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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #362860

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Using high-spatiotemporal ET retrievals in near real time for operational irrigation management in a California vineyard

item Knipper, Kyle
item Kustas, William - Bill
item Anderson, Martha
item ALSINA, M. - E & J Gallo Winery
item HAIN, C. - Goddard Space Flight Center
item Alfieri, Joseph
item Prueger, John
item Gao, Bo
item McKee, Lynn
item SANCHEZ, L. - E & J Gallo Winery

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 4/5/2019
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: A challenge to sustainable wine grape production in California is the frequent and extreme droughts seriously affecting water availability for irrigation. However, even when water is not a limiting factor, deficit irrigation strategies are often implemented to control vine canopy growth and improve grape quality. For California viticulture to remain viable, accurate and timely mapping of vineyard water use or evapotranspiration (ET) is considered paramount for improving water use efficiency and conservation, as well as maximizing quality. In the current study, we investigate the utility of ET maps derived from thermal infrared satellite imagery in near-real-time over a Variable Rate Drip Irrigation (VRDI) equipped vineyard in the Central Valley of California, where irrigation can be independently varied at the scale of 30x30m blocks. To support irrigation management at that scale, we utilized a thermal-based multi-sensor data fusion approach to generate weekly total ET estimates at 30m spatial resolution, coinciding with the resolution of the Landsat reflectance bands. The vineyard was sub-divided into 4 blocks with different management strategies and goals, targeting varying degrees of stress and different sources of ET information. Weekly total ET estimates, developed in near-real-time, were used to recommend weekly irrigation amounts for each 30x30m cell in the VRDI grid. Derived ET estimates are also compared to a business-as-usual (BAU) method employed by vineyard managers, rooted in a modified Food and Agriculture Organization (FAO-56) method and using Landsat retrievals of normalized difference vegetation index (NDVI). Results indicate derived weekly total ET from the thermal-based data fusion approach match well with observations. The NDVI-based BAU method also shows good agreement with tower observations. However, it was unable to produce the spatial heterogeneity in ET over the vineyard due to a water stress event that took place in two of the four vineyard blocks. The thermal-based data fusion modeling scheme can detect this stress event, showing lower weekly total ET estimates over the two stressed versus the two non-stressed blocks. While the data fusion system provided valuable information to vineyard managers for determining weekly irrigation amounts, latency in current satellite data availability, particularly Landsat, impact near-real-time applications in an operational setting over the course of a growing season.