Monitoring Gypsiferous Soils by Leveraging Advanced Spaceborne Hyperspectral Imagery via Spectral Indices and a Machine Learning Approach

Enhancing the spatial resolution of gypsiferous soil detection, as a valuable baseline information layer, is beneficial for investigating agroecological processes and tackling land degradation in semi-arid environments. This study evaluates the performance of PRISMA (PRecursore IperSpettrale della Missione Applicativa) and EnMAP (Environmental Mapping and Analysis Program) satellites in estimating soil gypsum content and compares models trained on satellite imagery versus lab data. To this end, 242 bare-soil samples were collected from southeast Iran. Gypsum content was measured using acetone precipitation, and spectral reflectance was acquired using the ASD (Analytical Spectral Devices)-Fieldspec 3 spectroradiometer. The gypsum content was retrieved by optical data using three approaches: narrowband indices, spectral absorption features, and machine learning (ML) algorithms. Four machine learning algorithms, including PLSR (Partial Least Squares Regression), RF (Random Forest), SVR (Support Vector Regression), and GPR (Gaussian Process Regression), achieved excellent performance (RPD > 2.5). The results showcased that the difference soil index (DSI) achieved the highest R2 scores of 0.96 (ASD), 0.79 (PRISMA), and 0.84 (EnMAP), slightly outperforming the normalized difference gypsum ratio (NDGI) and ratio soil index (RSI). Comparing the shape indices’, the slope parameter (SLP) index outperformed the half-area parameter (HAP) index. PRISMA, with SVR (R2 ≥ 0.83), and EnMAP, with PLSR (R2 ≥ 0.85), demonstrated that hyperspectral satellites proved reliable in detecting gypsum content, yielding results comparable to ASD with detailed algorithms.