Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #328273
Title: Investigating trends in water use over the Choptank River watershed using a multi-satellite data fusion approach

Author
item SUN, LIANG - Beijing Normal University
item Anderson, Martha
item Gao, Feng
item Yang, Yun
item YANG, YANG - Beijing Normal University
item Dulaney, Wayne

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 4/11/2016
Publication Date: 4/27/2016
Citation: Sun, L., Anderson, M.C., Gao, F., Yang, Yun, Yang, Yang, Dulaney, W.P. 2016. Investigaing trends in water use over the Choptank River watershed using a multi-satellite data fusion approach [abstract]. 2016 BARC Poster Day, April 27, 2016, Beltsville, Maryland

Interpretive Summary:

Technical Abstract: Satellite remote sensing technologies have been widely used to map spatiotemporal variability in consumptive water use (or evapotranspiration; ET) for agricultural water management applications. However, current satellite-based sensors with the high spatial resolution required to map ET at sub-field scales (<100 m) typically provide infrequent temporal sampling (bi-weekly), while satellites with hourly or daily revisit have to coarse a resolution (>1 km) to see individual fields. To overcome these limitations, our group has developed a multi-sensor satellite data fusion methodology (STARFM: Spatial and Temporal Adaptive Reflective Fusion Model), combined with a multi-scale ET retrieval algorithm (DisALEXI: Disaggregated Atmosphere-Land Exchange Inverse model). This system fuses ET maps generated with 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 Landsat satellite (sharpened to 30-m resolution, 16-day acquisition) to create geospatial water use datasets with both high spatial (30-m) and temporal (daily) detail. In this study, the ET fusion system was applied to a 30 by 30 km region including the Choptank River watershed located on the Eastern Shore of Maryland, USA - a focus watershed within the Lower Chesapeake Bay Long-Term Agricultural Research (LCB LTAR) site. Evaluations using in-situ flux tower measurements indicate that ET estimates directly retrieved on Landsat overpass dates have high accuracy with bias of 0.17 mm/day, Root Mean Square Error (RMSE) of 1.18 mm/day, and relative error (RE) of 9%. The fused daily ET, using MODIS to inform interpolation between Landsat dates, has reasonable errors of 18% - an improvement from 27% errors using standard interpolation techniques. Maps of cumulative annual ET for 2013 and 2014 showed similar spatial patterns, with high evaporative water use rates in the west close to the river, with large contributions from riparian and wetland systems, and relative low ET in the drier eastern portion of the domain. An accounting of annual water consumption by different landcover types was performed, showing reasonable distributions of water use. Developed areas showed lowest evaporative water loss, with 765 mm total annual ET on average. Wetlands have the highest consumption rates, with 1316 mm annually. Forest and crops annually consume 1137 and 1054 mm, respectively. Additionally, the ratio of actual-to-potential ET (ET/PET) was assessed as a metric of water use dynamics, revealing different temporal behaviors between land cover types. Wetlands have relatively low temporal variability in ET/PET, reflecting ancillary sources of water availability from shallow groundwater that keep ET close to potential rates. Forested, cropped and developed areas showed progressively higher variability in ET/PET. Efforts are underway to integrate these detailed water use datasets into hydrologic models to improve assessments of water quality and best management practices within the Chesapeake Bay.