REMOTE SENSING OF TEXTURAL CLASSES IN SW UNITED STATES

Current soil mapping methods are time consuming and expensive, especially at small scales and in remote areas. Traditional methods requires analysis of aerial photographs and descriptions of soils (Soil Survey Staff, 2017). After data is collected the scientist will create a conceptual model of soil formation to predict the soil map unit throughout an area. Principle components of map boundaries such as land forms, lithology, slope angle and slope orientation, are identified. Traditional soil mapping methods have high propensity for error, with accuracy ranging from 60-80\% (Wilding, 1965; Drohan, 2003). Modern soil surveys produce maps at 1:24,000 or 1:12,000; (Soil Survey Staff, 2017). At such large scales acres of soils can be misidentified. To create a soil map that is more detailed with current methods is logistically cost prohibitive to undertake, especially in range-land areas. The need remains for a method to produce a high-resolution, accurate, and descriptive soil map.

This study hypothesized that an accurate and detailed soil map can be produced using data collected by way of Unmanned Aerial Vehicles (UAVs) and energy balance algorithms. Multi-spectral data necessary to estimate soil moisture was collected using UAVs. In an effort to reduce cost an alternative method of collecting soil moisture was tested against the energy balance algorithm. This alternative method identified large scale differences but was as effective as the energy balance algorithm. The soil moisture maps informed the soil map unit boundaries. Physical sampling informed map unit designation and hydraulogical modeling. The hydraulogical modeling produced drying curves, which are correlated to soil texture (Cosby, 1984; Miller, 1973). The soil drying curves made it possible to correlate the soil moisture maps and the soil physical properties. This informed the distribution of the soil map units, resulting in a soil map with increased resolution, higher accuracy and more descriptive units, than traditional methods.