The University of Arizona

Estimating soil water content in tallgrass prairie using remote sensing.

P.J. Starks, T.J. Jackson

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 (theta(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 (theta(vBL)) by combining remotely sensed estimates of theta(vSL), in situ measurements, and modeling techniques. A simple soil water budget model was modified to estimate theta(vBL) from assimilated values of theta(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 theta(vSL) at 2-day intervals, 3) assimilation of field-measured theta(vSL) matching dates of remote sensing data acquisitions during the study period, and 4) assimilation of remotely sensed theta(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 less than or equal to 0.01 m3m(-3), RMSE less than or equal to 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) greater than or equal to ME less than or equal to 0.02 m3m(-3), RMSE less than or equal to 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 theta(vSL) values. These preliminary results suggest that it is feasible to infer theta(vSL) in tallgrass prairies by combining remotely sensed estimates of theta(vSL), in situ field measurements, and modeling, provided that the remotely sensed data correctly estimates surface conditions.

DOI:10.2458/azu_jrm_v55i5_starks


Keywords


algorithms;microwave radiation;data assimilation;particle size distribution;soil profiles;data analysis;simulation models;water balance;remote sensing;leaf area index;sampling;Oklahoma;soil water content;prairies;biomass;prairie soils

Full Text:

PDF