A hyperspectral and multi-angular synthetic dataset for algorithm development in waters of varying trophic levels and optical complexity

The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. Therefore, there is a strong need to validate the OLCI products to be sure that the technical capabilities provided will be used in the best possible way in water quality monitoring and research. The Baltic Sea is an optically complex waterbody where many ocean colour products, performing well in other waterbodies, fail. We tested the performance of standard Case-2 Regional/Coast Colour (C2RCC) processing chain in retrieving water reflectance, inherent optical properties (IOPs), and water quality parameters such as chlorophyll a, total suspended matter (TSM) and coloured dissolved organic matter (CDOM) in the Baltic Sea. The reflectance spectra produced by the C2RCC are realistic in both shape and magnitude. However, the IOPs, and consequently the water quality parameters estimated by the C2RCC, did not have correlation with in situ data. On the other hand, some tested empirical remote sensing algorithms performed well in retrieving chlorophyll a, TSM, CDOM and Secchi depth from the reflectance produced by the C2RCC. This suggests that the atmospheric correction part of the processor performs relatively well while IOP retrieval part of the neural network needs extensive training with actual IOP data before it can produce reasonable estimates for the Baltic Sea.

Instantaneous determinations of photosynthetically active radiation (PAR) over the sea from the Ocean and Land Color Instrument (OLCI) on Sentinel-3 are compared with in-situ measurements at the island of Lampedusa in the central Mediterranean Sea. Radiative transfer calculations show that the PAR measured at the island site is representative for open ocean conditions. Satellite data show a good agreement (5.2% positive bias, R2 = 0.97) with in-situ data, in line with similar analyses for other satellite sensors. Larger satellite-in situ differences are found during summer, and the possible role of aerosols in degrading PAR estimate has been investigated by comparing AOD values measured at Lampedusa and derived by OLCI. The relative difference between OLCI and in-situ PAR appears to be negatively correlated with the relative differences between OLCI and in-situ AOD, suggesting that a more accurate determination of AOD, in particular, for cases with AOD > 0.2, mostly related to Saharan dust, may lead to improved satellite PAR estimates.

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

Algal blooms by eutrophication are regarded as a serious issue in many regions including Korea’s Four Major Rivers. Accurately measuring water Chlorophyll-a (Chl-a) is essential to propose effective solutions for addressing this problem. However, it is very hard to obtain water quality data for all desired regions through direct measurement. By utilizing remote sensing to collect a large amount of data from various water bodies, an accurate and rapid model to estimate Chl-a concentration can be developed, playing a crucial role in addressing the algae problem.This study utilized Sentinel-3 OLCI (Ocean and Land Color Instrument) data with a spatial resolution of approximately 300 meters. Bio-optical algorithms were applied to estimate Chl-a concentration. Bio-optical algorithms vary in types depending on the parameters used, such as radiance, reflectance, and Inherent optical properties (IOPs). In this study, IOPs were utilized to use the inherent properties of water. Accurate IOPs estimation is important because the coefficients of IOPs estimation algorithms are influenced by regional and temporal variability. The Bottom of Atmosphere (BOA) reflectance, derived from radiance data of OLCI EFR using the C2RCC processor, was utilized to estimate IOPs. Based on the derived IOPs, bio-optical algorithms were applied to estimate Chl-a concentration. After that, reinforcement learning was employed to refine the IOPs estimation process, dynamically adjusting coefficients to improve Chl-a concentration accuracy across varying conditions. Observed Chl-a data from the Water Environment Information System were used for model training and validation. Therefore, this study aims to estimate and map algal concentrations across Korea’s Four Major Rivers. Reflectance-based NDWI was calculated to delineate inland water bodies, and the reflectance data were incorporated into the Chl-a reinforcement learning model developed in this study to generate detailed spatial maps. This study is expected to contribute to solving green algae problems and water quality management by enabling more accurate and rapid Chl-a concentration estimation as it is not swayed by regional and temporal variations.

Evaluation of the impact of turbidity on satellite-derived bathymetry (SDB) is a crucial step for selecting optimal scenes and for addressing the limitations of SDB. This study examines the relatively high-resolution MultiSpectral instrument (MSI) onboard Sentinel-2A (10–20–60 m) and the moderate-resolution Ocean and Land Color instrument (OLCI) onboard Sentinel-3A (300 m) for generating bathymetric maps through a conventional ratio transform model in environments with some turbidity in South Florida. Both sensors incorporate additional spectral bands in the red-edge near infrared (NIR) region, allowing turbidity detection in optically shallow waters. The ratio model only requires two calibration parameters for vertical referencing using available chart data, whereas independent lidar surveys are used for validation and error analysis. The MSI retrieves bathymetry at 10 m with errors of 0.58 m at depths ranging between 0–18 m (limit of lidar survey) in West Palm Beach and of 0.22 m at depths ranging between 0–5 m in Key West, in conditions with low turbidity. In addition, this research presents an assessment of the SDB depth limit caused by turbidity as determined with the reflectance of the red-edge bands at 709 nm (OLCI) and 704 nm (MSI) and a standard ocean color chlorophyll concentration. OLCI and MSI results are comparable, indicating the potential of the two optical missions as interchangeable sensors that can help determine the selection of the optimal scenes for SDB mapping. OLCI can provide temporal data to identify water quality characteristics and general SDB patterns. The relationship of turbidity with depth detection may help to enhance the operational use of SDB over environments with varying water transparency conditions, particularly in remote and inaccessible regions of the world.

The unprecedented availability of remote sensing data from different complementary Sentinel missions provides increasing opportunities to alleviate the spatial limitations of Sentinel-3 (S3) from an intersensor perspective. Nonetheless, effectively exploiting such intersensor synergies still raises important challenges for super-resolution (SR) algorithms in terms of operational data availability, sensor alignment and substantial resolution changes, among others. In this scenario, this article sets a new SR framework for spatially enhancing S3 ocean and land color instrument (OLCI) products by taking advantage of the higher spatial resolution of the Sentinel-2 (S2) multispectral instrument (MSI). To achieve this goal, we initially study some of the most important deep learning-based approaches. Then, we define a novel Level-4 SR framework which integrates a new convolutional neural network specially designed for super-resolving OLCI data. In contrast to other networks, the proposed SR architecture (termed as SRS3) employs a dense multireceptive field together with a residual channel attention mechanism to relieve the particularly low spatial resolution of OLCI while extracting more discriminating features for the large spatial resolution differences with respect to MSI. The experimental part of the work, conducted using ten coupled OLCI and MSI operational data, reveals the suitability of the presented Level-4 SR framework within the Copernicus programme context as well as the advantages of the proposed architecture with respect different state-of-the-art models when spatially enhancing OLCI products. The related codes will be publicly available at https://github.com/rufernan/SRS3 .

The on-orbit calibration performance of the Ocean and Land Color Instrument (OLCI) onboard the Sentinel-3A satellite, launched on February 16, 2016, is evaluated via a radiometric intersensor comparison with reference to the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Among the 21 OLCI bands (designated as “Oa” bands), which are reflective solar bands (RSBs), seven OLCI bands match up sufficiently well with the seven shortest wavelength SNPP VIIRS bands (M1–M7)—they are Oa02 at 412.5 nm, Oa03 at 442.5 nm, Oa04 at 490 nm, Oa06 at 560 nm, Oa08 at 665 nm, Oa12 at 754 nm, and Oa17 at 865 nm. The radiometric comparison adopts a “nadir-only” refinement of the simultaneous nadir overpass (SNO) approach and uses the official SNPP VIIRS RSB data processed by the Interface Data Processing Segment (IDPS). The time-series result for bands Oa02, Oa03, Oa08, and Oa17, with spectral coverage that well represents the spectral range of OLCI, shows two-year stability at the level of 0.3% that supports nominally correct on-orbit calibration. The result for Oa08, Oa09, and Oa10, the three spectrally adjacent bands matching M5, demonstrates the effects of spectral mismatch—different radiometric ratio baselines and seasonally modulating patterns. Lastly, this result clarifies some key findings of earlier studies involving SNPP VIIRS and illustrates great potential for significantly more radiometric evaluation activities in the new era of Earth observations with many more powerful multispectral sensors coming into operation.

The integrated water vapor (IWV) products collected from June 1, 2019 to May 31, 2020 from the ocean and land color instrument (OLCI) sensor, onboard the Sentinel-3 satellites, are evaluated against reference water vapor data estimated from ground-based 214 global positioning system (GPS) stations in the Mainland China. This is the first time to thoroughly evaluate the quality of Sentinel-3 OLCI IWV products by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> GPS-measured IWV data from such a large spatial coverage as China. The validation results show that, under cloud-free conditions, the OLCI IWV measurements agree very well with the ground-based GPS water vapor data, with a root-mean-square error (RMSE) of 3.03 mm for Sentinel-3A satellite and 3.13 mm for Sentinel-3B satellite. The dependence of OLCI IWV on various parameters was also analyzed. Analysis showed that the accuracy of inland OLCI IWV products was superior to that in coastal areas and that OLCI tended to overestimate IWV value in lower elevation and underestimate IWV value in higher elevation. The accuracy of OLCI IWV measurements increased as IWV decreased. Solar zenith angle analysis showed that the OLCI IWV product had a higher accuracy at a larger solar zenith angle. In spring and winter, the OLCI IWV observations had higher accuracy than those in summer and autumn. OLCI IWV tended to underestimate IWV value in most land cover types. Except for the polar climatic zone, the Sentinel-3 OLCI IWV products tended to overestimate IWV value. The validation results against previous studies were also discussed in this work.

<strong class="journal-contentHeaderColor">Abstract.</strong> Validation of the Integrated Water Vapour (IWV) from Sentinel-3 Ocean and Land Colour Instrument (OLCI) was performed as a part of the “ESA/Copernicus Space Component Validation for Land Surface Temperature, Aerosol Optical Depth and Water Vapour Sentinel-3 Products” (LAW) project. High-spatial-resolution IWV observations in the near-infrared spectral region from the OLCI instruments aboard the Sentinel-3A and Sentinel-3B satellites provide continuity with observations from MERIS (Medium Resolution Imaging Spectrometer). The IWV was compared with reference observations from two networks: GNSS (Global Navigation Satellite System) precipitable water vapour from the SuomiNet network and integrated lower tropospheric columns from radio-soundings from the IGRA (Integrated Radiosonde Archive) database. Results for cloud-free matchups over land show a wet bias of 7 %–10 % for OLCI, with a high correlation against the reference observations (0.98 against SuomiNet and 0.90 against IGRA). Both OLCI-A and OLCI-B instruments show almost identical results, apart from an anomaly observed in camera 3 of the OLCI-B instrument, where observed biases are lower than in other cameras in either instrument. The wavelength drift in sensors was investigated, and biases in different cameras were found to be independent of wavelength. Effect of cloud proximity was found to have almost no effect on observed biases, indicating that cloud flagging in the OLCI IWV product is sufficiently reliable. We performed validation of random uncertainty estimates and found them to be consistent with the statistical a posteriori estimates, but somewhat higher.

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

The Quasi-Analytical Algorithm (QAA) is effective in retrieving water inherent optical properties (IOPs) from remote sensing spectral reflectance and has wider applications in studies of the open ocean and coastal waters than of inland waters. This research aimed to modify the QAA model based on measured field spectral reflectance and absorption coefficients to render it applicable to studies of Daihai Lake, China. The improvements mainly included the reference wavelength selection, the power index of the particle backscattering coefficient and the exponential slope of the absorption coefficient of the colored detrital matter estimation. The average relative error between the inverse and measured absorption coefficients was less than 20%. A linear model was established between the phytoplankton absorption coefficient at a wavelength of 674 nm (aph(674)) and the chlorophyll-a (Chl-a) concentration, with a determination coefficient of 0.88. Additionally, the modified Quasi-Analytical Algorithm (MQAA) model was applied to the Ocean and Land Color Instrument (OLCI) data aboard the Sentinel-3 satellite. Finally, a spatial distribution map for the Chl-a concentrations in Daihai Lake on August 10, 2017, was drawn and the mid-eutrophication area was found to occur in the north and border.

Recurrent transnational Sargassum blooms across the Caribbean Sea and Atlantic Ocean have received growing attention. Different multispectral sensors, including Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imager Radiometer Suite (VIIRS), and Ocean and Land Color Instrument (OLCI), have been used to map their distributions. However, the synergistic use of multi-sensor observations for high temporal resolution Sargassum monitoring is lacking. Here, by combining MODIS (on Aqua and Terra), VIIRS (on JPSS1 and SNPP), and OLCI (on Sentinel-3A and -3B) observations, 3-day mean Sargassum distributions were mapped across the Caribbean Sea and Central Atlantic. The Sargassum biomass densities were derived using the sensor-specific Alternative Floating Algae Index (AFAI)-biomass model, and the consistency between the six sensors was examined using MODIS Aqua as the reference sensor. Comparison of the Sargassum biomass derived from different sensors shows that they have strong linear correlations (R2 ≥ 0.95), demonstrating high consistency and continuity between the six-sensor observations. On average, the combined six-sensor datasets provide ∼1.6 times more valid observations compared to the MODIS-only dataset in 2021, enabling the generation of the 0.5° 3-day mean products over ∼90% of the study region. Such 0.5° 3-day mean products detected ∼10-20% more biomass in the bloom peak month (June 2021) compared to the monthly mean counterpart. Increasing the spatial resolution to 0.1°, the 3-day mean products can continuously monitor Sargassum dynamics with eddies and tropical cyclones, which cannot be well captured by single sensors. This study highlights that combining multiple polar-orbiting satellite observations can achieve 3-day gap-free monitoring of floating macroalgae dynamics in the Caribbean Sea and tropical Atlantic, thus facilitating the analyses of the bloom response to different environmental conditions and the prediction of future bloom events.

Global estimates of surface albedo from Sentinel-3 OLCI and SLSTR data for Copernicus Climate Change Service: Algorithm and preliminary validation

We present here results that demonstrate the potential of the recently launched Ocean and Land Colour Instrument (OLCI) onboard the satellite Sentinel-3A to deliver accurate estimates of chlorophyll-a (chl-a) concentration in coastal waters using reflectances in the red and near-infrared (NIR) spectral regions. Two-band and three-band NIR-red models that were previously used for data from the MEdium Resolution Imaging Spectrometer (MERIS) were applied to OLCI data from the Sea of Azov and the Taganrog Bay, Russia. Atmospherically corrected reflectance data from OLCI were compared to in situ reflectance data collected concurrently with a field spectrometer. Results show that the default atmospheric correction procedure currently applied to OLCI data performs well in preserving the spectral shape of chl-a-specific reflectance features in the red and NIR regions. Similar to what was achieved with MERIS data, the NIR-red models yield accurate estimates of chl-a concentration, with accuracies on the order of 90%, though the parameters of the NIR-red algorithms based on OLCI data are slightly different from what was obtained with MERIS data. More data, from various geographical locations, need to be analyzed to establish robust NIR-red algorithms for OLCI data.

Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European Space Agency (ESA) Sentinel 3 (S3) satellite, accurate LST retrieval methodologies are being developed by exploiting the synergy between the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). In this paper we explain the implementation in the Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM) and the use of one LST algorithm developed in the framework of the Synergistic Use of The Sentinel Missions For Estimating And Monitoring Land Surface Temperature (SEN4LST) project. The LST algorithm is based on the split-window technique with an explicit dependence on the surface emissivity. Performance of the methodology is assessed by using MEdium Resolution Imaging Spectrometer/Advanced Along-Track Scanning Radiometer (MERIS/AATSR) pairs, instruments with similar characteristics than OLCI/ SLSTR, respectively. The LST retrievals were properly validated against in situ data measured along one year (2011) in three test sites, and inter-compared to the standard AATSR level-2 product with satisfactory results. The algorithm is implemented in BEAM using as a basis the MERIS/AATSR Synergy Toolbox. Specific details about the processor validation can be found in the validation report of the SEN4LST project.