Integrating Proximal and Remote Sensing for Regional Soil Characterization and Mapping in Rural Haiti

Agronomic optimization is essential in developing countries, particularly in regions where soil resources are limited and spatial variability is poorly characterized. This study, the first of its kind in Haiti, applied predictive modeling to link laboratory-derived physical and chemical soil properties with proximal and remotely sensed data collected from 32,949 georeferenced surface soil (0–20 cm) samples across the Arcahaie region. A representative subset of samples (n = 300) was analyzed using multiple machine-learning models, including Random Forest, Gradient Boosting, Stacking Ensemble, and XGBoost, to predict soil pH, texture components (sand, silt, clay), soil organic carbon, soil organic matter, cation exchange capacity, and plant-available P, K, Si, Fe, and Cu from proximal sensing data. Strong predictive performance was achieved for sand, silt, clay, soil organic carbon, soil organic matter, and cation exchange capacity (R² ≥ 0.80), with particularly robust results for soil texture and carbon-related properties, while predictions for other parameters were statistically significant but less accurate. The optimized models were subsequently applied to the full dataset, and spatial interpolation was performed to generate high-resolution maps of soil physical and chemical variability across the region. These outputs provide a decision-support framework for site-specific agronomic management. The methodology demonstrated here is readily transferable to other agriculturally important regions of Haiti and comparable developing-country contexts and could be further extended to three-dimensional modeling and mapping of subsoil properties to better characterize fertility within the root zone.