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Evaluation of Plant Stress Monitoring Capabilities Using a Portable Spectrometer and Blue-Red Grow Light. SENSORS 2022; 22:s22093411. [PMID: 35591102 PMCID: PMC9099694 DOI: 10.3390/s22093411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/11/2022] [Accepted: 04/21/2022] [Indexed: 11/25/2022]
Abstract
Remote sensing offers a non-destructive method to detect plant physiological response to the environment by measuring chlorophyll fluorescence (CF). Most methods to estimate CF require relatively complex retrieval, spectral fitting, or modelling methods. An investigation was undertaken to evaluate measurements of CF using a relatively straightforward technique to detect and monitor plant stress with a spectroradiometer and blue-red light emitting diode (LED). CF spectral response of tomato plants treated with a photosystem inhibitor were assessed and compared to traditional reflectance-based indices: normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI). The blue-red LEDs provided input irradiance and a “window” in the CF emission range of plants (~650 to 850 nm) sufficient to capture distinctive “two-peak” spectra and to distinguish plant health from day to day of the experiment, while within day differences were noisy. CF-based metrics calculated from CF spectra clearly captured signs of vegetation stress earlier than reflectance-based indices and by visual inspection. This CF monitoring technique is a flexible and scalable option for collecting plant function data, especially for indicating early signs of stress. The technique can be applied to a single plant or larger canopies using LED in dark conditions by an individual, or a manned or unmanned vehicle for agricultural or military purposes.
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2
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Research progress of crop diseases monitoring based on reflectance and chlorophyll fluorescence data. ACTA AGRONOMICA SINICA 2021. [DOI: 10.3724/sp.j.1006.2021.03057] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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3
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Morata M, Siegmann B, Morcillo-Pallarés P, Rivera-Caicedo JP, Verrelst J. Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer. REMOTE SENSING 2021; 13:4368. [PMID: 36081451 PMCID: PMC7613348 DOI: 10.3390/rs13214368] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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.
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Affiliation(s)
- Miguel Morata
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain
| | - Bastian Siegmann
- Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Pablo Morcillo-Pallarés
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain
- Institute ITACA, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Juan Pablo Rivera-Caicedo
- CONACyT-UAN, Secretaría de Investigación y Posgrado, Universidad Autónoma de Nayarit, Ciudad de la Cultura Amado Nervo, Tepic 63155, Mexico
| | - Jochem Verrelst
- Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain
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Paynter I, Cook B, Corp L, Nagol J, McCorkel J. Characterization of FIREFLY, an Imaging Spectrometer Designed for Remote Sensing of Solar Induced Fluorescence. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4682. [PMID: 32825075 PMCID: PMC7506705 DOI: 10.3390/s20174682] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 08/01/2020] [Accepted: 08/11/2020] [Indexed: 11/17/2022]
Abstract
Solar induced fluorescence (SIF) is an ecological variable of interest to remote sensing retrievals, as it is directly related to vegetation composition and condition. FIREFLY (fluorescence imaging of red and far-red light yield) is a high performance spectrometer for estimating SIF. FIREFLY was flown in conjunction with NASA Goddard's lidar, hyperspectral, and thermal (G-LiHT) instrument package in 2017, as a technology demonstration for airborne retrievals of SIF. Attributes of FIREFLY relevant to SIF retrieval, including detector response and linearity; full-width at half maximum (FWHM); stray light; dark current; and shot noise were characterized with a combination of observations from Goddard's laser for absolute measurement of radiance calibration facility; an integrating sphere; controlled acquisitions of known targets; in-flight acquisitions; and forward modelling. FWHM, stray light, and dark current were found to be of acceptable magnitude, and characterized to within acceptable limits for SIF retrieval. FIREFLY observations were found to represent oxygen absorption features, along with a large number of solar absorption features. Shot noise was acceptable for direct SIF retrievals at native resolution, but indirect SIF retrievals from absorption features would require spatial aggregation, or repeated observations of targets.
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Affiliation(s)
- Ian Paynter
- Universities Space Research Association, Columbia, MD 21046, USA;
- Earth Sciences, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA; (J.N.); (J.M.)
| | - Bruce Cook
- Earth Sciences, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA; (J.N.); (J.M.)
| | - Lawrence Corp
- Science Systems & Applications Inc., Lanham, MD 20771, USA;
| | - Jyoteshwar Nagol
- Earth Sciences, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA; (J.N.); (J.M.)
- Department of Geographical Sciences, University of Maryland, College Park, MD 20771, USA
| | - Joel McCorkel
- Earth Sciences, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA; (J.N.); (J.M.)
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5
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Bandopadhyay S, Rastogi A, Juszczak R. Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1144. [PMID: 32093068 PMCID: PMC7070282 DOI: 10.3390/s20041144] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 02/10/2020] [Accepted: 02/14/2020] [Indexed: 11/16/2022]
Abstract
Remote sensing (RS) of sun-induced fluorescence (SIF) has emerged as a promising indicator of photosynthetic activity and related stress from the leaf to the ecosystem level. The implementation of modern RS technology on SIF is highly motivated by the direct link of SIF to the core of photosynthetic machinery. In the last few decades, a lot of studies have been conducted on SIF measurement techniques, retrieval algorithms, modeling, application, validation, and radiative transfer processes, incorporating different RS observations (i.e., ground, unmanned aerial vehicle (UAV), airborne, and spaceborne). These studies have made a significant contribution to the enrichment of SIF science over time. However, to realize the potential of SIF and to explore its full spectrum using different RS observations, a complete document of existing SIF studies is needed. Considering this gap, we have performed a detailed review of current SIF studies from the ground, UAV, airborne, and spaceborne observations. In this review, we have discussed the in-depth interpretation of each SIF study using four RS platforms. The limitations and challenges of SIF studies have also been discussed to motivate future research and subsequently overcome them. This detailed review of SIF studies will help, support, and inspire the researchers and application-based users to consider SIF science with confidence.
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Affiliation(s)
- Subhajit Bandopadhyay
- Laboratory of Bioclimatology, Department of Ecology and Environmental Protection, Faculty of Environmental Engineering and Spatial Management, Poznan University of Life Sciences, 60-649 Poznan, Poland;
| | | | - Radosław Juszczak
- Laboratory of Bioclimatology, Department of Ecology and Environmental Protection, Faculty of Environmental Engineering and Spatial Management, Poznan University of Life Sciences, 60-649 Poznan, Poland;
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6
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Du S, Liu L, Liu X, Zhang X, Gao X, Wang W. The Solar-Induced Chlorophyll Fluorescence Imaging Spectrometer (SIFIS) Onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1): Specifications and Prospects. SENSORS (BASEL, SWITZERLAND) 2020; 20:E815. [PMID: 32028694 PMCID: PMC7038700 DOI: 10.3390/s20030815] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 01/26/2020] [Accepted: 02/01/2020] [Indexed: 11/22/2022]
Abstract
The global monitoring of solar-induced chlorophyll fluorescence (SIF) using satellite-based observations provides a new way of monitoring the status of terrestrial vegetation photosynthesis on a global scale. Several global SIF products that make use of atmospheric satellite data have been successfully developed in recent decades. The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1), the first Chinese terrestrial ecosystem carbon inventory satellite, which is due to be launched in 2021, will carry an imaging spectrometer specifically designed for SIF monitoring. Here, we use an extensive set of simulated data derived from the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) and Soil Canopy Observation Photosynthesis and Energy (SCOPE) models to evaluate and optimize the specifications of the SIF Imaging Spectrometer (SIFIS) onboard TECIS for accurate SIF retrievals. The wide spectral range of 670-780 nm was recommended to obtain the SIF at both the red and far-red bands. The results illustrate that the combination of a spectral resolution (SR) of 0.1 nm and a signal-to-noise ratio (SNR) of 127 performs better than an SR of 0.3 nm and SNR of 322 or an SR of 0.5 nm and SNR of 472 nm. The resulting SIF retrievals have a root-mean-squared (RMS) diff* value of 0.15 mW m-2 sr-1 nm-1 at the far-red band and 0.43 mW m-2 sr-1 nm-1 at the red band. This compares with 0.20 and 0.26 mW m-2 sr-1 nm-1 at the far-red band and 0.62 and 1.30 mW m-2 sr-1 nm-1 at the red band for the other two configurations described above. Given an SR of 0.3 nm, the increase in the SNR can also improve the SIF retrieval at both bands. If the SNR is improved to 450, the RMS diff* will be 0.17 mW m-2 sr-1 nm-1 at the far-red band and 0.47 mW m-2 sr-1 nm-1 at the red band. Therefore, the SIFIS onboard TECIS-1 will provide another set of observations dedicated to monitoring SIF at the global scale, which will benefit investigations of terrestrial vegetation photosynthesis from space.
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Affiliation(s)
- Shanshan Du
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; (S.D.); (X.L.)
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liangyun Liu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; (S.D.); (X.L.)
| | - Xinjie Liu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; (S.D.); (X.L.)
| | - Xinwei Zhang
- Beijing Institute of Spacecraft System Engineering, Beijing 100094, China;
| | - Xianlian Gao
- Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China;
| | - Weigang Wang
- Beijing Institute of Space Mechanics and Electricity, China Academy of Space Technology, Beijing 100094, China;
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7
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Mohammed GH, Colombo R, Middleton EM, Rascher U, van der Tol C, Nedbal L, Goulas Y, Pérez-Priego O, Damm A, Meroni M, Joiner J, Cogliati S, Verhoef W, Malenovský Z, Gastellu-Etchegorry JP, Miller JR, Guanter L, Moreno J, Moya I, Berry JA, Frankenberg C, Zarco-Tejada PJ. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. REMOTE SENSING OF ENVIRONMENT 2019; 231:111177. [PMID: 33414568 PMCID: PMC7787158 DOI: 10.1016/j.rse.2019.04.030] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF - especially from space - is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.
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Affiliation(s)
| | - Roberto Colombo
- Remote Sensing of Environmental Dynamics Lab., University of Milano - Bicocca, Milan, Italy
| | | | - Uwe Rascher
- Forschungszentrum Jülich, Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Jülich, Germany
| | - Christiaan van der Tol
- University of Twente, Faculty of Geo-Information Science and Earth Observation, Enschede, The Netherlands
| | - Ladislav Nedbal
- Forschungszentrum Jülich, Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Jülich, Germany
| | - Yves Goulas
- CNRS, Laboratoire de Météorologie Dynamique (LMD), Ecole Polytechnique, Palaiseau, France
| | - Oscar Pérez-Priego
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Alexander Damm
- Department of Geography, University of Zurich, Zurich, Switzerland
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland
| | - Michele Meroni
- European Commission, Joint Research Centre (JRC), Ispra (VA), Italy
| | - Joanna Joiner
- NASA/Goddard Space Flight Center, Greenbelt, Maryland, United States
| | - Sergio Cogliati
- Remote Sensing of Environmental Dynamics Lab., University of Milano - Bicocca, Milan, Italy
| | - Wouter Verhoef
- University of Twente, Faculty of Geo-Information Science and Earth Observation, Enschede, The Netherlands
| | - Zbyněk Malenovský
- Department of Geography and Spatial Sciences, School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, Hobart, Australia
| | | | - John R. Miller
- Department of Earth and Space Science and Engineering, York University, Toronto, Canada
| | - Luis Guanter
- German Research Center for Geosciences (GFZ), Remote Sensing Section, Potsdam, Germany
| | - Jose Moreno
- Department of Earth Physics and Thermodynamics, University of Valencia, Valencia, Spain
| | - Ismael Moya
- CNRS, Laboratoire de Météorologie Dynamique (LMD), Ecole Polytechnique, Palaiseau, France
| | - Joseph A. Berry
- Department of Global Ecology, Carnegie Institution of Washington, Stanford, California, United States
| | - Christian Frankenberg
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, United States
| | - Pablo J. Zarco-Tejada
- European Commission, Joint Research Centre (JRC), Ispra (VA), Italy
- Instituto de Agriculture Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
- Department of Infrastructure Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, Victoria, Australia
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8
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Zhang X, He Y, Wang C, Xu F, Li X, Tan C, Chen D, Wang G, Shi L. Estimation of Corn Canopy Chlorophyll Content Using Derivative Spectra in the O 2-A Absorption Band. FRONTIERS IN PLANT SCIENCE 2019; 10:1047. [PMID: 31507626 PMCID: PMC6718702 DOI: 10.3389/fpls.2019.01047] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 07/29/2019] [Indexed: 06/10/2023]
Abstract
Chlorophyll (Chl) is one of the most important classes of light-absorbing pigments in photosynthesis, and the proportion of Chl in leaves is closely related to vegetation nutrient status. Remote sensing-based estimation of Chl content holds great potential for evaluating crop growth status in agricultural management, precision farming and ecosystem monitoring. Recent studies have shown that steady-state fluorescence contributed up to 2% on the apparent reflectance in the 750-nm spectral region of plant and also provided additional evidence for fluorescence in-filling of the atmospheric oxygen absorption band at a central wavelength of 760 nm (O2-A band). In this study, an in situ hyperspectral remote sensing approach zwas employed to estimate corn Chl content at the canopy level by using chlorophyll fluorescence (ChlF) signals in the O2-A absorption band. Two new spectral indices, REArea760 (sum of first derivative reflectance between 755 and 763 nm) and REA760 (maximum of first derivative reflectance between 755 and 763 nm), derived from the first derivative spectra in the O2-A band, were proposed for estimating the corn canopy Chl content (CCC). They were compared with the performance of published indices measured at ground level, including the MERIS Terrestrial Chlorophyll Index (MTCI), Optimized Soil-Adjusted Vegetation Index 2 (OSAVI2), Modified Chlorophyll Absorption Ratio Index 2 (MCARI2), SR710, REArea (sum of first derivative reflectance between 680 and 780 nm), REA (maximum value of first derivative reflectance between 680 and 780 nm), and mND705. The results indicated that corn Chl content at the canopy level was better predicted by the new indices (with R2 = 0.835) than the published indices (with R2 ranging from 0.676 to 0.826). The two new indices ranked in the top four according to their summed ranks by integrating the ranks of RMSE and R2 of CCC linear regression models. ChlF originates only from chlorophyll in the photosynthetic apparatus and therefore is less sensitive to soil, wood, and dead biomass interference. Moreover, due to the fluorescence in-filling of the O2-A band and the amplified effect on spectrum signals by derivative operation, the spectral derivative indices in the O2-A band have great potential for estimating the CCC.
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Affiliation(s)
- Xuehong Zhang
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteotological Disasters (CIC-FEMD), School of Remote Sensing & Geomatics Engineering,School of Electronic and Information Engineering, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, China
- Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Meteorological Institute of Hebei Province, Shijiazhuang, China
| | - Yang He
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteotological Disasters (CIC-FEMD), School of Remote Sensing & Geomatics Engineering,School of Electronic and Information Engineering, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, China
| | - Chao Wang
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteotological Disasters (CIC-FEMD), School of Remote Sensing & Geomatics Engineering,School of Electronic and Information Engineering, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, China
| | - Fan Xu
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteotological Disasters (CIC-FEMD), School of Remote Sensing & Geomatics Engineering,School of Electronic and Information Engineering, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, China
| | - Xinhui Li
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteotological Disasters (CIC-FEMD), School of Remote Sensing & Geomatics Engineering,School of Electronic and Information Engineering, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, China
| | - Changwei Tan
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Yangzhou University, Yangzhou, China
| | - Dongmei Chen
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteotological Disasters (CIC-FEMD), School of Remote Sensing & Geomatics Engineering,School of Electronic and Information Engineering, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, China
- Department of Geography and Planning, Queen’s University, Kingston, ON, Canada
| | - Guojie Wang
- Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environment Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteotological Disasters (CIC-FEMD), School of Remote Sensing & Geomatics Engineering,School of Electronic and Information Engineering, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing, China
| | - Lixin Shi
- Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Meteorological Institute of Hebei Province, Shijiazhuang, China
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9
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A Spectral Fitting Algorithm to Retrieve the Fluorescence Spectrum from Canopy Radiance. REMOTE SENSING 2019. [DOI: 10.3390/rs11161840] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Retrieval of Sun-Induced Chlorophyll Fluorescence (F) spectrum is one of the challenging perspectives for further advancing F studies towards a better characterization of vegetation structure and functioning. In this study, a simplified Spectral Fitting retrieval algorithm suitable for retrieving the F spectrum with a limited number of parameters is proposed (two parameters for F). The novel algorithm is developed and tested on a set of radiative transfer simulations obtained by coupling SCOPE and MODTRAN5 codes, considering different chlorophyll content, leaf area index and noise levels to produce a large variability in fluorescence and reflectance spectra. The retrieval accuracy is quantified based on several metrics derived from the F spectrum (i.e., red and far-red peaks, O2 bands and spectrally-integrated values). Further, the algorithm is employed to process experimental field spectroscopy measurements collected over different crops during a long-lasting field campaign. The reliability of the retrieval algorithm on experimental measurements is evaluated by cross-comparison with F values computed by an independent retrieval method (i.e., SFM at O2 bands). For the first time, the evolution of the F spectrum along the entire growing season for a forage crop is analyzed and three diverse F spectra are identified at different growing stages. The results show that red F is larger for young canopy; while red and far-red F have similar intensity in an intermediate stage; finally, far-red F is significantly larger for the rest of the season.
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Ni Z, Lu Q, Huo H, Zhang H. Estimation of Chlorophyll Fluorescence at Different Scales: A Review. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3000. [PMID: 31288380 PMCID: PMC6651496 DOI: 10.3390/s19133000] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/19/2019] [Accepted: 06/24/2019] [Indexed: 11/16/2022]
Abstract
Measuring chlorophyll fluorescence is a direct and non-destructive way to monitor vegetation. In this paper, the fluorescence retrieval methods from multiple scales, ranging from near the ground to the use of space-borne sensors, are analyzed and summarized in detail. At the leaf-scale, the chlorophyll fluorescence is measured using active and passive technology. Active remote sensing technology uses a fluorimeter to measure the chlorophyll fluorescence, and passive remote sensing technology mainly depends on the sun-induced chlorophyll fluorescence filling in the Fraunhofer lines or oxygen absorptions bands. Based on these retrieval principles, many retrieval methods have been developed, including the radiance-based methods and the reflectance-based methods near the ground, as well as physically and statistically-based methods that make use of satellite data. The advantages and disadvantages of different approaches for sun-induced chlorophyll fluorescence retrieval are compared and the key issues of the current sun-induced chlorophyll fluorescence retrieval algorithms are discussed. Finally, conclusions and key problems are proposed for the future research.
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Affiliation(s)
- Zhuoya Ni
- Key Laboratory of Radiometric Calibration and Validation for Environment Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
| | - Qifeng Lu
- Key Laboratory of Radiometric Calibration and Validation for Environment Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
| | - Hongyuan Huo
- College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China.
| | - Huili Zhang
- Jiangxi Technical College Of Manufacturing, Nanchang 330095, China
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11
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Du S, Liu L, Liu X, Guo J, Hu J, Wang S, Zhang Y. SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3009. [PMID: 31288443 PMCID: PMC6651194 DOI: 10.3390/s19133009] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 07/04/2019] [Accepted: 07/05/2019] [Indexed: 11/16/2022]
Abstract
Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2-A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.
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Affiliation(s)
- Shanshan Du
- Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liangyun Liu
- Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China.
| | - Xinjie Liu
- Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
| | - Jian Guo
- Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
- College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Jiaochan Hu
- Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shaoqiang Wang
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Yongguang Zhang
- International Institute for Earth System Sciences, Nanjing University, Nanjing 21002, China
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Effects of the Temporal Aggregation and Meteorological Conditions on the Parameter Robustness of OCO-2 SIF-Based and LUE-Based GPP Models for Croplands. REMOTE SENSING 2019. [DOI: 10.3390/rs11111328] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a very important and active area. However, compared with other gross primary production (GPP) models, the robustness of the parameterization of the SIF model under different circumstances has rarely been investigated. In this study, we examined and compared the effects of temporal aggregation and meteorological conditions on the stability of model parameters for the SIF model ( ε / S I F yield ), the one-leaf light-use efficiency (SL-LUE) model ( ε max ), and the two-leaf LUE (TL-LUE) model ( ε msu and ε msh ). The three models were parameterized based on a maize–wheat rotation eddy-covariance flux tower data in Yucheng, Shandong Province, China by using the Metropolis–Hasting algorithm. The results showed that the values of the ε / S I F yield and ε max were similarly robust and considerably more stable than ε msu and ε msh for all temporal aggregation levels. Under different meteorological conditions, all the parameters showed a certain degree of fluctuation and were most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale. Nonetheless, the averaged coefficient of variation ( C V ) of ε / S I F yield was relatively small (15.0%) and was obviously lower than ε max ( C V = 27.0%), ε msu ( C V = 43.2%), and ε msh ( C V = 53.1%). Furthermore, the SIF model’s performance for estimating GPP was better than that of the SL-LUE model and was comparable to that of the TL-LUE model. This study indicates that, compared with the LUE-based models, the SIF-based model without climate-dependence is a good predictor of GPP and its parameter is more likely to converge for different temporal aggregation levels and under varying environmental restrictions in croplands. We suggest that more flux tower data should be used for further validation of parameter convergence in other vegetation types.
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Abstract
Aerosol retrieval algorithms used in conjunction with remote sensing are subject to ill-posedness. To mitigate non-uniqueness, extra constraints (in addition to observations) are valuable for stabilizing the inversion process. This paper focuses on the imposition of an empirical correlation constraint on the retrieved aerosol parameters. This constraint reflects the empirical dependency between different aerosol parameters, thereby reducing the number of degrees of freedom and enabling accelerated computation of the radiation fields associated with neighboring pixels. A cross-pixel constraint that capitalizes on the smooth spatial variations of aerosol properties was built into the original multi-pixel inversion approach. Here, the spatial smoothness condition is imposed on principal components (PCs) of the aerosol model, and on the corresponding PC weights, where the PCs are used to characterize departures from the mean. Mutual orthogonality and unit length of the PC vectors, as well as zero sum of the PC weights also impose stabilizing constraints on the retrieval. Capitalizing on the dependencies among aerosol parameters and the mutual orthogonality of PCs, a perturbation-based radiative transfer computation scheme is developed. It uses a few dominant PCs to capture the difference in the radiation fields across an imaged area. The approach is tested using 27 observations acquired by the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) during multiple NASA field campaigns and validated using collocated AERONET observations. In particular, aerosol optical depth, single scattering albedo, aerosol size, and refractive index are compared with AERONET aerosol reference data. Retrieval uncertainty is formulated by accounting for both instrumental errors and the effects of multiple types of constraints.
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Evaluating the Performance of the SCOPE Model in Simulating Canopy Solar-Induced Chlorophyll Fluorescence. REMOTE SENSING 2018. [DOI: 10.3390/rs10020250] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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15
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Chlorophyll Fluorescence Data Reveals Climate-Related Photosynthesis Seasonality in Amazonian Forests. REMOTE SENSING 2017. [DOI: 10.3390/rs9121275] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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Impact of Atmospheric Inversion Effects on Solar-Induced Chlorophyll Fluorescence: Exploitation of the Apparent Reflectance as a Quality Indicator. REMOTE SENSING 2017. [DOI: 10.3390/rs9060622] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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17
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Ni Z, Liu Z, Li ZL, Nerry F, Huo H, Sun R, Yang P, Zhang W. Investigation of Atmospheric Effects on Retrieval of Sun-Induced Fluorescence Using Hyperspectral Imagery. SENSORS 2016; 16:s16040480. [PMID: 27058542 PMCID: PMC4850994 DOI: 10.3390/s16040480] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 03/25/2016] [Accepted: 03/29/2016] [Indexed: 11/16/2022]
Abstract
Significant research progress has recently been made in estimating fluorescence in the oxygen absorption bands, however, quantitative retrieval of fluorescence data is still affected by factors such as atmospheric effects. In this paper, top-of-atmosphere (TOA) radiance is generated by the MODTRAN 4 and SCOPE models. Based on simulated data, sensitivity analysis is conducted to assess the sensitivities of four indicators-depth_absorption_band, depth_nofs-depth_withfs, radiance and Fs/radiance-to atmospheric parameters (sun zenith angle (SZA), sensor height, elevation, visibility (VIS) and water content) in the oxygen absorption bands. The results indicate that the SZA and sensor height are the most sensitive parameters and that variations in these two parameters result in large variations calculated as the variation value/the base value in the oxygen absorption depth in the O₂-A and O₂-B bands (111.4% and 77.1% in the O₂-A band; and 27.5% and 32.6% in the O₂-B band, respectively). A comparison of fluorescence retrieval using three methods (Damm method, Braun method and DOAS) and SCOPE Fs indicates that the Damm method yields good results and that atmospheric correction can improve the accuracy of fluorescence retrieval. Damm method is the improved 3FLD method but considering atmospheric effects. Finally, hyperspectral airborne images combined with other parameters (SZA, VIS and water content) are exploited to estimate fluorescence using the Damm method and 3FLD method. The retrieval fluorescence is compared with the field measured fluorescence, yielding good results (R² = 0.91 for Damm vs. SCOPE SIF; R² = 0.65 for 3FLD vs. SCOPE SIF). Five types of vegetation, including ailanthus, elm, mountain peach, willow and Chinese ash, exhibit consistent associations between the retrieved fluorescence and field measured fluorescence.
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Affiliation(s)
- Zhuoya Ni
- State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China.
- ICube, CNRS, Université de Strasbourg, 300 Boulevard Sébastien Brant, CS10413, Illkirch 67412, France.
| | - Zhigang Liu
- State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China.
| | - Zhao-Liang Li
- ICube, CNRS, Université de Strasbourg, 300 Boulevard Sébastien Brant, CS10413, Illkirch 67412, France.
- Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Françoise Nerry
- ICube, CNRS, Université de Strasbourg, 300 Boulevard Sébastien Brant, CS10413, Illkirch 67412, France.
| | - Hongyuan Huo
- College of Urban and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China.
| | - Rui Sun
- State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China.
| | - Peiqi Yang
- ITC-Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede 7514AE, The Netherlands.
| | - Weiwei Zhang
- State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China.
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18
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Comparison of Sun-Induced Chlorophyll Fluorescence Estimates Obtained from Four Portable Field Spectroradiometers. REMOTE SENSING 2016. [DOI: 10.3390/rs8020122] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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