Integrating remote sensing and GIS for soil health monitoring: A comprehensive review

Abstract

Soil health underpins sustainable agriculture, ecosystem integrity, and food security. Conventional soil monitoring relies on field sampling and laboratory analysis, which are often time‑consuming, labour-intensive, and spatially limited. Recent developments in remote sensing (RS), geographic information systems (GIS), and digital soil mapping (DSM) offer efficient, scalable alternatives enabling frequent, large-area assessment of soil physical, chemical, and biological properties. This review synthesises state-of-the-art methods integrating RS, GIS, and machine learning (ML) to monitor soil health indicators—such as soil organic carbon (SOC), bulk density, texture, moisture, and nutrient status. We examine successes and limitations, explore case studies (including in India), and highlight recent advances in high-resolution mapping, spatio-temporal monitoring, and predictive modelling. We also discuss how RS-GIS integration supports land-use planning, soil degradation assessment, and targeted soil restoration interventions. The review concludes with a summary of research gaps and recommendations for operational soil-health monitoring systems.