Integrating RS data with fuzzy decision systems for innovative crop water needs assessment

Irrigation is a critical component of global water usage, accounting for approximately 70 % of water consumption. As the world's population continues to grow, the demand for food will increase, making it essential to improve irrigation management by reducing water waste and increasing efficiency. This study aims to develop and validate a fuzzy decision-making system that determines crop irrigation needs based on parameters that affect plant water requirements. These parameters can be monitored using Remote sensing (RS) satellites, enabling large-scale agricultural irrigation monitoring. The study utilized Landsat-8 satellite data and meteorological data. It also employed a fuzzy decision system with inputs of estimated evapotranspiration, Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST), Crop Water Stress Index (CWSI), Stress Index (SI), and Soil Moisture (SM). The output of the fuzzy model is a map that effectively determines the irrigation requirements for agricultural land relatively. The system was tested on six Landsat images of winter wheat crops in Tehran University's agricultural fields. The estimated evapotranspiration was compared to Reference Evapotranspiration (ET_r) obtained from the FAO-Penman-Monteith equation, resulting in a root mean square error of 0.33 mm. The fuzzy decision system was evaluated by comparing its results with Vegetation Water Content (VWC) measurements during satellite overpass time. The NDVI, CWSI, SI, and SM variables had the highest R² with VWC data (0.71––0.92) on all six dates. This approach has significant implications for improving irrigation management practices, reducing water waste, and increasing crop yields, which can contribute to global food security. The study highlights the potential of RS technology and fuzzy decision-making systems in promoting sustainable agriculture.