Bio-optical Modeling of Sun-Induced Chlorophyll- a Fluorescence

Gilerson, Alexander; Huot, Yannick

doi:10.1016/b978-0-12-804644-9.00007-0

Cited by 8 publications

(6 citation statements)

References 72 publications

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“…SIF was retrieved from in-situ, airborne and spaceborne measurements in SNAP using the Fluorescence Line Height (FLH) algorithm, an established empirical algorithm that exploits radiances at and around the SIF emission in three spectral bands (for this work 665 nm, 681 nm and 709 nm). For more details on the algorithm approach, its general formula and the selected spectral bands see [11], [12], [13]. The FLH method is typically used in the open ocean since low signal from elastic scattering provides good resulting baseline curves [14], [15] .…”

Section: Methodsmentioning

confidence: 99%

“…The FLH method is typically used in the open ocean since low signal from elastic scattering provides good resulting baseline curves [14], [15] . High TChla and TSM concentrations in optically complex waters could significantly affect FLH results due to a peak from the elastic reflectance overlapping with the fluorescence signal and therefore potentially contaminating fluorescence retrieval [11].…”

Section: Methodsmentioning

confidence: 99%

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“Flex 2018” Cruise: an opportunity to assess phytoplankton chlorophyll fluorescence retrieval at different observative scales

Angelini

Gupana

Damm

et al. 2020

Proceedings E Report

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The “FLEX 2018” cruise, organized by the CNR-ISMAR in frame of the ESA “FLEXSense Campaign 2018” and CMEMS project, provided a ground station for several bio-optical instruments that investigated the coastal waters of the Tyrrhenian Sea in June 2018. The field measurements were performed in time synergy with Sentinel 3A and Sentinel 3B satellites and HyPlant airborne imaging spectrometer. Active and passive fluorescence were investigated at different scales in coastal waters to support preparatory activities of the FLuorescence EXplorer (FLEX) satellite mission.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“Flex 2018” Cruise: an opportunity to assess phytoplankton chlorophyll fluorescence retrieval at different observative scales

Angelini

Gupana

Damm

et al. 2020

Proceedings E Report

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“…As discussed above, however, fluorescence per chla (or more correctly per absorbed photon) is not constant. For this reason, a number of studies have explored the use of SIF signatures as a diagnostic tool to assess phytoplankton physiological state (e.g., Letelier et al, 1997;Morrison, 2003;Schallenberg et al, 2008;Behrenfeld et al, 2009;O'Malley et al, 2014;Gilerson and Huot, 2017). Remote sensing of phytoplankton physiology over entire ocean basins would significantly advance the development of global primary productivity models.…”

Section: St-chlf and The Interpretation Of Sun Induced Chlfmentioning

confidence: 99%

Single-Turnover Variable Chlorophyll Fluorescence as a Tool for Assessing Phytoplankton Photosynthesis and Primary Productivity: Opportunities, Caveats and Recommendations

et al. 2021

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Phytoplankton photosynthetic physiology can be investigated through single-turnover variable chlorophyll fluorescence (ST-ChlF) approaches, which carry unique potential to autonomously collect data at high spatial and temporal resolution. Over the past decades, significant progress has been made in the development and application of ST-ChlF methods in aquatic ecosystems, and in the interpretation of the resulting observations. At the same time, however, an increasing number of sensor types, sampling protocols, and data processing algorithms have created confusion and uncertainty among potential users, with a growing divergence of practice among different research groups. In this review, we assist the existing and upcoming user community by providing an overview of current approaches and consensus recommendations for the use of ST-ChlF measurements to examine in-situ phytoplankton productivity and photo-physiology. We argue that a consistency of practice and adherence to basic operational and quality control standards is critical to ensuring data inter-comparability. Large datasets of inter-comparable and globally coherent ST-ChlF observations hold the potential to reveal large-scale patterns and trends in phytoplankton photo-physiology, photosynthetic rates and bottom-up controls on primary productivity. As such, they hold great potential to provide invaluable physiological observations on the scales relevant for the development and validation of ecosystem models and remote sensing algorithms.

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“…The models can be roughly divided into analytical methods and empirical methods [29][30][31]. The former is to use the theory of radiation transfer to deduce the optical characteristics of each component in the water column, which extends the semi-analytical methods and quasi-analytical methods, generally used to estimate concentrations of chlorophyll-a(Chla), total suspended solids and other optically active substances [29]. For example, Lee et al put forward a quasi-analytical model for Chl-a in class II water in 2002 [32].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Eutrophic Water in Highly Urbanized Area Coupling UAV Multispectral Data and Machine Learning Algorithms

Jiang

Wang

et al. 2023

Water

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With the rapid development of urbanization and a population surge, the drawback of water pollution, especially eutrophication, poses a severe threat to ecosystem as well as human well-being. Timely monitoring the variations of water quality is a precedent to preventing the occurrence of eutrophication. Traditional monitoring methods (station monitoring or satellite remote sensing), however, fail to real-time obtain water quality in an accurate and economical way. In this study, an unmanned aerial vehicle (UAV) with a multispectral camera is used to acquire the refined remote sensing data of water bodies. Meanwhile, in situ measurement and sampling in-lab testing are carried out to obtain the observed values of four water quality parameters; subsequently, the comprehensive trophic level index (TLI) is calculated. Then three machine learning algorithms (i.e., Extreme Gradient Boosting (XGB), Random Forest (RF) and Artificial Neural Network (ANN)) are applied to construct the inversion model for water quality estimation. The measured values of water quality showed that the trophic status of the study area was mesotrophic or light eutrophic, which was consistent with the government’s water-control ambition. Among the four water quality parameters, TN had the highest correlation (r = 0.81, p = 0.001) with TLI, indicating that the variation in TLI was inextricably linked to TN. The performances of the three models were satisfactory, among which XGB was considered the optimal model with the best accuracy validation metrics (R2 = 0.83, RMSE = 0.52). The spatial distribution map of water quality drawn by the XGB model was in good agreement with the actual situation, manifesting the spatial applicability of the XGB model inversion. The research helps guide effective monitoring and the development of timely warning for eutrophication.

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Bio-optical Modeling of Sun-Induced Chlorophyll- a Fluorescence

Cited by 8 publications

References 72 publications

“Flex 2018” Cruise: an opportunity to assess phytoplankton chlorophyll fluorescence retrieval at different observative scales

“Flex 2018” Cruise: an opportunity to assess phytoplankton chlorophyll fluorescence retrieval at different observative scales

Single-Turnover Variable Chlorophyll Fluorescence as a Tool for Assessing Phytoplankton Photosynthesis and Primary Productivity: Opportunities, Caveats and Recommendations

Retrieving Eutrophic Water in Highly Urbanized Area Coupling UAV Multispectral Data and Machine Learning Algorithms

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