Comparison of four models to determine surface soil moisture from C‐band radar imagery in a sparsely vegetated semiarid landscape

Abstract

[1] Four approaches for deriving estimates of near-surface soil moisture from radar imagery in a semiarid, sparsely vegetated rangeland were evaluated against in situ measurements of soil moisture. The approaches were based on empirical, physical, semiempirical, and image difference techniques. The empirical approach involved simple linear regression of radar backscatter on soil moisture, while the integral equation method (IEM) model was used in both the physical and semiempirical approaches. The image difference or delta index approach is a new technique presented here for the first time. In all cases, spatial averaging to the watershed scale improved agreement with observed soil moisture. In the empirical approach, variation in radar backscatter explained 85% of the variation in observed soil moisture at the watershed scale. For the physical and best semiempirical adjustment to the physical model, the root-mean-square errors (RMSE) between modeled and observed soil moisture were 0.13 and 0.04, respectively. Practical limitations to obtaining surface roughness measurements limit IEM utility for large areas. The purely image-based delta index has significant operational advantage in soil moisture estimates for broad areas. Additionally, satellite observations of backscatter used in the delta index indicated an approximate 1:1 relationship with soil moisture that explained 91% of the variability, with RMSE = 0.03. Results showed that the delta index is scaled to the range in observed soil moisture and may provide a purely image based model. It should be tested in other watersheds to determine if it implicitly accounts for surface roughness, topography, and vegetation. These are parameters that are difficult to measure over large areas, and may influence the delta index.

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

“…The ratio method is usually preferred and generally more effective as it is more robust to calibration errors [135], as shown in Villasensor et al [138]. Shoshany et al [139] introduced the normalised radar backscatter soil moisture index (NBMI), similar to the normalised difference vegetation index (NDVI) concept, obtained from the backscatter measurements at two different times (t 1 and t 2 ) over the same location, expressed as: An image difference technique originally proposed by Thoma et al [93] and later adapted by Thoma et al [79], known as the delta index (or Δ-index), is similar to image differencing except that the backscatter difference is divided by the 'dry' reference backscatter image, thereby scaling the index to the soil moisture range. The delta index is defined as: where :…”

“…The resulting backscatter changes between repeat passes can therefore be attributed to changes in soil moisture. This approach has been used successfully used [79,93,140] where two techniques, the theoretical IEM and the Δ-index for retrieving surface soil moisture were compared and the Δ-index was found to be a better predictor of soil moisture content.…”

“…In order to invert the IEM and directly relate σº to the model predictions over both bare and sparsely vegetated surfaces, several algorithms have been devised based on the fitting of IEM numerical simulations for a variety of soil moisture and roughness conditions, including Look Up Tables (LUTs) [79][80][81], Neural Networks (NN) [75,[82][83][84], Bayesian approaches [85][86][87] and minimisation techniques (Nelder-Mead minimisation method devised by Nelder and Mead [88,89] and later adapted by Paloscia et al [89]). Santi et al [90] compared the performances of three of these approaches (Bayes, Neural Networks and Nelder-Mead minimisation) and found them to yield satisfactory results, although the Nelder-Mead minimisation tended to slightly overestimate soil moisture values.…”

Comparison of Simulated Backscattering Signal and ALOS PALSAR Backscattering over Arid Environment Using Experimental Measurement

“…The varieties of backscattering coefficient (σ˚) of simulated model give rise to a wide variety of surface roughness, dielectric constants and water content of soil surface and vegetation water content in vegetated land cover [16]. Meanwhile, most frequently cases study the researcher overcomes the vegetation affected to the radar backscattering coefficient because of this complexity.…”

Monitoring Volumetric Surface Soil Moisture Content at the La Grande Basin Boreal Wetland by Radar Multi Polarization Data

“…This approach is based on the assumption that the temporal variability of surface roughness and natural vegetation biomass is generally at a much longer time scale than that of soil moisture [29,30]. Thus, the change in SAR between repeat passes results from the change in soil moisture (with images acquired under similar satellite configuration and incidence angle).…”