Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
Advances in Hyperspectral Remote Sensing for Sustainable Natural Resource Management
Authors
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Yuying Ma
School of Electronic Engineering, Shandong University of Engineering and Vocational Technology, Jinan 250200, China
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Guanghui Li
Shandong Panlong Information Technology Co., Ltd., Jinan 250000, China
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Maohu Wei
School of Electronic Engineering, Shandong University of Engineering and Vocational Technology, Jinan 250200, China
DOI:
https://doi.org/10.30564/jees.v8i3.12866Abstract
Hyperspectral remote sensing has emerged as a transformative technology for sustainable natural resource management by providing unprecedented insight into the biochemical, biophysical, and compositional properties of Earth’s surface. The high spectral resolution of hyperspectral sensors allows a very specific discrimination of materials, monitoring of environmental stress at a very early stage, and provides quantitative retrieval of ecological and geochemical parameters in a wide range of landscapes. The booming technology in sensor design, machine learning, spectral unmixing, and multi-sensor data fusion has further improved the analysis potential and application of imaging spectroscopy to a large extent. This paper involves a discussion of the oversight of such technological advances and the manner in which they are utilized in the principal fields that include forestry, agriculture, water, mineral exploration, and coastal ecosystems. Case studies allow us to identify the potential practical consequences of both spaceborne and unmanned aerial vehicles (UAV)-based hyperspectral systems and AI-based workflows that can be used to aid in more efficient and accurate environmental review. Even though the issues associated with data volume, atmospheric impacts, lack of uniformity in the calibration process, and socioeconomic limits continue to exist, the new technology in sensor miniaturization, cloud computing, and artificial intelligence indicates a fast-changing environment. All these developments make hyperspectral remote sensing a key instrument in solving global sustainability problems and evidence-based management of natural resources in an evolving world.
Keywords:
Hyperspectral Remote Sensing; Sustainable Natural Resource Management; Imaging Spectroscopy; Machine Learning; Environmental MonitoringReferences
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Ma, Y., Li, G., & Wei, M. (2026). Advances in Hyperspectral Remote Sensing for Sustainable Natural Resource Management. Journal of Environmental & Earth Sciences, 8(3), 30–50. https://doi.org/10.30564/jees.v8i3.12866
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Copyright © 2026 Yuying Ma, Guanghui Li, Maohu Wei
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
