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Linking Photosynthesis, Gross Primary Productivity and Sun-Induced Fluorescence

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 6923

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Dr. Anshu Rastogi

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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
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Prof. Dr. Peiqi Yang

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School of Geography, Nanjing Normal University, Nanjing 210023, China
Interests: quantitative remote sensing; radiative transfer modelling; plant-climate interaction via photosynthetic and hydrologic processes
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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

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Keywords

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

Published Papers (3 papers)

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Research

18 pages, 3772 KiB

Open AccessArticle

Improving the Estimation of Canopy Fluorescence Escape Probability in the Near-Infrared Band by Accounting for Soil Reflectance

by Mengjia Qi, Xinjie Liu, Shanshan Du, Linlin Guan, Ruonan Chen and Liangyun Liu

Remote Sens. 2023, 15(18), 4361; https://doi.org/10.3390/rs15184361 - 5 Sep 2023

Cited by 3 | Viewed by 1982

Abstract

Solar-induced chlorophyll fluorescence (SIF) has been found to be a useful indicator of vegetation’s gross primary productivity (GPP). However, the directional SIF observations obtained from a canopy only represent a portion of the total fluorescence emitted by all the leaf photosystems because of scattering and reabsorption effects inside the leaves and canopy. Hence, it is crucial to downscale the SIF from canopy level to leaf level by modeling fluorescence escape probability (f_esc) for improved comprehension of the relationship between SIF and GPP. Most methods for estimating f_esc rely on the assumption of a “black soil background,” ignoring soil reflectance and the effect of scattering between soils and leaves, which creates significant uncertainties for sparse canopies. In this study, we added a correction factor considering soil reflectance, which was modeled using the Gaussian process regression algorithm, to the semi-empirical NIRv/FAPAR model and obtained the improved f_esc model accounting for soil reflectance (called the f_{esc_GPR-SR} model), which is suitable for near-infrared SIF downscaling. The evaluation results using two simulation datasets from the Soil–Canopy–Observation of Photosynthesis and the Energy Balance (SCOPE) model and the Discrete Anisotropic Radiative Transfer (DART) model showed that the f_{esc_GPR-SR} model outperformed the NIRv/FAPAR model, especially for sparse vegetation, with higher accuracy for estimating f_esc (R² = 0.954 and RMSE = 0.012 for SCOPE simulations; R² = 0.982 and RMSE = 0.026 for DART simulations) compared with the NIRv/FAPAR model (R² = 0.866 and RMSE = 0.100 for SCOPE simulations; R² = 0.984 and RMSE = 0.070 for DART simulations). The evaluation results using in situ observation data from multi-species canopies also suggested that the leaf-level SIF calculated by the f_{esc_GPR-SR} model tracked better with photosynthetic active radiation absorbed by green components (APAR_green) for sparse vegetation (R² = 0.937, RMSE = 0.656 mW/m²/nm) compared with the NIRv/FAPAR model (R² = 0.921, RMSE = 0.904 mW/m²/nm). The leaf-level SIF calculated by the f_{esc_GPR-SR} model was less sensitive to observation angles and differences in canopy structure among multiple species. These results emphasize the significance of accounting for soil reflectance in the estimation of f_esc and demonstrate that the f_{esc_GPR-SR} model can contribute to further exploring the physiological mechanism between SIF and GPP. Full article

(This article belongs to the Special Issue Linking Photosynthesis, Gross Primary Productivity and Sun-Induced Fluorescence)

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19 pages, 4717 KiB

Open AccessArticle

Dynamic of Fluorescence Emissions at O₂A and O₂B Telluric Absorption Bands in Forested Areas with Seasonal APAR and GPP Variations

by Daniel Kováč, Jan Novotný, Ladislav Šigut, John Grace and Otmar Urban

Remote Sens. 2023, 15(1), 67; https://doi.org/10.3390/rs15010067 - 23 Dec 2022

Cited by 3 | Viewed by 1980

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 canopy shadow fraction (α_S) 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

(This article belongs to the Special Issue Linking Photosynthesis, Gross Primary Productivity and Sun-Induced Fluorescence)

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15 pages, 5158 KiB

Open AccessArticle

Exploring the Potential of SCOPE Model for Detection of Leaf Area Index and Sun-Induced Fluorescence of Peatland Canopy

by Anshu Rastogi, Michal Antala, Egor Prikaziuk, Peiqi Yang, Christiaan van der Tol and Radoslaw Juszczak

Remote Sens. 2022, 14(16), 4010; https://doi.org/10.3390/rs14164010 - 18 Aug 2022

Cited by 2 | Viewed by 1675

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 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

(This article belongs to the Special Issue Linking Photosynthesis, Gross Primary Productivity and Sun-Induced Fluorescence)

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