Special Issue "Hyperspectral Remote Sensing of Vegetation Functions"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editors

Prof. Dr. Quan Wang
E-Mail Website
Guest Editor
Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan
Interests: hyperspectral RTM; ecophysiology; gas exchange; ecological modelling; remote sensing applications
Dr. Jia Jin
E-Mail Website
Guest Editor
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 Issue Information

Dear Colleagues,

Hyperspectral information remotely sensed from different platforms at multiple spatial, temporal, and spectral scales offers more unprecedented data sources for revealing the properties of vegetation than ever before, opening the door for not only retrieving vegetation’s biophysical (structural), biochemical, and physiological traits, but also the possibility of tracing the dynamics of functions that are impossible with previous remote sensing activities. However, lacking the profound mechanical understanding of involved physical and physiological processes of hyperspectral data, which are scale-dependent, prevents their proper applications and needs to be explicitly addressed. This Special Issue is, thus, calling for state-of-the-art studies on processing and analyzing hyperspectral information acquired from different platforms (leaf spectroscopy, tower-based proximal remote sensing, UAV mounts, airplane/satellite-borne devices), with the target fof clarifying the underlying physical and physiological mechanisms and for accurately tracking the dynamics of vegetation functions. Special focus will be placed on, but is not limited to:

  • Novel techniques (statistical/RTM/machine-learning or deep-learning) for retrieving and tracing vegetation functions (especially ecophysiological processes) from hyperspectral data.
  • Novel research on clarifying the physical and physiological bases of hyperspectral information using field monitoring, laboratory-controlled experiments, or RTM simulation datasets.
  • Insightful research on upscaling/downscaling mechanisms of the relationships between hyperspectral information and vegetation functions from leaf to canopy and plot levels.

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 papers will be 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 2400 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

  • leaf spectroscopy
  • proximal
  • hyperspectral imaging
  • RTM
  • physical and physiological mechanisms
  • ecological processes
  • scaling
  • inversion
  • machine-learning
  • deep-learning

Published Papers (1 paper)

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Research

Article
Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes
Remote Sens. 2021, 13(9), 1679; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091679 - 27 Apr 2021
Viewed by 565
Abstract
Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise [...] Read more.
Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R2 = 0.80–0.92) and moderate to weak relationships with BDW (R2 = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes. Full article
(This article belongs to the Special Issue Hyperspectral Remote Sensing of Vegetation Functions)
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