Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Remote Sensing of Fluorescence, Photosynthesis and Vegetation Status

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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: closed (31 December 2021) | Viewed by 11997

Share This Special Issue

Special Issue Editors

Dr. Anshu Rastogi

E-Mail Website
Guest Editor

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

Dr. Jochem Verrelst

E-Mail Website
Guest Editor

Laboratory for Earth Observation, Image Processing Laboratory - Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
Interests: imaging spectroscopy; vegetation properties retrieval; FLEX; vegetation fluorescence; optical remote sensing; radiative transfer models; retrieval methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photosynthesis is the basis of life on Earth. During photosynthesis, part of the radiation absorbed by chlorophyll is re-emitted in the form of fluorescence. Therefore, the measurement of chlorophyll fluorescence is considered to be a valuable tool to monitor photosynthetic activity. Remote sensing of chlorophyll fluorescence is a fast-growing field, which is important for quantifying vegetation’s health status, encompassing its functional activity from the canopy to ecosystem levels at global scale. In the last decade, the research on Sun-Induced Chlorophyll Fluorescence (SIF) has increased rapidly. Novel instruments, measuring systems and platforms are developed to acquire reflectance spectra with ultrafine spectral resolution. At the same time, new and improved retrieval methods have been developed with the purpose of extracting the weak SIF signal from reflectance spectra with higher accuracy. All kinds of research experiments have been performed recently through different platforms (Unmanned aerial vehicle, airborne, spaceborne) to study the sensitivity of SIF signals on environmental factors (pollutants, herbicides, etc.) to better interpret the SIF and its relation to photosynthesis at multiple scales (leaf, canopy, ecosystem). For this purpose, advanced physiological and radiative transfer models are being developed, which helps in combining SIF data with other available biophysical information.

In the meantime, space satellite missions such as OCO-2, GOSAT, GOME-2, and others are already exploiting the SIF variability to understand the earth's vegetation, whereas the dedicated Fluorescence Explorer (FLEX)—Sentinel-3 tandem mission by European Space Agency is in its implementation phase.

The field of SIF-related is rapidly evolving and SIF has been generally recognized as the most direct remote sensing proxy for photosynthesis estimation. However, additional information is required for a better understanding of the complex vegetation physiology mechanisms, such as absorbed photosynthetic radiation by plants, non-photochemical energy dissipation, plant structure, the role of the atmosphere, among others.

Altogether, this Special Issue calls for research and review articles that contribute to: (1) the progress in the field of remote sensing of chlorophyll fluorescence, and (2) SIF-related applications for a better understanding of photosynthesis and vegetation status.

Dr. Anshu Rastogi
Dr. Jochem Verrelst
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

Fluorescence
Sun Induced fluorescence
SIFChlorophyll
Photosynthesis
Fluorescence Explorer
SCOPE
Radiative Transfer Models

Published Papers (4 papers)

Download All Papers

Research

22 pages, 4450 KiB

Open AccessArticle

Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer

by Miguel Morata, Bastian Siegmann, Pablo Morcillo-Pallarés, Juan Pablo Rivera-Caicedo and Jochem Verrelst

Remote Sens. 2021, 13(21), 4368; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214368 - 29 Oct 2021

Cited by 7 | Viewed by 2232

Abstract

The retrieval of sun-induced fluorescence (SIF) from hyperspectral radiance data grew to maturity with research activities around the FLuorescence EXplorer satellite mission FLEX, yet full-spectrum estimation methods such as the spectral fitting method (SFM) are computationally expensive. To bypass this computational load, this work aims to approximate the SFM-based SIF retrieval by means of statistical learning, i.e., emulation. While emulators emerged as fast surrogate models of simulators, the accuracy-speedup trade-offs are still to be analyzed when the emulation concept is applied to experimental data. We evaluated the possibility of approximating the SFM-like SIF output directly based on radiance data while minimizing the loss in precision as opposed to SFM-based SIF. To do so, we implemented a double principal component analysis (PCA) dimensionality reduction, i.e., in both input and output, to achieve emulation of multispectral SIF output based on hyperspectral radiance data. We then evaluated systematically: (1) multiple machine learning regression algorithms, (2) number of principal components, (3) number of training samples, and (4) quality of training samples. The best performing SIF emulator was then applied to a HyPlant flight line containing at sensor radiance information, and the results were compared to the SFM SIF map of the same flight line. The emulated SIF map was quasi-instantaneously generated, and a good agreement against the reference SFM map was obtained with a

R^{2}

of 0.88 and NRMSE of 3.77%. The SIF emulator was subsequently applied to 7 HyPlant flight lines to evaluate its robustness and portability, leading to a

R^{2}

between 0.68 and 0.95, and a NRMSE between 6.42% and 4.13%. Emulated SIF maps proved to be consistent while processing time was in the order of 3 min. In comparison, the original SFM needed approximately 78 min to complete the SIF processing. Our results suggest that emulation can be used to efficiently reduce computational loads of SIF retrieval methods. Full article

(This article belongs to the Special Issue Remote Sensing of Fluorescence, Photosynthesis and Vegetation Status)

► Show Figures

Graphical abstract

22 pages, 5849 KiB

Open AccessArticle

Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data

by Subhajit Bandopadhyay, Anshu Rastogi, Sergio Cogliati, Uwe Rascher, Maciej Gąbka and Radosław Juszczak

Remote Sens. 2021, 13(13), 2545; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132545 - 29 Jun 2021

Cited by 10 | Viewed by 3420

Abstract

In this study, we are testing a proxy for red and far-red Sun-induced fluorescence (SIF) using an integrated fuzzy logic modelling approach, termed as SIF_fuzzy and SIF_fuzzy-APAR. The SIF emitted from the core of the photosynthesis and observed at the top-of-canopy is regulated by three major controlling factors: (1) light interception and absorption by canopy plant cover; (2) escape fraction of SIF photons (fesc); (3) light use efficiency and non-photochemical quenching (NPQ) processes. In our study, we proposed and validated a fuzzy logic modelling approach that uses different combinations of spectral vegetation indices (SVIs) reflecting such controlling factors to approximate the potential SIF signals at 760 nm and 687 nm. The HyPlant derived and field validated SVIs (i.e., SR, NDVI, EVI, NDVIre, PRI) have been processed through the membership transformation in the first stage, and in the next stage the membership transformed maps have been processed through the Fuzzy Gamma simulation to calculate the SIF_fuzzy. To test whether the inclusion of absorbed photosynthetic active radiation (APAR) increases the accuracy of the model, the SIF_fuzzy was multiplied by APAR (SIF_fuzzy-APAR). The agreement between the modelled SIF_fuzzy and actual SIF airborne retrievals expressed by R² ranged from 0.38 to 0.69 for SIF₇₆₀ and from 0.85 to 0.92 for SIF₆₈₇. The inclusion of APAR improved the R² value between SIF_fuzzy-APAR and actual SIF. This study showed, for the first time, that a diverse set of SVIs considered as proxies of different vegetation traits, such as biochemical, structural, and functional, can be successfully combined to work as a first-order proxy of SIF. The previous studies mainly included the far-red SIF whereas, in this study, we have also focused on red SIF along with far-red SIF. The analysis carried out at 1 m spatial resolution permits to better infer SIF behaviour at an ecosystem-relevant scale. Full article

(This article belongs to the Special Issue Remote Sensing of Fluorescence, Photosynthesis and Vegetation Status)

► Show Figures

Graphical abstract

18 pages, 11342 KiB

Open AccessArticle

Satellite-Based Observations Reveal the Altitude-Dependent Patterns of SIF_yield and Its Sensitivity to Ambient Temperature in Tibetan Meadows

by Ruonan Chen, Liangyun Liu and Xinjie Liu

Remote Sens. 2021, 13(7), 1400; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071400 - 05 Apr 2021

Cited by 3 | Viewed by 2392

Abstract

Photosynthesis and its sensitivity to the changing environment in alpine regions are of great significance to the understanding of vegetation–environment interactions and other global ecological processes in the context of global change, while their variations along the elevation gradient remain unclear. Using solar-induced [...] Read more.

S I F_{y i e l d}

) with rising elevation in Tibetan meadows in the summer, related to the altitudinal variation in temperature sensitivity at both seasonal and interannual scales. Results of the altitudinal patterns of

S I F_{y i e l d}

demonstrated higher temperature sensitivity at high altitudes, and the sensitivity at the interannual scale even exceeds that at seasonal scale when the elevation reaches above 4700 m. This high-temperature sensitivity of

S I F_{y i e l d}

at high altitudes implies potential adaptation of alpine plants and also indicates that changes in photosynthesis-related physiological functions at high altitudes should receive more attention in climate change research. The altitudinal

S I F_{y i e l d}

patterns revealed in this study also highlight that variations in temperature sensitivity should be considered in models, otherwise the increasing trend of

S I F_{y i e l d}

observations can never be discovered in empirical simulations. Full article

(This article belongs to the Special Issue Remote Sensing of Fluorescence, Photosynthesis and Vegetation Status)

► Show Figures

Graphical abstract

23 pages, 6068 KiB

Open AccessArticle

A New Fluorescence Quantum Yield Efficiency Retrieval Method to Simulate Chlorophyll Fluorescence under Natural Conditions

by Tianyuan Zou and Jing Zhang

Remote Sens. 2020, 12(24), 4053; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244053 - 11 Dec 2020

Cited by 6 | Viewed by 2411

Abstract

Chlorophyll fluorescence (ChlF) is a useful indicator of plant photosynthesis and stress conditions. ChlF spectra can be simulated with the Fluspect model, which is a radiative transfer model that simulates leaf reflectance, transmittance, and fluorescence; however, it has never been used or validated under natural conditions. In this paper, a new fluorescence quantum yield efficiency of photosystem (FQE) retrieval method based on the Fluspect model is proposed for use in simulating ChlF in two healthy varieties of soybeans grown under natural conditions. The parameters, Chlorophyll a + b content (C_ab), carotenoid (C_ca), dry matter content (C_dm), indicator of leaf water content (C_w) and leaf mesophyll structure (N) and the simulated fluorescence from the experiment were compared with the measured values to validate the model under natural conditions. The results show a good correlation (coefficient of determination R² = 0.7–0.9) with the measured data at wavelengths of 650–880 nm. However, there is a large relative error (RE) that extends up to 150% at the peak of the fluorescence curve. To improve the accuracy of the simulation, an inversion code containing the emission efficiency parameters for photosystems I and II was added, which retrieves FQE I and II from the measured fluorescence spectra. The evaluation results for all wavelengths and two peaks demonstrated a significant reduction in the error at the peak of the curve by the Fluspect model with the FQE inversion code. This new method reduced the overestimation of fluorescence from 150% to 20% for the RE, and the R² value was higher than 0.9 at the spectra peaks. Additionally, the original plant parameter information remained mostly unchanged upon the addition of the inversion code. Full article

(This article belongs to the Special Issue Remote Sensing of Fluorescence, Photosynthesis and Vegetation Status)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Remote Sensing of Fluorescence, Photosynthesis and Vegetation Status

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI