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Radiative transfer (RT) simulations have long been used to study relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean from which the ocean apparent optical properties (AOPs) can be calculated. For example, inverse models to estimate IOPs from ocean color radiometric measurements have been developed or validated using results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range from the near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development was the generation of the synthetic dataset of seawater IOPs which served as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by 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 the 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 and three simulation scenarios associated with 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 wind speed of 5 m s−1. Thus, overall 29880 RT simulations were performed. The output results of these simulations include the radiance distributions, plane and scalar irradiances, and the whole set of AOPs including the remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines where all optical variables are reported in the spectral range from 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 the IOPs and AOPs.

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.

International audience; 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.

The Dissolved Organic Carbon (DOC) represents the largest pool of organic carbon and the most active carbon compartment in the ocean. Describing the spatio-temporal dynamics of the oceanic DOC in response to variation in the physical of biological forcings is therefore crucial for better understanding the global carbon cycle. The DOC distribution and its temporal dynamics is however currently not well known.In the recent years several works have demonstrated the possibility to assess from space the DOC distribution in the coastal ocean thanks to direct relationships between DOC and the optical properties of colored dissolved organic matter (CDOM). Such CDOM-DOC relationships are not applicable for the open ocean water due making more complex the DOC estimation from space in the latter environments. Here we present first results documenting an alternative method for estimating DOC from satellite imagery which rely on the use of a neural network which combines different physical and biogeochemical input variables (SST, SSS, PAR, aCDOM and Chl-a).

International audience; 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 (Kd) 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 Kd retrieval using a sizeable (n = 239) and diverse dataset (e.g., Kd (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 Kd retrieval using in-situ and Sentinel-2/MSI (n = 49) derived from Remote Sensing Reflectance (Rrs). Overall, the performance of the calibrated SAA was satisfactory for both in-situ and satellite Rrs. 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 Kd mapping in Amazon Floodplain Lakes. Therefore, this study demonstrates a successful application of satellite remote sensing data for the spatialization of the Kd in the optically complex waters of Amazon Basin, which is essential for the ecological management of the Amazon Floodplain Lakes.

International audience; 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. View Full-Text

Given the importance of DOM in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. This study assesses the use of available absorption optical indices based on in situ data to both characterize the seasonal variability of the DOM dynamics in a highly complex environment and their viability of being used for satellite remote sensing on large scale studies. The study area comprises four lakes located at the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of coloured dissolved organic matter (CDOM) and remote sensing reflectance (Rrs) were acquired in situ. The Rrs was applied to simulate MSI visible bands and used in the proposed models. Differences between lakes were tested regarding CDOM indices. Significant difference in the average of aCDOM (440), aCDOM spectra and S275–295 were found between lakes located inside the flood forest and those near the river bank. The proposed model showed that aCDOM can be used as proxy of S275–295 during rising water with good validation results, demonstrating the potential of Sentinel/MSI imagery data in large scale studies on the dynamics of DOM.

Uncertainties in the estimates of water constituents are among the main issues concerning the orbital remote sensing of inland waters. Those uncertainties result from sensor design, atmosphere correction, model equations, and in situ conditions (cloud cover, lake size/shape, and adjacency effects). In the Amazon floodplain lakes, such uncertainties are amplified due to their seasonal dynamic. Therefore, it is imperative to understand the suitability of a sensor to cope with them and assess their impact on the algorithms for the retrieval of constituents. The objective of this paper is to assess the impact of the SNR on the Chl-a and TSS algorithms in four lakes located at Mamirauá Sustainable Development Reserve (Amazonia, Brazil). Two data sets were simulated (noisy and noiseless spectra) based on in situ measurements and on sensor design (MSI/Sentinel-2, OLCI/Sentinel-3, and OLI/Landsat 8). The dataset was tested using three and four algorithms for TSS and Chl-a, respectively. The results showed that the impact of the SNR on each algorithm displayed similar patterns for both constituents. For additive and single band algorithms, the error amplitude is constant for the entire concentration range. However, for multiplicative algorithms, the error changes according to the model equation and the Rrs magnitude. Lastly, for the exponential algorithm, the retrieval amplitude is higher for a low concentration. The OLCI sensor has the best retrieval performance (error of up to 2 μg/L for Chl-a and 3 mg/L for TSS). For MSI, the error of the additive and single band algorithms for TSS and Chl-a are low (up to 5 mg/L and 1 μg/L, respectively); but for the multiplicative algorithm, the errors were above 10 μg/L. The OLI simulation resulted in errors below 3 mg/L for TSS. However, the number and position of OLI bands restrict Chl-a retrieval. Sensor and algorithm selection need a comprehensive analysis of key factors such as sensor design, in situ conditions, water brightness (Rrs), and model equations before being applied for inland water studies.

Chlorophyll-a (chl-a) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the Funil reservoir (Rio de Janeiro, Brazil). A Monte Carlo simulation was applied for all the algorithms to achieve the best calibration. The Normalized Difference Chlorophyll Index (NDCI) got the lowest error (17.85%). The in situ hyperspectral dataset was used to simulate the Ocean Land Color Instrument (OLCI) spectral bands by applying its spectral response function. Therefore, we evaluated its applicability to monitor water quality in tropical turbid inland waters using algorithms developed for MEdium Resolution Imaging Spectrometer (MERIS) data. The application of OLCI simulated spectral bands to the algorithms generated results similar to the in situ hyperspectral: an error of 17.64% was found for NDCI. Thus, OLCI data will be suitable for inland water quality monitoring using MERIS reflectance based bio-optical algorithms.

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.

A Lagoa dos Patos (LP) é um dos ambientes oticamente complexos mais bem estudados no Brasil, e sua grande abrangência espacial, permite a união de diferentes medidas in situ com produtos de sensoriamento remoto, sendo possível entender melhor como os componentes óticos da água influenciam na sua cor. Florações de fitoplâncton possuem grande relevância ecológica e econômica, sendo o desenvolvimento de metologias simples para o seu monitoramento de vital importância. O presente trabalho utilizou produtos de coloração do oceano de imagens diárias dos sensores MODIS/Aqua e SeaWiFS durante os anos de 2002-2005, dados de modelos meteorológicos de reanálise para precipitação e velocidade do vento e dados de clorofila-a e salinidade obtidos in situ. Foi identificado que o espectro de reflectância de sensoriamento remoto é controlado pelo regime de El Niño e La Niña, variação intra anual e espacial (p