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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 #316895
Title: Daily stand-scale evapotranspiration estimation over a managed pine plantation in North Carolina, USA, using multisatellite data fusion

Author
item Yang, Yun
item Anderson, Martha
item Gao, Feng
item HAIN, C. - Collaborator
item Kustas, William - Bill
item NOORMETS, A. - Collaborator
item WYNNE, R.H. - Virginia Polytechnic Institution & State University
item THOMAS, V.A. - Virginia-Maryland Regional College Of Veterinary Medicine (VMRCVM)
item GE, S. - Collaborator

Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 5/5/2015
Publication Date: 5/10/2015
Citation: Yang, Y., Anderson, M.C., Gao, F.N., Hain, C., Kustas, W.P., Noormets, A., Wynne, R., Thomas, V., Ge, S. 2015. Daily stand-scale evapotranspiration estimation over a managed pine plantation in North Carolina, USA, using multisatellite data fusion. BARC Poster Day. 63.

Interpretive Summary:

Technical Abstract: Evapotranspiration (ET) over agricultural land surfaces represents the rate at which soil water is consumed in growing crops. Maps of ET, produced at high spatial and temporal resolution using satellite imagery, can provide detailed information about daily vegetation water use and soil moisture status at scales of individual farm fields, and can be valuable for irrigation management and monitoring crop health. This research employs a multi-scale ET modeling system over a managed pine plantation site in North Carolina during the 2013 growing season to determine the accuracy of stand-scale forest water-use estimates. The influence of land cover type and stand age on water use was also examined using time-series of daily ET maps generated at 30-m spatial resolution. The multi-scale ET modeling system integrates the Atmosphere-Land Exchange Inverse surface energy balance model and associated disaggregation scheme (ALEXI/DisALEXI), generating ET estimates from multiple satellite platforms with varying spatial and temporal resolution characteristics. The Spatial and Temporal Adaptive Reflective Fusion Model (STARFM) was used to combine these water use maps, creating ET maps with both the fine spatial detail and good temporal sampling required for agricultural water management applications. Daily high ET retrievals at 30-m spatial resolution, generated using multi-satellite data fusion, were compared with observations acquired in two Loblolly Pine stands within the plantation: a 20-year old mid-rotation stand (US-NC2), and a recent clear-cut (US-NC3). The modeled ET agreed well with observations at both sites, with root-mean square errors of for NC2 is 0.99 mm d-1 and 1.07 mm d-1 for NC3, with relative errors on the order of 20% of the peak observed summer ET rates. Analyses show that seasonal water use patterns vary significantly with stand age, with higher rates of ET mid-season from mid- and late rotation stands in comparison with younger stands, but little difference in water use during the dormant season. The dataset also shows that pixels with natural forest had higher ET than the managed plantation and cropped areas. Our study demonstrates the capability of the multi-scale ET model to estimate daily field-scale ET over forested landscape, providing useful information for local water resource management.