Environmental Assessment of Semi-Arid Agriculture Using Sentinel Remote Sensing

Semi-arid agricultural regions face increasing environmental pressures driven by declining groundwater levels, rising irrigation demand, and intensified climate variability. This study presents an integrated remote-sensing-based framework to evaluate the environmental sustainability of semi-arid farming systems using Sentinel-1 and Sentinel-2 observations combined with open-source environmental modelling. Multi-temporal spectral indices, including NDVI, NDWI, MSI, and chlorophyll-sensitive bands, are used to assess crop water status, canopy moisture variability, and early stress signals during critical growth stages. Sentinel-1 backscatter further supports estimation of soil moisture proxies and surface roughness, enabling robust characterization of irrigation-driven landscape patterns. To translate these remote-sensing-derived indicators into environmental metrics, irrigation intensity maps are integrated with life cycle assessment (LCA) using OpenLCA. Pumping energy, blue-water use, and irrigation-related emissions are quantified using standard emission factors and region-specific groundwater data. This combined EO-LCA workflow allows estimation of spatially explicit carbon footprints associated with irrigation, water-use efficiency gradients, and zones with unsustainable extraction pressure. The case study over semi-arid croplands in northern India identified irrigation hotspots with estimated carbon footprints ranging from approximately 0.25 to 0.58 kg CO₂-eq m⁻³ of groundwater applied, while NDVI varied between 0.34 and 0.82 across the growing season, reflecting substantial differences in crop vigor. Moisture-stress indicators (MSI) increased by up to 30% in water-stressed fields compared with well-irrigated areas, highlighting localized irrigation inefficiencies. The results highlight considerable intra-field and inter-field variability driven by microclimate, soil texture, and irrigation scheduling differences. The proposed methodology is fully reproducible using open-access datasets, cloud-based processing, and free environmental modelling tools, making it suitable for large-scale monitoring and policy assessment. The study demonstrates how integrating remote sensing with environmental footprint analysis can support climate-smart agriculture, guide water allocation decisions, and strengthen sustainability planning in semi-arid agroecosystems.