Chlorophyll-a Algorithms Research Articles

Calibration and validation of chlorophyll-a algorithms based on global optical complex water

Calibration and validation of chlorophyll-a algorithms based on global optical complex water

Evaluation and Refinement of Chlorophyll-a Algorithms for High-Biomass Blooms in San Francisco Bay (USA)

A massive bloom of the raphidophyte Heterosigma akashiwo occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll a is typically <30 mg m−3, while also capturing bloom periods of >100 mg m−3 chlorophyll a.

Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms

The aim of this work is to test the state-of-the-art of water constituent retrieval algorithms for phycocyanin (PC) and chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from hyperspectral PRISMA images and concurrent in situ data. One near-coincident Sentinel-3 OLCI dataset has also been considered for PC mapping as its high revisit time is a relevant element for mapping cyanobacterial blooms. The testing was first performed on remote sensing reflectance (Rrs), as derived by applying two atmospheric correction methods (6SV, ACOLITE) to Level 1 data and as provided in the corresponding Level 2 products (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by sun glint, the testing of three de-glint models was also performed. The applicability of Semi-Analytical (SA) and Mixture Density Network (MDN) algorithms in enabling PC and chl-a concentration retrieval was then tested over three PRISMA scenes; in the case of PC concentration estimation, a Random Forest (RF) algorithm was further applied. Regarding OLCI, the SA algorithm was tested for PC estimation; notably, only SA was calibrated with site-specific data from the reservoir. The algorithms were applied to the Rrs spectra provided by PRISMA L2C products—and those derived with ACOLITE, in the case of OLCI—as these data showed better agreement with in situ measurements. The SA model provided low median absolute error (MdAE) for PRISMA-derived (MdAE = 3.06 mg.m−3) and OLCI-derived (MdAE = 3.93 mg.m−3) PC concentrations, while it overestimated PRISMA-derived chl-a (MdAE = 42.11 mg.m−3). The RF model for PC applied to PRISMA performed slightly worse than SA (MdAE = 5.21 mg.m−3). The MDN showed a rather different performance, with higher errors for PC (MdAE = 40.94 mg.m−3) and lower error for chl-a (MdAE = 23.21 mg.m−3). The results overall suggest that the model calibrated with site-specific measurements performed better and indicates that SA could be applied to PRISMA and OLCI for remote sensing of PC in Brazilian reservoirs.

Revised chlorophyll-a algorithms for satellite ocean color sensors in the East/Japan Sea

The East/Japan Sea (hereafter, East Sea (ES)) is a semi-enclosed marginal sea and considered as a miniature ocean with oceanic features (e.g., currents, eddies, fronts, upwelling) and dynamic marine environments. The ES is significantly influenced by the global ocean warming with fast increases in sea surface temperature during the last several decades. The climate-induced environmental changes can affect marine ecosystem and food webs in the ES. Therefore, more accurate estimates in phytoplankton biomass in the ES with high spatial and temporal resolutions are necessary to investigate phytoplankton production, fisheries, and carbon budget as well as biological responses to the environmental and climate changes. The NASA ocean chlorophyll-type (OCx) chlorophyll-a (Chl-a) algorithms based on blue to green band ratio for the satellite ocean color data in the global ocean have been evaluated using the in situ radiometric and Chl-a measurements in the ES. The model-derived Chl-a data using the OC Chl-a algorithms present systematic overestimation in the lower Chl-a concentrations between about 0.1 and 1.0 mg/m3 in the ES (with ∼40% uncertainties). New Chl-a algorithms for the ES has been derived from the 3rd polynomial regression fits of in situ Chl-a and maximum band ratios of remote sensing reflectance (Rrs). The revised Chl-a algorithms considerably improve the Chl-a data in the ES from the satellite ocean color data in the lower Chl-a concentrations. Thus, the new Chl-a algorithms can provide more accurate assessments in biological and biogeochemical processes such as primary productivity, phytoplankton phenology, phytoplankton dynamics, and carbon cycles.

Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties

A robust and reliable chlorophyll-a (Chla) concentration algorithm is still lacking for optically complex waters due to the lack of understanding of the bio-optical process. Machine learning approaches, which excel at detecting potential complex nonlinear relationships, provide opportunities to estimate Chla accurately for optically complex waters. However, the uncertainties in atmospheric correction (AC) may be amplified in different Chla algorithms. Here, we aim to select one state-of-the-art algorithm or establish a new algorithm based on machine learning approaches that less sensitive to AC uncertainties. Firstly, nine state-of-the-art empirical, semianalytical, and optical water types (OWT) classification-based Chla algorithms were implemented. These existing algorithms showed good performance by using in situ database, however, failed in actual OLCI applications due to their sensitivity to AC uncertainties. Thus, four popular machine learning approaches (random forest regression (RFR), extreme gradient boosting (XGBoost), deep neural network (DNN), and support vector regression (SVR)) were then employed. Among them, the “RFR-Chla” model performed the best and showed less sensitivity to AC uncertainties. Finally, the Chla spatiotemporal variations in 163 major lakes across eastern China were mapped from OLCI between May 2016 and April 2020 using the proposed RFR-Chla model. Generally, the lakes in eastern China are severely eutrophic, with an average Chla concentration of 33.39 ± 6.95 μg/L. Spatially, Chla in the south of eastern China was significantly higher than those in northern lakes. Seasonally, Chla was high in the summer and autumn and low in the spring and winter. This study provides a reference for water quality monitoring in turbid inland waters suffering certain AC uncertainties and supports aquatic management and SDG 6 reporting.

Evaluating the Efficacy of Five Chlorophyll-a Algorithms in Chesapeake Bay (USA) for Operational Monitoring and Assessment

This manuscript describes methods for evaluating the efficacy of five satellite-based Chlorophyll-a algorithms in Chesapeake Bay, spanning three separate sensors: Ocean Land Color Imager (OLCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and MODerate Resolution Imaging Spectroradiometer (MODIS). The algorithms were compared using in situ Chlorophyll-a measurements from 38 separate stations, provided through the Chesapeake Bay Program (CBP). These stations span nearly the entire 300 km length of the optically complex Chesapeake Bay, the largest estuary in the United States. Overall accuracy was examined for the entire dataset, in addition to assessing the differences related to the distance from the turbidity maximum to the north by grouping the results into the upper bay, middle bay, or lower bay. The mean bias and the Mean Absolute Error (MAE) as well as the median bias and Median Absolute Error (MedAE) were conducted for comparison. A two-band algorithm, that is based on the red-edge portion of the electromagnetic spectrum (RE10), when applied to OLCI imagery, exhibited the lowest overall MedAE of 36% at all stations. As a result, it is recommended that the RE10 algorithm be applied to OLCI and provided as an operational product through NOAA’s CoastWatch program. The paper will conclude with results from a brief climatological analysis using the OLCI RE10 algorithm.

Characterising retrieval uncertainty of chlorophyll-a algorithms in oligotrophic and mesotrophic lakes and reservoirs

Remote sensing product uncertainties for phytoplankton chlorophyll-a (chla) concentration in oligotrophic and mesotrophic lakes and reservoirs were characterised across 13 existing algorithms using an in situ dataset of water constituent concentrations, inherent optical properties (IOPs) and remote-sensing reflectance spectra Rrsλ collected from 53 lakes and reservoirs (346 observations; chla concentration < 10 mg m-3, dataset median 2.5 mg m-3). Substantial shortcomings in retrieval accuracy were evident with median absolute percentage differences (MAPD) > 37% and mean absolute differences (MAD) > 1.82 mg m-3. Using the Hyperspectral Imager for the Coastal Ocean (HICO) band configuration improved the accuracies by 10–20% compared to the Ocean and Land Colour Instrument (OLCI) configuration. Retrieval uncertainties were attributed to optical and biogeochemical properties using machine learning models through SHapley Additive exPlanations (SHAP). The chla retrieval uncertainty of most semi-analytical algorithms was primarily determined by phytoplankton absorption and composition. Machine learning chla algorithms showed relatively high sensitivity to light absorption by coloured dissolved organic matter (CDOM) and non-algal pigment particulates (NAP). In contrast, the uncertainties of red/near-infrared algorithms, which aim for lower uncertainty in the presence of CDOM and NAP, were primarily explained through the total absorption by phytoplankton at 673 nm (aϕ(673)) and variables related to backscatter. Based on these uncertainty characterisations we discuss the suitability of the evaluated algorithm formulations, and we make recommendations for chla estimation improvements in oligo- and mesotrophic lakes and reservoirs.

Merging of the Case 2 Regional Coast Colour and Maximum-Peak Height chlorophyll-a algorithms: validation and demonstration of satellite-derived retrievals across US lakes

Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L−1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15−M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15−M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L−1; if the MPH(P) value was ≤ 10 µg L−1, the C2RCC value was selected, as long as that value was < 15 µg L−1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.

Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms

High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll-a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll-a retrieval, empirical ‘ocean colour’ blue-green band ratios and a near infra-red (NIR) band ratio algorithm, as well as a semi-analytical three-band NIR-red ratio algorithm, were included in the analysis. Six million co-registrations with MSI and OLCI spanning 24 lakes across five continents were analysed. Following atmospheric correction with POLYMER, the reflectance distributions of the red and NIR bands showed close similarity between the two sensors, whereas the distribution for blue and green bands was positively skewed in the MSI results compared to OLCI. Whilst it is not possible from this analysis to determine the accuracy of reflectance retrieved with either MSI or OLCI results, optimizing water quality algorithms for MSI against those previously derived for the Envisat Medium Resolution Imaging Spectrometer (MERIS) and its follow-on OLCI, supports the wider use of MSI for aquatic applications. Chlorophyll-a algorithms were thus tuned for MSI against concurrent OLCI observations, resulting in significant improvements against the original algorithm coefficients. The mean absolute difference (MAD) for the blue-green band ratio algorithm decreased from 1.95 mg m−3 to 1.11 mg m−3, whilst the correlation coefficient increased from 0.61 to 0.80. For the NIR-red band ratio algorithms improvements were modest, with the MAD decreasing from 4.68 to 4.64 mg m−3 for the empirical red band ratio algorithm, and 3.73 to 3.67 for the semi-analytical 3-band algorithm. Three implementations of the turbidity algorithm showed improvement after tuning with the resulting distributions having reduced bias. The MAD reduced from 0.85 to 0.72, 1.22 to 1.10 and 1.93 to 1.55 FNU for the 665, 708 and 778 nm implementations respectively. However, several sources of uncertainty remain: adjacent land showed high divergence between the sensors, suggesting that high product uncertainty near land continues to be an issue for small water bodies, while it cannot be stated at this point whether MSI or OLCI results are differentially affected. The effect of spectrally wider bands of the MSI on algorithm sensitivity to chlorophyll-a and turbidity cannot be fully established without further availability of in situ optical measurements.

A machine learning approach for monitoring Brazilian optical water types using Sentinel-2 MSI

Optical Water Type (OWT) is a useful parameter for assessing water quality changes related to different turbidity levels, trophic state and colored dissolved organic matter (CDOM) while also helpful for tuning chlorophyll-a algorithms. For this reason, interest in the satellite remote sensing of OWTs has recently increased in recent years. This study develops a machine learning method for monitoring Brazilian OWTs using the Sentinel-2 MSI, which can detect OWTs already assessed by field measurements and recognize new OWTs. The already assessed OWTs used for calibrating the machine learning algorithm are clear, moderate turbid, eutrophic turbid, eutrophic clear, hypereutrophic, CDOM richest, turbid, and very turbid waters. The classification method consists of two Support Vector Machines for classifying the known OWTs, while a novelty detection method based on sigmoid functions is used for assessing new OWTs. Results show the classification based on Sentinel-2 MSI bands simulated using field radiometric data is accurate (accuracy = 0.94). However, when radiometric errors are simulated, the accuracy significantly decreases to 0.75, 0.56, 0.45, and 0.37 as the mean absolute percent error increases to 10%, 20%, 30%, and 40%, respectively. Considering the errors retrieved when comparing the field and satellite measurements, the expected accuracy of Sentinel-2 MSI images is 0.78. In the satellite images, the novelty detection distinguishes new OWTs originated from the mixture among the known OWTs and a new OWT that was not part of the training database (clear blue waters). Two examples of time series in the Funil reservoir and the Curuai lake are used to show the applicability of monitoring OWTs. In the Funil reservoir, OWTs could indicate eutrophication and turbid changes caused by river inflow and sediment sinking. In the Curuai lake, OWTs could indicate areas susceptible to algae bloom and turbidity increases related to river inflow and particle resuspension. In the future, the proposed algorithm could be used for large-scale assessment of water quality degradation and supports rapid mitigation and recovery responses. For improving the classification accuracy, adjacency correction and more robust glint removal methods should be developed.

Assessing responses of phytoplankton to consecutive typhoons by combining Argo, remote sensing and numerical simulation data

Typhoons can affect various hydrodynamic processes, such as upwelling, vertical mixing, and entrainment, which in turn influence the growth and redistribution of phytoplankton. In this study, responses of phytoplankton to consecutive typhoons Barijat and Mangkhut in summer 2018 in the northern South China Sea (NSCS) were investigated through combining remote sensing, numerical simulation, and Argo profile data. Variations of physical and biological properties induced by Barijat and Mangkhut over the study region were observed. Regional chlorophyll-a (Chla) algorithms over the NSCS were assessed in order to accurately quantify typhoon-induced variations of phytoplankton. Chla concentration decreased slightly over the region of interest after the passage of Barijiat while increased obviously after the passage of Mangkhut. Satellite-derived Chla increased by ~63% on average after the consecutive typhoons. Responses of phytoplankton size structure (PSS) were further investigated over the continental shelf and the deep ocean in the NSCS. For the continental shelf, the increment of micro- and nano-phytoplankton was about 1.5-fold as high as that of pico-phytoplankton, compared with a 0.5-fold increment for the deep ocean. This study attested the discrepant responses of different phytoplankton size classes to typhoons. It was of great significance to expand our understanding of ocean physical and ecological responses to extreme phenomena. Our finding underscores the potential of remote sensing to investigate typhoon-induced variation of PSS and could be the key to accurately assess typhoon-induced carbon fluxes, primary production, and potential fishing ground in the future.

Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters

Reliable satellite estimates of chlorophyll-a concentration (Chl-a) are needed in coastal waters for applications such as eutrophication monitoring. However, because of the optical complexity of coastal waters, retrieving accurate Chl-a is still challenging. Many algorithms exist and give quite different performance for different optical conditions but there is no clear definition of the limits of applicability of each algorithm and no clear basis for deciding which algorithm to apply to any given image pixel (reflectance spectrum). Poor quality satellite Chl-a data can easily reach end-users. To remedy this and provide a clear decision on when a specific Chl-a algorithm can be used, we propose simple quality control tests, based on MERIS water leaving reflectance (ρw) bands, to determine on a pixel-by-pixel basis if any of three popular and complementary algorithms can be used. The algorithms being tested are: 1. the OC4 blue-green band ratio algorithm which was designed for open ocean waters; 2. the OC5 algorithm which is based on look-up tables and corrects OC4 overestimation in moderately turbid waters and 3. a near infrared-red (NIR-red) band ratio algorithm designed for eutrophic waters.Using a dataset of 348 in situ Chl-a / MERIS matchups, the conditions for reliable performance of each of the selected algorithms are determined. The approach proposed here looks for the best compromise between the minimization of the relative difference between In situ measurements and satellite estimations and the number of pixels processed. Conditions for a reliable application of OC4 and OC5 depend on ρw412/ρw443 and ρw560, used as proxies of coloured dissolved organic matter and suspended particulate matter (SPM), as compared to ρw560/ρw490, used as a proxy for Chl-a. Conditions for reliable application of the NIR-red band ratio algorithm depend on Chl-a and SPM. These conditions are translated into pixel-based quality control (QC) tests with appropriately chosen thresholds. Results show that by removing data which do not pass QC, the performance of the three selected algorithms is significantly improved. After combining these algorithms, 70% of the dataset could be processed with a median absolute percent difference of 30.5%. The QC tests and algorithm merging methodology were then tested on four MERIS images of European waters. The OC5 algorithm was found to be suitable for most pixels, except in very turbid and eutrophic waters along the coasts where the NIR-red band ratio algorithm helps to fill the gap. Finally, a test was performed on an OLCI-S3A image. Although some validations of water reflectance are still needed for the OLCI sensors, results show similar behavior to the MERIS applications which suggests that when applied to OLCI data the present methodology will help to accurately estimate Chl-a in coastal waters for the next decade.

Evaluation of ocean chlorophyll-a remote sensing algorithms using in situ fluorescence data in Southern Brazilian Coastal Waters

Abstract A performance evaluation of ocean color chlorophyll-a algorithms was conducted based on the in situ fluorescence chlorophyll concentration (Fchl) measured by a sensor on the buoy SiMCosta-SC01 in coastal waters of South Brazil. The operational algorithms are used in MODIS and VIIRS sensors to derive satellite chlorophyll concentration (Csat). Fchl values were successfully corrected for nonphotochemical quenching (NPQ) by an interpolation of sunrise and sunset daily measurements. A laboratory-derived calibration coefficient was applied to convert the unquenching Fchl values into chlorophyll concentration (Cflu). Overall, linear regression analysis between Cflu and Csat for both sensors showed good results, with the coefficient of determination (R2) varying between 0.88 and 0.96, slopes between 0.92 and 1.02 and intercepts between -0.17 and 0.13. The MODIS algorithm (R2 = 0.96, slope = 1.02, RMSE = 0.16 mg m-3, BIAS = 0.16 mg m-3, for N = 222 and time interval ±1 h) presented slightly better performance than VIIRS (R2 = 0.92, slope = 0.96, RMSE = 0.25 mg m-3, BIAS = -0.25 mg m-3, for N = 284 and time interval ±1 h). These results represent the most comprehensive satellite data analysis for this region, suggesting that the approach may be applicable to other SiMCosta buoys.

Water Quality Properties Derived from VIIRS Measurements in the Great Lakes

Refined empirical algorithms for chlorophyll-a (Chl-a) concentration, using the maximum ratio of normalized water-leaving radiance nLw(λ) at the blue and green bands, and Secchi depth (SD) from nLw(λ) at 551 nm, nLw(551), are proposed for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in the Great Lakes. We demonstrated that water quality properties and phytoplankton production can be successfully monitored and assessed using the new regional Chl-a and SD algorithms, with reasonably accurate estimates of Chl-a and SD from the VIIRS-SNPP ocean color data in the Great Lakes. VIIRS-derived Chl-a and SD products using the proposed algorithms provide the temporal and spatial variabilities in the Great Lakes. Overall, Chl-a concentrations are generally low in lakes Michigan and Huron, while Chl-a data are highest in Lake Erie. The seasonal pattern shows that overall low Chl-a concentrations appear in winter and high values in June to September in the lakes. The distribution of SD in the Great Lakes is spatially and temporally different from that of Chl-a. The SD data are generally lower in summer and higher in winter in most of the Great Lakes. However, the highest SD in Lake Erie appears in summer, and lower values in winter. Significantly high values in Chl-a, and lower values in SD, in the nearshore regions, such as Thunder Bay, Saginaw Bay, and Whitefish Bay, can be related to the very shallow bathymetry and freshwater inputs from the land. The time series of VIIRS-derived Chl-a and SD data provide strong interannual variability in most of the Great Lakes.

Inter-annual variability in the inherent optical properties along the southeastern Arabian Sea from 2009 to 2015

A study was performed on the coastal waters of the southeastern Arabian Sea to understand the changes in the inherent properties of optically active substances (OAS). The objective of this study was to obtain insight into the temporal dynamics of OASs for various remote sensing applications. Both the absorption of light by phytoplankton (aph(440)) and detritus (ad(440)) varied the 2nd orders of magnitude, whereas the absorption of light by chromophoric dissolved organic matter (CDOM; aCDOM(440)) exhibited an increasing trend over the years. aCDOM(440) increased from 2009 to 2015 at a rate 0.012 m−1 year−1. The validation of the satellite data with in situ data indicated that ad(443) underestimated the in situ ad(443) when the in situ ad(443) was less than 0.3 m−1. The ternary analysis of ocean-colour-climate-change initiative (OC-CCI)–derived inherent optical properties (IOP) during the postmonsoon season indicated increased contribution by ad(443). Detritus was the primary light absorber in the coastal waters of Kochi followed by phytoplankton and CDOM. These long-term data sets and their seasonal variation will enable development of seasonal specific regional algorithms for chlorophyll-a, which are employed in potential fishing zone advisories and other applications.

Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3–1000 mg/m3). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89–22.83 mg/m3, class 2 varies 19.51–87.63 mg/m3, and class 3 varies 75.89–938.97 mg/m3. The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R2 = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R2 = 0.93; MAPE = 23.35%), and 2-band (R2 = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM’s optical trophic classes. The overall performance of OHA was satisfactory (R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters.

Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans

Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean science studies. Here, the performance of two generic chlorophyll-a algorithms (i.e., a band ratio one, Ocean Colour X (OCx), and a semi-analytical one, Garver–Siegel Maritorena (GSM)) was assessed against two large in situ datasets of chlorophyll-a concentration collected between 1999 and 2016 in the Northeast Pacific (NEP) and Northwest Atlantic (NWA) for three ocean colour sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). In addition, new regionally-tuned versions of these two algorithms are presented, which reduced the mean error (mg m−3) of chlorophyll-a concentration modelled by OCx in the NWA from −0.40, −0.58 and −0.45 to 0.037, −0.087 and −0.018 for MODIS, SeaWiFS, and VIIRS respectively, and −0.34 and −0.36 to −0.0055 and −0.17 for SeaWiFS and VIIRS in the NEP. An analysis of the uncertainties in chlorophyll-a concentration retrieval showed a strong seasonal pattern in the NWA, which could be attributed to changes in phytoplankton community composition, but no long-term trends were found for all sensors and regions. It was also found that removing the 443 nm waveband for the OCx algorithms significantly improved the results in the NWA. Overall, GSM performed better than the OCx algorithms in both regions for all three sensors but generated fewer chlorophyll-a retrievals than the OCx algorithms.

Monitoring Tangier (Morocco) coastal waters for As, Fe and P concentrations using ESA Sentinels-2 and 3 data: An exploratory study

An exploratory study is presented to investigate human activities pollution in the coastal waters of a popular tourist destination in the west coast of Tangier, Morocco. A series of bathing water samples were examined for arsenic (As), iron (Fe) and phosphorus (P) concentrations in the western coastal waters of Tangier. Additionally, quasi-simultaneous satellite images from Sentinels-2 and 3 were processed to retrieve water leaving reflectances in the same locations. The bathing water samples showed high concentrations of As, P and Fe in the Boukhalef River mouth and in two nearby beaches; Jbila and Sidikacem, which appeared closely correlated with Sentinel-3 reflectances at bands 6 (560 nm) and 7 (620 nm). Since algorithms for Chlorophyll-a and Total Suspended Matter Concentrations rely on these wavelengths, they too were found to be closely related to high As, Fe and P concentrations. Therefore, caution must be taken when analysing these level-2 products in close proximity to the Boukhalef River mouth, since results may be biased due to the presence of significant As, Fe and P concentrations. In this case, Sentinel data proved to be a potentially useful tool with prospects to be used for monitoring environmental hazardous substances such as metals (e.g. As and Fe) spatial distributions in the west coast of Tangier, Morocco.

Assessment of Satellite-Based Chlorophyll-a Algorithms in Eutrophic Korean Coastal Waters: Jinhae Bay Case Study

Jinhae Bay, one of the most important aquaculture areas in Korean coastal waters, has suffered from serious environmental problems due to intensive anthropogenic activities since the 1970s. Determining the response of coastal ecosystems in Korea to anthropogenic activities requires understanding the characteristics of chlorophyll-a concentration (Chl-a), and the high spatiotemporal resolution of the Geostationary Ocean Color Imager (GOCI) can aid these efforts. However, producing reliable satellite-based Chl-a estimates is challenging in optically complex coastal waters and the Chl-a estimation algorithms must be assessed regionally. Based on in situ Chl-a measurements collected in Jinhae Bay between 2011 and 2016, we evaluated GOCI-derived Chl-a estimates obtained using six ocean color Chl-a algorithms: two standard open ocean algorithms, one GOCI-standard algorithm, and three Tassan’s algorithms regionally modified for Korean waters. All of the algorithms tended to underestimate high Chl-a values >0.9 mg m–3. The Yellow Sea Large Marine Ecosystem Ocean Color Project (YOC) algorithm, one of the modified Tassan’s algorithms, provided the best fit to the in situ Chl-a measurements in Jinhae Bay (r=0.51 , p<0.05 ), including appropriate representations of the spatial and temporal variation. Therefore, this algorithm can be considered a baseline approach for satellite-based long-term coastal monitoring systems in Jinhae Bay.

Evaluation of standard and regional satellite chlorophyll-a algorithms for moderate-resolution imaging spectroradiometer (MODIS) in the Bohai and Yellow Seas, China: a comparison of chlorophyll-a magnitude and seasonality

ABSTRACTRetrieval of satellite remotely sensed chlorophyll-a (chl-a) concentrations in coastal regions such as the Bohai and Yellow Seas (BYS) is challenging due to their complex oceanic and atmospheric optical properties. The standard OC3M (ocean chl-a three-band algorithm for MODIS (moderate-resolution imaging spectroradiometer)) algorithm has been widely used in the BYS, despite well-known uncertainties about its accuracy in terms of absolute magnitude. This was based on the belief that OC3M chl-a is capable of representing reliable relative spatial and temporal patterns of sea surface chl-a concentrations. In this study, the ability of the standard OC3M chl-a algorithm to reproduce accurate seasonality patterns was evaluated, based on comparisons with in situ chl-a measurements in the BYS. The results quantified the overestimation by the standard OC3M algorithm with a median absolute percentage difference of 98.48% and a median relative difference of 1.13 mg m−3.More importantly, the seasonality from OC3M chl-a was significantly biased relative to the seasonal patterns of in situ chl-a. In addition, a regional GAM (generalized additive model)-based satellite chl-a algorithm was evaluated and compared with OC3M chl-a. The results showed the GAM chl-a improved accuracy in both magnitude and seasonality when compared with in situ chl-a, relative to that from OC3M chl-a.