Hyperspectral Remote Sensing of Vegetation Functions: Assessing Vegetation Ecophysiology II

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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