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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 1690

Special Issue Editors


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Guest Editor
Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan
Interests: hyperspectral RTM; ecophysiology; gas exchange; ecological modelling; remote sensing applications
Special Issues, Collections and Topics in MDPI journals
Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hyperspectral remote sensing
  • ecophysiology
  • data mining
  • radiative transfer model
  • photosynthesis/evapotranspiration
  • proximal

Published Papers (1 paper)

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Research

16 pages, 3519 KiB  
Article
Validating and Developing Hyperspectral Indices for Tracing Leaf Chlorophyll Fluorescence Parameters under Varying Light Conditions
by Jie Zhuang, Quan Wang, Guangman Song and Jia Jin
Remote Sens. 2023, 15(19), 4890; https://doi.org/10.3390/rs15194890 - 9 Oct 2023
Cited by 5 | Viewed by 1251
Abstract
Chlorophyll a fluorescence (ChlFa) parameters provide insight into the physiological and biochemical processes of plants and have been widely applied to monitor and evaluate the photochemical process and photosynthetic capacity of plants in a variety of environments. Recent advances in remote sensing provide [...] Read more.
Chlorophyll a fluorescence (ChlFa) parameters provide insight into the physiological and biochemical processes of plants and have been widely applied to monitor and evaluate the photochemical process and photosynthetic capacity of plants in a variety of environments. Recent advances in remote sensing provide new opportunities for the detection of ChlFa at large scales but demand further tremendous efforts. Among such efforts, application of the hyperspectral index is always possible, but the performance of hyperspectral indices in detecting ChlFa parameters under varying light conditions is much less investigated. The objective of this study is to investigate the performance of reported hyperspectral indices for tracking ChlFa parameters under different light conditions and to develop and evaluate novel spectral indices. Therefore, an experiment was conducted to simultaneously measure ChlFa parameters and spectral reflectance of sunlit and shaded leaves under varying light conditions, and 28 reported hyperspectral indices were examined for their performance in tracking the ChlFa parameters. Furthermore, we developed novel hyperspectral indices based on various spectral transformations. The results indicated that the maximum quantum efficiency of photosystem II (PSIImax), the cumulative quantum yield of photochemistry (ΦP), and the fraction of open reaction centers in photosystem II (qL) of sunlit leaves were significantly higher than those of shaded leaves, while the cumulative quantum yield of regulated thermal dissipation (ΦN) and fluorescence (ΦF) of shaded leaves was higher than that of sunlit leaves. Efficient tracing of ChlFa parameters could not be achieved from previously published spectral indices. In comparison, all ChlFa parameters were well quantified in shaded leaves when using novel hyperspectral indices, although the hyperspectral indices for tracing the non-photochemical quenching (NPQ) and ΦF were not stable, especially for sunlit leaves. Our findings justify the use of hyperspectral indices as a practical approach to estimating ChlFa parameters. However, caution should be used when using spectral indices to track ChlFa parameters based on the differences in sunlit and shaded leaves. Full article
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