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Special Issue "Linking Photosynthesis, Gross Primary Productivity and Sun-Induced Fluorescence"

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

Deadline for manuscript submissions: 20 December 2023 | Viewed by 2869

Special Issue Editors

Laboratory of Bioclimatology, Department of Ecology and Environmental Protection, Poznan University of Life Sciences Piątkowska 94, 60-649 Poznań, Poland
Interests: remote sensing; plant stress physiology; sun induced fluorescence; chlorophyll fluorescence; gross primary productivity
Special Issues, Collections and Topics in MDPI journals
School of Geography, Nanjing Normal University, Nanjing 210023, China
Interests: quantitative remote sensing; radiative transfer modelling; plant-climate interaction via photosynthetic and hydrologic processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photosynthesis is a basic phenomenon on which the Earth is dependent. Without it, most organisms would disappear and the Earth’s atmosphere would slowly become one without gaseous oxygen. Photosynthesis is also very sensitive to stress factors (abiotic and biotic), which often disturb the photosynthetic phenomena, often resulting in less productivity and thus food shortages. Due to its importance, it is necessary to monitor photosynthetic activities, but measuring photosynthetic activity on a global scale is not an easy task. In recent decades, a fluorescence-based technique was developed through which researchers are trying to estimate the photosynthetic processes. Remote sensing techniques are able to detect sun-induced fluorescence (SIF), providing a possibility to monitor photosynthesis from space. However, the relationship between SIF and photosynthesis is not direct. Instead, it is regulated by other phenomena such as non-photochemical quenching. Several recent works have linked SIF with the gross primary productivity (GPP) of the plant. However, a lot of work is still needed to understand the relationship between SIF, GPP, and photosynthesis under changing environmental conditions.

The purpose of this Special Issue is to publish the recent work in the area, for the purpose to build a better understanding of this remote sensing signal to observe the Earth's agriculture and vegetation.

We invite all types of articles (reviews, original research, opinions) related to SIF, which can help to better understand the SIF signals and their relationship with GPP and photosynthesis.

Dr. Anshu Rastogi
Prof. Dr. Peiqi Yang
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 2500 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

  • sun-induced chlorophyll fluorescence
  • photosynthesis
  • gross primary productivity
  • plant physiology
  • radiative transfer models

Published Papers (2 papers)

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Research

Article
Dynamic of Fluorescence Emissions at O2A and O2B Telluric Absorption Bands in Forested Areas with Seasonal APAR and GPP Variations
Remote Sens. 2023, 15(1), 67; https://doi.org/10.3390/rs15010067 - 23 Dec 2022
Viewed by 1157
Abstract
We measured dynamics of solar-induced chlorophyll fluorescence at telluric oxygen absorption bands O2A and O2B in evergreen spruce and deciduous beech forests. Seasonal variations in fluorescence emissions were compared with NDVI. Daily changes in fluorescence emissions were compared with [...] Read more.
We measured dynamics of solar-induced chlorophyll fluorescence at telluric oxygen absorption bands O2A and O2B in evergreen spruce and deciduous beech forests. Seasonal variations in fluorescence emissions were compared with NDVI. Daily changes in fluorescence emissions were compared with canopy shadow fraction (αS) dynamics, which showed impact of branch and leaf positions on detected fluorescence signals based on comparison with canopy height model. Absorbed photosynthetically active radiation (APAR) was recognized as a large determinant of fluorescence changes within the O2A band (SIFA), with R2 > 0.68. Fluorescence within the O2B band was more directly linked to NDVI. Although, the seasonal dynamics of fluorescence within the O2B band (SIFB) were similar to SIFA in the spruce forest. In the beech forest, SIFB showed different seasonal dynamics as compared with SIFA. SIFA in the spruce forest showed a relationship to gross primary productivity (GPP), with R2 = 0.48, and a relationship of R2 = 0.37 was estimated for the SIFA-GPP connection in the beech forest. SIFB was better linked to seasonal GPP in the beech forest, but with a negative slope in the relationship with R2 = 0.61. We have shown that measurements of passive fluorescence signals at telluric oxygen absorption bands can contribute to understanding to photosynthesis processes in forest canopies. Full article
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Article
Exploring the Potential of SCOPE Model for Detection of Leaf Area Index and Sun-Induced Fluorescence of Peatland Canopy
Remote Sens. 2022, 14(16), 4010; https://doi.org/10.3390/rs14164010 - 18 Aug 2022
Viewed by 1037
Abstract
The study of peatland is challenging due to the water saturation and evergreen mixed vegetation that ranges from simple forms of plants such as mosses to higher forms of plants such as cranberries, grasses, etc. The changing water level through the growing season [...] Read more.
The study of peatland is challenging due to the water saturation and evergreen mixed vegetation that ranges from simple forms of plants such as mosses to higher forms of plants such as cranberries, grasses, etc. The changing water level through the growing season makes the peatland vegetation very dynamic. In this work, we have used ground-level remote-sensing signals to understand the dynamic nature of peatland vegetation. We have also estimated the leaf area index (LAI) and Sun-Induced fluorescence (SIF) through the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model. The estimated LAI and SIF were compared with the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-Infrared Reflectance of vegetation (NIRv), and measured SIF. The modeled LAI was observed to be significantly correlated with NDVI, EVI, and NIRv, whereas a good correlation was observed between measured and modeled SIF. Along with showing the dynamic behavior of peatland vegetation, the study indicates that SCOPE in its inverted form can be used to estimate reflectance-based LAI for peatland, which can be more reliable to present biomass and productivity of peatland ecosystem in comparison to transmittance-based LAI measurement for such ecosystem. The good correlation between measured and modeled SIF at 760 nm indicates that a reliable SIF value can be estimated through the SCOPE model for a complex ecosystem such as peatland, which can be very helpful in the absence of high-resolution hyperspectral data (usually used for SIF measurements). Full article
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