Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
Pushing the Boundaries of Sustainability: Advances in Hyperspectral Remote Sensing for Ecosystem and Natural Resource Management
Authors
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Yongfei Han
Xining Land Survey and Planning Research Institute Co., Ltd., Xining 810000, China
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Hailin Zhang
Xining Land Survey and Planning Research Institute Co., Ltd., Xining 810000, China
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Xiushan Sun
Xining Surveying and Mapping Institute, Xining 810000, China
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Ning Luo
Xining Land Survey and Planning Research Institute Co., Ltd., Xining 810000, China
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Dengbiao Ma
Xining Land Survey and Planning Research Institute Co., Ltd., Xining 810000, China
DOI:
https://doi.org/10.30564/jees.v8i1.13068Abstract
Also known as imaging spectroscopy, hyperspectral remote sensing is becoming a key technology for ecosystem and natural resource management sustainability. Hyperspectral observations can be used to measure tens to hundreds of narrow bands of reflected radiation to resolve diagnostic absorption bands and spectral shape variations associated with vegetation pigments, water status of the canopy, biochemical composition, mineralogies, and organic matter of the soil, and water quality constituents of aquatic water. These abilities allow one to make a transition between the descriptive mapping and the functional monitoring, the anticipation of stress and disturbance early, and the more accurate attribution of environmental change. This summary encompasses improvements on the entire sensor-to-product pipeline, including field and UAV (Unmanned Aerial Vehicle) system platform developments, airborne campaign and spaceborne mission developments, calibration and analysis-ready preprocessing improvements, empirical learning methodology improvements, radiative transfer-based inversion method, spectral unmixing, deep learning, and hybrid physics-machine learning. We underline the increased importance of the combination of data with LiDAR (Light Detection and Ranging), SAR (Synthetic Aperture Radar), and thermal features aimed at decreasing the level of ambiguity and enhancing operational resilience. Applications based on decision are evaluated in terms of biodiversity and habitat evaluation, vegetation functionality and restoration, stress and disturbance, sustainable agricultural production, inland water quality and coastal water quality, land degradation and soil status, and environmental impact assessment. Inhibiting factors to operational adoption have always been perceived to be domain shift by region, season, and sensor, ground truth and validation, mixed pixels and scale mismatch, preprocessing sensitivities, and desirable uncertainty quantification and product output that is interpretable. We conclude with the scalability, sustainability, service priorities, such as harmonization standards, representative benchmarking, uncertainty-aware delivery, and co-design of stakeholders.
Keywords:
Hyperspectral Remote Sensing; Imaging Spectroscopy; Ecosystem Monitoring; Data Fusion; Uncertainty QuantificationReferences
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Han, Y., Zhang, H., Sun, X., Luo, N., & Ma, D. (2026). Pushing the Boundaries of Sustainability: Advances in Hyperspectral Remote Sensing for Ecosystem and Natural Resource Management. Journal of Environmental & Earth Sciences, 8(1), 324–353. https://doi.org/10.30564/jees.v8i1.13068
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Copyright © 2026 Yongfei Han, Hailin Zhang, Xiushan Sun, Ning Luo, Dengbiao Ma
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
