Remote sensing technology has emerged as a transformative tool in agricultural resource management, enabling large-scale monitoring of crops, soils, and water resources with unprecedented spatial and temporal resolution. This review paper provides a comprehensive examination of the applications of remote sensing in agriculture, encompassing satellite-based platforms such as Landsat and Sentinel, aerial systems, and unmanned aerial vehicles (UAVs). The paper discusses key vegetation indices including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI) that are widely employed for crop health assessment, yield estimation, and stress detection. Furthermore, the role of multispectral, hyperspectral, and synthetic aperture radar (SAR) imaging in soil moisture estimation, irrigation management, nutrient stress detection, and pest and disease identification is critically analyzed. The integration of remote sensing with Geographic Information Systems (GIS) and machine learning algorithms has significantly enhanced decision-support systems for precision agriculture. Despite remarkable advancements, challenges persist in terms of data resolution trade-offs, computational requirements, high costs, and accessibility for smallholder farmers. This review synthesizes current knowledge, identifies research gaps, and proposes future directions for optimizing remote sensing applications to achieve sustainable agricultural resource management and global food security.

Keywords: Remote Sensing; Precision Agriculture; Vegetation Indices; Crop Monitoring; Agricultural Resource Management.