Retrieving long-term topsoil moisture in Qingtongxia irrigation district using a modified OPTRAM model

Qingtongxia Irrigation District (QID), Ningxia, China, a fully irrigated and arid region. The Optical Trapezoid Model (OPTRAM) estimates surface soil moisture (SSM) using optical remote sensing data by linking SSM to Shortwave Infrared Transformed Reflectance (STR). It defines linear dry and wet edges of the STR-NDVI trapezoidal space under minimum dry and maximum wet soil conditions. However, these edges may not always be linear, and OPTRAM's long-term performance in large-scale irrigation areas remains underexplored. This study, set in Ningxia’s Qingtongxia Irrigation District, uses Sentinel 2 and Landsat 8 images (2022–2024) across crop growth and fallow periods. A modified OPTRAM model introduces a quadratic function to better capture non-linear STR-NDVI edges, improving long-term SSM estimates. Our result showed that the modified OPTRAM achieved the highest accuracy in SSM estimation, especially with Sentinel 2 data, compared with OPTRAM and TOTRAM models. Despite cloud cover, the model captured field-scale SSM dynamics, including irrigation events. It also showed potential for crop type mapping, growth stage analysis, and irrigation detection. By incorporating the entire crop growth and fallow periods, a distinct STR-NDVI feature space for QID was revealed. These results offer new insights into soil moisture heterogeneity and water use patterns in irrigated dryland regions, supporting improved irrigation management and precision agriculture. • Modified OPTRAM model for wet and dry edges improved SSM estimates. • New STR-NDVI space including crop growth and fallow periods was revealed. • OPTRAM model can track the growth of crops in a large-scale irrigation district. • Developed auto and post-visual matching parameterization approach.