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Novel Interpretations of Solar-Induced Chlorophyll Fluorescence and Photochemical Reflectance Using Remote Sensing

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 2024 | Viewed by 1908

Special Issue Editors


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Guest Editor
Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Interests: chlorophyll fluorescence; remote sensing; photosynthesis

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Guest Editor
1. Sciences Faculty, Porto University (FCUP) Rua do Campo Alegre, s.n. 4169-007 Porto, Portugal
2. Researcher at Institute for Systems and Computer Engineering, Technology (INESC TEC) Portugal, R. Dr. Roberto Frias, Porto, Portugal
Interests: remote sensing; crop modelling; climate change; precision agriculture; orchards/vineyards monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, Xianyang 712100, China
Interests: solar-induced chlorophyll fluorescence; photosynthesis; agriculture; optical sensing

Special Issue Information

Dear Colleagues,

Solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance indices (PRIs) provide opportunities to investigate vegetation dynamics under different environmental conditions. Strong relationships between SIF and photosynthesis have been idenitified across multiple temporal and spatial scales, and PRIs are linked with non-photochemical quenching (NPQ) and light use efficiency (LUE). Recent advances in remote sensing techniques have enabled measurements of SIF and PRIs from leaf, canopy, and landscape scales. However, these measurements have been transformed into valuable information, though new applications still require more effort dedicated to the scaling, modelling, and interpretation of SIF and PRIs. Instrumental, atmospheric, structural, and physiological factors are accounted for to establish a quantitative link between SIF, PRIs, and photosynthesis.

This Special Issue aims to showcase studies covering SIF, PRIs, and photosynthesis acquired from different sensors, platforms, species, and environments. Potential topics include, but are not limited to, the following:

  • Instrumentations and measurement protocols;
  • Retrieval algorithms and calibration/validation methods;
  • Vegetation stress detection;
  • SIF and PRI modelling,as well as radiative transfer models;
  • New applications of SIF and PRIs;
  • Vegetation dynamics monitoring;
  • Gross primary production estimation;
  • Scaling from leaf to canopy;
  • Relationships among SIF, PRIs, and photosynthesis.

Dr. Genghong Wu
Dr. Mario Cunha
Dr. Zhunqiao Liu
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

  • photosynthesis
  • vegetation dynamics
  • vegetation stress
  • solar-induced chlorophyll fluorescence
  • photochemical reflectance index
  • radiative transfer models
  • multiscale measurements
  • retrieval algorithms

Published Papers (2 papers)

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Research

22 pages, 11888 KiB  
Article
The Relationship of Gross Primary Productivity with NDVI Rather than Solar-Induced Chlorophyll Fluorescence Is Weakened under the Stress of Drought
by Wenhui Zhao, Yuping Rong, Yangzhen Zhou, Yanrong Zhang, Sheng Li and Leizhen Liu
Remote Sens. 2024, 16(3), 555; https://doi.org/10.3390/rs16030555 - 31 Jan 2024
Viewed by 808
Abstract
Grasslands cover approximately one-fourth of the land in the world and play a crucial role in the carbon cycle. Therefore, quantifying the gross primary productivity (GPP) of grasslands is crucial to assess the sustainable development of terrestrial ecosystems. Drought is a widespread and [...] Read more.
Grasslands cover approximately one-fourth of the land in the world and play a crucial role in the carbon cycle. Therefore, quantifying the gross primary productivity (GPP) of grasslands is crucial to assess the sustainable development of terrestrial ecosystems. Drought is a widespread and damaging natural disaster worldwide, which introduces uncertainties in estimating GPP. Solar-induced chlorophyll fluorescence (SIF) is considered as an effective indicator of vegetation photosynthesis and provides new opportunities for monitoring vegetation growth under drought conditions. In this study, using downscaled GOME-2 SIF satellite products and focusing on the drought event in the Xilingol grasslands in 2009, the ability of SIF to evaluate the variations in GPP due to drought was explored. The results showed that the anomalies of SIF in July–August exhibited spatiotemporal characteristics similar to drought indicators, indicating the capability of SIF in monitoring drought. Moreover, the determination coefficient (R2) between SIF and GPP reached 0.95, indicating that SIF is a good indicator for estimating GPP. Particularly under drought conditions, the relationship between SIF and GPP (R2 = 0.90) was significantly higher than NDVI and GPP (R2 = 0.62), demonstrating the superior capability of SIF in tracking changes in grassland photosynthesis caused by drought compared to NDVI. Drought reduces the ability of NDVI to monitor GPP but does not affect that of SIF to monitor GPP. Our study provides a new approach for accurately estimating changes in GPP under drought conditions and is of significant importance for assessing the carbon dynamics of ecosystems. Full article
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16 pages, 8813 KiB  
Article
Regional Analysis of Dominant Factors Influencing Leaf Chlorophyll Content in Complex Terrain Regions Using a Geographic Statistical Model
by Tianjia Chu, Jing Li, Jing Zhao, Chenpeng Gu, Faisal Mumtaz, Yadong Dong, Hu Zhang and Qinhuo Liu
Remote Sens. 2024, 16(3), 479; https://doi.org/10.3390/rs16030479 - 26 Jan 2024
Viewed by 760
Abstract
Chlorophyll is a vital indicator of vegetation growth; exploring its relationship with external influencing factors is essential for studies such as chlorophyll remote sensing retrieval and vegetation growth monitoring. However, there has been limited in-depth exploration of the spatial distribution of leaf chlorophyll [...] Read more.
Chlorophyll is a vital indicator of vegetation growth; exploring its relationship with external influencing factors is essential for studies such as chlorophyll remote sensing retrieval and vegetation growth monitoring. However, there has been limited in-depth exploration of the spatial distribution of leaf chlorophyll content (LCC) and its influencing factors across large-scale areas with varying climates and terrains. To investigate the primary influencing factors and degrees of various environmental factors on LCC, this study employed the Geodetector Model (GDM) and the LCC satellite products in Sichuan Province in 2020 to investigate the impact of relationships between nine environmental factors (meteorology, topography, and vegetation types) and the ecosystem LCC at a regional scale. The results indicated the following: (1) Elevation (q-value = 49.31%) is the primary factor determining photosynthesis in Sichuan Province, followed by temperature (46.10%) and vegetation types (40.73%). The impact of topographical factors on LCC distribution is higher than that of meteorological factors and vegetation types in terrain with complex topography. The elevation effectively distinguishes the variations in climate factors and vegetation types. (2) Combining the influencing factors pairwise increased the combined q-values. The combination of elevation with other factors yielded the highest combined q-value. (3) The q-values for all influencing factors are higher in winter and spring and lowest in summer. Different influencing factors exhibited more substantial constraints on vegetation photosynthesis during winter and spring, significantly reducing influence during summer. (4) The different primary factors drive or constrain vegetation photosynthesis in different climate zones due to their distinct temperature and humidity characteristics. The findings of this study provide a basis for future research on vegetation change analysis and dynamic monitoring of vegetation LCC in different terrains. Full article
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