Free‐access geospatial data and machine learning deliver high‐resolution soil salinity maps in the Sonoran Basin and Range

Abstract Soil salinity is a serious threat to crop productivity and is anticipated to increase in the coming decades, particularly in semi‐arid to arid agricultural regions. Accessible geospatial data and data mining techniques can enable high‐resolution mapping of soil salinity to improve predictions and soil management. In this study, 64 features derived from Sentinel‐1, Sentinel‐2, land surface temperature, apparent electrical conductivity, and auxiliary geospatial datasets were used to classify salinity in the Sonoran Basin and Range (SBR) ecoregion. As ground truth data, 361 soil samples were collected, and electrical conductivity was measured using the saturated paste method. Recursive feature elimination selected 12 variables for the optimized model (OM), while inferential analysis further reduced them to six for the simplified model (SM). Cross‐validation analysis of OM and SM yielded comparable accuracy (0.68 ±0.04 and 0.68 ±0.05). Based on this and additional analysis (classification report, confusion matrix, etc.), the SM was utilized to predict salinity and non‐salinity maps (10 m resolution) in two areas within the SBR ecoregion (121,110 ha). Based on uncertainty analysis, the non‐saline predicted class comprised larger areas with uncertainties <0.2 compared to the saline class. Overall, the aridity index and Sentinel‐2 features were the most relevant predictors. Findings provide valuable insights for identifying non‐saline areas potentially suitable for agriculture and highlight the importance of salinity mapping and predictions for soil and water resource planning for sustaining crop and range production in arid regions.