Emerging Role of Hyperspectral Remote Sensing in Predictive Soil Health Monitoring Under Climate-Induced Stress Scenarios

Abstract

Soil health is a critical determinant of agricultural productivity, ecological sustainability, and climate resilience. As climate change intensifies the frequency and severity of stressors such as drought, salinization, and thermal extremes, there is an urgent need for advanced tools to monitor and predict soil degradation. Hyperspectral remote sensing (HRS) has emerged as a transformative technology capable of capturing subtle spectral signatures of key soil properties with high precision and spatial detail. This review explores the evolving role of hyperspectral imaging in predictive soil health monitoring, emphasizing its application in climate-stressed environments. The paper presents a conceptual framework for soil health prediction, discusses hyperspectral indicators of soil properties, and highlights the importance of temporal monitoring and change detection. It also examines the integration of hyperspectral data with vegetation and atmospheric parameters to better understand the soil–vegetation–atmosphere continuum. Advances in artificial intelligence (AI), data fusion techniques, and deep learning are reviewed as enablers of real-time, scalable, and accurate soil health forecasting. While the technology shows immense promise, challenges remain, including sensor calibration, data dimensionality, ground truth requirements, and accessibility. The paper concludes with a roadmap for future research, calling for interdisciplinary collaboration, technological innovation, and policy support to mainstream hyperspectral soil monitoring in the context of global climate change. Hyperspectral remote sensing, when coupled with AI-driven analytics and integrated with broader environmental datasets, stands poised to redefine how we assess, forecast, and manage soil health in the 21st century.