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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 #345137

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: Monitoring and validating spatio-temporal continuously daily evapotranspiration and its components at river the basin scale

item SONG, L. - South China Normal University
item LIU, S. - Beijing Normal University
item Kustas, William - Bill
item NIETO, H. - Institute De Recerca I Tecnologia Agroalimentaries (IRTA)
item SUN, L. - US Department Of Agriculture (USDA)
item XU, Z. - Beijing Normal University
item Skaggs, Todd
item MA, M. - Southwest University
item XU, T. - Beijing Normal University
item TANG, X. - Southwest University
item LI, Q. - Southwest University

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/2/2018
Publication Date: 12/10/2018
Citation: Song, L., Liu, S., Kustas, W.P., Nieto, H., Sun, L., Xu, Z., Skaggs, T.H., Ma, M., Xu, T., Tang, X., Li, Q. 2018. Monitoring and validating spatio-temporal continuously daily evapotranspiration and its components at river the basin scale. Remote Sensing of Environment.

Interpretive Summary: Arid and semi-arid areas in northwestern China encompass a large land mass that has experienced significant environmental stress from rapid growth in human population and economic development. In the Heihe River Basin, the second largest inland river basin in China, water requirements for sustaining economic growth while maintaining ecosystem functioning have recently exceeded the availability. To effectively implement a sustainable water resource strategy there is a need for reliable monitoring of plant water use and stress. This will enable the development of efficient irrigation strategies under different land cover conditions throughout the Heihe River Basin. The only way to efficiently assess and monitor spatially distributed water use and stress at a large scale is through satellite-based estimates of evapotranspiration (ET) and its components of evaporation (E) and transpiration (T). A land surface temperature-based technique that utilizes the operational MODerate resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites is used to estimate daily ET, E and T over the Heihe River Basin. Model estimates are validated with ET measurements over croplands, semi-arid shrubland-forests, and upland grasslands. Results suggest that reliable ET and T and E are obtained with the satellite-based approach under clear sky conditions, but are more problematic under cloudy skies. More reliable inputs under all-weather conditions will improve model performance and will provide an operational remote sensing method for monitoring daily water use and stress at field to watershed scales, and as a result lead to improvement in water resource management of water-limited regions.

Technical Abstract: Operational estimation of spatio-temporal continuously daily evapotranspiration (ET), and the components evaporation (E) and transpiration (T), at river basin scale is very useful for developing a sustainable water resource strategy in semi-arid and arid areas. In this study, multi-year all-weather daily ET, E and T were estimated using MODIS-based (Dual Temperature Difference) DTD model under different land covers in Heihe river basin, China. The remotely sensed ET was validated using ground measurements from large aperture scintillometer systems, with a source area of several kilometers, under grassland, cropland and riparian shrub-forest. The results showed that the remotely sensed ET produced mean absolute percent deviation (MAPD) errors of about 30% during the growing season for all-weather conditions, but the model performed better under clear sky conditions. However, uncertainty in interpolated MODIS land surface temperature input data under cloudy conditions to the DTD model, and the representativeness of LAS measurements for the heterogeneous land surfaces contribute to the discrepancies between the modeled and ground measured surface heat fluxes, especially for the more humid grassland and heterogeneous shrub-forest sites. This suggests that further studies need to produce more accurate model inputs under all-weather conditions, obtain direct ET observations from microwave LAS systems or aggregated from EC systems, and a more effective approach to derive the components of E and T at larger scales for a more robust validation of ET, E and T from remote sensing-based models.