A response surface-based calibration framework for accurate soil water content determination under saline conditions: a case study in Guangdong Province, China

Abstract

Understanding soil water content dynamics under saline conditions remains a major challenge for effective soil and water management in agricultural systems. This study aimed to optimize the correction of soil water content measurements obtained from gypsum blocks under the combined effects of salinity and matric suction using Response Surface Methodology (RSM). Soil samples were collected from agricultural fields in Guangdong Province, China, representing three distinct soil textures: sandy loam, clay, and clay loam. The samples were exposed to seven salinity levels ranging from 2 to 24 dS m⁻¹. Calibration of the gypsum blocks involved establishing the relationship between electrical resistance and soil matric potential, followed by developing a second-order polynomial model to predict the soil water content correction value (Δθ = predicted−measured). Statistical analysis showed that the model provided strong predictive performance within the experimentally investigated domain (R² = 0.98, RMSE = 0.021, CRM ≈ 0, indicating negligible bias). The interaction between salinity and matric suction significantly influenced the sensor response; higher salinity levels progressively degraded uncorrected reading accuracy. Across the three soil textures (sandy loam, clay, and clay loam), the proposed RSM calibration reduced the root mean square error (RMSE) by more than 95% relative to uncorrected raw sensor readings. These results demonstrate that RSM is an effective empirical approach for correcting gypsum block data under saline regimes, offering a practical solution for improving soil water content estimation and irrigation management in saline agricultural environments.