Highlights What are the main findings? Applying explainable machine learning to both proximal and remote sensing data yields strong insights into the dynamics of soil salinity, especially in data-scarce semi-arid regions. When used with PLSR, regression kriging is an effective accuracy booster; however, the choice of the underlying predictive model still has a significant impact on how effective it is. What are the implications of the main findings? Topographic features significantly enhance the prediction power of UAV-derived models and are crucial in soil salinization processes. PlanetScope and UAV-derived topographic covariates are highly recommended for timely high-resolution monitoring of soil salinity.Highlights What are the main findings? Applying explainable machine learning to both proximal and remote sensing data yields strong insights into the dynamics of soil salinity, especially in data-scarce semi-arid regions. When used with PLSR, regression kriging is an effective accuracy booster; however, the choice of the underlying predictive model still has a significant impact on how effective it is. What are the implications of the main findings? Topographic features significantly enhance the prediction power of UAV-derived models and are crucial in soil salinization processes. PlanetScope and UAV-derived topographic covariates are highly recommended for timely high-resolution monitoring of soil salinity.Abstract Soil salinity severely constrains agricultural productivity and soil health, particularly in arid and semi-arid regions. Conventional salinity assessment methods are labor-intensive, time-consuming, and spatially limited. This study developed a data-scarce workflow integrating proximal sensing (EM38-MK2), very high-resolution multispectral imagery, and machine learning to map soil salinity at field scale in the semi-arid Sehb El Masjoune area, central Morocco. A total of 26 soil samples were analyzed for Electrical Conductivity (EC), and 500 Apparent Electrical Conductivity (ECa) measurements were collected and calibrated using the field samples. Spectral and topographic covariates derived from Unmanned Aerial Vehicle (UAV) and PlanetScope imagery supported model training using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and a Stacked Ensemble Learning Model (ELM). Regression Kriging (RK) was applied to model residuals to improve spatial prediction. ELM achieved the highest accuracy (R2 = 0.87, RMSE approximate to 4.15), followed by RF, which effectively captured nonlinear spatial patterns. RK improved PLSR accuracy (by 11.1% for PlanetScope, 13.8% for UAV) but offered limited gains for RF, SVR, and ELM. SHAP analysis identified topographic covariates as the most influential predictors. Both UAV and PlanetScope delineated similar saline-sodic zones. The study demonstrates the following: (1) a scalable, data-efficient workflow for salinity mapping; (2) model and RK performance depend more on algorithmic design than sensor type; (3) interpretable ML and spatial modeling enhance understanding of salinity processes in semi-arid systems.