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Optically complex waters present significant challenges for remote sensing due to high concentrations of optically active substances (OASs) and their inherent optical properties (IOPs), as well as the adjacency effect. OASs and IOPs can be derived from atmospheric correction processors’ in-water algorithms applied to data from Sentinel-2 MultiSpectral Instrument (S2 MSI) and Sentinel-3 Ocean and Land Color Instrument (S3 OLCI). This study compared S3 OLCI Level-2 in-water products for Case-2 waters with alternative in-water algorithms derived from ACOLITE, POLYMER, C2RCC, and A4O. Fifty in-water algorithms were evaluated using an extensive match-up dataset from lakes and coastal areas, focusing particularly on small lakes with high colored dissolved organic matter absorption at 442 nm (up to 48 m-1). The Chl a band ratio introduced by Gons et al. (2022) applied to data processed by ACOLITE performed best for S3 OLCI Chl a retrieval (dispersion = 23%, bias = 10%). Gons et al. (2022) band ratio also showed consistent agreement between S3 OLCI and S2 MSI resampled data (intercept of 6.27 and slope of 0.83, close to the 1:1 line); however, lower Chl a values (<20 mg/m³) were overestimated by S2 MSI. When estimating errors associated with proximity to land, S2 MSI Chl a in-water algorithms had higher errors close to the shore (on average 315%) compared to S3 OLCI (on average 150%). Chl a retrieved with POLYMER had the lowest errors close to the shore for both S2 MSI and S3 OLCI data (on average 70%). Total suspended matter (TSM) retrieval with C2RCC performed well for S2 MSI (dispersion 24% and bias −12%). Total absorption was most accurately derived from C2RCC applied to S3 OLCI L1 data (dispersion < 43% and bias < −39%), and it was better estimated than its individual components: phytoplankton, mineral particles, and colored dissolved organic matter absorption. However, none of the colored dissolved organic matter absorption in-water algorithms performed well (dispersion > 59% and bias < −29%). [ABSTRACT FROM AUTHOR]

Optically complex waters present significant challenges for remote sensing due to high concentrations of optically active substances (OASs) and their inherent optical properties (IOPs), as well as the adjacency effect. OASs and IOPs can be derived from atmospheric correction processors’ in-water algorithms applied to data from Sentinel-2 MultiSpectral Instrument (S2 MSI) and Sentinel-3 Ocean and Land Color Instrument (S3 OLCI). This study compared S3 OLCI Level-2 in-water products for Case-2 waters with alternative in-water algorithms derived from ACOLITE, POLYMER, C2RCC, and A4O. Fifty in-water algorithms were evaluated using an extensive match-up dataset from lakes and coastal areas, focusing particularly on small lakes with high colored dissolved organic matter absorption at 442 nm (up to 48 m-1). The Chl a band ratio introduced by Gons et al. (2022) applied to data processed by ACOLITE performed best for S3 OLCI Chl a retrieval (dispersion = 23%, bias = 10%). Gons et al. (2022) band ratio also showed consistent agreement between S3 OLCI and S2 MSI resampled data (intercept of 6.27 and slope of 0.83, close to the 1:1 line); however, lower Chl a values ( 59% and bias < −29%).

The Baltic Sea is characterized by large gradients in salinity, high concentrations of colored dissolved organic matter, and a phytoplankton phenology with two seasonal blooms. Satellite retrievals of chlorophyll-a concentration (chl-a) are hindered by the optical complexity of this basin and the reduced performance of the atmospheric correction in its highly absorbing waters. Within the development of a regional ocean color operational processing chain for the Baltic Sea based on Sentinel-3 Ocean and Land Colour Instrument (OLCI) fullresolution data, the performance of four atmospheric correction processors for the retrieval of remote-sensing reflectance (Rrs) was analyzed. Assessments based on three Aerosol Robotic Network-Ocean Color (AERONET-OC) sites and shipborne hyperspectral radiometers show that POLYMER was the best-performing processor in the visible spectral range, also providing a better spatial coverage compared with the other processors. Hence, OLCI Rrs spectra retrieved with POLYMER were chosen as input for a bio-optical ensemble scheme that computes chl-a as a weighted sum of different regional multilayer perceptron neural nets. This study also evaluated the operational Rrs and chl-a datasets for the Baltic Sea based on OC-CCI v.6. The chl-a retrievals based on OC-CCI v.6 and OLCI Rrs, assessed against in-situ chl-a measurements, yielded similar results (OC-CCI v.6: R² = 0.11, bias = -0.22; OLCI: R² = 0.16, bias = -0.03) using a common set of match-ups for the same period. Finally, an overall good agreement was found between chl-a retrievals from OLCI and OC-CCI v.6 although differences between Rrs were amplified in terms of chl-a estimates. [ABSTRACT FROM AUTHOR]

Accurate atmospheric correction (AC) is one fundamental and essential step for successful ocean colour remote-sensing applications. Currently, most ACs and the associated ocean colour remote-sensing applications are restricted to solar zenith angles (SZAs) lower than 70°. The ACs under high SZAs present degraded accuracy or even failure problems, rendering the satellite retrievals of water quality parameters more challenging. Additionally, the complexity of the bio-optical properties of the coastal waters and the presence of complex aerosols add to the difficulty of AC. To address this challenge, this study proposed an AC algorithm based on extreme gradient boosting (XGBoost) for optically complex waters under high SZAs. The algorithm presented in this research has been developed using pairs of Geostationary Ocean Colour Imager (GOCI) high-quality noontime remote-sensing reflectance (Rrs) and the Rayleigh-corrected reflectance (ρrc) derived from the Ocean Colour–Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) in the morning (08:55 LT) and at dusk (15:55 LT). The algorithm was further examined using the daily GOCI images acquired in the morning and at dusk, and the hourly (total suspended sediment) TSS concentration was also obtained based on the atmospherically corrected GOCI data. The results showed that: (i) the model produced an accurate fitting performance (R2 ≥ 0.90, RMSD ≤ 0.0034 sr−1); (ii) the model had a high validation accuracy with an independent dataset (R2 = 0.92–0.97, MAPD = 8.2–26.81% and quality assurance (QA) score = 0.9–1); and (iii) the model successfully retrieved more valid Rrs for GOCI images under high SZAs and enhanced the accuracy and coverage of TSS mapping. This algorithm has great potential to be applied to AC for optically complex waters under high SZAs, thus increasing the frequency of available observations in a day. [ABSTRACT FROM AUTHOR]

A spectral deconvolution model (SDM) for inversion of light absorption, a(λ) and backscattering, bb(λ), to estimate concentrations of chlorophyll (CHL), colored dissolved organic material (CDOM) and non‐biogenic mineral suspended solids (MSS) in offshore and shelf waters is presented. This approach exploits the spectral information embedded in the ratio bb(λ)/a(λ), without the need to know each parameter separately. The model has been applied to in situ inherent optical properties (IOPs), a(λ) and bb(λ), and to in situ remote sensing reflectance, rrs(λ). CHL, MSS, and CDOM estimates are provided by propagating uncertainties in input IOPs and material‐specific IOPs using a bootstrapping approach. Application of the SDM to a data set collected in the Ligurian Sea provides Mean Average Errors (MAE) of <0.7 mg m−3 for CHL, <0.02 m−1 for CDOM, and <0.2 g m−3 for MSS. The SDM is found to perform as well as, or in some cases better than, single parameter algorithms and other semi‐analytical algorithms (SAA) for each parameter for the Ligurian Sea data set. The SDM CHL product is tested using the NOMAD, Case 1 dominated, global data set and found to perform consistently with the quasi‐analytical algorithm (Lee et al., 2002, https://doi.org/10.1364/ao.41.005755) but with slightly poorer performance than standard OCx algorithms. However, the additional estimates of CDOM and MSS provided by the SDM suggest that the approach may be particularly useful for Case 2 waters. Successful retrieval of constituent concentrations with uncertainties suggests good potential to adapt this technique for satellite remote sensing. Plain Language Summary: A key goal for ocean color sensing is to be able to retrieve concentrations of optically significant constituents such as chlorophyll, colored dissolved organic material and inorganic particles or sediment. It is also important to assess the accuracy of these concentrations. We present a spectral deconvolution model that allows us to determine concentrations of all of these materials and to provide an estimate of associated uncertainties. The model operates on the ratio of backscattering to absorption, which is easily derived from measurements of remote sensing reflectance. This gives us a shortcut to bypass the difficult step of estimating absorption and backscattering separately that other similar approaches require, and lets us more directly use the information contained in remote sensing reflectance signals. We show that the approach works similarly well in clear oceanic waters and in shallow coastal waters in the Ligurian Sea (part of the North Western Mediterranean Sea). The spectral deconvolution model performs as well as other established algorithms for each product for the Ligurian Sea and is competitive when applied to the NOMAD global data set. Simultaneous sensing of all three constituent concentrations with robust uncertainties is a useful development for monitoring optical water quality in natural waters. Key Points: A spectral deconvolution model operating on the ratio of backscattering to absorption provides multiple constituent concentrationsThe model uses bootstrapping to provide propagated product uncertaintiesThe model can be applied to in situ inherent optical properties or to remote sensing reflectance data [ABSTRACT FROM AUTHOR]

ABSTRACTThe Río de la Plata estuary, located in southeast South America, is of great social and environmental relevance for the region. In the summer of 2018–2019, an exceptional cyanobacterial bloom was detected along the northern coast of the estuary, which greatly affected tourism and recreation, as reported in previous studies and the public press. This is the first work that uses satellite images to quantitatively monitor the evolution of this bloom in an intermediate zone of the estuary, including its initial and ending stages. Chlorophyll-a threshold maps of 10 and 24 µg/L were detected from Sentinel-2 imagery between November 2018-May 2019, while the presence of cyanobacteria was confirmed from phytoplankton field samples and a continuous (point) record of phycocyanin fluorescence. The combination of both satellite and in-situ information was crucial to analyze the spatiotemporal variability of the bloom, and to study its qualitative relationship to environmental drivers: local winds, discharge of the Río de la Plata main tributaries, discharge of a local tributary, water temperature and salinity. Our study shows the complexity of short-term bloom dynamics in a coastal region of the Río de la Plata, particularly highlighting the role of local winds on surface transport and mixing conditions.

The Baltic Sea is characterized by large gradients in salinity, high concentrations of colored dissolved organic matter, and a phytoplankton phenology with two seasonal blooms. Satellite retrievals of chlorophyll-a concentration (chl-a) are hindered by the optical complexity of this basin and the reduced performance of the atmospheric correction in its highly absorbing waters. Within the development of a regional ocean color operational processing chain for the Baltic Sea based on Sentinel-3 Ocean and Land Colour Instrument (OLCI) full-resolution data, the performance of four atmospheric correction processors for the retrieval of remote-sensing reflectance (Rrs) was analyzed. Assessments based on three Aerosol Robotic Network-Ocean Color (AERONET-OC) sites and shipborne hyperspectral radiometers show that POLYMER was the best-performing processor in the visible spectral range, also providing a better spatial coverage compared with the other processors. Hence, OLCI Rrs spectra retrieved with POLYMER were chosen as input for a bio-optical ensemble scheme that computes chl-a as a weighted sum of different regional multilayer perceptron neural nets. This study also evaluated the operational Rrs and chl-a datasets for the Baltic Sea based on OC-CCI v.6. The chl-a retrievals based on OC-CCI v.6 and OLCI Rrs, assessed against in-situ chl-a measurements, yielded similar results (OC-CCI v.6: R2 = 0.11, bias = −0.22; OLCI: R2 = 0.16, bias = −0.03) using a common set of match-ups for the same period. Finally, an overall good agreement was found between chl-a retrievals from OLCI and OC-CCI v.6 although differences between Rrs were amplified in terms of chl-a estimates.

The Pomeranian lakes in Northern Poland and the nearby coastal waters of the Baltic Sea belong to optically complex water bodies characterised by high eutrophication levels. These water types require a local approach when developing bio-optical algorithms that combine the inherent and the apparent properties of seawater. Well-established local algorithms are of great value for understanding and addressing rapid changes in water quality related mostly to human activities in coastal and near-shore zones, as well as in optically similar lakes. Our research analyses the possibility of using the multi-parameter algorithms of absorption a(λ), backscattering bb(λ) and remote sensing reflectance Rrs(λ), originally developed for the coastal waters of the Southern Baltic Sea, for three selected Pomeranian lakes. Our multi-parameter algorithms are based on the input concentrations of the biogeochemical components measured in the lake waters, i.e., chlorophyll a (Chl a), suspended particulate matter (SPM), inorganic suspended particulate matter (SPMinorg), the sum of the surface concentrations of accessory pigments (ΣC) and coloured dissolved organic matter with a wavelength of 400 nm (aCDOM(400)). Rrs(λ) and a(λ) output values were compared with independent measurements of these parameters conducted in the lake waters at 20 sampling stations. Our algorithm output values of bb(λ) were compared to the values obtained based on the algorithm provided by Ficek, previously developed and validated for Pomeranian lakes, at the same stations. The statistical analyses conducted afterwards showed that the multi-parameter algorithms of Rrs(λ) and a(λ) for the Southern Baltic Sea are sufficient to be used for the stations investigated in the aforementioned three lakes. Specifically, the correlations between the bb(λ) values obtained based on the Ficek algorithm and the bb(λ) values obtained using our multi-parameter algorithm reveal a statistical error rate of less than 20%. [ABSTRACT FROM AUTHOR]

The color of natural waters – oceanic, coastal, and inland – is determined by the spectral absorption and scattering properties of dissolved and particulate water constituents. Remote sensing of aquatic ecosystems requires a comprehensive understanding of these inherent optical properties (IOPs), their interdependencies, and their impact on ocean (water) color, i.e., remote-sensing reflectance. We introduce a bio-geo-optical model for natural waters that includes revised spectral absorption and scattering parameterizations, based on a comprehensive analysis of precisely measured IOPs and water constituents. In addition, specific IOPs of the most significant phytoplankton groups are modeled and a system is proposed to represent the optical variability of phytoplankton diversity and community structures. The model provides a more accurate representation of the relationship between bio-geo-optical properties and can better capture optical variability across different water types. Based on the evaluation both using the training and independent testing data, our model demonstrates an accuracy of within ±5% for most component IOPs throughout the visible spectrum. We also discuss the potential of this model for radiative transfer simulations and building a comprehensive synthetic dataset especially for optically complex waters. Such datasets are the crucial basis for the development of satellite-based ocean (water) color algorithms and atmospheric correction methods. Our model reduces uncertainties in ocean color remote sensing by enhancing the distinction of optically active water constituents and provides a valuable tool for predicting the optical properties of natural waters across different water types.

A novel semi-analytical algorithm was developed to retrieve total suspended matter (TSM) in turbid waters. A turbidity index developed from remote sensing reflectance ( R rs ) of three bands of visible spectrum was employed to estimate TSM. Adopting a partial differentiation approach, wavelength λ 1 at which maximum absorption of TSM was identified between 650 nm and 750 nm. The second ( λ 2 ) and third ( λ 3 ) wavelengths were identified through radiative transfer modelling and parameterization of particulate backscattering to minimize the effect of other optically active substances and account for backscattering effects, respectively. Thus, the wavelengths identified were 679 nm ( λ 1 ), 695 nm ( λ 2 ), and 704 nm ( λ 3 ). Regressing the index with 50 randomly chosen in situ data points (Zuary River estuary, Goa) resulted in a best-fit polynomial form of algorithm. This algorithm was validated with a different set of in situ data points (n = 116), and resulted in a correlation coefficient, r = 0.88. In addition, a comparative analysis of the developed algorithm with forty-one empirical and semi-analytical models of TSM indicated their non-suitability in varying optical conditions. The study further pointed out the significance of 695 nm as an inseparable band of any future optical sensor to retrieve TSM from remotely sensed data. [ABSTRACT FROM AUTHOR]

Adjacency effect (AE) corrections over inland water surfaces has been a known issue in space-borne optical remote sensing over more than four decades. Here we present a novel algorithm able to simultaneously retrieve the aerosol optical depth, sun glint, AE, water reflectance, and water inherent optical properties (IOPs). The method was evaluated against an in situ data set of remote sensing reflectance (R r s) collected in ∼ 100 lakes across Canada. The new algorithm is based on a genetic optimization scheme (GAAC: Genetic Algorithm for Atmospheric Correction), and was here compared to the most popular atmospheric correction algorithms available (ACOLITE, iCOR+SIMEC). The statistical metrics of the R r s retrieval were improved by a factor of almost 2 in all wavelengths, and for all metrics (Bias, Error, Similarity Angle) relative to other algorithms. Demonstrations of GAAC on scenes of Lansdat-8 OLI, and Sentinel-2 MSI sensors demonstrate the algorithm's robustness when applied to spatially complex small lake (∼ 10 km of width) surfaces. [Display omitted] • A novel genetic-based algorithm for atmospheric correction (AC) for medium spatial resolution multispectral instruments (Landsat OLI and Sentinel MSI). • The algorithm corrects the adjacency effects affecting small inland waters surrounded by diverse land cover. • The algorithm is tested on 200+ Canadian lakes, and its performance is compared to other state-of-the-art AC algorithms (ICOR and ACOLITE). [ABSTRACT FROM AUTHOR]

In most coastal waters, riverine inputs of suspended particulate matter (SPM) and colored dissolved organic matter (CDOM) are the primary optically active constituents. Moderate- and high-resolution satellite optical sensors, such as the Operational Land Imager (OLI) on Landsat-8 and the MultiSpectral Instrument (MSI) on Sentinel-2, offer a synoptic view at high spatial resolution (10–30 m) with weekly revisits allowing the study of coastal dynamics (e.g., river plumes and sediment re-suspension events). Accurate estimations of CDOM and SPM from space require regionally tuned bio-optical algorithms. Using an in situ dataset of CDOM, SPM, and optical properties (both apparent and inherent) from various field campaigns carried out in the coastal waters of the estuary and Gulf of St. Lawrence (EGSL) and eastern James Bay (JB) (N = 347), we developed regional algorithms for OLI and MSI sensors. We found that CDOM absorption at 440 nm [ag (440)] can be retrieved using the red-to-green band ratio for both EGSL and JB. In contrast, the SPM algorithm required regional adjustments due to significant differences in mass-specific inherent optical properties. Finally, the application of regional algorithms to satellite images from OLI and MSI indicated that the atmospheric correction (AC) algorithm C2RCC gives the most accurate remote-sensing reflectance (Rrs) absolute values. However, the ACOLITE algorithm gives the best results for CDOM estimation (almost null bias; median symmetric accuracy of 45% and R2 of 0.78) as it preserved the Rrs spectral shape, while tending to yield positively bias SPM (88%). We conclude that the choice of the algorithm depends on the parameter of interest.

Satellite bathymetry estimation (SBE) is a cost-effective method for producing depth information for shallow marine and aquatic areas. However, most SBE methods have been developed and are refined in clear water conditions and their feasibility in optically complex waters is poorly understood. This hinders the use of SBE in optically complex waters. To test the feasibility of SBE in optically complex waters, we used multi-temporal Sentinel-2 (S-2) imagery and in-situ measured depth information from dive computers to fit bathymetric models for 14 study areas in the northern Baltic Sea. We compared different methods of using multi-temporal S2-scenes, atmospheric correction, and statistical modeling, as well as different band combinations to define the optimal model for each study area. We tested the dependence of the depth estimation on 13 environmental variables with potential influence on the accuracy of the SBE. The optimal bathymetric models differed among the study sites, with the differences manifested in different requirements for atmospheric correction, band combination, statistical model, and the use of multiple S2-scenes. The coefficients of determination (R2) of the optimal models varied between 0.42 and 0.79, with mean (X) R2 equaling to 0.65. The coefficients of determination were positively related to the proportion of sea within the 5 km search kernel and to the exposure of the study site. At the predicted depth of 2 m, the differences between observed and predicted depths varied between 0.11 and 0.60 m (X = 0.35 m). At the predicted depth of 1 m, the differences varied between 0.04 and 0.34 m (X = 0.21 m). SBE provides accurate depth estimates in optically complex waters, even when in-situ calibration data are limited. However, the maximum detectable depth obtained with SBE in optically complex waters is significantly lower than in clear water conditions. The SBE process can be fine-tuned to the specific characteristics of an area, improving its performance in optically complex conditions. In particular, the identification of the optimal band combination is critical for successful SBE. The depth estimates produced in this study can be used in a variety of applications that require accurate information on the seafloor bathymetry. • Satellite bathymetry estimation (SBE) provides useful depth estimates even in optically complex waters. • The quality of SBE can be improved by defining an optimal SBE process for a given site. • Our study provides practical steps for defining the optimal SBE process in optically complex waters. [ABSTRACT FROM AUTHOR]

A relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration (Chl-a). Alas, ocean color remote sensing applications to estimate Chl-a in this brackish basin, characterized by large gradients in salinity and dissolved organic matter, are hampered by its optical complexity and atmospheric correction limits. This study presents Chl-a retrieval improvements for a fully reprocessed multi-sensor time series of remote-sensing reflectances (Rrs) at ~1 km spatial resolution for the Baltic Sea. A new ensemble scheme based on multilayer perceptron neural net (MLP) bio-optical algorithms has been implemented to this end. The study documents that this approach outperforms band-ratio algorithms when compared to in situ datasets, reducing the gross overestimates of Chl-a observed in the literature for this basin. The Rrs and Chl-a time series were then exploited for eutrophication monitoring, providing a quantitative description of spring and summer phytoplankton blooms in the Baltic Sea over 1998–2019. The analysis of the phytoplankton dynamics enabled the identification of the latitudinal variations in the spring bloom phenology across the basin, the early blooming in spring in the last two decades, and the description of the spatiotemporal coverage of summer cyanobacterial blooms in the central and southern Baltic Sea.

Coloured dissolved and detrital matter (CDM) forms a significant fraction of the total dissolved organic matter (DOM) in water bodies. It absorbs light strongly in the ultraviolet and blue domains of the electromagnetic spectrum. The present study maps CDM absorption of the entire Chilika lagoon, Odisha, India (an optically complex water body) using hyperspectral data of AVIRIS-NG. This study takes advantage of hyperspectral data which use SWIR bands for the estimation of remote sensing reflectance in highly turbid waters of Chilika lagoon (northern sector, which otherwise is masked using standard atmospheric correction schemes). During 24-27 December 2015, we have collected in situ bio-optical data over waters of Chilika lagoon, for studying the distribution of CDM. AVIRIS-NG data have also been acquired synchronous to in situ measurements over the study area. CDM absorption coefficient is retrieved using quasi analytical algorithm and the distribution of CDM is discussed in detail in three different sectors of Chilika lagoon (southern, central, northern) and at the outer channel. The variability of CDM absorption at 412 nm shows that in the north sector of Chilika lagoon, CDM absorption is quite high compared to other sectors (5.5 m-1 with a standard deviation of 0.06 m-1). In the southern sector and at the outer channel it is 1.8 m-1 with a standard deviation of 0.02 m-1 and in the central sector it is 3.76 m-1 with a standard deviation of 0.22 m-1. High CDM in the northern sector is attributed to the inflow of terrestrial organic matter. The advantage of hyperspectral data is that it gives CDM absorption contiguous in the range of 375-425 nm, where the absorption by CDM is strong and away from chlorophyll-a absorption. [ABSTRACT FROM AUTHOR]

Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be 117 million lakes). In this study, bio-optical modelling and remote sensing data (Sentinel-3 Ocean and Land Colour Instrument) was combined to investigate the spatial and temporal variation of PP in four Baltic lakes during 2018. The model used has three input parameters: concentration of chlorophyll-a, the diffuse attenuation coefficient, and incident downwelling irradiance. The largest of our studied lakes, Võrtsjärv (270 km2), had the highest total yearly estimated production (61 Gg C y−1) compared to the smaller lakes Lubans (18 Gg C y−1) and Razna (7 Gg C y−1). However, the most productive was the smallest studied, Lake Burtnieks (40.2 km2); although the total yearly production was 13 Gg C y−1, the daily average areal production was 910 mg C m−2 d−1 in 2018. Even if lake size plays a significant role in the total PP of the lake, the abundance of small and medium-sized lakes would sum up to a significant contribution of carbon fixation. Our method is applicable to larger regions to monitor the spatial and temporal variability of lake PP.

Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM) clustering. Four typical types of waters were studied: (1) highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM), phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2) CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3) nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4) cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. POLarization and Directionality of the Earth's Reflectances (POLDER), Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS) were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709)/Rrs (681), Rrs (560)/Rrs (709), Rrs (560)/Rrs (620), and Rrs (709)/Rrs (761) are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively.

Remotely sensed hyperspectral data has widely been used to determine water quality parameters in oceanic waters. However in freshwater basins the dependence between the hyperspectral data and the parameters is more complicated. In this work some ideas are presented concerning the study of this dependence. The data used in this study were collected from the lake Hiidenvesi in southern Finland. The hyperspectral data consists of reflectances in 36 bands in the wavelength area 508...878 nm and the separately measured water quality parameters are turbidity, blue-green algae, chlorophyll, pH and dissolved oxygen. Hyperspectral data was used as bare band reflectances, but also in the form of two simple spectral indices: ratio A/B and difference A-B, where A and B go through all the bands. The correlations of the indices with the parameters were presented visually as 1- or 2-dimensional arrays. To examine the significance on the results of different variables, the data was classified in two different ways: the natural basins and the values of the water quality parameters. It was noticed that the variability of the correlation arrays was particularly strong among different basins in both the magnitude of correlation and the best performing indices. Further studies are needed to clarify which features of the basins are of most importance in predicting the shapes of the correlation arrays. [ABSTRACT FROM AUTHOR]

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84−0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

Coastal zones are sensitive areas responding at various scales (events to long-term trends) where the monitoring and management of physico-chemical, biological, morphological processes, and fluxes are highly challenging [...]

Water transparency is one of the ecological indicators for describing water quality and the underwater light field which determines its productivity. In the European Water Framework Directive (WFD) as well as in the European Marine Strategy Framework Directive (MSFD) water transparency is used for ecological status classification of inland, coastal and open sea waters and it is regarded as an indicator for eutrophication in Baltic Sea management (HELCOM, 2007). We developed and compared different empirical and semi-analytical algorithms for lakes and coastal Nordic waters to retrieve Secchi depth (ZSD) from remote sensing data (MERIS, 300 m resolution). The algorithms were developed in water bodies with high coloured dissolved organic matter absorption (aCDOM(442) ranging 1.7-4.0 m-1), Chl a concentration (0.5-73 mg m-3) and total suspended matter (0.7-37.5 g m-3) and validated against an independent data set over inland and coastal waters (0.6 m < ZSD < 14.8 m). The results indicate that for empirical algorithms, using longer wavelengths in the visible spectrum as a reference band decreases the RMSE and increases the coefficient of determination (R2). The accuracy increased (R2 = 0.75, RMSE = 1.33 m, n = 134) when ZSD was retrieved via an empirical relationship between ZSD and K d(490). The best agreement with in situ data was attained when ZSD was calculated via both the diffuse and the beam attenuation coefficient (R2 = 0.89, RMSE = 0.77 m, n = 89). The results demonstrate that transparency can be retrieved with high accuracy over various optical water types by the means of ocean color remote sensing, improving both the spatial and temporal coverage. The satellite derived ZSD product could be therefore used as an additional source of information for WFD and MSFD reporting purposes. [ABSTRACT FROM AUTHOR]

The European Parliament and The Council of the European Union have established the Water Framework Directive (2000/60/EC) for all European Union member states to achieve, at least, “good” ecological status of all water bodies larger than 50 hectares in Europe. The MultiSpectral Instrument onboard European Space Agency satellite Sentinel-2 has suitable 10, 20, 60 m spatial resolution to monitor most of the Estonian lakes as required by the Water Framework Directive. The study aims to analyze the suitability of Sentinel-2 MultiSpectral Instrument data to monitor water quality in inland waters. This consists of testing various atmospheric correction processors to remove the influence of atmosphere and comparing and developing chlorophyll a algorithms to estimate the ecological status of water in Estonian lakes. This study shows that the Sentinel-2 MultiSpectral Instrument is suitable for estimating chlorophyll a in water bodies and tracking the spatial and temporal dynamics in the lakes. However, atmospheric corrections are sensitive to surrounding land and often fail in narrow and small lakes. Due to that, deriving satellite-based chlorophyll a is not possible in every case, but initial results show the Sentinel-2 MultiSpectral Instrument could still provide complementary information to in situ data to support Water Framework Directive monitoring requirements.

Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The effects of variations in the environmental noise on water surface reflectance and the accuracy of environmental variable retrievals were quantified. Two WorldView-2 satellite images were acquired, within one minute of each other, with Image 1 placed in a near-optimal sun-sensor geometric configuration and Image 2 placed close to the specular point of the Bidirectional Reflectance Distribution Function (BRDF). Image 2 had higher total environmental noise due to increased surface glint and higher atmospheric path-scattering. Generally, depths were under-estimated from Image 2, compared to Image 1. A partial improvement in retrieval error after glint correction of Image 2 resulted in an increase of the maximum depth to which accurate depth estimations were returned. This case-study indicates that critical analysis of individual images, accounting for the entire sun elevation and azimuth and satellite sensor pointing and geometry as well as anticipated wave height and direction, is required to ensure an image is fit for purpose for aquatic data analysis. [ABSTRACT FROM AUTHOR]

The Special Issue (SI) on "Remote Sensing of Water Resources" presents a diverse range of papers studying remote sensing tools, methods, and models to better monitor water resources which include inland, coastal, and open ocean waters. The SI is comprised of fifteen articles on widely ranging research topics related to water bodies. This preface summarizes each article published in the SI. [ABSTRACT FROM AUTHOR]

Optical remote sensing in complex waters is challenging because the optically active constituents may vary independently and have a combined and interacting influence on the remote sensing signal. Additionally, the remote sensing signal is influenced by noise and spectral contamination by confounding factors, resulting in ill-posedness and ill-conditionedness in the inversion of the model. There is a need for inversion methods that are less sensitive to these changing or shifting spectral features. We propose WaveIN, a wavelet-enhanced inversion method, specifically designed for complex waters. It integrates wavelet-transformed high-spectral resolution reflectance spectra in a multiscale analysis tool. Wavelets are less sensitive to a bias in the spectra and can avoid the changing or shifting spectral features by selecting specific wavelet scales. This paper applied WaveIN to simulated reflectance spectra for the Scheldt River. We tested different scenarios, where we added specific noise or confounding factors, specifically uncorrelated noise, contamination due to spectral mixing, a different sun zenith angle, and specific inherent optical property (SIOP) variation. WaveIN improved the constituent estimation in case of the reference scenario, contamination due to spectral mixing, and a different sun zenith angle. WaveIN could reduce, but not overcome, the influence of variation in SIOPs. Furthermore, it is sensitive to wavelet edge effects. In addition, it still requires in situ data for the wavelet scale selection. Future research should therefore improve the wavelet scale selection. [ABSTRACT FROM PUBLISHER]

Abstract: Atmospheric correction of satellite ocean color imagery in sediment-laden and algal bloom waters remains to be a challenging task because of the inefficiency of present methods to accurately assess aerosol radiances in the near-infrared (NIR) bands and extrapolate these into the visible spectrum. This study presents a novel method for estimation and extrapolation of aerosol radiances in MODIS–Aqua bands over optically complex oceanic waters dominated by suspended sediments and algal blooms. The Rayleigh-corrected radiance at 748nm, after correcting the influence of the elevated radiance contributed by suspended sediments and algal blooms, is employed in the present method to estimate aerosol radiance in a MODIS–Aqua band centered at 531nm. Then, the normalized Gaussian distribution function is used to extrapolate aerosol radiance over the entire wavebands (from 412 to 748nm). This method retrieves water-leaving radiances Lw(λ) nearly comparable to those of the NIR scheme for extremely clear waters (chlorophyll <0.15mgm−3) with an error of <5% which is an acceptable limit for such waters. Evaluation of the performance of the new method with NIR and NIR–SWIR switching schemes using in-situ Lw(λ) measurements from highly turbid waters off Point Calimere (on the southeast coast of India) and other regional waters (NOMAD in-situ Lw(λ) data) shows an apparent improvement in Lw(λ) retrievals with the new method over NIR and NIR–SWIR switching schemes (i.e., with a decrease in mean relative error by 22.59% and 23.06% respectively). The performance of the new method is demonstrated for several MODIS-Aqua imageries of optically contrasted and complex waters (containing different in-water and floating algal blooms), and yields physically realistic water-leaving (or surface-leaving) radiances in all visible bands. By contrast, the NIR and NIR–SWIR switching schemes show large distortions in Lw(λ) structures with large negative values across the visible bands. These results suggest that the new method will have important implications for satellite remote sensing of optically complex waters, since the accuracy of bio-geophysical products derived from such data mainly depends on the water-leaving radiance products delivered by the atmospheric correction algorithm. [Copyright &y& Elsevier]

This letter presents an application of continuous wavelet analysis, providing a new semi-empirical approach to estimate Chlorophyll-a (Chl-a) in optically complex inland waters. Traditionally spectral narrow band ratios have been used to quantify key diagnostic features in the remote sensing signal to estimate concentrations of optically active water quality constituents. However, they cannot cope easily with shifts in reflectance features caused by multiple interactions between variable absorption and backscattering effects that typically occur in optically complex waters. We use continuous wavelet analysis to detect Chl-a features at various wavelengths and frequency scales. Using the wavelet decomposition, we build a 2-D correlation scalogram between in situ pond reflectance spectra and in situ Chl-a concentration. By isolating the most informative wavelet regions via thresholding, we could relate all five regions to known inherent optical properties. We select the optimal feature per region and compare them to three well-known narrow band ratio models. For this experimental application, the wavelet features outperform the NIR-red models, while fluorescence line height (FLH) yield comparable results. Because wavelets analyze the signal at different scales and synthesize information across bands, we hypothesize that the wavelet features are less sensitive to confounding factors, such as instrument noise, colored dissolved organic matter, and suspended matter. [ABSTRACT FROM PUBLISHER]

Several semi-analytical models for the satellite-based estimation of absorption coefficients of phytoplankton aph (\lambda ) have been used to routinely produce aph (\lambda ) product from satellite ocean color data. However, these models are generally applicable for clear ocean waters where they produce aph (\lambda ) values only at a few wavelengths in the blue-green domain; this causes the main difficulty in making these models more usable with any suite of wavelengths. Further, recent studies have shown the performance of these models to be highly questionable in optically complex waters. This emphasized the need for developing a more accurate model for the satellite-based estimation of aph (\lambda ) in a wide range of oceanic waters. In our previous study, we developed an empirical model (hereafter referred as Tiwari-Shanmugam model – “TS model”) based on the relationship of the in situ remote sensing reflectance ratio R rs (670)/R rs (490) and in situ aph(\lambda) which is best fit to a third order polynomial. In the present study, we rigorously test this model along with three global inversion models (e.g., Constrained Linear Matrix (LM) model, Quasi-analytical algorithm (QAA), and GSM semi-analytical model which are often used by the ocean color community) using three independent in situ data sets from clear to turbid coastal waters and satellite match-ups data from global waters. When applied to these data sets, the TS model produces more accurate aph values across the entire visible wavelengths (400–700 nm) in all these waters, whereas LM, QAA and GSM models yield significant errors in addition to being restricted to produce aph values only in the blue-green wavelengths (LM and GSM). Though the TS model is mathematically simple, it overcomes such limitations and yields excellent results in terms of reproducing measured aph spectra that are highly desired by the ocean color community for inputting in various bio-optical models and studying the spatial structure of the different phytoplankton communities from satellite remote sensing observations in global waters. [ABSTRACT FROM PUBLISHER]

Abstract: We provide a comprehensive overview of water constituent retrieval algorithms and underlying definitions and models for optically deep and complex (i.e. case 2) waters using earth observation data. The performance of constituent retrieval algorithms is assessed based on matchup validation experiments published between January 2006 and May 2011. Validation practices range from singular vicarious calibration experiments to comparisons using extensive in situ time series. Band arithmetic and spectral inversion algorithms for all water types are classified using a method based scheme that supports the interpretation of algorithm validity ranges. Based on these ranges we discuss groups of similar algorithms in view of their strengths and weaknesses. Such quantitative literature analysis reveals clear application boundaries. With regard to chlorophyll retrieval, validation of blue–green band ratios in coastal waters is limited to oligotrophic, predominantly ocean waters, while red-NIR ratios apply only at more than 10mg/m3. Spectral inversion techniques — although not validated to the same extent — are necessary to cover all other conditions. Suspended matter retrieval is the least critical, as long as the wavelengths used in empirical models are increased with concentrations. The retrieval of dissolved organic matter however remains relatively inaccurate and inconsistent, with large differences in the accuracy of comparable methods in similar validation experiments. We conclude that substantial progress has been made in understanding and improving retrieval of constituents in optically deep and complex waters, enabling specific solutions to almost any type of optically complex water. Further validation and intercomparison of spectral inversion procedures are however needed to learn if solutions with a larger validity range are feasible. [Copyright &y& Elsevier]

A relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration (Chl-a). Alas, ocean color remote sensing applications to estimate Chl-a in this brackish basin, characterized by large gradients in salinity and dissolved organic matter, are hampered by its optical complexity and atmospheric correction limits. This study presents Chl-a retrieval improvements for a fully reprocessed multi-sensor time series of remote-sensing reflectances (Rrs) at ~1 km spatial resolution for the Baltic Sea. A new ensemble scheme based on multilayer perceptron neural net (MLP) bio-optical algorithms has been implemented to this end. The study documents that this approach outperforms band-ratio algorithms when compared to in situ datasets, reducing the gross overestimates of Chl-a observed in the literature for this basin. The Rrs and Chl-a time series were then exploited for eutrophication monitoring, providing a quantitative description of spring and summer phytoplankton blooms in the Baltic Sea over 1998–2019. The analysis of the phytoplankton dynamics enabled the identification of the latitudinal variations in the spring bloom phenology across the basin, the early blooming in spring in the last two decades, and the description of the spatiotemporal coverage of summer cyanobacterial blooms in the central and southern Baltic Sea. [ABSTRACT FROM AUTHOR]

Freshwater quality maintenance is essential for human use and ecological functions. To ensure this objective, governments establish programs for a continuous monitoring of the inland waters state. This could be possible with Sentinel-2 (S2) and Sentinel-3 (S3), two remote sensing satellites of the European Space Agency, equipped with spectral optical sensors. To determine optimal water quality algorithms applicable to their spectral bands, 36 algorithms were tested for different key variables (chlorophyll a (Chl_a), colored dissolved organic matter (CDOM), colored dissolved organic matter (TSS), phycocyanin (PC) and Secchi disk depth (SDD)). A database of 296 water-leaving reflectance spectra were used, as well as concomitant water quality measurements of Mediterranean reservoirs and lakes of Spain. Two equal data sets were used for calibration and validation. The best algorithms were recalculated using all database and used the following band relations: SDD, R560/R700; CDOM, R665/R490; PC, R705/R665 for S2 and R620, R665, R709 and R779 for S3, using a semi-analytical algorithm; R700 for TSS < 20 mg/L and R783/R492 (S2) or R779/R510 (S3) for TSS > 20 mg/L; and for Chl_a, the maximum (R443; R492)/R560 for Chl_a < 5 mg/m3 and R700/R665 for Chl_a > 5 mg/m3. A preliminary test with a satellite image in a well-known reservoir showed results consistent with the expected ranges and spatial patterns of the variables. [ABSTRACT FROM AUTHOR]

The retrieval of sun-induced chlorophyll fluorescence is greatly beneficial to studies of marine phytoplankton biomass, physiology, and composition, and is required for user applications and services. Customarily phytoplankton chlorophyll fluorescence is determined from satellite measurements through a fluorescence line-height algorithm using three bands around 680 nm. We propose here a modified retrieval, making use of all available bands in the relevant wavelength range, with the goal to improve the effectiveness of the algorithm in optically complex waters. For the Ocean and Land Colour Instrument (OLCI), we quantify a Fluorescence Peak Height by fitting a Gaussian function and related terms to the top-of-atmosphere reflectance bands between 650 and 750 nm. This algorithm retrieves, what we call Fluorescence Peak Height by fitting a Gaussian function upon other terms to top-of-atmosphere reflectance bands between 650 and 750 nm. This approach is applicable to Level-1 and Level-2 data. We find a good correlation of the retrieved fluorescence product to global in-situ chlorophyll measurements, as well as a consistent relation between chlorophyll concentration and fluorescence from radiative transfer modelling and OLCI/in-situ comparison. Evidence suggests, the algorithm is applicable to complex waters without needing an atmospheric correction and vicarious calibration, and features an inherent correction of small spectral shifts, as required for OLCI measurements. [ABSTRACT FROM AUTHOR]

The paper presents the modelling results of selected characteristics of water-leaving light in an optically complex nearshore marine environment. The modelled quantities include the spectra of the remote-sensing reflectance Rrs(λ) and the hue angle α, which quantitatively describes the colour of water visible to the unaided human eye. Based on the latter value, it is also possible to match water-leaving light spectra to classes on the traditional Forel-Ule water colour scale. We applied a simple model that assumes that seawater is made up of chemically pure water and three types of additional optically significant components: particulate organic matter (POM) (which includes living phytoplankton), particulate inorganic matter (PIM), and chromophoric dissolved organic matter (CDOM). We also utilised the specific inherent optical properties (SIOPs) of these components, determined from measurements made at a nearshore location on the Gulf of Gdańsk. To a first approximation, the simple model assumes that the Rrs spectrum can be described by a simple function of the ratio of the light backscattering coefficient to the sum of the light absorption and backscattering coefficients (u = bb/(a + bb)). The model calculations illustrate the complexity of possible relationships between the seawater composition and the optical characteristics of an environment in which the concentrations of individual optically significant components may be mutually uncorrelated. The calculations permit a quantitative interpretation of the Forel-Ule scale. The following parameters were determined for the several classes on this scale: typical spectral shapes of the u ratio, possible ranges of the total light absorption coefficient in the blue band (a(440)), as well as upper limits for concentrations of total and organic and inorganic fractions of suspended particles (SPM, POM and PIM concentrations). The paper gives examples of practical algorithms that, based on a given Rrs spectrum or some of its features, and using lookup tables containing the modelling results, enable to estimate the approximate composition of seawater. [ABSTRACT FROM AUTHOR]

Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be 117 million lakes). In this study, bio-optical modelling and remote sensing data (Sentinel-3 Ocean and Land Colour Instrument) was combined to investigate the spatial and temporal variation of PP in four Baltic lakes during 2018. The model used has three input parameters: concentration of chlorophyll-a, the diffuse attenuation coefficient, and incident downwelling irradiance. The largest of our studied lakes, Võrtsjärv (270 km2), had the highest total yearly estimated production (61 Gg C y−1) compared to the smaller lakes Lubans (18 Gg C y−1) and Razna (7 Gg C y−1). However, the most productive was the smallest studied, Lake Burtnieks (40.2 km2); although the total yearly production was 13 Gg C y−1, the daily average areal production was 910 mg C m−2 d−1 in 2018. Even if lake size plays a significant role in the total PP of the lake, the abundance of small and medium-sized lakes would sum up to a significant contribution of carbon fixation. Our method is applicable to larger regions to monitor the spatial and temporal variability of lake PP. [ABSTRACT FROM AUTHOR]

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84–0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing. [ABSTRACT FROM AUTHOR]

Inland waters play a critical role in our drinking water supply. Additionally, they are important providers of food and recreation possibilities. Inland waters are known to be optically complex and more diverse than marine or ocean waters. The optical properties of natural waters are influenced by three different and independent sources: phytoplankton, suspended matter, and colored dissolved organic matter. Thus, the remote sensing of these waters is more challenging. Different types of waters need different approaches to obtain correct water quality products; therefore, the first step in remote sensing of lakes should be the classification of the water types. The classification of optical water types (OWTs) is based on the differences in the reflectance spectra of the lake water. This classification groups lake and coastal waters into five optical classes: Clear, Moderate, Turbid, Very Turbid, and Brown. We studied the OWTs in three different Latvian lakes: Burtnieks, Lubans, and Razna, and in a large Estonian lake, Lake Võrtsjärv. The primary goal of this study was a comparison of two different Copernicus optical instrument data for optical classification in lakes: Ocean and Land Color Instrument (OLCI) on Sentinel-3 and Multispectral Instrument (MSI) on Sentinel-2. We found that both satellite OWT classifications in lakes were comparable (R2 = 0.74). We were also able to study the spatial and temporal changes in the OWTs of the study lakes during 2017. The comparison between two satellites was carried out to understand if the classification of the OWTs with both satellites is compatible. Our results could give us not only a better overview of the changes in the lake water by studying the temporal and spatial variability of the OWTs, but also possibly better retrieval of Level 2 satellite products when using OWT guided approach. [ABSTRACT FROM AUTHOR]

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors. [ABSTRACT FROM AUTHOR]