Soil Unit Delineation in Semi-Arid Regions Using Remotely Sensed Data

Creating accurate, high resolution soil maps in remote semi-arid areas is time consuming and expensive. The traditional methods of soil mapping in areas of high economic value involve spending considerable amounts of time in the field, often verifying boundaries identified from aerial photographs and validating the boundaries with point observations in soil pits. In contrast, soils in semi-arid rangelands often receive very little attention because the land is of little economic use with the exception of some high-value agricultural soils. Even areas of high interest are generally mapped broadly, with map units on the scale of associations and complexes. However, today societal demands require accurate soil information in semi-arid regions for climate change and echohydrological modeling as well as the estimation of carbon sequestration. Remote sensing techniques can provide data that is spatially and spectrally contiguous and have been used successfully to conduct landuse and landcover surveys as well as to obtain surface information about soils and soil properties. We used the Surface Energy Balance Algorithms for Land (SEBAL) algorithm that solves the surface energy balance on an instantaneous time scale for every pixel of a satellite image to produce maps of root zone soil moisture. Landsat images during the growing season covering several years were analyzed to identify recurring patterns in soil moisture and compared to existing soil and landform maps. Initial investigations using this technique provided good correlations between soil map unit boundaries, landform boundaries and the patterns of soil moisture suggesting that this method may be a useful tool for mapping semi-arid rangeland soils. Recent work has indicated that the soil boundaries detected previously using the remote sensing approach can be further verified using electromagnetic induction, depth to calcic horizon, and percentage of calcium carbonate by depth. Overall, these data illustrate the strengths of remote sensing techniques in mapping soils in remote semi-arid areas.