11.4 Evaluation of vegetation and thermal infrared-based ET maps for real-time water use and stress monitoring in a California vineyard

Wednesday, 15 January 2020: 3:45 PM
Kyle Knipper, USDA-ARS, Beltsville, MD; and W. P. Kustas, M. C. Anderson, M. M. Alsina, C. R. Hain, J. G. Alfieri, J. Prueger, F. Gao, A. McElrone, N. Bambach-Ortiz, L. G. McKee, and L. Sanchez

The efficient application of water resources through irrigation is predicated on the accurate prediction and constant monitoring of evapotranspiration (ET). This is especially true in viticulture, where irrigation practices aim to control vine canopy growth and regulate vine water status at critical stages to improve wine grape quality and yield. To support efficient irrigation strategies, satellite-based remote sensing technologies can be employed in mapping ET at field to sub-field scales, quantifying time- and space-varying vineyard water use and stress. In the current study, we investigate the utility of thermal infrared-based and vegetation-based ET maps over a vineyard in the Central Valley of California equipped with a Variable Rate Drip Irrigation (VRDI) system which enables differential water applications at the 30 x 30 m scale. Weekly total actual ET (ETa) estimates at 30 m spatial resolution, coinciding with Landsat reflectance bands and the VRDI grid, were derived in near real-time from a thermal-based multi-sensor data fusion approach. Crop water requirements (ETc) were also calculated in near real-time, but used a vegetative index (VI)-based approach. To test the capacity of ETa and ETc to capture stress signals in near real-time, the vineyard was sub-divided into 4 blocks with different irrigation management strategies and goals, inducing varying degrees of stress during the growing season. Results show that the thermal-based method could indicate areas of the vineyard under stressed conditions. These areas of stress were not reflected in the VI-based ETc estimate, highlighting the value of thermal band imaging. While the thermal-based multi-sensor data fusion approach provided valuable spatial information, latency in current satellite data availability, particularly from Landsat, impacts near real-time applications in an operational setting over the course of a growing season.
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