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ARS Home » Southeast Area » Fayetteville, Arkansas » Poultry Production and Product Safety Research » Research » Publications at this Location » Publication #428351
Research Project: Developing Best Management Practices for Poultry Litter to Improve Agronomic Value and Reduce Air, Soil and Water Pollution

Location: Poultry Production and Product Safety Research

Title: Free-access geospatial data and machine learning delivers high-resolution soil salinity maps in the Sonoran Basin and Range ecoregion

Author
item VALENCIA-ORTIZ, MILTON - University Of Arkansas
item Ashworth, Amanda
item WINZLER, EDWIN - University Of Texas At Arlington
item Blackstock, Joshua
item MANCINI, MARCELO - Federal University - Brazil
item Owens, Phillip
item MUENICH, REBECCA - University Of Arkansas

Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/9/2025
Publication Date: 1/23/2026
Citation: Valencia-Ortiz, M., Ashworth, A.J., Winzler, E., Blackstock, J.M., Mancini, M., Owens, P.R., Muenich, R. 2026. Free-access geospatial data and machine learning delivers high-resolution soil salinity maps in the Sonoran Basin and Range ecoregion. Vadose Zone Journal. https://doi.org/10.1002/vzj2.70070.
DOI: https://doi.org/10.1002/vzj2.70070

Interpretive Summary: Soil salinization represents a serious threat for farmers’ sustained crop productivity as it can render land untenable, requiring farmers to abandon production. Consequently, mapping the spatial occurrence of salinity is essential for crop management and can aid in future salinity predictions for proactive interventions and informed policymaking. Although large-scale monitoring of soil salinity is challenging due to its time and space variability, satellites and advanced mathematical procedures now make soil salinization mapping and predictions of future salt-affected areas possible. Researchers set out to create high-resolution qualitative salinity maps using machine learning models. This mapping product will provide critical soil management strategies at the farmer level.

Technical Abstract: Soil salinity is a serious threat to crop productivity and is anticipated to increase in coming decades. 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 SM and OM 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 of 10 m of spatial resolution in two areas within the SBR ecoregion. Based on uncertainty analysis, the non-saline predicted class comprised larger areas with uncertainties less than <0.2 compared to the saline class. Overall, an aridity index and Sentinel 2 features were the most relevant predictors. Results indicated that only LST showed comparable potential as AI. 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.