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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





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Title:   Author  item
                                            SEMMENS, K. - Collaboratoritem
                                            Anderson, Marthaitem
                                            Kustas, William

                                                         - *Bill*item
                                            Gao, Fengitem
                                            Alfieri, Josephitem
                                            McKee, Lynnitem
                                            Prueger, Johnitem
                                            HAIN, C. - Collaboratoritem
                                            CAMMALLERI, C. - Collaboratoritem
                                            Yang, Yunitem
                                            XIA, TING - Tsinghua Universityitem
                                            SANCHEZ, L. - E & J Gallo Wineryitem
                                            ALSINA, MIMAR - E & J Gallo Wineryitem
                                            VELEZ, M. - University Of Puerto Rico
  Submitted to: Remote Sensing of Environment
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 10/25/2015
                                    Publication  Date: 3/1/2016
                                    Publication URL: https://handle.nal.usda.gov/10113/5450822Citation: Semmens, K., Anderson, M.C., Kustas, W.P., Gao, F.N., Alfieri, J.G., Mckee, L.G., Prueger, J.H., Hain, C., Cammalleri, C., Yang, Y., Xia, T., Sanchez, L., Alsina, M., Velez, M. 2016. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sensing of Environment. doi: 10.1016/j.rse.2015.1010.1025.Interpretive Summary: The recent multi-year drought in California is highlighting need for improved water management for irrigated agriculture within the state. Viticultural systems for wine grapes in particular require accurate and spatially detailed information about crop water use and soil moisture status throughout the growing season, since grape quality is highly sensitive to soil moisture. This paper describes a data fusion technique for mapping daily water use, or evapotranspiration (ET), over agricultural landscapes at sub-field scale (30 meter spatial resolution). The technique fuses remote sensing data from several satellite platforms with different spatial and temporal characteristics, working toward the goal of producing ET maps at the highest spatial and temporal resolution possible – best serving the needs of agricultural water management applications. The Landsat satellite, for example, collects imagery with good spatial detail (30 m) but relatively infrequently (8-16 day revisit). The Moderate Resolution Imaging Spectroradiometer (MODIS), on the other hand, collects data almost every day but with coarser detail (1 km). In this study, the data fusion system was applied to Landsat and MODIS data collected over two Pinot Noir vineyards near Lodi, California in the Central Valley. The fused daily ET estimates over these two sites agreed well with measurements made within the fields, indicating good potential for operational applications. Future research will investigate the cause and solutions for a small early season bias in water use estimates at the younger vineyard site.
                                Technical Abstract: California’s Central Valley grows a significant fraction of grapes used for wine production in the United States. With increasing vineyard acreage, reduced water availability in much of California, and competing water use interests, it is critical to be able to monitor regional water use, or evapotranspiration (ET), over large areas, but also in detail at individual field scales to improve water management within these viticulture production systems. This can be achieved by integrating remote sensing data from multiple satellite systems with different spatiotemporal characteristics. In this research, we evaluate the utility of a multi-scale system for monitoring ET as applied over two vineyard sites near Lodi, California during the 2013 growing season, leading into the drought in early 2014. The system employs a multisensor satellite data fusion methodology (STARFM: Spatial and Temporal Adaptive Reflective Fusion Model) combined with a multi-scale ET retrieval algorithm based on the Two-Source Energy Balance (TSEB) land-surface representation to compute daily ET at 30 m resolution. In this system, TSEB is run using thermal band imagery from the Geostationary Environmental Operational Satellites (GOES; 4-km spatial resolution, hourly temporal sampling), the Moderate Resolution Imaging Spectroradiometer (MODIS) data (1 km resolution, daily acquisition) and the new Landsat 8 satellite (sharpened to 30 m resolution, ~16 day acquisition). Estimates of daily ET generated in two neighboring fields of Pinot Noir vines of different maturity agree well with ground-based flux measurements acquired in-field during most of the 2013 season with relative errors on the order of 12-16%. A model overestimation of ET in the early season was detected in the younger vineyard, perhaps relating to an inter-row grass cover crop. Spatial patterns of cumulative ET correspond to measured yield maps and indicate areas of variable crop moisture, soil condition, and yield within the vineyards that could require adaptive management. The work suggests that multi-sensor remote sensing observations provide a unique means for monitoring crop water use and soil moisture status at field-scales over extended growing regions, and may have value in supporting operational water management decisions in vineyards and other high value crops.


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