Estimation of Chlorophyll-a concentration (Chl-a) across diverse aquatic systems using Moderate Resolution Imaging Spectroradiometer-Aqua (MODIS-A) data has posed challenges, particularly the inability of existing algorithms to maintain consistent accuracy across varying optical water conditions, from oligotrophic clear waters to highly turbid productive systems. Traditional Blue/Green ratio approaches often show limitations over optically complex waters where colored dissolved organic matter and suspended sediments interfere with phytoplankton signal detection. In contrast, Red/NIR (Near-Infrared) models perform relatively well in productive coastal domains but are less effective in open ocean waters where phytoplankton absorption is too weak to produce detectable signals in these longer wavelengths. To address these challenges, we developed a Combination Of Neural Network models for Estimating Chlorophyll-a over Turbid and clear waters (CONNECT model) based on the principle that different Optical Water Types (OWTs) require specialized bio-optical algorithms. The methodology involves the development of two Multi-Layer Perceptron (MLP) models (NN-Clear & NN-Turbid) that are trained and evaluated on a comprehensive in-situ dataset with simultaneous measurements of Remote Sensing Reflectance (Rrs) and Chl-a gathered in various environments from clear to ultra-turbid waters (N = 5,358) with Chl-a ranging between 0.017 and 838.24 µg.L-1. These specialized models are then combined through a weighted blending approach to produce unified Chl-a estimates that adapts to the optical conditions of various water types. In particular, the algorithm merging process involves the use of probability values corresponding to 2 groups of Optical Water Types as the blending coefficients. Accuracy evaluations performed on both in-situ and matchup datasets indicate a remarkable advancement of the CONNECT model compared to the traditional Blue/Green approaches over different trophic conditions with an improvement of 49.65% on the matchup validation considering the Symmetric Signed Percentage Bias (SSPB) metric.

The concentration of particulate organic nitrogen (PON) in seawater plays a central role in ocean biogeochemistry. Limited availability of PON data obtained directly from in situ sampling methods hinders our ability to better characterize the spatio-temporal variability of PON within the global ocean. Tight relationships have recently been developed between in situ measurements of seawater inherent optical properties (IOPs) and PON. Knowing that IOPs can now be estimated from ocean color remote-sensing, these relationships could then be used to assess PON from semi-analytical algorithms applied to satellite ocean color observations. The present study aims at evaluating which IOPs, as estimated from space, can be used as the best proxy for the remote sensing retrieval of PON. The different considered IOPs are the absorption coefficients of total particulate matter, ap(λ), phytoplankton, aph(λ), and non-algal particles, ad(λ), as well as the particulate backscattering coefficient, bbp(l). IOPs have been derived from satellite ocean remote-sensing reflectance, Rrs(l), using different available inverse methods. The validation of the algorithms is based on matchup between an extensive dataset of 156 concurrent measurements of in situ PON and satellite-derived particulate IOPs. Our results show that reasonably strong PON vs satellite-derived IOPs relationships hold across a range of diverse oceanic and coastal environments. aph(443) shows the best ability to serve as a PON proxy over a broad range of PON from open ocean oligotrophic to coastal waters (MdAPD of 25.39 %). bbp(555) can also be considered as a good proxy of PON in open ocean environments (MdAPD of 22.03 %). Comparison with in situ time series over ten years shows also the good performance of the algorithm to reproduce the seasonal variation of PON. The application of this algorithm to Moderate-Resolution Imaging Spectroradiometer (MODIS; 2002-present) observations provides global PON distributions pattern which agree with in situ PON expected geographical distribution. High PON concentrations are observed in turbid shelf and coastal regions as well as in upwelling areas; while low PON are observed in oligotrophic regions. The presented relationships demonstrate a promising means to assess long-term trends and/or budget of PON in specific areas of the ocean or at the global oceanic scale.

The present study evaluates the influence of industrialization on suspended particulate matter (SPM) dynamics along the northern coast of Rio de Janeiro, focusing specifically on the Açu Port Industrial Complex (APIC). A 20-year MODIS-Aqua (1 km) dataset (2002–2022) was processed using the OC-SMART atmospheric correction. For SPM estimation, a retrieval approach for coastal turbid waters that integrates two optimized bio-optical algorithms based on Optical Water Types (OWTs) was developed. The validity of this approach was substantiated through the utilization of the GLORIA in situ dataset and satellite matchups, which demonstrated its robust performance across a range of turbidity conditions. Its main innovation lies in the OWT-based fusion of two optimized SPM models, enabling robust retrievals across diverse coastal optical conditions. Statistical analyses based on Census X11 decomposition and the Seasonal Mann–Kendall test revealed strong spatial and temporal variability, with SPM concentrations increasing by up to 60% near the APIC during the study period, coinciding with dredging, port expansion, and sediment disposal. These findings indicate a pronounced anthropogenic signal, while spatial and temporal correlation analyses demonstrated that sediment dispersion is consistently directed northward, primarily controlled by currents and wind forcing. The results indicate that industrial activities augment the supply of sediments, while natural hydrodynamic processes govern their dispersion and transport, emphasizing the impact of human pressures and physical drivers on coastal sediments.

Carbon monitoring from space is critical for the reporting and verification of carbon stocks and changes in both coastal and open ocean waters. In the frame of the OCROC project, funded by the Copernicus 2 – 1st Service Evolution Call for Tenders (2022-2024), we focus on the particulate (POC) and dissolved (DOC) organic carbon of surface oceanic and coastal waters, which represent the two components of the total organic carbon (TOC) pool in the ocean. The present presentation is mainly dedicated to the estimation of DOC, the main contributor to TOC, over open ocean waters. An enhanced version of the Ocean and Land Color Instrument's (OLCI) DOC algorithm of Bonelli et al. (2022) is presented and adapted to historical and present ocean color sensors. This algorithm employs two different Artificial Neural Network (ANN) algorithms depending on the Optical Water Classes, and four input parameters namely the absorption coefficient of Colored Dissolved Organic Matter (acdom(443)) chlorophyll-a concentration (Chl-α), Sea Surface Temperature (SST), and Mixed Layer Depth (MLD). In this new version of the algorithm SST and MLD are both delivered by COPERNICUS (Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year reprocessing). Each of the four input parameters is provided at a distinct time lag to enhance the accuracy of the model. Furthermore, a revisited “match-up” database, compared to the one used in Bonelli et al. (2022), is utilized to validate the algorithm across multiple ocean color missions.

The site AERONET-OC Bahía Blanca (BB-AERONET-OC) is located at the mouth of the Bahía Blanca Estuary, Argentina (Southwestern Atlantic Ocean), a coastal system defined by its high suspended loads and relatively low colored dissolved organic matter. The typically high turbidity of these waters makes the BB-AERONET-OC distinctive within the AERONET-OC network stations, providing exceptional opportunities not only for the validation of atmospheric correction algorithms but also for the development of regional algorithms for coastal complex waters. A SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM) instrument was deployed in January 2020 in the upper rail of a Mareograph Tower, a 15 m tall structure, located 10 miles away from the coast in optically deep waters. In this work we used the remote sensing reflectance (Rrs) derived from the BB-AERONET-OC measurements along with in situ hyperspectral radiometric data to classify optical water types (OWTs). We assigned each Rrs(λ) spectra to one of the five OWTs defined by Tran et al., and OWTs were further characterized with the concentrations of optically significant components (chlorophyll-a and suspended particulate matter) and inherent optical properties (absorptions of phytoplankton, non-algal particles, and dissolved organic matter), retrieved from water samples obtained simultaneously with radiometric spectra. Based on a match-up exercise with in situ data, different schemes of atmospheric correction methods were applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images. The operational product OLCI Level 2 European Space Agency (ESA) standard (hereafter referred to as “Standard Neural Network (NN)”) proves to be the most suitable atmospheric correction algorithm, which was then used to describe spatial patterns and temporal variability of the different OWTs in the region. The BB-AERONET-OC site is located in a sharp transition between estuarine and coastal waters that present contrasting optical conditions: OWT 4 dominates over time (73.72% of the observations), followed by OWT 3 (24.74%) and OWT 5 (1.53%). OWTs 4 and 5 are associated with turbid waters of the Bahía Blanca Estuary, especially OWT 5, which typifies the very turbid waters from the inner estuary, with the particulate load dominated by mineral sediments and detritus. OWT 3, in turn, depicts the eutrophic coastal waters of the inner shelf. The variability of OWTs and the relative contribution of organic and inorganic compounds to the suspended material would be mostly related with the prevalence of northwest winds in the area, which would drive the export of estuarine sediments to the shelf.

Abstract. Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development is the generation of a synthetic dataset of seawater IOPs that serves as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by the probability distributions of IOPs that are consistent with global distributions representative of vast areas of open-ocean pelagic environments and coastal regions, covering a broad range of optical water types. The generation of synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs, assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column (not considered in previous synthetically generated optical databases) and three simulation scenarios associated with the sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0, 30, and 60∘ assuming clear skies, standard atmosphere, and a wind speed of 5 m s−1. Thus, overall 29 880 RT simulations were performed. The output results of these simulations include radiance distributions, plane and scalar irradiances, and a whole set of AOPs, including remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines, where all optical variables are reported in the spectral range of 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving IOPs and AOPs. The database is available at the Dryad open-access repository of research data (https://doi.org/10.6076/D1630T, Loisel et al., 2023).

Chlorophyll-a concentration (Chl-a) is a crucial parameter for monitoring the water quality in coastal waters. The principal aim of this study is to evaluate the performance of existing Chl-a band ratio inversion models for estimating Chl-a from Sentinel2-MSI and Sentinel3-OLCI observation. This was performed using an extensive in situ Rrs-Chl-a dataset covering contrasted coastal waters (N = 1244, Chl-a (0.03–555.99) µg/L), which has been clustered into five optical water types (OWTs). Our results show that the blue/green inversion models are suitable to derive Chl-a over clear to medium turbid waters (OWTs 1, 2, and 3) while red/NIR models are adapted to retrieve Chl-a in turbid/high-Chl-a environments. As they exhibited the optimal performance considering these two groups of OWTs, MuBR (multiple band ratio) and NDCI (Normalized Difference Chlorophyll-a Index)-based models were merged using the probability values of the defined OWTs as the blending coefficients. Such a combination provides a reliable Chl-a prediction over the vast majority of the global coastal turbid waters (94%), as evidenced by a good performance on the validation dataset (e.g., MAPD = 21.64%). However, our study further illustrated that none of the evaluated algorithms yield satisfying Chl-a estimates in ultra-turbid waters, which are mainly associated with turbid river plumes (OWT 5). This finding highlights the limitation of multispectral ocean color observation in such optically extreme environments and also implies the interest to better explore hyperspectral Rrs information to predict Chl-a.

The mechanistic approach consisting of coupling Dynamic Energy Budget (DEB) models to Individual-Based Models (IBMs) allows simulating individual and population biological traits and their dynamics. This approach was developed here to study population dynamics of two sympatric intertidal ecosystem engineers, Arenicola marina and Arenicola defodiens (Annelida Polychaeta) occurring in the North-East Atlantic from Portugal to Sweden. Latitudinal heterogeneity of the two species’ performances were investigated in terms of population dynamics and biological traits using latitudinal differences in environmental forcing variables. The impact of the forcing variables on population dynamics processes (shore colonisation and migration, spawning and recruitment, etc.) within a specific foreshore (mean values and seasonal patterns) was also assessed. Published DEB parameters were used for A. marina and a specific calibration was undertaken for A. defodiens, combining literature data and new laboratory experiments and field data. Our DEB-IBM simulated super-individuals’ growth and reproduction while lugworms were colonising, migrating and dying over a simulated foreshore. Density rules affected population dynamics. Environmental forcings consisted in monthly values of chlorophyll-a (chl-a) concentrations and daily values of SST. Scenarios focusing on the two most contrasted of these forcing variables time series were used to explore their relative effects over populations’ dynamics and on-shore processes were investigated at two sites displaying highly different simulated population abundances. Overall, northern sites with higher chl-a levels performed better displaying higher biomass, maximum length and reproductive outputs for both species. As expected, Sea Surface Temperature (SST) changes between sites did not impact greatly populations dynamics. Under favourable environmental conditions, intra- and inter-specific competitions emerged from the model. Under non-favourable environmental conditions, A. defodiens’ populations crashed and A. marina displayed atypical population processes, with rare spawning events barely allowing the population’s renewal, and lower size at maturity. Further use and development of this model will lead to better insights on the lugworm populations’ evolution over the next decades.

The particle size distribution (PSD) of suspended particles in near-surface seawater is a key property linking biogeochemical and ecosystem characteristics with optical properties that affect ocean color remote sensing. Phytoplankton size affects their physiological characteristics and ecosystem and biogeochemical roles, e.g., in the biological carbon pump, which has an important role in the global carbon cycle and thus climate. It is thus important to develop capabilities for measurement and predictive understanding of the structure and function of oceanic ecosystems, including the PSD, phytoplankton size classes (PSCs), and phytoplankton functional types (PFTs). Here, we present an ocean color satellite algorithm for the retrieval of the parameters of an assumed power-law PSD. The forward optical model considers two distinct particle populations: phytoplankton and non-algal particles (NAPs). Phytoplankton are modeled as coated spheres following the Equivalent Algal Populations (EAP) framework, and NAPs are modeled as homogeneous spheres. The forward model uses Mie and Aden-Kerker scattering computations, for homogeneous and coated spheres, respectively, to model the total particulate spectral backscattering coefficient as the sum of phytoplankton and NAP backscattering. The PSD retrieval is achieved via spectral angle mapping (SAM), which uses backscattering end-members created by the forward model. The PSD is used to retrieve size-partitioned absolute and fractional phytoplankton carbon concentrations (i.e., carbon-based PSCs), as well as particulate organic carbon (POC), using allometric coefficients. This model formulation also allows the estimation of chlorophyll a concentration via the retrieved PSD, as well as percent of backscattering due to NAPs vs. phytoplankton. The PSD algorithm is operationally applied to the merged Ocean Colour Climate Change Initiative (OC-CCI) v5.0 ocean color data set. Results of an initial validation effort are also presented using PSD, POC, and picophytoplankton carbon in situ measurements. Validation results indicate the need for an empirical tuning for the absolute phytoplankton carbon concentrations; however these results and comparison with other phytoplankton carbon algorithms are ambiguous as to the need for the tuning. The latter finding illustrates the continued need for high-quality, consistent, large global data sets of PSD, phytoplankton carbon, and related variables to facilitate future algorithm improvements.

The Dissolved Organic Carbon (DOC) represents the largest organic carbon reservoir in the ocean. Therefore, describing its spatio-temporal distribution is crucial for better understanding the global carbon cycle. Recent studies have demonstrated the possibility to determine DOC in coastal waters from ocean color radiometry (OCR) based on its strong correlation with the absorption coefficient of Colored Dissolved Organic Matter (acdom(lambda)). However, in the open ocean, the CDOM to DOC relationship is highly variable as they present different sources, sinks, and kinetics. Here we present a new approach to estimating DOC over the open ocean based on an Artificial Neural Network (ANN) algorithm. This model accounts for i) Optical Water Classes (OWC) ii) sea surface tem-perature (SST), mixed layer depth (MLD), acdom(443), and chlorophyll-a (Chl-a) concentration, and iii) different time lags depending on the input parameter. The satellite DOC estimated with this model is in good agreement with in situ measurements (MAPD = 7.04%), while the spatial patterns follow former observations and model outputs. A sensitivity analysis has shown that the main descriptors to assess satellite DOC at a given time for oligotrophic and mesotrophic open ocean waters are SST one week before, and acdom(443) two weeks before; with Chl-a one week before as an additional input parameter for more productive waters. This study allows for the first time the assessment of the contribution of the particulate organic carbon (POC) to the total organic carbon (TOC) over the global ocean. The POC/TOC ratio value varies between 1.31% and 9.07%, with a mean value of about 4.57 +/- 1.87%.

S1 Assessment and propagation of uncertaintiesThe Monte Carlo simulations (N=3000) of the two-population Mie scattering model were used to estimate uncertainty of the retrieved PSD parameters that is due to assumptions of the values of the model input parameters.The input distributions from which the varying input parameters were sampled for the Monte Carlo simulations are shown in main text Tables 1 and2.This approach resulted in 3000 different realizations of modeled spectral b bp , for each ξ input value.The median of all realizations is used as the SAM end-members, and Kruskal-Wallis analysis of variance is used to determine class similarity (where each input ξ value represents a class).This analysis determined, for each class, the neighboring classes that are statistically not different from it (at the 5% significance level) in terms of spectral angle.This gave a range of possible retrieved ξ values.This range is not always symmetric, but it is approximated as a symmetric 95% confidence interval (CI) and converted to standard deviation for use in subsequent analytical error propagation.The standard deviation of the corresponding N 0 parameter across all similar classes and all Monte Carlo realizations is used as the uncertainty estimate for N 0 .

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.

Abstract. Knowledge of the organic and inorganic particulate fractions of suspended material in coastal waters is essential for the study of particle dynamics and biogeochemical cycles in these complex and highly variable environments. Thanks to the availability of appropriate spatial sensors, and to the considerable improvements of algorithms dedicated to the satellite observation of coastal waters from ocean color radiometry (OCR) achieved these two last decades, various optical and biogeochemical parameters can now routinely be monitored over coastal waters. Here we show that a Proxy of Particulate Composition (PPC) can be estimated from OCR observations. The present algorithm, based on Neural Network approach, has been validated using a broad range of biogeochemical data collected in various contrasted coastal waters, and applied to MERIS observations over the global coastal ocean at a 1 km×1 km spatial resolution from 2002 to 2012. The relevance of the temporal occurrence of PPC at a given water pixel has been illustrated over the global coastal ocean, and its pertinence has been deeply discussed over the English Channel and southern North Sea which are characterized by a well-documented variability of suspended particulate matter composition. The present algorithm can directly be applied to all OCR sensors.

We present a three-step inverse model (3SAA) for estimating the inherent optical properties (IOPs) of surface waters from the remote sensing reflectance spectra, Rrs(λ). The derived IOPs include the total (a(λ)), phytoplankton (aphy(λ)), and colored detrital matter (acdm(λ)), absorption coefficients, and the total (bb(λ)) and particulate (bbp(λ)) backscattering coefficients. The first step uses an improved neural network approach to estimate the diffuse attenuation coefficient of downwelling irradiance from Rrs. a(λ) and bbp(λ) are then estimated using the LS2 model (Loisel et al., 2018), which does not require spectral assumptions on IOPs and hence can assess a(λ) and bb(λ) at any wavelength at which Rrs(λ) is measured. Then, an inverse optimization algorithm is combined with an optical water class (OWC) approach to assess aphy(λ) and acdm(λ) from anw(λ).The proposed model is evaluated using an in situ dataset collected in open oceanic, coastal, and inland waters. Comparisons with other standard semi-analytical algorithms (QAA and GSM), as well as match-up exercises, have also been performed. The applicability of the algorithm on OLCI observations was assessed through the analysis of global IOPs spatial patterns derived from 3SAA and GSM. The good performance of 3SAA is manifested by median absolute percentage differences (MAPD) of 13%, 23%, 34% and 34% for bbp(443), anw(443), aphy(443) and acdm(443), respectively for oceanic waters. Due to the absence of spectral constraints on IOPs in the inversion of total IOPs, and the adoption of an OWC-based approach, the performance of 3SAA is only slightly degraded in bio-optical complex inland waters.

A semi-analytical model (CDOM-KD2) based on the light vertical attenuation coefficient (K-d(lambda)) has been developed for estimating the absorption by colored dissolved organic matter, a(cdom)(443), from ocean color remote sensing at global scale. The performance of this new inversion model together with that of former models by Shanmugam (2011) (S2011), Chen et al. (2017) (C2017) and Aurin et al. (2018) (A2018) was evaluated from in situ and matchup validation data sets gathering worldwide distributed samples. An overall consistency in the a(cdom)(443) estimated from S2011, C2017 and CDOM-KD2 models with a slightly better performance of the latter method was observed (MAPD of 27.42% and 30.85% for open ocean with in situ and satellite data, respectively), emphasizing the possible specific assessment of a(cdom)(443) dynamics from satellite remote sensing over the global ocean including the most oligotrophic waters. At 443 nm the global average relative contribution of a(cdom)(443) to the absorption by colored detrital matter, a(cdm)(443) is of 61% +/- 14%, while the contribution of a(cdom)(443) to the non-water absorption, anw(443), is of 35% +/- 26%. Strong spatial disparities are however observed for both a(cdom)(443) temporal dynamics and relative contribution in the absorption budget. A decoupling is observed between acdom(443) and particulate detrital (i.e. non-living) matter and phytoplankton in the gyre areas where a low temporal variability is globally observed. This is contrasting with water masses influenced by terrestrial inputs as well as in equatorial and subtropical areas impacted by main oceanic currents where CDOM loads and a(cdom)(443) contribution in the water absorption budget are more variable.

Given the importance of dissolved organic matter (DOM) in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. In this study we assess the use of optical absorption indices available in the literature based on in situ data to both characterize the seasonal variability of DOM in a highly complex environment and for application in large-scale studies using remote sensing data. The study area comprises four lakes located in the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of colored dissolved organic matter (CDOM) and measurements of remote sensing reflectance (Rrs) were acquired in situ. The Rrs was used to simulate the response of the visible bands of the Sentinel-2 MultiSpectral Instrument (MSI), which was used in the proposed models. Differences between lakes were tested using the CDOM indices. The results highlight the role of the flood pulse in the DOM dynamics at the floodplain lakes. The validation results show that the use of the absorption coefficient of CDOM (aCDOM) as a proxy of the spectral slope between 275 and 295 nm (S275–295) during rising water is worthwhile, demonstrating its potential application to Sentinel-2 MSI imagery data for studying DOM dynamics on the large scale.

The modeling of underwater light field is essential for the understanding of biogeochemical processes, such as photosynthesis, carbon fluxes, and sediment transports in inland waters. Water-column light attenuation can be quantified by the diffuse attenuation coefficient of the downwelling irradiance (K-d) using semi-analytical algorithms (SAA). However, the accuracy of these algorithms is currently limited in highly turbid environments, such as Amazon Floodplains, due to the SAA parametrization steps. In this study, we assessed an SAA approach for K-d retrieval using a sizeable (n = 239) and diverse dataset (e.g., K-d (490) ranging from almost 0 to up to 30 m(-1) with mean values of 5.75 +/- 3.94 m(-1)) in Amazon freshwater ecosystem. The main framework of this study consists of i) re-parametrization of a quasi-analytical algorithm using regional in-situ inherent optical properties (IOPs) and ii) application and validation of SAA for K-d retrieval using in-situ and Sentinel-2/MSI (n = 49) derived from Remote Sensing Reflectance (R-IS). Overall, the performance of the calibrated SAA was satisfactory for both in-situ and satellite R-rs. The validation results with in-situ data achieved a Mean Absolute Percentage Error (MAPE) lower than 22%, Correlation Coefficient (R) > 0.80, Root Mean Square Error (RMSE) lower than 1.7 m(-1) and bias between 0.73 and 1.34 for simulated visible bands of Sentinel-2/MSI (490, 560 and 660 nm) (VIS). The results using MSI imagery were similar to those of in-situ, with R > 0.9, MAPE < 20%, RMSE < 1.25 m(-1), and bias between 0.98 and 1.10 for VIS bands, which illustrate the viability of this methodology for K-d mapping in Amazon Floodplain Lakes. Therefore, this study demonstrates a successful application of satellite remote sensing data for the spatialization of the K-d in the optically complex waters of Amazon Basin, which is essential for the ecological management of the Amazon Floodplain Lakes.

Recently, different algorithms have been developed to assess near-surface particulate organic matter (POC) concentration over coastal waters. In this study, we gathered an extensive in situ dataset representing various contrasted bio-optical coastal environments at low, medium, and high latitudes, with various bulk particulate matter chemical compositions (mineral-dominated, 50% of the data set, mixed, 40%, or organic-dominated, 10%). The dataset includes 606 coincident measurements of POC concentration and remote-sensing reflectance, Rrs, with POC concentrations covering three orders of magnitude. Twelve existing algorithms have then been tested on this data set, and a new one was proposed. The results show that the performance of historical algorithms depends on the type of water, with an overall low performance observed for mineral-dominated waters. Furthermore, none of the tested algorithms provided satisfactory results over the whole POC range. A novel approach was thus developed based on a maximum band ratio of Rrs (red/blue, red/yellow or red/green ratio). Based on the standard statistical metric for the evaluation of inverse models, the new algorithm presents the best performance. The root-mean square deviation for log-transformed data (RMSDlog) is 0.25. The mean absolute percentage difference (MAPD) is 37.48%. The mean bias (MB) and median ratio (MR) values are 0.54 μg L−1 and 1.02, respectively. This algorithm replicates quite well the distribution of in situ data. The new algorithm was also tested on a matchup dataset gathering 154 coincident MERIS (MEdium Resolution Imaging Spectrometer) Rrs and in situ POC concentration sampled along the French coast. The matchup analysis showed that the performance of the new algorithm is satisfactory (RMSDlog = 0.24, MAPD = 34.16%, MR = 0.92). A regional illustration of the model performance for the Louisiana continental shelf shows that monthly mean POC concentrations derived from MERIS with the new algorithm are consistent with those derived from the 2016 algorithm of Le et al. which was specifically developed for this region.

Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance ( R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency’s Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of ~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%–96%) and blue (84%–92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes ( R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.