Applications of Remote Sensing and GIS in Agricultural Resource Management: A Review

Agricultural resource management increasingly requires spatially explicit, timely and repeatable information on crops, soils, water, terrain and production risks. Remote sensing (RS) and geographic information systems (GIS) provide a complementary framework for acquiring, integrating and analysing such information from plant to regional scales. This review synthesises major applications of satellite, airborne, unmanned aerial vehicle (UAV), thermal, hyperspectral, synthetic aperture radar (SAR) and geospatial technologies in agricultural resource management. Emphasis is placed on crop inventory and phenology, soil and land-resource characterisation, irrigation and evapotranspiration mapping, drought and water-stress assessment, nutrient management, pest and disease surveillance, yield forecasting, land suitability, degradation monitoring and precision agriculture. The review also examines the role of GIS in combining remotely sensed products with soils, weather, terrain, infrastructure and administrative data to support multi-criteria decision analysis and operational decision support. The evidence base was updated through 11 July 2026, with particular emphasis on peer-reviewed reviews and systematic reviews published during 2024–2026, while retaining landmark papers where they define established indices, missions or physical methods. Recent advances in cloud computing, data cubes, machine learning, deep learning, foundation-model concepts and multi-sensor fusion have improved the scalability and timeliness of agricultural monitoring, while open Landsat and Sentinel archives have lowered data-access barriers. Persistent limitations include cloud contamination, mixed pixels, scale mismatch, limited ground truth, model transferability, uncertainty communication, data latency, smallholder field fragmentation and the gap between research products and actionable farm decisions. Indian operational developments, including technology-assisted crop insurance and yield estimation, illustrate the movement from experimental geospatial analysis towards institutional use. Future progress will depend on harmonised multi-source observations, physics-informed and explainable artificial intelligence, robust validation, interoperable GIS platforms and decision products designed around farmer and policy needs. RS and GIS should therefore be viewed not as substitutes for field observations, but as force multipliers that extend field knowledge across space and time and enable more efficient, climate-resilient and evidence-based management of agricultural resources.