Development of a novel machine learning framework for sustainable agricultural development

To ensure the sustainable development of agriculture, the vegetation and water resources must be monitored accurately in the context of a changing climate. Spatial resolution, however, is a limitation of conventional satellite-based remote sensing methods, and they may not be able to detect the fine-scale patterns of agricultural activity in areas with low water availability. The integrated framework of multisensor remote sensing with super-resolution image enhancement for detecting agriculture and groundwater (GW) conditions in the Al Qassim region of Saudi Arabia is presented. Landsat, Gravity Recovery and Climate Experiment (GRACE), PlanetScope, and Climatic Research Unit Time-Series (CRU-TS) climate data were combined to assess the vegetation indices (VIs) Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI2), and their associations with temperature, rainfall, and groundwater storage (GWS). Three image enhancement techniques were considered, such as Bicubic interpolation, Super-Resolution Convolutional Neural Network (SRCNN), and the Super-Resolution Generative Adversarial Network (SRGNN)-based super-resolution model. The SRGAN model had the smallest root mean square error (RMSE) of 8.27 image intensity units in the 0–255 scaled reflectance space, and the highest peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) scores of 36.48 and 0.935, respectively. The results indicate that there is clear seasonal and annual variability in the vegetation indices, and there is a relationship between the vegetation condition at the regional scale and the climate variables, such as temperature, rainfall, and GWS. Spatial analysis also suggests that responses to vegetation and GW are not the same throughout the study area, due to the variable nature of agriculture, water availability, and climatic stresses encountered locally. The results show that SRGAN can enhance the spatial interpretability of the multi-sensor remote sensing imagery (RSE) and aid in climate-resilient monitoring of agriculture and water resource management.