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

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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: 25 April 2025 | Viewed by 5156

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Special Issue Editors

Dr. Genghong Wu

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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

Dr. Mario Cunha

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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
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Dr. Zhunqiao Liu

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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

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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

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Published Papers (3 papers)

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Research

17 pages, 3326 KiB

Open AccessArticle

Improving Soybean Gross Primary Productivity Modeling Using Solar-Induced Chlorophyll Fluorescence and the Photochemical Reflectance Index by Accounting for the Clearness Index

by Jidai Chen and Jiasong Shi

Remote Sens. 2024, 16(16), 2874; https://doi.org/10.3390/rs16162874 - 6 Aug 2024

Viewed by 1286

Abstract

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (

{S I F}_{T O C_760}

) was downscaled to the broadband photosystem-level SIF for photosystem II (

{S I F}_{T O T_F U L L_P S I I}

). Our results show that GPP can be accurately estimated using a multi-linear model with

{S I F}_{T O T_F U L L_P S I I}

and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (

{G P P}_{E C}

) to GPP estimated using

{S I F}_{T O T_F U L L_P S I I}

and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R² = 0.21) and daily scales (R² = 0.25). The GPP estimates using

{S I F}_{T O T_F U L L_P S I I}

and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R² improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42

μ

mol•m⁻²•s⁻¹; for the daily data, R² improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34

μ

mol•m⁻²•s⁻¹). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using

{S I F}_{T O T_F U L L_P S I I}

and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions. Full article

(This article belongs to the Special Issue Novel Interpretations of Solar-Induced Chlorophyll Fluorescence and Photochemical Reflectance Using Remote Sensing)

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22 pages, 11888 KiB

Open AccessArticle

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

Cited by 3 | Viewed by 1540

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 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

(This article belongs to the Special Issue Novel Interpretations of Solar-Induced Chlorophyll Fluorescence and Photochemical Reflectance Using Remote Sensing)

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16 pages, 8813 KiB

Open AccessArticle

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 1731

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 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

(This article belongs to the Special Issue Novel Interpretations of Solar-Induced Chlorophyll Fluorescence and Photochemical Reflectance Using Remote Sensing)

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