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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 #304517

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Monitoring water use and crop condition in California vineyards at multiple scales using multi-sensor satellite data fusion

Author
item Semmens, Kathryn
item Anderson, Martha
item Gao, Feng
item Kustas, William - Bill
item Alfieri, Joseph
item McKee, Lynn
item Prueger, John
item HAIN, C. - University Of Maryland

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 4/9/2014
Publication Date: 4/23/2014
Citation: Semmens, K.A., Anderson, M.C., Gao, F.N., Kustas, W.P., Alfieri, J.G., Mckee, L.G., Prueger, J.H., Hain, C. 2014. Monitoring water use and crop condition in California vineyards at multiple scales using multi-sensor satellite data fusion [abstract]. 2014 BARC Poster Day, Beltsville, MD.

Interpretive Summary:

Technical Abstract: Recent weather patterns have left California’s agricultural areas in severe drought. Given the reduced water availability in much of California it is critical to be able to measure water use and crop condition over large areas, but also in fine detail at scales of individual fields to support water management decisions. Such multi-scale monitoring capabilities can be supported using satellite remote sensing, combining information from multiple satellite platforms collected at different spatial resolutions and temporal frequencies. In this research, we evaluate the utility of a multi-scale system for monitoring daily evapotranspiration (ET) and crop stress as applied over two vineyard sites near Lodi, California during the 2013 growing season, leading into the current year’s drought. Accurate water use and plant stress information is particularly critical in vinicultural production systems, where grape vines must be maintained under highly controlled conditions of moderate water stress to obtain high production with good quality fruit. In this experiment, regional scale ET was mapped at 4-km resolution with the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model using geostationary satellite data collected in the thermal infrared with coarse resolution but broad coverage and high temporal frequency (acquisitions every 15 minutes). Stress conditions are mapped regionally using the Evaporative Stress Index (ESI), representing anomalies in the ratio of actual-to-potential ET estimates derived by ALEXI. ALEXI ET fluxes were also disaggregated to a finer spatial resolution using a multi-sensor data fusion methodology. This approach uses both Landsat data (30 m resolution, ~16 day acquisition) and Moderate Resolution Imaging Spectroradiometer (MODIS) data (1 km resolution, daily acquisition). The high spatial resolution Landsat retrievals are fused with high temporal frequency MODIS data using the Spatial and Temporal Adaptive Reflective Fusion Model (STARFM), resulting in daily estimates of crop water use at 30 m resolution - sufficient for discriminating individual vineyards. Estimates of daily ET generated in two fields of Pinot Noir of different vine maturity agree well with ground-based flux measurements acquired in-field during the 2013 season. Field scale water use dynamics are compared with broader scale ET and ESI maps generated with ALEXI over California and the western U.S. to provide spatial context. This research case study, showing both broad and detailed scales of multi-sensor remote sensing observations, provides a unique and valuable perspective of evapotranspiration and drought estimation with implications for crop health and yield estimation in vineyard sites. It is also the first study utilizing data from the new Landsat 8 satellite, launched in February 2013, to estimate evapotranspiration using the ALEXI and STARFM methodologies.