| Patrick J. Starks and Thomas J. Jackson |
| Authors are Soil Scientist, USDA-ARS, Grazinglands Research Laboratory, El Reno, Okla. 73036, and Hydrologist, USDA-ARS, Hydrology Laboratory, Beltsville, Md. 20705. |
Abstract |
| Increased demand for available water supplies necessitates that tools and techniques be developed to quantify soil water reserves over large land areas as an aid in management of water resources and watersheds. Microwave remote sensing can provide measurements of volumetric water content of the soil surface (O vSL) up to about 10 cm deep. The objective of this study was to examine the feasibility of inferring the volumetric water content of the soil profile (O vBL) by combining remotely sensed estimates of vSL, in situ measurements, and modeling techniques. A simple soil water budget model was modified to estimate vBL from assimilated values of vSL. Four modeling scenarios were evaluated at 4 tallgrass prairie sites located in central and south central Oklahoma: 1) unmodified model, 2) assimilation of field-measured vSL at 2-day intervals, 3) assimilation of field-measured vSL matching dates of remote sensing data acquisitions during the study period, and 4) assimilation of remotely sensed vSL. The unmodified model (scenario 1) underestimated measurements with root mean square errors (RMSE) between 0.03 and 0.06 m3m-3 and mean errors (ME) between 0.02 and 0.04 m3m-3. Model output from scenario 2 agreed well with measurements at all study sites (|ME| 0.01 m3m-3, RMSE 0.03 m3m-3). The RMSE and ME values from scenario 3 were comparable to those of scenario 2. Simulations from scenario 4 agreed well with measured data at 2 study sites (0.00 m3m-3 ME 0.02 m3m-3, RMSE 0.03 m3m-3) but underestimated measurements at the remaining sites, in one case by as much as 0.15 m3m-3. The underestimation was due largely to inaccurate remotely sensed vSL values. These preliminary results suggest that it is feasible to infer qvSL in tallgrass prairies by combining remotely sensed estimates of vSL, in situ field measurements, and modeling, provided that the remotely sensed data correctly estimates surface conditions. |
| Key Words: hydrology, microwave, water budget |