Remote Sensing and Precision Agronomy: A Comprehensive Review of Applications and Prospects

Abstract

Remote sensing and precision agronomy now underpin data-driven crop management across scales. Advances in satellite constellations, synthetic aperture radar (SAR), thermal sensors, and low-altitude drones have transformed field observation from periodic scouting to continuous, quantitative monitoring. This review synthesises foundations, platforms, and analytical methods, and connects them to core agronomic decisions: soil and crop characterisation, irrigation scheduling, nutrient management, weed and disease control, and yield forecasting. We summarise established indices (e.g., NDVI, EVI, SAVI), physics-based energy balance methods for evapotranspiration, SAR for all-weather crop mapping, and emerging hyperspectral and deep learning approaches. We discuss multisensor data fusion, model–data integration with crop system models (e.g., DSSAT, APSIM), and operational products such as OpenET and GEOGLAM Crop Monitor. We outline implementation pathways, economics, and barriers, including interoperability, calibration, and data governance. Finally, we identify near-term prospects in UAV satellite fusion, fieldscale ET, edge AI, and standards for interoperable farm data, as well as long-term needs in privacy, equitable access, and decision support. The evidence indicates remote sensing is mature for many tasks and rapidly improving for others, enabling more precise, profitable, and sustainable agronomy when integrated into repeatable, validated workflows. Key enabling conditions are robust calibration/validation, transparent data contracts, and farmer-centric design.