The monitoring of coastal waters using satellite data, from sensors such as Sentinel-3 OLCI, has become a vital tool in the management of these water environments, especially when it comes to improving our understanding of the effects of climate change on these regions. In this study, the latest Level-2 water products derived from different OLCI Sentinel-3 processors were validated against a comprehensive in situ dataset from the NW Baltic Sea proper region through a matchup analysis. The products validated were those of the regionally adapted Case-2 Regional Coast Colour (C2RCC) OLCI processor (v1.0 and v2.1), as well as the latest standard Level-2 OLCI Case-2 (neural network) products from Sentinel-3’s processing baseline, listed as follows: Baseline Collection 003 (BC003), including “CHL_NN”, “TSM_NN”, and “ADG443_NN”. These products have not yet been validated to such an extent in the region. Furthermore, the effect of the current EUMETSAT system vicarious calibration (SVC) on the Level-2 water products was also validated. The results showed that the system vicarious calibration (SVC) reduces the reliability of the Level-2 OLCI products. For example, the application of these SVC gains to the OLCI data for the regionally adapted v2.1 C2RCC products resulted in RMSD increases of 36% for “conc_tsm”; 118% for “conc_chl”; 33% for “iop_agelb”; 50% for “iop_adg”; and 10% for “kd_z90max” using a ±3 h validation window. This is the first time the effects of these SVC gains on the Level-2 OLCI water products has been isolated and quantified in the study region. The findings indicate that the current EUMETSAT SVC gains should be applied and interpreted with caution in the region of study at present. A key outcome of the paper recommends the development of a regionally specific SVC against AERONET-OC data in order to improve the Level-2 water product retrieval in the region. The results of this study are important for end users and the water authorities making use of the satellite water products in the Baltic Sea region.

The optical complexity of coastal waters is mostly caused by the water discharged from land carrying optical components (such as dissolved and particulate matter) into coastal bays and estuaries, and increasing the attenuation of light. This paper aims to investigate the links between in-water optical properties in four Swedish bays (from the northern Baltic proper up to the Bothnian bay) and the land use and land cover (LULC) in the respective catchment of each bay. The optical properties were measured in situ over the last decade by various research and monitoring groups while the LULC in each bay was classified using the Copernicus Land Monitoring Service based on Landsat 8/OLI data. The absorption coefficient of colored dissolve organic matter (CDOM) at 440 nm, aCDOM (440), was significantly correlated to Wetlands which may act as sources of CDOM, while Developed areas (Agricultural and Urban classes) were negatively correlated. The Agriculture class was also negatively related to suspended particulate organic matter (SPOM), whilst Coniferous Forests and Mixed Forests as well as Meadows were positively correlated. SPOM seems thus to mostly originate from Natural classes, possibly due to the release of pollen and other organic matter. Overall, the methods applied here allow for a better understanding of effects of land use and land cover on the bio-optical properties, and thus coastal water quality, on a macroscopic scale.

PRISMA is the Italian Space Agency’s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA’s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications.

The Aerosol Robotic Network - Ocean Color (AERONET-OC) instrument located at the Pålgrunden site in Lake Vänern provides values of remote sensing spectral reflectance R_RS(λ) since 2008. These in situ R_RS(λ) indicated a temporal increase from 2015 at center-wavelengths in the green and red spectral regions. To investigate the environmental and climate processes responsible for this increase, water color trends in Lake Vänern were analyzed considering in situ limnological measurements, meteo-climatic quantities and additionally satellite-derived data products from the Moderate Resolution Imaging Spectroradiometer on board the Aqua platform (MODIS-A). Satellite ocean color R_RS(λ) data assessed against in situ R_RS(λ) from the Pålgrunden site showed satisfactory agreement at a number of spectral bands. Relying on these validation results, comprehensive statistical analysis were performed using MODIS-A R_RS(λ). These indicated periodical changes between 2002 and 2021 with clear minima occurring between 2010 and 2013. The complementary analyses of temporal changes characterizing limological and meteo-climatic quantities, and also relationships between these quantities and R_RS(λ), indicated the existence of complex and concurrent bio-geochemical processes influencing water color in Lake Vänern. In particular, significant correlations were observed between R_RS(λ) and turbidity, and also between R_RS(λ) and total biovolume. Additionally, an early warming of Lake Vänern surface waters was identified since spring 2014. This occurrence could potentially affect the vertical mixing and water exchange between turbid coastal and pelagic waters with implications for phytoplankton phenology.

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022).

The Ocean and Land Color Instrument (OLCI) on Sentinel-3 is one of the most advanced ocean color satellite sensors for aquatic environment monitoring. However, limited studies have been focused on a comprehensive assessment of atmospheric correction (AC) methods for OLCI. In an attempt to fill the gap, this study evaluated seven different AC methods for OLCI using global automatic in situ observations from Aerosol Robotic Network-Ocean Color (AERONET-OC). Results showed that the POLYnomial-based algorithm applied to MERIS (POLYMER) had the best performance for bands with wavelength ≤ 443 nm, and the SeaDAS method based on 779 and 865 nm was the best for longer spectral bands; however, SeaDAS (SeaWiFS Data Analysis System) processing algorithm based on 779 and 1020 nm, as well as 865 and 1020 nm, obtained degraded AC performance; Case 2 Regional CoastColor (C2RCC) also produced large uncertainties; Baseline AC (BAC) method might be better than SeaDAS method; and simple subtraction method was the worst except for turbid waters. POLYMER and C2RCC underestimated high remote sensing reflectance (Rrs) at red and green bands; SeaDAS method based on 779 and 865 nm held an advantage for clear waters over the other two band combinations, while their difference turned small for turbid waters. AC uncertainties generally impacted the performance of chlorophyll retrievals. POLYMER outperformed other methods for chlorophyll retrieval. This study provides a good reference for selecting a suitable AC method for aquatic environment monitoring with Sentinel-3 OLCI.

Satellites have generated extensive data of remote sensing reflectance spectra (R_rs(λ)) covering diverse water classes or types across global waters. Spectral classification of satellite R_rs(λ) data allows for the distinguishing and grouping of waters with characteristic bio-optical/biogeochemical features that may influence the productivity of a given water body. This study reports new satellite water class products (Level-2 and Level-3) from the Visible Infrared Imaging Radiometer Suite (VIIRS). We developed and implemented a hyperspectral scheme that accounts for the R_rs(λ) spectral shapes and globally resolves oceanic, coastal, and inland waters into 23 water classes. We characterized the light absorption and scattering coefficients, chlorophyll-a concentration, diffuse attenuation coefficient, and suspended particulate matter for individual water classes. It is shown that the water classes are separable by their distinct bio-optical and biogeochemical properties. Furthermore, validation result suggests that the VIIRS water class products are accurate globally. Finally, we examined the spatial and temporal variability of the water classes in case studies for a demonstration of applications. The water class data in open oceans reveal that the subtropical ocean gyres have experienced dramatic expansion over the last decade. In addition, the water class data appear to be a valuable (and qualitative) indicator for water quality in coastal and inland waters with compelling evidence. We stress that this new satellite product is an excellent addition to the aquatic science database, despite the need for continuous improvement toward perfection.

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA - ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances ((rho) over cap (w)). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in (rho) over cap (w)(560) and (rho) over cap (w)(664) were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15-30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20-30% uncertainties in (rho) over cap (w)(490 <= lambda <= 743 nm) yielded 25-70% uncertainties in derived Chla and TSS products for top-performing AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative performance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems.

In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms.

A Water Quality Monitor (WQM) equipped with a range of oceanographic sensors was deployed from April 2017 to October 2017 in the North Western (NW) Baltic Sea. We assessed here if the data from a moored chlorophyll-a fluorometer can be used to improve satellite validation in coastal waters. Calibrated mooring data and ship-based chlorophyll-a concentrations from 2017 and 2018 were matched with OLCI (Ocean and Land Colour Instrument) data to validate the C2RCC (Case-2 Regional Coast Colour) processor, a locally-adapted version of C2RCC (LA-C2RCC), as well as the POLYMER processor. Using additional mooring data resulted in a substantial increase in paired observations compared to using ship-based data alone (C2RCC; N = 41-63, LA-C2RCC; N = 37-59, POLYMER; N = 108-166). However, the addition of mooring data only reduced the error and bias of the LA-C2RCC (MNB: from 24 % to 22 %, RMSE: from 60 % to 57 %, APD: both 47 %). In contrast, the statistical errors increased with the addition of mooring data both for C2RCC (MNB: -26 % to -33 %, RMSE: 50 %-51 %, APD 84 %-96 %) and for POLYMER (MNB: 26 %-36 %, RMSE: 79 % to 79 %, APD 64 %-64 %). The results indicate that the locally-adapted version of the C2RCC should be used for the area of investigation. These results are most likely also related to the effect of the System Vicarious Calibration (SVC). As opposed to C2RCC, the locally-adapted version had not been vicariously calibrated. The results indicate that SVC is not beneficial for Baltic Sea data and that more work needs to be done to improve SVC for Baltic Sea waters or for other waters with high CDOM absorption. In order to improve the validation capabilities of moored fluorometers in general, they should be strategically placed in waters with representative ranges of chl-a concentrations for the area of research in question.