A comparison of modeling schemes for mapping daily evapotranspiration at high resolution using remote sensing

Wednesday, 17 December 2014: 11:20 AM
Theresa Ring1, Richard H Cuenca1, Martha C. Anderson2, Christopher Hain3, Kathryn A Semmens2, William P Kustas2 and Joseph G Alfieri2,4, (1)Oregon State University, Corvallis, OR, United States, (2)Agricultural Research Service Beltsville, Beltsville, MD, United States, (3)Earth System Science Interdisciplinary Center, COLLEGE PARK, MD, United States, (4)Organization Not Listed, Washington, DC, United States
Evapotranspiration (ET) is an important component of the hydrologic cycle that transfers large quantities of water vapor away from Earth’s surface into the atmosphere. In addition to having water management applications in agriculture, including monitoring water rights compliance and irrigation scheduling, it is also important to be able to accurately measure water used by other landscapes for soil-vegetation-atmosphere-transfer (SVAT) models. This can only be done with large scale estimations which are most efficiently achieved with remote sensing.

This research compares daily ET retrieved from two remote sensing modeling schemes: a) Reconstructed METRIC: Mapping EvapoTranspiration at high Resolution with Internalized Calibration; and b) ALEXI/DisALEXI: Atmosphere-Land EXchange Inverse /Disaggregated ALEXI, over two predominately forested landscapes. ET flux estimates are retrieved from ALEXI/DisALEXI using GOES (daily, 4km), MODIS (daily, 1km) and Landsat 8 (16 days, 30m) and from Reconstructed METRIC using Landsat 8. We develop daily Landsat scale ET maps for the summer months of 2013.

The flux-tower footprint is calculated at each site to match the remotely sensed retrieval with that of the flux tower such that modeled output can be evaluated against ground based observations, taken from the AmeriFlux network. In addition, surface and evaporative fluxes retrieved from the two models are inter-compared over the different land cover types in the scenes. Differences in input and data processing requirements for each of the two methods will be also described