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Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 35604

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Special Issue Editors

Dr. Sergio Cogliati

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Guest Editor

Remote Sensing of Environmental Dynamics Laboratory, University of Milano - Bicocca, 20126 Milan, Italy
Interests: optical remote sensing; hyperspectral; imaging spectroscopy; vegetation fluorescence; geo-physical parameters retrieval; retrieval methods; radiative transfer modeling; design of future earth observation missions; calibration/validation field campaigns

Dr. Luis Alonso

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Guest Editor

IPL: Laboratory for Earth Observation, University of Valencia, Paterna, Spain
Interests: hyperspectral imaging; sun-induced fluorescence; laboratory and field spectroscopy; instrumental design, calibration and validation; instrument characterization; data processing; leaf to canopy relations; 3D structural effects; angular effects; radiative transfer models

Dr. Feng Zhao

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Guest Editor

School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
Interests: radiative transfer modeling; estimation of land surface variables from remote sensing observations; sun-induced fluorescence; calibration and validation; 3D vegetation simulations; BRDF modeling

Special Issue Information

Dear Colleagues,

Sun-induced chlorophyll fluorescence (SIF) is rapidly becoming a relevant tool in remote sensing for characterizing the vegetation structure and dynamics from in-situ, ecosystem, regional, and global scale observations. The growing interest in SIF is prompted by the increasing availability of measurements from satellite (GOSAT, GOME-2, OCO-2, Sentinel-5P, TanSat and the upcoming ESA’s FLEX), airborne (HyPlant, Ibis, FireFly, etc.), and ground-based instruments. Relevant developments have also been made in the radiative transfer modeling of leaves and plant canopies, offering a theoretical understanding of the linkage between remote sensing observations with canopy characteristics and functioning. Although unprecedented developments have been made in the last decades, research that requires further attention includes improved instrumentations, ground-based measurement networks, advanced retrieval techniques, physical and process-based models, applications of SIF for vegetation primary productivity estimation, and plant disease studies.

The aim of this Special Issue is to present state-of-the-art research about technological and methodological developments on sun-induced chlorophyll fluorescence. Articles covering but not limited to recent researches about the following topics are invited for this Special Issue:

Novel sensor designs for detecting sun-induced fluorescence from ground-based drones and airborne and satellite platforms;
Instrumental characterization protocols, noise reduction algorithms, and novel data processing aimed at improving accuracies in SIF retrieval;
Quantitative methods for retrieving red, far-red, and full fluorescence spectrum based on terrestrial atmospheric absorption lines and Fraunhofer lines;
Atmospheric correction algorithms for SIF retrieval from proximal and remote sensing;
Physical, statistical, and machine learning methods;
Novel radiative transfer models for leaves and canopies, including sun-induced fluorescence;
Linking fluorescence measurement at different scales, from leaf to canopy and ecosystem;
Studies making use of remote sensing data from multiple sources (satellite, airborne, drones, and field spectroscopy) are encouraged;
Methodologies for calibration/validation of airborne and satellite remote sensing fluorescence observations and associated products;
Usage of vegetation fluorescence information in agriculture and forestry and applications in quantifying vegetation stress and diseases;
The use of sun-induced fluorescence to improve modelling of primary productivity and ecosystem and global scale;
Innovative usage of sun-induced fluorescence information;
Assimilation of sun-induced fluorescence into process-based models for monitoring the functional status of vegetation.

Dr. Sergio Cogliati
Dr. Luis Alonso
Dr. Feng Zhao
Guest Editors

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Keywords

Sun-induced chlorophyll fluorescence
Upscaling techniques from leaf to top of atmosphere
SIF retrieval methods
Calibration/validation methods
Radiative transfer models
In-situ sun-induced fluorescence
Applications in agriculture and forestry
Vegetation stress conditions
Global vegetation dynamics.

Published Papers (10 papers)

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Research

13 pages, 2741 KiB

Open AccessCommunication

Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods

by Huaize Feng, Tongren Xu, Liangyun Liu, Sha Zhou, Jingxue Zhao, Shaomin Liu, Ziwei Xu, Kebiao Mao, Xinlei He, Zhongli Zhu and Linna Chai

Remote Sens. 2021, 13(4), 804; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040804 - 22 Feb 2021

Cited by 13 | Viewed by 3204

Abstract

Successfully applied in the carbon research area, sun-induced chlorophyll fluorescence (SIF) has raised the interest of researchers from the water research domain. However, current works focused on the empirical relationship between SIF and plant transpiration (T), while the mechanistic linkage between them has not been fully explored. Two mechanism methods were developed to estimate T via SIF, namely the water-use efficiency (WUE) method and conductance method based on the carbon–water coupling framework. The T estimated by these two methods was compared with T partitioned from eddy covariance instrument measured evapotranspiration at four different sites. Both methods showed good performance at the hourly (R² = 0.57 for the WUE method and 0.67 for the conductance method) and daily scales (R² = 0.67 for the WUE method and 0.78 for the conductance method). The developed mechanism methods provide theoretical support and have a great potential basis for deriving ecosystem T by satellite SIF observations. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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17 pages, 4678 KiB

Open AccessArticle

Simulation-Based Evaluation of the Estimation Methods of Far-Red Solar-Induced Chlorophyll Fluorescence Escape Probability in Discontinuous Forest Canopies

by Weiwei Liu, Shezhou Luo, Xiaoliang Lu, Jon Atherton and Jean-Philippe Gastellu-Etchegorry

Remote Sens. 2020, 12(23), 3962; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233962 - 03 Dec 2020

Cited by 7 | Viewed by 2309

Abstract

The escape probability of Solar-induced chlorophyll fluorescence (SIF) can be remotely estimated using reflectance measurements based on spectral invariants theory. This can then be used to correct the effects of canopy structure on canopy-leaving SIF. However, the feasibility of these estimation methods is untested in heterogeneous vegetation such as the discontinuous forest canopy layer under evaluation here. In this study, the Discrete Anisotropic Radiative Transfer (DART) model is used to simulate canopy-leaving SIF, canopy total emitted SIF, canopy interceptance, and the fraction of absorbed photosynthetically active radiation (fAPAR) in order to evaluate the estimation methods of SIF escape probability in discontinuous forest canopies. Our simulation results show that the normalized difference vegetation index (NDVI) can be used to partly eliminate the effects of background reflectance on the estimation of SIF escape probability in most cases, but fails to produce accurate estimations if the background is partly or totally covered by vegetation. We also found that SIF escape probabilities estimated at a high solar zenith angle have better estimation accuracy than those estimated at a lower solar zenith angle. Our results show that additional errors will be introduced to the estimation of SIF escape probability with the use of satellite products, especially when the product of leaf area index (LAI) and clumping index (CI) was underestimated. In other results, fAPAR has comparable estimation accuracy of SIF escape probability when compared to canopy interceptance. Additionally, fAPAR for the entire canopy has better estimation accuracy of SIF escape probability than fPAR for leaf only in sparse forest canopies. These results help us to better understand the current estimation results of SIF escape probability based on spectral invariants theory, and to improve its estimation accuracy in discontinuous forest canopies. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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24 pages, 4712 KiB

Open AccessArticle

A New Retrieval of Sun-Induced Chlorophyll Fluorescence in Water from Ocean Colour Measurements Applied on OLCI L-1b and L-2

by Lena Kritten, Rene Preusker and Jürgen Fischer

Remote Sens. 2020, 12(23), 3949; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233949 - 02 Dec 2020

Cited by 7 | Viewed by 2859

Abstract

The retrieval of sun-induced chlorophyll fluorescence is greatly beneficial to studies of marine phytoplankton biomass, physiology, and composition, and is required for user applications and services. Customarily phytoplankton chlorophyll fluorescence is determined from satellite measurements through a fluorescence line-height algorithm using three bands around 680 nm. We propose here a modified retrieval, making use of all available bands in the relevant wavelength range, with the goal to improve the effectiveness of the algorithm in optically complex waters. For the Ocean and Land Colour Instrument (OLCI), we quantify a Fluorescence Peak Height by fitting a Gaussian function and related terms to the top-of-atmosphere reflectance bands between 650 and 750 nm. This algorithm retrieves, what we call Fluorescence Peak Height by fitting a Gaussian function upon other terms to top-of-atmosphere reflectance bands between 650 and 750 nm. This approach is applicable to Level-1 and Level-2 data. We find a good correlation of the retrieved fluorescence product to global in-situ chlorophyll measurements, as well as a consistent relation between chlorophyll concentration and fluorescence from radiative transfer modelling and OLCI/in-situ comparison. Evidence suggests, the algorithm is applicable to complex waters without needing an atmospheric correction and vicarious calibration, and features an inherent correction of small spectral shifts, as required for OLCI measurements. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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25 pages, 3854 KiB

Open AccessArticle

Estimating Crop and Grass Productivity over the United States Using Satellite Solar-Induced Chlorophyll Fluorescence, Precipitation and Soil Moisture Data

by Maryia Halubok and Zong-Liang Yang

Remote Sens. 2020, 12(20), 3434; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203434 - 19 Oct 2020

Cited by 6 | Viewed by 2810

Abstract

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m

^{2}

/month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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15 pages, 6691 KiB

Open AccessArticle

Satellite Solar-Induced Chlorophyll Fluorescence Reveals Heat Stress Impacts on Wheat Yield in India

by Yang Song, Jing Wang and Lixin Wang

Remote Sens. 2020, 12(20), 3277; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203277 - 09 Oct 2020

Cited by 15 | Viewed by 3042

Abstract

With continued global warming, the frequency and severity of heat wave events increased over the past decades, threatening both regional and global food security in the future. There are growing interests to study the impacts of drought on crop. However, studies on the impacts of heat stress on crop photosynthesis and yield are still lacking. To fill this knowledge gap, we used both statistical models and satellite solar-induced chlorophyll fluorescence (SIF) data to assess the impacts of heat stress on wheat yield in a major wheat growing region, the Indo-Gangetic Plains (IGP), India. The statistical model showed that the relationships between different accumulated degree days (ADD) and reported wheat yield were significantly negative. The results confirmed that heat stress affected wheat yield across this region. Building on such information, satellite SIF observations were used to further explore the physiological basis of heat stress impacts on wheat yield. Our results showed that SIF had strong negative correlations with ADDs and was capable of monitoring heat stress. The SIF results also indicated that heat stress caused yield loss by directly impacting the photosynthetic capacity in wheat. Overall, our findings demonstrated that SIF as an effective proxy for photosynthetic activity would improve our understanding of the impacts of heat stress on wheat yield. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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29 pages, 8592 KiB

Open AccessArticle

Systematic Orbital Geometry-Dependent Variations in Satellite Solar-Induced Fluorescence (SIF) Retrievals

by Joanna Joiner, Yasuko Yoshida, Philipp Köehler, Petya Campbell, Christian Frankenberg, Christiaan van der Tol, Peiqi Yang, Nicholas Parazoo, Luis Guanter and Ying Sun

Remote Sens. 2020, 12(15), 2346; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152346 - 22 Jul 2020

Cited by 26 | Viewed by 4507

Abstract

While solar-induced fluorescence (SIF) shows promise as a remotely-sensed measurement directly related to photosynthesis, interpretation and validation of satellite-based SIF retrievals remains a challenge. SIF is influenced by the fraction of absorbed photosynthetically-active radiation at the canopy level that depends upon illumination geometry as well as the escape of SIF through the canopy that depends upon the viewing geometry. Several approaches to estimate the effects of sun-sensor geometry on satellite-based SIF have been proposed, and some have been implemented, most relying upon satellite reflectance measurements and/or other ancillary data sets. These approaches, designed to ultimately estimate intrinsic or physiological components of SIF related to photosynthesis, have not generally been applied globally to satellite measurements. Here, we examine in detail how SIF and related reflectance-based indices from wide swath polar orbiting satellites in low Earth orbit vary systematically due to the host satellite orbital characteristics. We compare SIF and reflectance-based parameters from the Global Ozone Mapping Experiment 2 (GOME-2) on the MetOp-B platform and from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel 5 Precursor satellite with a focus on high northern latitudes in summer where observations at similar geometries and local times occur. We show that GOME-2 and TROPOMI SIF observations agree nearly to within estimated uncertainties when they are compared at similar observing geometries. We show that the cross-track dependence of SIF normalized by PAR and related reflectance-based indices are highly correlated for dense canopies, but diverge substantially as the vegetation within a field-of-view becomes more sparse. This has implications for approaches that utilize reflectance measurements to help account for SIF geometrical dependences in satellite measurements. To further help interpret the GOME-2 and TROPOMI SIF observations, we simulated cross-track dependences of PAR normalized SIF and reflectance-based indices with the one dimensional Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) canopy radiative transfer model at sun–satellite geometries that occur across the wide swaths of these instruments and examine the geometrical dependencies of the various components (e.g., fraction of absorbed PAR, SIF yield, and escape of SIF from the canopy) of the observed SIF signal. The simulations show that most of the cross-track variations in SIF result from the escape of SIF through the scattering canopy and not the illumination. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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18 pages, 5816 KiB

Open AccessArticle

Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity

by Yan Ma, Liangyun Liu, Ruonan Chen, Shanshan Du and Xinjie Liu

Remote Sens. 2020, 12(13), 2167; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12132167 - 07 Jul 2020

Cited by 24 | Viewed by 3311

Abstract

Solar-induced chlorophyll fluorescence (SIF) provides a new and direct way of monitoring photosynthetic activity. However, current SIF products are limited by low spatial resolution or sparse sampling. In this paper, we present a data-driven method of generating a global, spatially continuous TanSat SIF product. Firstly, the key explanatory variables for modelling canopy SIF were investigated using in-situ and satellite observations. According to theoretical and experimental analysis, the solar radiation intensity was found to be a dominant driving environmental variable for the SIF yield at both the canopy and global scales; this has, however, been neglected in previous research. The cosine value of the solar zenith angle at noon (cos (SZA₀)), a proxy for solar radiation intensity, was found to be a dominant abiotic factor for the SIF yield. Next, a Random Forest (RF) approach was employed for SIF prediction based on Moderate Resolution Imaging Spectroradiometer (MODIS) visible-to-NIR reflectance data, the normalized difference vegetation (NDVI), cos (SZA₀), and air temperature. The machine learning model performed well at predicting SIF, giving R² values of 0.73, an RMSE of 0.30 mW m⁻² nm⁻¹ sr⁻¹ and a bias of 0.22 mW m⁻² nm⁻¹ sr⁻¹ for 2018. If cos (SZA₀) was not included, the accuracy of the RF model decreased: the R² value was then 0.65, the RMSE 0.34 mW m⁻² nm⁻¹ sr⁻¹ and an bias of 0.26 mW m⁻² nm⁻¹ sr⁻¹, further verifying the importance of cos (SZA₀). Finally, the globally continuous TanSat SIF product was developed and compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIF data. The results showed that the globally continuous TanSat SIF product agreed well with the TROPOMI SIF data, with an R² value of 0.73. Thus, this paper presents an improved approach to modelling satellite SIF that has a better accuracy, and the study also generated a global, spatially continuous TanSat SIF product with a spatial resolution of 0.05°. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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21 pages, 4324 KiB

Open AccessArticle

Unmanned Aerial Systems (UAS)-Based Methods for Solar Induced Chlorophyll Fluorescence (SIF) Retrieval with Non-Imaging Spectrometers: State of the Art

by Juan Quirós Vargas, Juliane Bendig, Alasdair Mac Arthur, Andreas Burkart, Tommaso Julitta, Kadmiel Maseyk, Rick Thomas, Bastian Siegmann, Micol Rossini, Marco Celesti, Dirk Schüttemeyer, Thorsten Kraska, Onno Muller and Uwe Rascher

Remote Sens. 2020, 12(10), 1624; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101624 - 19 May 2020

Cited by 28 | Viewed by 5773

Abstract

Chlorophyll fluorescence (ChlF) information offers a deep insight into the plant physiological status by reason of the close relationship it has with the photosynthetic activity. The unmanned aerial systems (UAS)-based assessment of solar induced ChlF (SIF) using non-imaging spectrometers and radiance-based retrieval methods, has the potential to provide spatio-temporal photosynthetic performance information at field scale. The objective of this manuscript is to report the main advances in the development of UAS-based methods for SIF retrieval with non-imaging spectrometers through the latest scientific contributions, some of which are being developed within the frame of the Training on Remote Sensing for Ecosystem Modelling (TRuStEE) program. Investigations from the Universities of Edinburgh (School of Geosciences) and Tasmania (School of Technology, Environments and Design) are first presented, both sharing the principle of the spectroradiometer optical path bifurcation throughout, the so called ‘Piccolo-Doppio’ and ‘AirSIF’ systems, respectively. Furthermore, JB Hyperspectral Devices’ ongoing investigations towards the closest possible characterization of the atmospheric interference suffered by orbital platforms are outlined. The latest approach focuses on the observation of one single ground point across a multiple-kilometer atmosphere vertical column using the high altitude UAS named as AirFloX, mounted on a specifically designed and manufactured fixed wing platform: ‘FloXPlane’. We present technical details and preliminary results obtained from each instrument, a summary of their main characteristics, and finally the remaining challenges and open research questions are addressed. On the basis of the presented findings, the consensus is that SIF can be retrieved from low altitude spectroscopy. However, the UAS-based methods for SIF retrieval still present uncertainties associated with the current sensor characteristics and the spatio-temporal mismatching between aerial and ground measurements, which complicate robust validations. Complementary studies regarding the standardization of calibration methods and the characterization of spectroradiometers and data processing workflows are also required. Moreover, other open research questions such as those related to the implementation of atmospheric correction, bidirectional reflectance distribution function (BRDF) correction, and accurate surface elevation models remain to be addressed. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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19 pages, 2282 KiB

Open AccessArticle

CO₂ Concentration, A Critical Factor Influencing the Relationship between Solar-induced Chlorophyll Fluorescence and Gross Primary Productivity

by Ruonan Qiu, Ge Han, Xin Ma, Zongyao Sha, Tianqi Shi, Hao Xu and Miao Zhang

Remote Sens. 2020, 12(9), 1377; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091377 - 27 Apr 2020

Cited by 19 | Viewed by 3258

Abstract

The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO₂ concentration from OCO-2 as critical driven factors simultaneously (SIF-CO₂-GPP model). Evidences from all sites show that the introduction of the atmospheric CO₂ concentration improves accuracies of estimated GPP. Compared with the SIF-CO₂-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO₂ concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO₂ concentration. As OCO-2 is capable of providing SIF and XCO₂ products with identical spatial and temporal scales, the SIF-CO₂-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO₂-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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20 pages, 11901 KiB

Open AccessArticle

Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests

by Meng Guo, Jing Li, Shubo Huang and Lixiang Wen

Remote Sens. 2020, 12(4), 680; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040680 - 19 Feb 2020

Cited by 24 | Viewed by 3529

Abstract

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R² = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R² = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature. Full article

(This article belongs to the Special Issue Advances on Quantitative Remote Sensing of Sun-Induced Chlorophyll Fluorescence)

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