Predictive Soil Mapping in Southern Arizona's Basin and Range

Abstract

A fundamental knowledge gap in understanding land-atmosphere interactions is accurate, high-resolution soil properties. Remote sensing and spatial modeling techniques can bridge the gap between site-specific soil properties and landscape variability, thereby improving predictions of soil attributes. Three studies were completed to advance soil prediction models in semiarid areas. The first study developed a soil pre-mapping technique using automated image segmentation that utilized soil-landscape relationships and surface reflectance to produce an effective map unit design in a 160,000 ha soil survey area. Overall classification accuracy of soil taxonomic units at the suborder was 58 % after including soil temperature regime. Physical soil properties were not significantly different for individual transects; however, properties were significantly different between soil pre-map units when soils from the entire study area were compared. Other studies used a raster approach to predict physical soil properties at a 5 m spatial resolution for a 6,265 ha area using digital soil mapping. The second study utilized remotely-sensed auxiliary data to develop a sampling design and compared three geostatistical techniques for predicting surface soil properties. Ordinary kriging had the smallest prediction error; however, regression kriging preserved landscape features present in the study area and demonstrated the potential of this technique for quantifying variability of soil components within soil map units. The third study applied quantitative data from soil prediction models in study 2 and additional models of subsurface properties to a pedotransfer function for predicting hydraulic soil parameters at the landscape scale. Saturated hydraulic conductivity and water retention parameters were used to predict water residence times for loss to gravity and evapotranspiration across the landscape. High water residence time for gravitational water corresponded to both low drainage density and high clay content, whereas high residence of plant available water was related to increased vegetation response. These studies illustrate the utility of digital soil mapping techniques for improving soil information at landscape scales, while reducing required resources. Resulting soil information is useful for quantifying landscape-scale processes that require constraint of spatial variability and prediction error of soil properties to better model hydrological and ecological responses to climate and land use change.