Remote sensing, geographic information system, and artificial intelligence for soil health monitoring: Indicators, frameworks, advances, limitations, and pathways forward

Abstract, Soil health is a cornerstone of sustainable agriculture and ecosystem resilience, reflecting the soil's capacity to sustain productivity, biodiversity, nutrient cycling, water regulation, and environmental quality. In recent decades, rapid advances in geospatial technologies, particularly remote sensing (RS) and geographic information systems (GISs), have transformed soil health monitoring by enabling spatially explicit, multi‐scale assessments. This review synthesizes current knowledge, methods, and applications of RS and GIS for soil health evaluation, explicitly linking physical (e.g., soil structure, porosity, texture, and moisture), chemical (e.g., soil organic matter, total carbon, salinity, and nutrient status), and biological indicators (e.g., microbial activity, respiration, and biodiversity) to soil health functions. We discuss the use of optical, thermal, microwave, and hyperspectral data to estimate these attributes, while also acknowledging limitations such as restricted subsurface detection, mixed pixel effects, and challenges in capturing biological processes. The integration of RS‐derived indicators within GIS frameworks supports spatial modeling, soil health index development, and multi‐criteria decision analysis. Advances in cloud computing, artificial intelligence (AI), and open‐access satellite data have enabled more accurate and scalable assessments, yet challenges remain in data harmonization, model transferability, and the need for robust ground‐truth calibration across diverse agroecosystems. To reconcile these limitations with future opportunities, we highlight emerging solutions including low‐cost Internet of Things sensor networks, transfer learning, and hybrid modeling approaches. The paper concludes by outlining realistic future directions, emphasizing RS–GIS–AI integration and the potential of deep learning and digital twins for dynamic, real‐time soil health monitoring in both data‐rich and data‐scarce environments.