Re-galvanizing water and crop management using remote sensing-based techniques

Efficient irrigation management remains a critical challenge in modern agriculture, particularly in regions where water resources are limited and traditional practices lead to low water use efficiency and poor crop performance. Integrating satellite-based remote sensing with irrigation operations can be one of the solutions for optimizing irrigation scheduling and enhancing yield. We have estimated rabi season crop water requirements for the Dashela, Gandhinagar, Gujarat, covering an area of approximately 3,500 hectares (ha) using remote sensing techniques. Sentinel-2 imagery was used to identify significant crops in the area and then computed vegetation indices NDVI, NDMI, and EVI to monitor crop health and phenology. For crop classification, Ground truth data were collected through field visits and performed supervised classification within a GIS environment. It was found that castor (968.61 ha), wheat (915.07 ha), and potato (344.46 ha) were significant crops in the area. Next, using the crop coefficient method, the water requirements of different crops during different stages were estimated. The rabi seasonal water demand was 8.33 million cubic meters (MCM) for castor, 4.65 MCM for wheat, and 1.29 MCM for potato crops. Crop yields were estimated using the semi-physical approach by integrating APAR, intercepted PAR, and radiation use efficiency. The crop yields of castor, wheat, and potato were estimated to be 2.38 t/ha, 3.25 t/ha, and 27.28 t/ha respectively. The results demonstrate the effectiveness of remote sensing as a tool for decision-makers in the planning and performance evolution of irrigation and agricultural schemes, which can enhance productivity and resource-use efficiency in semi-arid farming systems.