Mukesh Kumar, Rajeev Kumar, Rekha Jangra, Balkar Singh and Neetu Kataria
Abstract
Soil health is a key factor in agricultural production, environmental sustainability, and climatic resilience. As climate change increases the frequency and intensity of stressors including drought, salinization, and heat extremes, there is an urgent need for better methods to monitor and predict soil deterioration. Hyperspectral remote sensing (HRS) has developed as a breakthrough technology capable of detecting minor spectral signatures of important soil parameters with high precision and spatial detail. This paper investigates the expanding importance of hyperspectral imaging in soil health monitoring, with a focus on its use in climate-stressed areas. The paper proposes a conceptual framework for predicting soil health, analyzes hyperspectral soil property indicators, and emphasizes the need of temporal monitoring and hange detection. The integration of hyperspectral data with vegetation and atmospheric factors is being examined in order to gain a better understanding of the continuity of soil, vegetation, and atmosphere. Deep learning, data fusion, and artificial intelligence (AI) developments are examined as enabling realtime, scalable, and precise soil health forecasting. There are still issues with sensor calibration, data complexity, ground truth requirements, and accessibility, despite the technologyâs enormous potential. A plan for future study is provided in the paperâs conclusion, which calls for interdisciplinary cooperation, technological advancement, and legislative backing to mainstream hyperspectral soil monitoring in light of climate change. The way we evaluate, predict, and manage soil health in the twenty-first century could be completely changed by hyperspectral remote sensing combined with AI-driven analytics and larger environmental databases.