Irrigation area monitoring technology and application based on the fusion of multi-source remote sensing data

Objectives Detection technology based on multi-source remote sensing data fusion, accurately identifying irrigated areas is crucial for ensuring the stability of food production and enhancing water resource efficiency. This study addresses the desert oasis irrigation districts in Northwestern China.

Methods In the context of desert oasis irrigation districts in northwest China, this study employs a comprehensive approach integrating MODIS and landsat remote sensing data, alongside the China meteorological administration's land data assimilation system (CLDAS-V2.0) and SMAP (Soil Moisture active passive) reanalysis data. By utilizing the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the random forest (RF) regression model, a daily high-resolution surface data set is constructed. This data set is then used to invert surface soil moisture, enabling the extraction of irrigation areas.

Results The surface soil moisture inversion model achieved a coefficient of determination (R²) of 0.77, a mean absolute error (MAE) of 0.024 cm³/cm³, and a root mean square error (RMSE) of 0.038 cm³/cm³. Based on the inverted daily scale surface soil moisture during the growing season, a change-point analysis of the daily soil moisture time series was conducted. This allowed for the identification of irrigation events in the Ruoqiang River irrigation district during the growing period. Comparison with field-observed irrigation data showed that the overall accuracy of irrigation event identification reached 95%.

Conclusions The results validated the effectiveness of integrating multi-source remote sensing data and machine learning algorithms in constructing a high spatiotemporal resolution soil moisture dataset. This method is capable of capturing the dynamic variations of farmland moisture and irrigation behaviors in desert oasis regions. It not only provides reliable technical support for the refined management of agricultural water resources and irrigation scheduling in arid areas, but also offers an important scientific basis for optimizing regional water resource allocation and ensuring food security.