Articles de revues sur le sujet « Chlorophyll fluorescence and reflectance retrieval »
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1
Kritten, Lena, Rene Preusker et Jürgen Fischer. « A New Retrieval of Sun-Induced Chlorophyll Fluorescence in Water from Ocean Colour Measurements Applied on OLCI L-1b and L-2 ». Remote Sensing 12, no 23 (2 décembre 2020) : 3949. http://dx.doi.org/10.3390/rs12233949.
Texte intégralRésumé :
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.
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Ni, Zhuoya, Qifeng Lu, Hongyuan Huo et Huili Zhang. « Estimation of Chlorophyll Fluorescence at Different Scales : A Review ». Sensors 19, no 13 (8 juillet 2019) : 3000. http://dx.doi.org/10.3390/s19133000.
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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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Tenjo, Carolina, Antonio Ruiz-Verdú, Shari Van Wittenberghe, Jesús Delegido et José Moreno. « A New Algorithm for the Retrieval of Sun Induced Chlorophyll Fluorescence of Water Bodies Exploiting the Detailed Spectral Shape of Water-Leaving Radiance ». Remote Sensing 13, no 2 (19 janvier 2021) : 329. http://dx.doi.org/10.3390/rs13020329.
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Sun induced chlorophyll fluorescence (SICF) emitted by phytoplankton provides considerable insights into the vital role of the carbon productivity of the earth’s aquatic ecosystems. However, the SICF signal leaving a water body is highly affected by the high spectral variability of its optically active constituents. To disentangle the SICF emission from the water-leaving radiance, a new high spectral resolution retrieval algorithm is presented, which significantly improves the fluorescence line height (FLH) method commonly used so far. The proposed algorithm retrieves the reflectance without SICF contribution by the extrapolation of the reflectance from the adjacent regions. Then, the SICF emission curve is obtained as the difference of the reflectance with SICF, the one actually obtained by any remote sensor (apparent reflectance), and the reflectance without SICF, the one estimated by the algorithm (true reflectance). The algorithm first normalizes the reflectance spectrum at 780 nm, following the similarity index approximation, to minimize the variability due to other optically active constituents different from chlorophyll. Then, the true reflectance is estimated empirically from the normalized reflectance at three wavelengths using a machine learning regression algorithm (MLRA) and a cubic spline fitting adjustment. Two large reflectance databases, representing a wide range of coastal and ocean water components and scattering conditions, were independently simulated with the radiative transfer model HydroLight and used for training and validation of the MLRA fitting strategy. The best results for the high spectral resolution SICF retrieval were obtained using support vector regression, with relative errors lower than 2% for the SICF peak value in 81% of the samples. This represents a significant improvement with respect to the classic FLH algorithm, applied for OLCI bands, for which the relative errors were higher than 40% in 59% of the samples.
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Pacheco-Labrador, Hueni, Mihai, Sakowska, Julitta, Kuusk, Sporea et al. « Sun-Induced Chlorophyll Fluorescence I : Instrumental Considerations for Proximal Spectroradiometers ». Remote Sensing 11, no 8 (22 avril 2019) : 960. http://dx.doi.org/10.3390/rs11080960.
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Growing interest in the proximal sensing of sun‐induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Action ES1309 “Innovative optical tools for proximal sensing of ecophysiological processes” (OPTIMISE, ES1309; https://optimise.dcs.aber.ac.uk/) has produced three manuscripts addressing the main current challenges in this field. This article provides a framework to model the impact of different instrument noise and bias on the retrieval of SIF; and to assess uncertainty requirements for the calibration and characterization of state-of-the-art SIF-oriented spectroradiometers. We developed a sensor simulator capable of reproducing biases and noises usually found in field spectroradiometers. First the sensor simulator was calibrated and characterized using synthetic datasets of known uncertainties defined from laboratory measurements and literature. Secondly, we used the sensor simulator and the characterized sensor models to simulate the acquisition of atmospheric and vegetation radiances from a synthetic dataset. Each of the sensor models predicted biases with propagated uncertainties that modified the simulated measurements as a function of different factors. Finally, the impact of each sensor model on SIF retrieval was analyzed. Results show that SIF retrieval can be significantly affected in situations where reflectance factors are barely modified. SIF errors were found to correlate with drivers of instrumental-induced biases which are as also drivers of plant physiology. This jeopardizes not only the retrieval of SIF, but also the understanding of its relationship with vegetation function, the study of diel and seasonal cycles and the validation of remote sensing SIF products. Further work is needed to determine the optimal requirements in terms of sensor design, characterization and signal correction for SIF retrieval by proximal sensing. In addition, evaluation/validation methods to characterize and correct instrumental responses should be developed and used to test sensors performance in operational conditions.
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Xinjie Liu et Liangyun Liu. « Improving Chlorophyll Fluorescence Retrieval Using Reflectance Reconstruction Based on Principal Components Analysis ». IEEE Geoscience and Remote Sensing Letters 12, no 8 (août 2015) : 1645–49. http://dx.doi.org/10.1109/lgrs.2015.2417857.
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Gilerson, Alexander, Jing Zhou, Min Oo, Jacek Chowdhary, Barry M. Gross, Fred Moshary et Samir Ahmed. « Retrieval of chlorophyll fluorescence from reflectance spectra through polarization discrimination : modeling and experiments ». Applied Optics 45, no 22 (1 août 2006) : 5568. http://dx.doi.org/10.1364/ao.45.005568.
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De Grave, Charlotte, Luca Pipia, Bastian Siegmann, Pablo Morcillo-Pallarés, Juan Pablo Rivera-Caicedo, José Moreno et Jochem Verrelst. « Retrieving and Validating Leaf and Canopy Chlorophyll Content at Moderate Resolution : A Multiscale Analysis with the Sentinel-3 OLCI Sensor ». Remote Sensing 13, no 8 (7 avril 2021) : 1419. http://dx.doi.org/10.3390/rs13081419.
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ESA’s Eighth Earth Explorer mission “FLuorescence EXplorer” (FLEX) will be dedicated to the global monitoring of the chlorophyll fluorescence emitted by vegetation. In order to properly interpret the measured fluorescence signal, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem formation with Sentinel-3 (S3), which conveys the Ocean and Land Color Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In support of FLEX’s preparatory activities, this paper presents a first validation exercise of OLCI vegetation products against in situ data coming from the 2018 FLEXSense campaign. During this campaign, leaf chlorophyll content (LCC) and leaf area index (LAI) measurements were collected over croplands, while HyPlant DUAL images of the area were acquired at a 3 m spatial resolution. A multiscale validation strategy was pursued. First, estimates of these two variables, together with the combined canopy chlorophyll content (CCC = LCC × LAI), were obtained at the HyPlant spatial resolution and were compared against the in situ measurements. Second, the fine-scale retrieval maps from HyPlant were coarsened to the S3 spatial scale as a reference to assess the quality of the OLCI vegetation products. As an intermediary step, vegetation products extracted from Sentinel-2 data were used to compare retrievals at the in-between spatial resolution of 20 m. For all spatial scales, CCC delivered the most accurate estimates with the smallest prediction error obtained at the 300 m resolution (R2 of 0.74 and RMSE = 26.8 μg cm−2). Results of a scaling analysis suggest that CCC performs well at the different tested spatial resolutions since it presents a linear behavior across scales. LCC, on the other hand, was poorly retrieved at the 300 m scale, showing overestimated values over heterogeneous pixels. The introduction of a new LCC model integrating mixed reflectance spectra in its training enabled to improve by 16% the retrieval accuracy for this variable (RMSE = 10 μg cm−2 for the new model versus RMSE = 11.9 μg cm−2 for the former model).
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Zou, Tianyuan, et Jing Zhang. « A New Fluorescence Quantum Yield Efficiency Retrieval Method to Simulate Chlorophyll Fluorescence under Natural Conditions ». Remote Sensing 12, no 24 (11 décembre 2020) : 4053. http://dx.doi.org/10.3390/rs12244053.
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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 (Cab), carotenoid (Cca), dry matter content (Cdm), indicator of leaf water content (Cw) 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.
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Cogliati, Sergio, Marco Celesti, Ilaria Cesana, Franco Miglietta, Lorenzo Genesio, Tommaso Julitta, Dirk Schuettemeyer et al. « A Spectral Fitting Algorithm to Retrieve the Fluorescence Spectrum from Canopy Radiance ». Remote Sensing 11, no 16 (7 août 2019) : 1840. http://dx.doi.org/10.3390/rs11161840.
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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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Zheng, Wei, Xia Lu, Yu Li, Shan Li et Yuanzhi Zhang. « Hyperspectral Identification of Chlorophyll Fluorescence Parameters of Suaeda salsa in Coastal Wetlands ». Remote Sensing 13, no 11 (24 mai 2021) : 2066. http://dx.doi.org/10.3390/rs13112066.
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The stomata of Suaeda salsa are closed and the photosynthetic efficiency is decreased under conditions of water–salt imbalance, with the change to photosynthesis closely related to the chlorophyll fluorescence parameters of the photosystem PSII. Accordingly, chlorophyll fluorescence parameters were selected to monitor the growth status of Suaeda salsa in coastal wetlands under conditions of water and salt. Taking Suaeda salsa in coastal wetlands as the research object, we set up five groundwater levels (0 cm, –5 cm, –10 cm, –20 cm, and –30 cm) and six NaCl salt concentrations (0%, 0.5%, 1 %, 1.5%, 2%, and 2.5%) to carry out independent tests of Suaeda salsa potted plants and measured the canopy reflectance spectrum and chlorophyll fluorescence parameters of Suaeda salsa. A polynomial regression method was used to carry out hyperspectral identification of Suaeda salsa chlorophyll fluorescence parameters under water and salt stress. The results indicated that the chlorophyll fluorescence parameters Fv/Fm, Fm', and ΦPSII of Suaeda salsa showed significant relationships with vegetation index under water and salt conditions. The sensitive canopy band ranges of Suaeda salsa under water and salt conditions were 680–750 nm, 480–560 nm, 950–1000 nm, 1800–1850 nm, and 1890–1910 nm. Based on the spectrum and the first-order differential spectrum, the spectral ratio of A/B was constructed to analyze the correlation between it and the chlorophyll fluorescence parameters of Suaeda salsa. We constructed thirteen new vegetation indices. In addition, we discovered that the hyperspectral vegetation index D690/D1320 retrieved Suaeda chlorophyll fluorescence parameter Fv/Fm with the highest accuracy, with a multiple determination coefficient R2 of 0.813 and an RMSE of 0.042, and that D725/D1284 retrieved Suaeda chlorophyll fluorescence parameter ΦPSII model with the highest accuracy, with a multiple determination coefficient R2 of 0.848 and an RMSE of 0.096. The hyperspectral vegetation index can be used to retrieve the chlorophyll fluorescence parameters of Suaeda salsa in coastal wetlands under water and salt conditions, providing theoretical and technical support for future large-scale remote sensing inversion of chlorophyll fluorescence parameters.
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Amir, Muhammad, Jinghua Chen, Bin Chen, Shaoqiang Wang, Kai Zhu, Yuelin Li, Ze Meng et al. « Reflectance and chlorophyll fluorescence-based retrieval of photosynthetic parameters improves the estimation of subtropical forest productivity ». Ecological Indicators 131 (novembre 2021) : 108133. http://dx.doi.org/10.1016/j.ecolind.2021.108133.
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Tan, Jing, Robert Frouin, Didier Ramon et François Steinmetz. « On the Adequacy of Representing Water Reflectance by Semi-Analytical Models in Ocean Color Remote Sensing ». Remote Sensing 11, no 23 (28 novembre 2019) : 2820. http://dx.doi.org/10.3390/rs11232820.
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Deterministic or statistical inversion schemes to retrieve ocean color from space often use a simplified water reflectance model that may introduce unrealistic constraints on the solution, a disadvantage compared with standard, two-step algorithms that make minimal assumptions about the water signal. In view of this, the semi-analytical models of Morel and Maritorena (2001), MM01, and Park and Ruddick (2005), PR05, used in the spectral matching POLYMER algorithm (Steinmetz et al., 2011), are examined in terms of their ability to restitute properly, i.e., with sufficient accuracy, water reflectance. The approach is to infer water reflectance at MODIS wavelengths, as in POLYMER, from theoretical simulations (using Hydrolight with fluorescence and Raman scattering) and, separately, from measurements (AERONET-OC network). A wide range of Case 1 and Case 2 waters, except extremely turbid waters, are included in the simulations and sampled in the measurements. The reflectance model parameters that give the best fit with the simulated data or the measurements are determined. The accuracy of the reconstructed water reflectance and its effect on the retrieval of inherent optical properties (IOPs) is quantified. The impact of cloud and aerosol transmittance, fixed to unity in the POLYMER scheme, on model performance is also evaluated. Agreement is generally good between model results and Hydrolight simulations or AERONET-OC values, even in optically complex waters, with discrepancies much smaller than typical atmospheric correction errors. Significant differences exist in some cases, but having a more intricate model (i.e., using more parameters) makes convergence more difficult. The trade-off is between efficiency/robustness and accuracy. Notable errors are obtained when using the model estimates to retrieve IOPs. Importantly, the model parameters that best fit the input data, in particular chlorophyll-a concentration, do not represent adequately actual values. The reconstructed water reflectance should be used in bio-optical algorithms. While neglecting cloud and aerosol transmittances degrades the accuracy of the reconstructed water reflectance and the retrieved IOPs, it negligibly affects water reflectance ratios and, therefore, any variable derived from such ratios.
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Joiner, J., L. Guanter, R. Lindstrot, M. Voigt, A. P. Vasilkov, E. M. Middleton, K. F. Huemmrich, Y. Yoshida et C. Frankenberg. « Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements : methodology, simulations, and application to GOME-2 ». Atmospheric Measurement Techniques 6, no 10 (25 octobre 2013) : 2803–23. http://dx.doi.org/10.5194/amt-6-2803-2013.
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Abstract. Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5° × 0.5°. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.
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Peng, Huaiyue, Maria Pilar Cendrero-Mateo, Juliane Bendig, Bastian Siegmann, Kelvin Acebron, Caspar Kneer, Kari Kataja, Onno Muller et Uwe Rascher. « HyScreen : A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence ». Sensors 22, no 23 (2 décembre 2022) : 9443. http://dx.doi.org/10.3390/s22239443.
Texte intégralRésumé :
Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy’s structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun–canopy–sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red (F687) and far-red (F760) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m−2 sr−1 nm−1. The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m−2 sr−1 nm−1, whereas vegetation targets had a mean F687 of 1.13 and F760 of 1.96 mW m−2 sr−1 nm−1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants’ photosynthetic capacity.
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Lu, Li, Zhaoning Gong, Yanan Liang et Shuang Liang. « Retrieval of Chlorophyll-a Concentrations of Class II Water Bodies of Inland Lakes and Reservoirs Based on ZY1-02D Satellite Hyperspectral Data ». Remote Sensing 14, no 8 (12 avril 2022) : 1842. http://dx.doi.org/10.3390/rs14081842.
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Chlorophyll-a is an important parameter that characterizes the eutrophication of water bodies. The advantage of ZY1-02D hyperspectral satellite subdivision in the visible light and near-infrared bands is that it highlights the unique characteristics of water bodies in the spectral dimension, and it helps to assess the Class II water bodies of inland lakes and reservoirs, making it an important tool for refined remote sensing detection of the environment. In this study, the Baiyangdian Nature Reserve in northern China, which contains a typical inland lake and wetland, was chosen as the study area. Using ZY1-02D hyperspectral synchronization transit images and in situ measured chlorophyll-a concentration as the data source, remote sensing of the chlorophyll-a concentration of inland lakes was conducted. By analyzing the correlation between the spectral reflectance of the ZY1-02D hyperspectral image and the chlorophyll-a concentration and using algorithms such as the single band, band ratio, and three bands to compare and filter characteristic wavelengths, a quantitative hyperspectral model of the chlorophyll-a concentration was established to determine the chlorophyll-a concentration of Baiyangdian Lake. The dynamic monitoring of the water body and the assessment of the nutritional status of the water body were determined. The results revealed that the estimation of the chlorophyll-a concentration of Baiyangdian Lake based on the hyperspectral Fluorescence Line Height (FLH) model was ideal, with an R2 value of 0.78. The FLH model not only comprehensively considers the effects of suspended solids, yellow substances, and backscattering of the water body on the estimation of the chlorophyll-a concentration, but also considers the influence of the elastic scattering efficiency of the chlorophyll. Based on the ZY1-02D hyperspectral data, a spatial distribution map of the chlorophyll-a concentration of Baiyangdian Lake was created to provide new ideas and technical support for monitoring inland water environments.
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Joiner, J., L. Guanter, R. Lindstrot, M. Voigt, A. P. Vasilkov, E. M. Middleton, K. F. Huemmrich, Y. Yoshida et C. Frankenberg. « Global monitoring of terrestrial chlorophyll fluorescence from moderate spectral resolution near-infrared satellite measurements : methodology, simulations, and application to GOME-2 ». Atmospheric Measurement Techniques Discussions 6, no 2 (22 avril 2013) : 3883–930. http://dx.doi.org/10.5194/amtd-6-3883-2013.
Texte intégralRésumé :
Abstract. Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. In addition, fluorescence can contaminate photon path estimates from the O2 A-band that has become an integral part of missions to accurately measure greenhouse gas concentrations. Global mapping of far-red (~ 755–770 nm) terrestrial vegetation solar-induced fluorescence from space has been accomplished using the high spectral resolution (ν/Δ ν > 35 000) interferometer on the Japanese Greenhouse gases Observing SATellite (GOSAT). These satellite retrievals of fluorescence rely solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data to disentangle the spectral signatures of three basic components in and surrounding the O2 A-band: atmospheric absorption, surface reflectance, and fluorescence radiance. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate spectral resolution measurements with a relatively high signal-to-noise ratio within and outside the O2 A-band can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with GOSAT. GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. It should be noted that both GOME-2 and GOSAT were designed to make atmospheric trace gas measurements and were not optimized for fluorescence measurements. Our approach can be applied to other existing and future space-based instruments that provide moderate spectral resolution observations in the near-infrared region.
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Vargas, Juan Quirós, Juliane Bendig, Alasdair Mac Arthur, Andreas Burkart, Tommaso Julitta, Kadmiel Maseyk, Rick Thomas et al. « Unmanned Aerial Systems (UAS)-Based Methods for Solar Induced Chlorophyll Fluorescence (SIF) Retrieval with Non-Imaging Spectrometers : State of the Art ». Remote Sensing 12, no 10 (19 mai 2020) : 1624. http://dx.doi.org/10.3390/rs12101624.
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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.
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Merrick, Trina, Ralf Bennartz, Maria Luisa S. P. Jorge, Stephanie Pau et John Rausch. « Evaluation of Plant Stress Monitoring Capabilities Using a Portable Spectrometer and Blue-Red Grow Light ». Sensors 22, no 9 (29 avril 2022) : 3411. http://dx.doi.org/10.3390/s22093411.
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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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Siegmann, Bastian, Luis Alonso, Marco Celesti, Sergio Cogliati, Roberto Colombo, Alexander Damm, Sarah Douglas et al. « The High-Performance Airborne Imaging Spectrometer HyPlant—From Raw Images to Top-of-Canopy Reflectance and Fluorescence Products : Introduction of an Automatized Processing Chain ». Remote Sensing 11, no 23 (23 novembre 2019) : 2760. http://dx.doi.org/10.3390/rs11232760.
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The HyPlant imaging spectrometer is a high-performance airborne instrument consisting of two sensor modules. The DUAL module records hyperspectral data in the spectral range from 400–2500 nm, which is useful to derive biochemical and structural plant properties. In parallel, the FLUO module acquires data in the red and near infrared range (670–780 nm), with a distinctly higher spectral sampling interval and finer spectral resolution. The technical specifications of HyPlant FLUO allow for the retrieval of sun-induced chlorophyll fluorescence (SIF), a small signal emitted by plants, which is directly linked to their photosynthetic efficiency. The combined use of both HyPlant modules opens up new opportunities in plant science. The processing of HyPlant image data, however, is a rather complex procedure, and, especially for the FLUO module, a precise characterization and calibration of the sensor is of utmost importance. The presented study gives an overview of this unique high-performance imaging spectrometer, introduces an automatized processing chain, and gives an overview of the different processing steps that must be executed to generate the final products, namely top of canopy (TOC) radiance, TOC reflectance, reflectance indices and SIF maps.
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Hu, Jiaochan, Liangyun Liu et Xinjie Liu. « Improving the retrieval of solar-induced chlorophyll fluorescence at canopy level by modeling the relative peak height of the apparent reflectance ». Journal of Applied Remote Sensing 11, no 2 (16 juin 2017) : 026032. http://dx.doi.org/10.1117/1.jrs.11.026032.
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Yang, Peiqi, Egor Prikaziuk, Wout Verhoef et Christiaan van der Tol. « SCOPE 2.0 : a model to simulate vegetated land surface fluxes and satellite signals ». Geoscientific Model Development 14, no 7 (29 juillet 2021) : 4697–712. http://dx.doi.org/10.5194/gmd-14-4697-2021.
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Abstract. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model aims at linking satellite observations in the visible, infrared, and thermal domains with land surface processes in a physically based manner, and quantifying the microclimate in vegetation canopies. It simulates radiative transfer in the soil, leaves, and vegetation canopies, as well as photosynthesis and non-radiative heat dissipation through convection and mechanical turbulence. Since the first publication 12 years ago, SCOPE has been applied in remote sensing studies of solar-induced chlorophyll fluorescence (SIF), energy balance fluxes, gross primary production (GPP), and directional thermal signals. Here, we present a thoroughly revised version, SCOPE 2.0, which features a number of new elements: (1) it enables the definition of layers consisting of leaves with different properties, thus enabling the simulation of vegetation with an understorey or with a vertical gradient in leaf chlorophyll concentration; (2) it enables the simulation of soil reflectance; (3) it includes the simulation of leaf and canopy reflectance changes induced by the xanthophyll cycle; and (4) the computation speed has been reduced by 90 % compared to earlier versions due to a fundamental optimization of the model. These new features improve the capability of the model to represent complex canopies and to explore the response of remote sensing signals to vegetation physiology. The improvements in computational efficiency make it possible to use SCOPE 2.0 routinely for the simulation of satellite data and land surface fluxes. It also strengthens the operability for the numerical retrieval of land surface products from satellite or airborne data.
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Zhao, Min, Yan Bai, Hao Li, Xianqiang He, Fang Gong et Teng Li. « Fluorescence Line Height Extraction Algorithm for the Geostationary Ocean Color Imager ». Remote Sensing 14, no 11 (24 mai 2022) : 2511. http://dx.doi.org/10.3390/rs14112511.
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Chlorophyll fluorescence is an important indicator of the physiological state of phytoplankton in water bodies. The new generation of ocean color satellite remote sensors usually sets fluorescence bands to detect the phytoplankton fluorescence line height (FLH). Yet, the Geostationary Ocean Color Imager (GOCI) offers no FLH products so far, and the FLH results calculated using the fluorescence band (680 nm) and the two baseline bands (660 and 745 nm) have numerous negative values and are quite different from the FLH products of other satellite ocean color sensors. To address this problem, we established an FLH retrieval algorithm suitable for GOCI. We simulated the spectral datasets of different water types using the radiative transfer model HydroLight and established the band conversion relationship from 680 to 685 nm based on the simulated datasets. The remote sensing reflectance after band conversion was applied to the FLH product inversion, significantly reducing the number of negative FLH values and appreciably improving data availability for GOCI FLH products (from 14.78% to 66.73% on average). The new FLH product has a good correlation with the field-measured data (R2 = 0.73), and the relative error was 6.95%. Moreover, after band conversion, the FLH products retrieved by GOCI are in good agreement with the FLH products of MODIS, and fusion products can be further produced to improve the spatiotemporal resolution of the data. In addition, the radiative transfer simulation datasets also revealed that changes in solar zenith angle have little effect on FLH inversion. The hourly GOCI-derived FLH has good spatiotemporal continuity and can clearly reflect the diurnal variation of FLH. It can provide a stable FLH algorithm for further recovery of time-series GOCI FLH products and research on diurnal changes in FLH.
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Du, Kaiqi, Xia Jing, Yelu Zeng, Qixing Ye, Bingyu Li et Jianxi Huang. « An Improved Approach to Monitoring Wheat Stripe Rust with Sun-Induced Chlorophyll Fluorescence ». Remote Sensing 15, no 3 (24 janvier 2023) : 693. http://dx.doi.org/10.3390/rs15030693.
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Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL<20%). The strongest correlation between NDVI and SL (R=0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R=0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress.
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Zhang, Zhang, Li, Wu et Zhang. « Comparison of Bi-Hemispherical and Hemispherical-Conical Configurations for In Situ Measurements of Solar-Induced Chlorophyll Fluorescence ». Remote Sensing 11, no 22 (12 novembre 2019) : 2642. http://dx.doi.org/10.3390/rs11222642.
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During recent decades, solar-induced chlorophyll fluorescence (SIF) has shown to be a good proxy for gross primary production (GPP), promoting the development of ground-based SIF observation systems and supporting a greater understanding of the relationship between SIF and GPP. However, it is unclear whether such SIF-oriented observation systems built from different materials and of different configurations are able to acquire consistent SIF signals from the same target. In this study, we used four different observation systems to measure the same targets together in order to investigate whether SIF from different systems is comparable. Integration time (IT), reflectance, and SIF retrieved from different systems with hemispherical-conical (hemi-con) and bi-hemispherical (bi-hemi) configurations were also evaluated. A newly built prism system (SIFprism, using prism to collect both solar and target radiation) has the shortest IT and highest signal to noise ratio (SNR). Reflectance collected from the different systems showed small differences, and the diurnal patterns of both red and far-red SIF derived from different systems showed a marginal difference when measuring the homogeneous vegetation canopy (grassland). However, when the target is heterogeneous, e.g., the Epipremnum aureum canopy, the values and diurnal pattern of far-red SIF derived from systems with a bi-hemi configuration were obviously different with those derived from the system with hemi-con configuration. These results demonstrate that different SIF systems are able to acquire consistent SIF for landscapes with a homogeneous canopy. However, SIF retrieved from bi-hemi and hemi-con configurations may be distinctive when the target is a heterogeneous (or discontinuous) canopy due to the different fields of view and viewing geometries. Our findings suggest that the bi-hemi configuration has an advantage to measure heterogeneous canopies due to the large field of view for upwelling sensors being representative for the footprint of the eddy covariance flux measurements.
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Molkov, Alexander A., Sergei V. Fedorov, Vadim V. Pelevin et Elena N. Korchemkina. « Regional Models for High-Resolution Retrieval of Chlorophyll a and TSM Concentrations in the Gorky Reservoir by Sentinel-2 Imagery ». Remote Sensing 11, no 10 (22 mai 2019) : 1215. http://dx.doi.org/10.3390/rs11101215.
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The possibilities of chlorophyll a (Chl a) and total suspended matter (TSM) retrieval using Sentinel-2/MSI imagery and in situ measurements in the Gorky Reservoir are investigated. This water body is an inland freshwater ecosystem within the territory of the Russian Federation. During the algal bloom period, the optical properties of water are extremely heterogeneous and vary on scales of tens of meters. Additionally, they vary in time under the influence of currents and wind forcing. In this case, the usage of the traditional station-based sampling to describe the state of the reservoir may be uninformative and not rational. Therefore, we proposed an original approach based on simultaneous in situ measurements of the remote sensing reflectance by a single radiometer and the concentration of water constituents by an ultraviolet fluorescence LiDAR from a high-speed gliding motorboat. This approach provided fast data collection including 4087 synchronized LiDAR and radiometric measurements with high spatial resolutions of 8 m for two hours. A part of the dataset was coincided with Sentinel-2 overpass and used for the development of regional algorithms for the retrieval of Chl a and TSM concentrations. For inland waters of the Russian Federation, such research was performed for the first time. The proposed algorithms can be used for regular environmental monitoring of the Gorky Reservoir using ship measurements or Sentinel-2 images. Additionally, they can be adapted for neighboring reservoirs, for example, for other seven reservoirs on the Volga River. Moreover, the proposed ship measurement approach can be useful in the practice of limnological monitoring of inland freshwater ecosystems with high spatiotemporal variability of the optical properties.
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Nichol, Caroline, Guillaume Drolet, Albert Porcar-Castell, Tom Wade, Neus Sabater, Elizabeth Middleton, Chris MacLellan et al. « Diurnal and Seasonal Solar Induced Chlorophyll Fluorescence and Photosynthesis in a Boreal Scots Pine Canopy ». Remote Sensing 11, no 3 (30 janvier 2019) : 273. http://dx.doi.org/10.3390/rs11030273.
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Solar induced chlorophyll fluorescence has been shown to be increasingly an useful proxy for the estimation of gross primary productivity (GPP), at a range of spatial scales. Here, we explore the seasonality in a continuous time series of canopy solar induced fluorescence (hereafter SiF) and its relation to canopy gross primary production (GPP), canopy light use efficiency (LUE), and direct estimates of leaf level photochemical efficiency in an evergreen canopy. SiF was calculated using infilling in two bands from the incoming and reflected radiance using a pair of Ocean Optics USB2000+ spectrometers operated in a dual field of view mode, sampling at a 30 min time step using custom written automated software, from early spring through until autumn in 2011. The optical system was mounted on a tower of 18 m height adjacent to an eddy covariance system, to observe a boreal forest ecosystem dominated by Scots pine. (Pinus sylvestris) A Walz MONITORING-PAM, multi fluorimeter system, was simultaneously mounted within the canopy adjacent to the footprint sampled by the optical system. Following correction of the SiF data for O2 and structural effects, SiF, SiF yield, LUE, the photochemicsl reflectance index (PRI), and the normalized difference vegetation index (NDVI) exhibited a seasonal pattern that followed GPP sampled by the eddy covariance system. Due to the complexities of solar azimuth and zenith angle (SZA) over the season on the SiF signal, correlations between SiF, SiF yield, GPP, and LUE were assessed on SZA <50° and under strictly clear sky conditions. Correlations found, even under these screened scenarios, resulted around ~r2 = 0.3. The diurnal responses of SiF, SiF yield, PAM estimates of effective quantum yield (ΔF/Fm′), and meteorological parameters demonstrated some agreement over the diurnal cycle. The challenges inherent in SiF retrievals in boreal evergreen ecosystems are discussed.
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Zhang, Yao, Joanna Joiner, Seyed Hamed Alemohammad, Sha Zhou et Pierre Gentine. « A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks ». Biogeosciences 15, no 19 (2 octobre 2018) : 5779–800. http://dx.doi.org/10.5194/bg-15-5779-2018.
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Abstract. Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.
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Maimaitiyiming, Matthew, Vasit Sagan, Paheding Sidike, Maitiniyazi Maimaitijiang, Allison J. Miller et Misha Kwasniewski. « Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology ». Remote Sensing 12, no 19 (2 octobre 2020) : 3216. http://dx.doi.org/10.3390/rs12193216.
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Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs.
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Xu, Zhao, Xu Qian, Baoguo Shan, Jinhui Duan et Xiangdong Sun. « A fuzzy function model for remote evaluation of chlorophyll-a concentration in the Northern of South China Sea-Hong Kong ». E3S Web of Conferences 158 (2020) : 05001. http://dx.doi.org/10.1051/e3sconf/202015805001.
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Chlorophyll-a (Chla) concentration is an important indicator to monitor eutrophication, which is a persistent problem that affects the ecological health of coastal water or shallow lakes. We have applied a Gaussian-like fuzzy function model for the estimation of Chla concentration in Hong Kong Coastal Waters, based on the spectral signature using the HJ-1A /1B CCD images and measured data. The method of this paper is as follows. Firstly, reflectance peak and fluorescence band were used to figure out the bands or bands combination which impact the Chla concentration significantly; and then calculated the value of all the pixels without measured data according to their similarity degree with the measured points; finally, the full Chla concentration maps in the study area were produced by GIS spatial interpolation. It is concluded in this paper that the method can retrieve the Chla concentration accurately and the result of changes detected coincides with the observed result extremely, what’s more, the maps generated are continuous and smooth which are quite different from traditional RS maps that can only accurate to pixel size.
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Guanter, Luis, Cédric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher et al. « The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission ». Earth System Science Data 13, no 11 (19 novembre 2021) : 5423–40. http://dx.doi.org/10.5194/essd-13-5423-2021.
Texte intégralRésumé :
Abstract. The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial, and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatiotemporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743–758 nm window. A secondary SIF dataset derived from an extended fitting window (735–758 nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3 nm windows devoid of atmospheric absorption within the 665–785 nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work. The TROPOSIF dataset is available through the following digital object identifier (DOI): https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104 (Guanter et al., 2021).
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Su, Hua, Xuemei Lu, Zuoqi Chen, Hongsheng Zhang, Wenfang Lu et Wenting Wu. « Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning ». Remote Sensing 13, no 4 (6 février 2021) : 576. http://dx.doi.org/10.3390/rs13040576.
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Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian’s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing.
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Wang, Yujie, Philipp Köhler, Liyin He, Russell Doughty, Renato K. Braghiere, Jeffrey D. Wood et Christian Frankenberg. « Testing stomatal models at the stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1) ». Geoscientific Model Development 14, no 11 (5 novembre 2021) : 6741–63. http://dx.doi.org/10.5194/gmd-14-6741-2021.
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Abstract. At the leaf level, stomata control the exchange of water and carbon across the air–leaf interface. Stomatal conductance is typically modeled empirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predicting carbon and water fluxes at both the leaf and tree levels. However, how well the optimization models perform at larger scales has not been extensively evaluated. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such as big-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves nor the entire canopy. As a result, they are unable to directly link canopy optical properties with light distribution within the canopy to remote sensing data observed from afar. Here, we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the land component of a new Earth system model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and water fluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIF retrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model, in particular, showed increased skill in predicting the water flux given the lower error (ca. 14.2 % and 21.8 % improvement for the gymnosperm and angiosperm forests, respectively) and better 1:1 comparison (slope increases from ca. 0.34 to 0.91 for the gymnosperm forest and from ca. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for both forests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth system modeling in general.
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Turner, Alexander J., Philipp Köhler, Troy S. Magney, Christian Frankenberg, Inez Fung et Ronald C. Cohen. « A double peak in the seasonality of California's photosynthesis as observed from space ». Biogeosciences 17, no 2 (29 janvier 2020) : 405–22. http://dx.doi.org/10.5194/bg-17-405-2020.
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Abstract. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIRv) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.
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Lv, Jie, et Zhen Guo Yan. « Retrieval of Chlorophyll Content from Leaf Reflectance Spectra Using Support Vector Machine ». Applied Mechanics and Materials 602-605 (août 2014) : 2313–16. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2313.
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The chlorophyll content in crop leaf is an indicator of health situation and the crop yield. Hence, it is very important to retrieval of accurate chlorophyll content in paddy rice. This research selected Zhengyi town of Suzhou city as the study area, measurements were acquired during the summer of 2009, in a field campaign in which for 288 rice leaf samples, rice hyperspectral data was measured by ASD FieldSpec3 spectrometer, chlorophyll content was measured by using a SPAD-502 chlorophyll meter. And the parameters of support vector machine were optimized by genetic algorithm, then support vector machine and PROSPECT radiative transfer model were adopted to build estimation model, which used to retrieve the chlorophyll content of rice. The results indicate that: the coefficient of determination for the rice chlorophyll estimation model is 0.8825, and RMSE is 8.7491. Research of this paper provides some reference for quickly and accurately estimating the chlorophyll content in rice.
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Siebke, Katharina, et Marilyn C. Ball. « Non-destructive measurement of chlorophyll b:a ratios and identification of photosynthetic pathways in grasses by reflectance spectroscopy ». Functional Plant Biology 36, no 11 (2009) : 857. http://dx.doi.org/10.1071/fp09201.
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Equations for non-destructive determination of chlorophyll b : a ratios in grasses were developed from reflectance spectra of intact leaves of barley (Hordeum vulgare L.) and two barley mutants: clorina f2, which lacks chlorophyll b and clorina f104, which has a low chlorophyll b content. These plants enabled separation of effects of chlorophyll composition on reflectance spectra due to differential light absorption by chlorophylls a and b and to measure the effects of chlorophyll b on the contribution of fluorescence emitted by chlorophyll a to the reflectance spectra. Indices developed from these data were then tested on growth chamber-grown leaves from six C3 and 17 C4 grass species (7 NAD-ME and 10 NADP-ME subtypes). We used the chlorophyll b : a ratio because the data were less skewed than the chlorophyll a : b ratio. The best index for determination of the chlorophyll b : a ratio utilised wavelengths affected by chlorophyll absorbance: [R626 – 0.5 (R603 + R647)]/[R552– R626]. The chlorophyll b : a ratio was significantly lower in the C4 than C3 grasses, but was not sufficient in itself to separate these two functional groups. However, because of differences in fluorescence characteristics, C3 and C4 species could be distinguished by an index based on wavelengths affected by chlorophyll fluorescence: [R696 to 709/R545 to 567].
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Šebela, David, Julie Olejníčková, Anita Župčanová et Radek Sotolář. « Response of grapevine leaves to Plasmopara viticola infection by means of measurement of reflectance and fluorescence signals ». Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no 8 (2012) : 229–38. http://dx.doi.org/10.11118/actaun201260080229.
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Response of grapevine leaf tissue naturally infected byPlasmopara viticolain field was measured by means of chlorophyll fluorescence and reflectance signals. Three susceptible grapevine varieties (Cabernet Sauvignon, Pinot Blanc and Pinot Gris) were used in this study. Since the infection impairs photosynthetic activity, distribution of FV/FMparameter (maximum quantum yield of Photosystem II) over the leaf was effective to discriminate healthy and naturally infected leaf tissue. FV/FMwas reduced ~ 25% in all infected leaf parts. Infected leaf spots expressed significantly altered chlorophyll fluorescence induction kinetics expressing much slower electron transport rate both on donor and acceptor site of PSII. Vegetation reflectance indices followed the variations in pigment content after the fungal infection. R750/R700 (R2= 0.877) and CRI (carotenoid reflectance index; R2=0.735) were the most potent to follow changes in chlorophylls and carotenoids contents, respectively. Infected leaf tissue exhibited decrease in chlorophyll a (~50 %) as well as carotenoids (~70%). We conclude that combination of chlorophyll fluorescence and reflectance measurements can be used as an effective non-invasive tool for an early detection ofPlasmopara viticolain field as well as for estimation of the level of infection.
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Mridha, Nilimesh, Rabi N. Sahoo, Vinay K. Sehgal, Gopal Krishna, Sourabh Pargal, Sanatan Pradhan, Vinod K. Gupta et Dasika Nagesh Kumar. « Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters ». International Agrophysics 29, no 2 (1 avril 2015) : 201–12. http://dx.doi.org/10.1515/intag-2015-0019.
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Abstract The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for parameters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.
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Qiu, Chen, Croft, Li, Zhang, Zhang et Ju. « Retrieving Leaf Chlorophyll Content by Incorporating Variable Leaf Surface Reflectance in the PROSPECT Model ». Remote Sensing 11, no 13 (2 juillet 2019) : 1572. http://dx.doi.org/10.3390/rs11131572.
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Leaf chlorophyll content plays a vital role in plant photosynthesis. The PROSPECT model has been widely used for retrieving leaf chlorophyll content from remote sensing data over various plant species. However, despite wide variations in leaf surface reflectance across different plant species and environmental conditions, leaf surface reflectance is assumed to be the same for different leaves in the PROSPECT model. This work extends the PROSPECT model by taking into account the variation of leaf surface reflection. In the modified model named PROSPECT-Rsurf, an additional surface layer with a variable refractive index is bounded on the N elementary layers. Leaf surface reflectance (Rs) is characterized by the difference between the refractive indices of leaf surface and interior layers. The specific absorption coefficients of the leaf total chlorophyll and carotenoids were recalibrated using a cross-calibration method and the refractive indices of leaf surface and interior layers were obtained during model inversion. Chlorophyll content (Cab) retrieval and spectral reconstruction in the visible spectral region (VIS, 400–750 nm) were greatly improved using PROSPECT-Rsurf, especially for leaves covered by heavy wax or hard cuticles that lead to high surface reflectance. The root mean square error (RMSE) of chlorophyll estimates decreased from 11.1 µg/cm2 to 8.9 µg/cm2 and the Pearson’s correlation coefficient (r) increased from 0.81 to 0.88 (p < 0.01) for broadleaf samples in validation, compared to PROSPECT-5. For needle leaves, r increased from 0.71 to 0.89 (p < 0.01), but systematic overestimation of Cab was found due to the edge effects of needles. After incorporating the edge effects in PROSPECT-Rsurf, the overestimation of Cab was alleviated and its estimation was improved for needle leaves. This study explores the influence of leaf surface reflectance on Cab estimation at the leaf level. By coupling PROSPECT-Rsurf with canopy models, the influence of leaf surface reflectance on canopy reflectance and therefore canopy chlorophyll content retrieval can be investigated across different spatial and temporal scales.
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Hikosaka, Kouki. « Photosynthesis, chlorophyll fluorescence and photochemical reflectance index in photoinhibited leaves ». Functional Plant Biology 48, no 8 (2021) : 815. http://dx.doi.org/10.1071/fp20365.
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Solar-induced chlorophyll (chl) fluorescence (SIF) has been shown to be positively correlated with vegetation photosynthesis, suggesting that it is a useful signal for understanding of environmental responses and spatial heterogeneity of photosynthetic activity at various scales from leaf to the globe. Photosynthesis is often inhibited in stressful environments (photoinhibition), but how photoinhibition influences the relationship between photosynthesis and chl fluorescence remains unclear. Here, I studied light energy allocation among photosynthesis, chl fluorescence and heat dissipation in photoinhibited leaves and tested whether photosynthesis in photoinhibited leaves can be evaluated from chl fluorescence and reflectance spectra in remote sensing. Chl fluorescence and reflection spectra were examined with the pulse amplified modulation (PAM) system and spectroradiometer, respectively. Photoinhibited leaves had lower photosynthetic rates and quantum yields of photochemistry (ΦP) and higher chl fluorescence yields. Consequently, photosynthesis was negatively correlated with chl fluorescence, which contrasts the positive relationships between photosynthesis and SIF observed in past remote sensing studies. This suggests that vegetation photosynthesis evaluated solely from chl fluorescence may be overestimated if the vegetation is dominated by severely photoinhibited leaves. When a model of energy allocation was applied, ΦP estimated from chl fluorescence and photochemical reflectance index (PRI) significantly correlated with the observed ΦP, suggesting that the model is useful to evaluate photosynthetic activities of photoinhibited leaves by remote sensing.
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Hu, Hao, Guangzhi Zhang et Kefeng Zheng. « Modeling Leaf Image, Chlorophyll Fluorescence, Reflectance From SPAD Readings ». IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no 11 (novembre 2014) : 4368–73. http://dx.doi.org/10.1109/jstars.2014.2325812.
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Campbell, P. K. Entcheva, E. M. Middleton, L. A. Corp et M. S. Kim. « Contribution of chlorophyll fluorescence to the apparent vegetation reflectance ». Science of The Total Environment 404, no 2-3 (15 octobre 2008) : 433–39. http://dx.doi.org/10.1016/j.scitotenv.2007.11.004.
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RAMA RAO, N., P. K. GARG, S. K. GHOSH et V. K. DADHWAL. « Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery ». Journal of Agricultural Science 146, no 1 (26 septembre 2007) : 65–75. http://dx.doi.org/10.1017/s0021859607007514.
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SUMMARYRemotely sensed estimates of biochemical parameters of agricultural crops are central to the precision management of agricultural crops (precision farming). Past research using in situ and airborne spectral reflectance measurements of various vegetation species has proved the usefulness of hyperspectral data for the estimation of various biochemical parameters of vegetation. In order to exploit the vast spectral and radiometric resources offered by space-borne hyperspectral remote sensing for the improved estimation of plant biochemical parameters, the relationships observed between spectral reflectance and various biochemical parameters at in situ and airborne levels needed to be evaluated in order to establish the existence of a reliable and stable relationship between spectral reflectance and plant biochemical parameters at the pixel scale. The potential of the EO-1 Hyperion hyperspectral sensor was investigated for the estimation of total chlorophyll and nitrogen concentrations of cotton crops in India by developing regression models between hyperspectral reflectance and laboratory measurements of leaf total chlorophyll and nitrogen concentrations. A comprehensive and rigorous analysis was carried out to identify the spectral bands and spectral indices for accurate retrieval of leaf total chlorophyll and nitrogen concentrations of cotton crop. The performance of these critical spectral reflectance indices was validated using independent samples. A new vegetation index, named the plant biochemical index (PBI), is proposed for improved estimation of the plant biochemicals from space-borne hyperspectral data; it is simply the ratio of reflectance at 810 and 560 nm. Further, the applicability of PBI to a different crop and at a different geographical location was also assessed. The present results suggest the use of space-borne hyperspectral data for accurate retrieval of leaf total chlorophyll and nitrogen concentrations and the proposed PBI has the potential to retrieve leaf total chlorophyll and nitrogen concentrations of various crops and at different geographical locations.
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Irteza, S. M., et J. E. Nichol. « MEASUREMENT OF SUN INDUCED CHLOROPHYLL FLUORESCENCE USING HYPERSPECTRAL SATELLITE IMAGERY ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (23 juin 2016) : 911–13. http://dx.doi.org/10.5194/isprs-archives-xli-b8-911-2016.
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Solar Induced Chlorophyll Fluorescence (SIF), can be used as an indicator of stress in vegetation. Several scientific approaches have been made and there is considerable evidence that steady state Chlorophyll fluorescence is an accurate indicator of plant stress hence a reliable tool to monitor vegetation health status. Retrieval of Chlorophyll fluorescence provides an insight into photochemical and carbon sequestration processes within vegetation. Detection of Chlorophyll fluorescence has been well understood in the laboratory and field measurement. Fluorescence retrieval methods were applied in and around the atmospheric absorption bands 02B (Red wavelength) approximately 690 nm and 02A (Far red wavelengths) 740 nm. Hyperion satellite images were acquired for the years 2012 to 2015 in different seasons. Atmospheric corrections were applied using the 6S Model. The Fraunhofer Line Discrimanator (FLD) method was applied for retrieval of SIF from the Hyperion images by measuring the signal around the absorption bands in both vegetated and non vegetated land cover types. Absorption values were extracted in all the selected bands and the fluorescence signal was detected. The relationships between NDVI and Fluorescence derived from the satellite images are investigated to understand vegetation response within the absorption bands.
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Irteza, S. M., et J. E. Nichol. « MEASUREMENT OF SUN INDUCED CHLOROPHYLL FLUORESCENCE USING HYPERSPECTRAL SATELLITE IMAGERY ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (23 juin 2016) : 911–13. http://dx.doi.org/10.5194/isprsarchives-xli-b8-911-2016.
Texte intégralRésumé :
Solar Induced Chlorophyll Fluorescence (SIF), can be used as an indicator of stress in vegetation. Several scientific approaches have been made and there is considerable evidence that steady state Chlorophyll fluorescence is an accurate indicator of plant stress hence a reliable tool to monitor vegetation health status. Retrieval of Chlorophyll fluorescence provides an insight into photochemical and carbon sequestration processes within vegetation. Detection of Chlorophyll fluorescence has been well understood in the laboratory and field measurement. Fluorescence retrieval methods were applied in and around the atmospheric absorption bands 02B (Red wavelength) approximately 690 nm and 02A (Far red wavelengths) 740 nm. Hyperion satellite images were acquired for the years 2012 to 2015 in different seasons. Atmospheric corrections were applied using the 6S Model. The Fraunhofer Line Discrimanator (FLD) method was applied for retrieval of SIF from the Hyperion images by measuring the signal around the absorption bands in both vegetated and non vegetated land cover types. Absorption values were extracted in all the selected bands and the fluorescence signal was detected. The relationships between NDVI and Fluorescence derived from the satellite images are investigated to understand vegetation response within the absorption bands.
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Kromkamp, Jacco C., Edward P. Morris, Rodney M. Forster, Claire Honeywill, Scott Hagerthey et David M. Paterson. « Relationship of intertidal surface sediment chlorophyll concentration to hyperspectral reflectance and chlorophyll fluorescence ». Estuaries and Coasts 29, no 2 (avril 2006) : 183–96. http://dx.doi.org/10.1007/bf02781988.
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Korchemkina, Elena, Dmitriy Deryagin, Mariia Pavlova, Anna Kostyleva, Igor E. Kozlov et Svetlana Vazyulya. « Advantage of Regional Algorithms for the Chlorophyll-a Concentration Retrieval from In Situ Optical Measurements in the Kara Sea ». Journal of Marine Science and Engineering 10, no 11 (27 octobre 2022) : 1587. http://dx.doi.org/10.3390/jmse10111587.
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The data of the sea reflectance coefficient were obtained during the cruise of the R/V “Akademik Ioffe” (10 August–8 September 2021) in the Kara Sea beyond the Arctic Circle. A total of 28 measurements of sea reflectance were performed in different conditions ranging from estuarine zones to open ocean. In addition, at 10 stations, water samples were taken, and chlorophyll-a concentrations were determined using the fluorometric method. In situ reflectance data were compared to satellite data (MODIS Aqua/Terra, Sentinel OLCI 3A/B, VIIRS SNPP) obtained within 24 h before and after the measurement. It was shown that, in general, an overestimation of remote sensing reflectance is observed in short-wave channels (412, 443, 469 nm for MODIS; 410, 412.5, 442.5 for OLCI) and underestimation in long-wave channels (708.75, 753.75 nm for OLCI). The obtained in situ chlorophyll-a concentrations were compared with the results of standard models (GIOP, OC2, OC3, OC4), and regional algorithms (semi-analytical MHI, empirical K17) were applied to the in situ and remotely sensed reflectances. In the cases of GIOP and OC4, unrealistic concentrations of chlorophyll-a were obtained. Regional algorithms developed earlier, specifically for the Kara Sea, showed good correlation (0.6 and 0.76) with in situ measurements. Despite the approximately 20% overestimation of chlorophyll-a by both regional algorithms, they can be used to obtain chlorophyll-a concentrations in the Kara Sea in a concentration range of <1 mg/m3.
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Huemmrich, K. Fred, Petya Campbell, Sergio A. Vargas Z, Sarah Sackett, Steven Unger, Jeremy May, Craig Tweedie et Elizabeth Middleton. « Leaf-level chlorophyll fluorescence and reflectance spectra of high latitude plants ». Environmental Research Communications 4, no 3 (1 mars 2022) : 035001. http://dx.doi.org/10.1088/2515-7620/ac5365.
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Abstract Little is known about the chlorophyll fluorescence spectra for high latitude plants. A FluoWat leaf clip was used to measure leaf-level reflectance and chlorophyll fluorescence spectra of leaves of common high latitude plants to examine general spectral characteristics of these species. Fluorescence yield (Fyield) was calculated as the ratio of the emitted fluorescence divided by the absorbed radiation for the wavelengths from 400 nm up to the wavelength of the cut-off for the FluoWat low pass filter (either 650 or 700 nm). The Fyield spectra grouped into distinctly different patterns among different plant functional types. Black spruce (Picea mariana) Fyield spectra had little red fluorescence, which was reabsorbed in the shoot, but displayed a distinct far-red peak. Quaking aspen (Populus tremuloides) had both high red and far-red Fyield peaks, as did sweet coltsfoot (Petasites frigidus). Cotton grass (Eriophorum spp.) had both red and far-red Fyield peaks, but these peaks were much lower than for aspen or coltsfoot. Sphagnum moss (Sphagnum spp.) had a distinct Fyield red peak but low far-red fluorescence. Reindeer moss lichen (Cladonia rangiferina) had very low fluorescence levels, although when damp displayed a small red Fyield peak. These high latitude vegetation samples showed wide variations in Fyield spectral shapes. The Fyield values for the individual red or far-red peaks were poorly correlated to chlorophyll content, however the ratio of far-red to red Fyield showed a strong correlation with chlorophyll content. The spectral variability of these plants may provide information for remote sensing of vegetation type but may also confound attempts to measure high latitude vegetation biophysical characteristics and function using solar induced fluorescence (SIF).
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Zhang, Jing, Wenjiang Huang et Qifa Zhou. « Reflectance Variation within the In-Chlorophyll Centre Waveband for Robust Retrieval of Leaf Chlorophyll Content ». PLoS ONE 9, no 11 (3 novembre 2014) : e110812. http://dx.doi.org/10.1371/journal.pone.0110812.
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He, Ming-Xia, Zhi-Shen Liu, Ke-Ping Du, Li-Ping Li, Rui Chen, Kendall L. Carder et Zhong-Ping Lee. « Retrieval of chlorophyll from remote-sensing reflectance in the China seas ». Applied Optics 39, no 15 (20 mai 2000) : 2467. http://dx.doi.org/10.1364/ao.39.002467.
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BLACKBURN, G., et J. FERWERDA. « Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis ». Remote Sensing of Environment 112, no 4 (15 avril 2008) : 1614–32. http://dx.doi.org/10.1016/j.rse.2007.08.005.
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