Landsat Spectral Data for Digital Soil Mapping

Abstract

We propose that Landsat remotely sensed spectral data represent useful environmental covariates for digitally mapping soil distribution on the landscape, especially in arid and semiarid areas. Based on the common conceptual model that unique soils are the products of unique sets of soil-forming factors, Landsat spectral data can represent environmental covariates for vegetation (e.g., normalized difference vegetation index, fractional vegetation cover) and parent material and/or soil (e.g., band ratios diagnostic for gypsic and calcareous materials). In areas with sufficient relief, topographic data (e.g., slope, compound topographic index) derived from digital elevation models (DEMs) can be combined with Landsat-derived data to quantitatively model soil distribution on the landscape. These digital data can by analyzed using commercially available image processing software. Various classification and analysis methods (e.g., optimum index factor; principle component analysis; unsupervised and supervised classification) can be used to recognize meaningful soil-landscape patterns. . Training sites can be selected from existing soil surveys or from areas that have actual field data collection points. Accuracy assessment with independent field observation can be performed, and various classification methods can be used to generate estimates of prediction error. Landsat scenes are spatially explicit, physical representations of environmental covariates on the land surface. While the 30-m spatial resolution and fairly coarse spectral resolution may limit some applications, the wide availability and low expense should facilitate the utility of Landsat spectral data in digital soil mapping.