Generation of Winter Wheat Irrigation Guarantee Capability Index Map Based on Deep Learning

Irrigation assurance capacity is an important aspect of farmland quality. This study aims to calculate the evaluation indicators of reference crop irrigation assurance capacity by integrating meteorological station data, remote sensing data, and winter wheat spatiotemporal distribution data. The annual effective irrigation volume is calculated using rainfall and actual evapotranspiration from MOD16A2. A regression equation is established to estimate the spatially continuous reference crop water requirement by combining reference crop evapotranspiration from ground meteorological stations and potential evapotranspiration from MOD16A2. The irrigation water requirement for winter wheat is calculated using the crop coefficient method and winter wheat distribution map. The ratio of effective irrigation volume to irrigation water requirement is used as the evaluation indicator for irrigation assurance capacity. This study utilizes deep learning technology to directly generate an irrigation assurance index map for winter wheat cultivation areas in the North China Plain. The results demonstrate that the index map meets the application requirements for visual monitoring of regional farmland quality. By integrating multiple data sources and deep learning technology, this research provides valuable support for agricultural water resource decision-making and serves as a reference for larger-scale indicator mapping studies.