Remote Sensing and Modeling to Study Ecohydrological Feedbacks
Southwest Watershed Research
2009 Annual Report
1a.Objectives (from AD-416)
a) Assimilation of remote sensing information into hydrologic modeling;
b) Interpretation of remote sensing products in the context of ecohydrologic feedbacks.
1b.Approach (from AD-416)
The experimental design is built on remote sensing, measurements and modeling to better understand the ecohydrologic impacts of climate change and woody plant encroachment. Experiments are planned for ARS watershed locations in Arizona, Oklahoma and Georgia. Documents SCA with U of AZ. Formerly 5342-12660-004-01S 58-5342-4-447.
A method was proposed to allow for calibrating land surface models directly to radar backscatter intensity measurements in a way which simultaneously accounts for model parameter- and speckle-induced uncertainty, and which exploits the correlation between soil moisture at different image times, due to land and atmosphere processes, to reduce the required spatial aggregation needed to obtain reliable surface level soil moisture estimates from the calibrated model. The method is demonstrated using the NOAH land surface model and Integral Equation Method (IEM) backscatter model calibrated using SAR imagery of an area in the Southwestern United States, and validated against in situ soil moisture measurements. At spatial resolutions finer than 100 m by 100 m square NOAH and IEM calibrated using the proposed radar intensity likelihood parameter estimation algorithm predict surface level soil moisture to within 4% volumetric water content 95% of the time, which is an improvement over a 95% prediction confidence of 7% volumetric water content by the same models calibrated directly to soil moisture information derived from synthetic aperture radar imagery at the same scales. Results demonstrate that the proposed algorithm consistently improves calibrated-model estimates of surface level soil moisture at fine spatial resolutions as compared to estimates by models calibrated to a priori-derived image information. The project was managed through close interaction (UA students on the project have office space at the ARS laboratory) and in person meetings on a regular basis between the primary UA faculty member, ARS scientists, and graduate students.