Special Issue "Recent Progress in Remote Sensing of Terrestrial and Aquatic Fluorescence"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 October 2019.

Special Issue Editors

Prof. Alexander Damm
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Guest Editor
Department of Geography, University of Zurich, Winterthurerstrasse 190 8057 Zurich, Switzerland
Interests: fluorescence spectroscopy; remote sensing of vegetation; plant–water relations; carbon and water cycle; plant photosynthesis; ecosystem functioning and environmental change
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Prof. Uwe Rascher
E-Mail Website
Guest Editor
IBG-2: Plant Sciences, Institute of Bio- und Geosciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Interests: ecophysiology of photosynthesis; plant stress physiology; field phenotyping; optical remote sensing; understanding of sun-induced fluorescence; high-resolution imaging spectroscopy
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Prof. Roberto Colombo
E-Mail Website
Guest Editor
Department of Environmental Sciences, University of Milano-Bicocca, 20126 Milano, Italy
Interests: remote sensing; imaging spectroscopy; sun-induced chlorophyll fluorescence; land surface modelling; environmental monitoring; calibration/validation field campaigns
Dr. Dirk Schuettemeyer
E-Mail
Guest Editor
European Space Research and Technology Centre, European Space Agency, 2201 Noordwijk, The Netherlands
Interests: boundary layer meteorology; numerical weather prediction; remote sensing; fluorescence; calibration/validation field campaigns
Dr. Matthias Drusch
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Guest Editor
Mission Science Division (EOP-SME) European Space Agency, ESTEC, Directorate of Earth Observation Programmes Postbus 299, 2200 AG, Noordwijk, The Netherlands
Interests: land surface hydrology; data assimilation; numerical weather forecasting; remote sensing; space system engineering; fluorescence
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

With the Fluorescence Explorer (FLEX)—Sentinel-3 tandem mission in its implementation phase and fluorescence measurements becoming increasingly available from missions like OCO-2 and Sentinel-5p, remote sensing of fluorescence has been established as important and complementary Earth observation resource to assess complex processes in terrestrial and aquatic ecosystems.

The meeting of the International Network on Remote Sensing of Terrestrial and Aquatic Fluorescence, held in Davos, Switzerland from 5th to 8th March 2019 follows a series of events addressing the remote sensing of vegetation fluorescence and will focus on the latest developments of this topic.

As a follow-up to this event, we are calling for papers on the work presented at the meeting. In addition to this, we welcome papers from the global research community actively involved in research involving fluorescence remote sensing. As such, the Special Issue is open to anyone doing research in this field. The selection of papers for publication will depend on quality and rigor of research. Specific topics include but are not limited to the following:

  • Emerging passive and active sensors (field, airborne, and satellite);
  • Sun-induced chlorophyll fluorescence retrieval methods over land and water;
  • Retrieval algorithms of biophysical parameters (including the future FLEX and Sentinel-3 tandem mission concept);
  • Modelling chlorophyll fluorescence emission;
  • Combining models and observations across spatial and temporal scales;
  • Analyses and results from Sentinel-3 measurements (including the tandem phase of the Sentinel-3 A and B units in summer 2018);
  • Validation activities, field studies, and campaigns related to land and water fluorescence studies;
  • Applications of fluorescence in stress detection in agriculture and forestry;
  • Linking fluorescence and ecosystem processes including gross primary productivity and transpiration;
  • Fluorescence for aquatic research over coastal and inland waters.

Prof. Alexander Damm
Prof. Uwe Rascher
Prof. Roberto Colombo
Dr. Dirk Schuettemeyer
Dr. Matthias Drusch
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 1800 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.

Published Papers (3 papers)

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Research

Open AccessArticle
Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus
Remote Sens. 2019, 11(20), 2424; https://doi.org/10.3390/rs11202424 - 18 Oct 2019
Abstract
Sentinel-3 satellite has provided simultaneous observations in the optical (visible, near infrared (NIR), shortwave infrared (SWIR)) and thermal infrared (TIR) domains since 2016, with a revisit time of 1–2 days. The high temporal resolution and spectral coverage make the data of this mission [...] Read more.
Sentinel-3 satellite has provided simultaneous observations in the optical (visible, near infrared (NIR), shortwave infrared (SWIR)) and thermal infrared (TIR) domains since 2016, with a revisit time of 1–2 days. The high temporal resolution and spectral coverage make the data of this mission attractive for vegetation monitoring. This study explores the possibilities of using the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model together with Sentinel-3 to exploit the two sensors onboard of Sentinel-3 (the ocean and land color instrument (OLCI) and sea and land surface temperature radiometer (SLSTR)) in synergy. Sobol’ variance based global sensitivity analysis (GSA) of top of atmosphere (TOA) radiance produced with a coupled SCOPE-6S model was conducted for optical bands of OLCI and SLSTR, while another GSA of SCOPE was conducted for the land surface temperature (LST) product of SLSTR. The results show that in addition to ESA level-2 Sentinel-3 products, SCOPE is able to retrieve leaf area index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf senescent material (Cs), leaf inclination distribution (LAD). Leaf dry matter content (Cdm) and soil brightness, despite being important, were not confidently retrieved in some cases. GSA of LST in TIR domain showed that plant biochemical parameters—maximum carboxylation rate (Vcmax) and stomata conductance-photosynthesis slope (Ball-Berry m)—can be constrained if prior information on near-surface weather conditions is available. We conclude that the combination of optical and thermal domains facilitates the constraint of the land surface energy balance using SCOPE. Full article
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Open AccessArticle
A Spectral Fitting Algorithm to Retrieve the Fluorescence Spectrum from Canopy Radiance
Remote Sens. 2019, 11(16), 1840; https://doi.org/10.3390/rs11161840 - 07 Aug 2019
Abstract
Retrieval of Sun-Induced Chlorophyll Fluorescence (F) spectrum is one of the challenging perspectives for further advancing F studies towards a better characterization of vegetation structure and functioning. In this study, a simplified Spectral Fitting retrieval algorithm suitable for retrieving the F [...] Read more.
Retrieval of Sun-Induced Chlorophyll Fluorescence (F) spectrum is one of the challenging perspectives for further advancing F studies towards a better characterization of vegetation structure and functioning. In this study, a simplified Spectral Fitting retrieval algorithm suitable for retrieving the F spectrum with a limited number of parameters is proposed (two parameters for F). The novel algorithm is developed and tested on a set of radiative transfer simulations obtained by coupling SCOPE and MODTRAN5 codes, considering different chlorophyll content, leaf area index and noise levels to produce a large variability in fluorescence and reflectance spectra. The retrieval accuracy is quantified based on several metrics derived from the F spectrum (i.e., red and far-red peaks, O2 bands and spectrally-integrated values). Further, the algorithm is employed to process experimental field spectroscopy measurements collected over different crops during a long-lasting field campaign. The reliability of the retrieval algorithm on experimental measurements is evaluated by cross-comparison with F values computed by an independent retrieval method (i.e., SFM at O2 bands). For the first time, the evolution of the F spectrum along the entire growing season for a forage crop is analyzed and three diverse F spectra are identified at different growing stages. The results show that red F is larger for young canopy; while red and far-red F have similar intensity in an intermediate stage; finally, far-red F is significantly larger for the rest of the season. Full article
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Open AccessArticle
Hyplant-Derived Sun-Induced Fluorescence—A New Opportunity to Disentangle Complex Vegetation Signals from Diverse Vegetation Types
Remote Sens. 2019, 11(14), 1691; https://doi.org/10.3390/rs11141691 - 17 Jul 2019
Cited by 1
Abstract
Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the [...] Read more.
Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the diverse vegetation traits from a heterogeneous landscape using complex RS signal is challenging, due to its wide biodiversity and distinctive plant species composition. In this work, we aim to demonstrate, for the first time, the large heterogeneity of peatland vegetation traits using well-established vegetation indices (VIs) and Sun-Induced Fluorescence (SIF) for describing the spatial heterogeneity of the signals which may correspond to spatial diversity of biochemical and structural traits. SIF originates from the initial reactions in photosystems and is emitted at wavelengths between 650–780 nm, with the first peak at around 687 nm and the second peak around 760 nm. We used the first HyPlant airborne data set recorded over a heterogeneous peatland area and its surrounding ecosystems (i.e., forest, grassland) in Poland. We deployed a comparative analysis of SIF and VIs obtained from differently managed and natural vegetation ecosystems, as well as from diverse small-scale peatland plant communities. Furthermore, spatial relationships between SIF and VIs from large-scale vegetation ecosystems to small-scale peatland plant communities were examined. Apart from signal variations, we observed a positive correlation between SIF and greenness-sensitive VIs, whereas a negative correlation between SIF and a VI sensitive to photosynthesis was observed for large-scale vegetation ecosystems. In general, higher values of SIF were associated with higher biomass of vascular plants (associated with higher Leaf Area Index (LAI)). SIF signals, especially SIF760, were strongly associated with the functional diversity of the peatland vegetation. At the peatland area, higher values of SIF760 were associated with plant communities of high perennials, whereas, lower values of SIF760 indicated peatland patches dominated by Sphagnum. In general, SIF760 reflected the productivity gradient on the fen peatland, from Sphagnum-dominated patches with the lowest SIF and fAPAR values indicating lowest productivity to the Carex-dominated patches with the highest SIF and fAPAR values indicating highest productivity. Full article
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