Multi‐scale Modeling of Soil Series Using Remote Sensing in a Wetland Ecosystem

Abstract

Remote sensing informed soil models have shown success to improve the predictive power and spatial resolution of predictions in upland systems. Yet not much is known if these models also perform well in wetland ecosystems. The objectives of this study were to (i) develop spectral informed soil taxonomic prediction models and assess their accuracy; (ii) quantify the relationships between soil classes and environmental co‐variates derived from remote sensing and geospatial sources; and (iii) compare the effects of spatial resolution (10, 30, and 250 m) of three remote sensing images to delineate soil classes. The study was conducted in a subtropical wetland: Water Conservation Area‐2A, the Florida Everglades, U.S. Soil series were collected at 108 sites and three satellite images acquired (i) satellite pour l'observation de la terre (SPOT, 10 m), (ii) Landsat Enhanced Thematic Mapper Plus (ETM+, 30 m), and (iii) Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m). Classification trees were used to predict soil series using spectral data and ancillary environmental datasets. Prediction models derived from spectral data performed better when compared to a control model without spectral inputs. The soil series prediction model derived using SPOT spectral data “without bedrock depth” and the model derived using MODIS spectral data “with bedrock depth” showed the best results based on accuracy measures and Kappa coefficient. Results suggest that the variability of soil series can be explained by bedrock/parent material > topographic variables > vegetation properties derived from remote sensing.