Hyperspectral Remote Sensing of Vegetation Functions: Assessing Vegetation Ecophysiology II
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 3118
Special Issue Editors
Interests: hyperspectral RTM; ecophysiology; gas exchange; ecological modelling; remote sensing applications
Special Issues, Collections and Topics in MDPI journals
Interests: quantitative remote sensing; plant physiology; biochemistry; ecosystem monitoring; radiative transfer model
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The ecophysiological processes of plants in terrestrial ecosystems play an important role in the exchange of gases between vegetation and the atmosphere. While the ability to infer vegetation functions and traits related to physiological and ecological processes from hyperspectral remote sensing data has improved considerably over the past decades, the physical and physiological mechanisms involved remain poorly understood. Considerable inputs from laboratory-controlled and field experiments, long-term monitoring from different platforms (especially ground-based, UAV, and space-based), data mining, and radiative transfer modeling are required to reveal the underlying link between hyperspectral remote sensing signals (reflected/emitted/transmitted) and ecophysiological status and processes. The aim of this Special Issue is to report on recent advances in hyperspectral remote sensing retrieval algorithms and radiative transfer modeling in relation to plant physiological and ecological status and processes, as well as their applications in assessing vegetation responses to various stresses. Special focus will be placed on, but not limited to:
- Different approaches (statistical/RTM/machine learning or deep learning) to hyperspectral remote sensing of key vegetation parameters of ecophysiological processes.
- Mechanistic understanding of hyperspectral information and vegetation ecophysiological parameters through theoretical and experimental developments.
- Integrated modeling of radiative transfer and ecophysiological processes.
- Hyperspectral remote assessment of vegetation responses to stress conditions at different scales.
- Research into the application of hyperspectral remote sensing products for a better understanding of vegetation ecophysiology across a range of spatial and temporal scales.
Prof. Dr. Quan Wang
Dr. Jia Jin
Guest Editors
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Keywords
- hyperspectral remote sensing
- ecophysiology
- data mining
- radiative transfer model
- photosynthesis/evapotranspiration
- proximal
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