas, William; Nieto, Hector; Song, Lisheng; Alfieri, Joseph G.; Prueger, John H.; McKee, Lynn; Anderson, Martha; Sanchez, Luis; Alsina, Mima; Hipps, Larry; Jensen, Austin; and McKee, Mac, "Use of Aggieair UAS Remote Sensing Data to Estimate Crop ET at High Spatial Resolution" (2015). AggieAir Presentations. Paper 54.