Mapping infiltration in an urbanizing mixed-land-use watershed with multi-temporal satellite imagery

Abstract

Digital soil mapping (DSM) is a field of soil science that aims to improve traditional soil maps by producing higher resolution predictive maps of soil properties using spatial environmental data. DSM has historically relied primarily on static environmental covariates—such as slope gradient, slope aspect, and other topographic variables derived from digital terrain models—for predicting static soil properties, like soil texture. Advancements in satellite imagery and statistical modeling improve the accuracy of digital soil maps by incorporating multi-temporal data that can capture landscape-scale change over relatively short periods of time. Adding these dynamic environmental covariates may be especially useful for spatial prediction of dynamic soil properties, like infiltration rate, that are strongly affected by phenomenon that satellite imagery can detect, like land use that changes rapidly due to human activity. Infiltration strongly impacts soil health and hydrologic characteristics in a watershed. Understanding infiltration for sustainable land management is vital for making best management decisions in urbanizing environments like the West Run Watershed in Morgantown, West Virginia. We hypothesized that infiltration could be predicted at a higher accuracy and a finer spatiotemporal scale using digital soil mapping techniques than is currently provided by the current official soil data and maps produced by the National Cooperative Soil Survey. Spatial predictions of infiltration rate were produced for the West Run watershed using both static and dynamic environmental covariates as inputs into multiple linear regression (MLR) and random forest (RF) models, each of which were made using 10-fold cross validation. Training and independent validation sampling locations were selected using a conditioned Latin hypercube sampling scheme and observed saturated hydraulic conductivity of the soil surface was collected using automated dual-head infiltrometers. The MLR and RF models had R 2 of 0.302 and 0.201, respectively. Validation sampling was stratified by the predicted infiltration values of the MLR model. Validation R 2 values for the MLR and RF models were 0.080 and 0.103. The results from this study will benefit the development of a dynamic soil survey and will improve hydrologic models in this and potentially other mixed-land-use watersheds.