Land Colour Instrument Research Articles

Comparison of iCOR and Rayleigh atmospheric correction methods on Sentinel-3 OLCI images for a shallow eutrophic reservoir.

Remote sensing of inland waters is challenging, but also important, due to the need to monitor the ever-increasing harmful algal blooms (HABs), which have serious effects on water quality. The Ocean and Land Color Instrument (OLCI) of the Sentinel-3 satellites program is capable of providing images for the monitoring of such waters. Atmospheric correction is a necessary process in order to retrieve the desired surface-leaving radiance signal and several atmospheric correction methods have been developed through the years. However, many of these correction methods require programming language skills, or function as commercial software plugins, limiting their possibility of use by end users. Accordingly, in this study, the free SNAP software provided by the European Space Agency (ESA) was used to evaluate the possible differences between a partial atmospheric correction method accounting for Rayleigh scattering and a full atmospheric correction method (iCOR), applied on Sentinel-3 OLCI images of a shallow, highly eutrophic water reservoir. For the complete evaluation of the two methods, in addition to the comparison of the band reflectance values, chlorophyll (CHL) and cyanobacteria (CI) indices were also calculated and their values were intercompared. The results showed, that although the absolute values between the two correction methods did not coincide, there was a very good correlation between the two methods for both bands' reflectance (r>0.73) and the CHL and CI indices values (r>0.95). Therefore, since iCOR correction image processing time is 25 times longer than Rayleigh correction, it is proposed that the Rayleigh partial correction method may be alternatively used for seasonal water monitoring, especially in cases of long time-series, enhancing time and resources use efficiency. Further comparisons of the two methods in other inland water bodies and evaluation with in situ chlorophyll and cyanobacteria measurements will enhance the applicability of the methodology.

Natural and human activities driving the spatiotemporal variability of water clarity in lakes across Eastern China

• The water clarity presents a pattern of “high in the south and low in the north”. • The month of highest Z sd is postponed from June in the south to August in the north. • Natural and human activities contributed equally to Z sd in Eastern lakes. Water clarity, indexed by Secchi disk depth ( Z sd ), comprehensively reflects water quality and can be monitored by satellite. Z sd may vary across lakes globally, driven by natural and human activities (climate, land use/land cover, lake morphological characteristics, etc.). Based on Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery, we explored the driving factors of Z sd spatiotemporal patterns in lakes across Eastern China (including the Northeast Plain and Mountain Lake (NPML) Zone and the Eastern Plain Lake (EPL) Zone). Spatially, the responses of driving factors were assessed using feature importance and partial dependence in the random forest model. The results showed that the impacts of natural factors on the spatial variations of Z sd (accounting for 52.00 % of contributions) are comparable to that of human activities (accounting for 48.00 % of contributions). The dynamic sediment ratio (DSR = the square root of the surface area divided by the average depth) showed the highest impact (accounting for 19.64 % of contributions). Thus, small lakes have relatively small DSR indices, making them less susceptible to wind- and wave-induced sediment resuspension, leading to higher Z sd than large lakes. Seasonally, the variations could be linked with the East Asian monsoon climate, characterized by hot and rainy summers. Moreover, the month with the highest Z sd was postponed from June-July in EPL lakes (south) to August-September in NPML lakes (north), consistent with the movement of the rainfall belt of the East Asian monsoon. This study could facilitate the knowledge of water quality in Eastern China as well as other developed areas and provide support for the sustainable development and management of lakes.

Seasonal dynamics of dissolved organic matter in the Mackenzie Delta, Canadian Arctic waters: Implications for ocean colour remote sensing

Increasing air temperatures and associated permafrost thaw in Arctic river watersheds, such as the Mackenzie River catchment, are directly affecting the aquatic environment. As a consequence, the quantity and the quality of dissolved organic carbon (DOC) that is transported via the Mackenzie River into the Arctic Ocean is expected to change. Particularly in these remote permafrost regions of the Arctic, monitoring of terrigenous organic carbon fluxes is insufficient and knowledge of distribution and fate of organic carbon when released to the coastal waters is remarkably lacking. Despite its poorly evaluated performance in Arctic coastal waters, Satellite Ocean Colour Remote Sensing (SOCRS) remains a powerful tool to complement monitoring of land-ocean DOC fluxes, detect their trends, and help in understanding their propagation in the Arctic Ocean.In this study, we use in situ and SOCRS data to show the strong seasonal dynamics of the Mackenzie River plume and the spatial distribution of associated terrigenous DOC on the Beaufort Sea Shelf for the first time. Using a dataset collected during an extensive field campaign in 2019, the performance of three commonly-used atmospheric correction (AC) algorithms and two available colored dissolved organic matter (CDOM) retrieval algorithms were evaluated using the Ocean and Land Colour Instrument (OLCI). Our results showed that in optically-complex Arctic coastal waters the Polymer AC algorithm performed the best. For the retrieval of CDOM, the gsmA algorithm (Mean Percentage Error (MPE) = 35.7%) showed slightly more consistent results compared to the ONNS algorithm (MPE = 37.9%). By merging our measurements with published datasets, the newly-established DOC-CDOM relationship for the Mackenzie-Beaufort Sea region allowed estimations of DOC concentrations from SOCRS across the entire fluvial-marine transition zone with an MPE of 20.5%. Finally, we applied SOCRS with data from the Sentinel-3 OLCI sensor to illustrate the seasonal variation of DOC concentrations in the surface waters of the Beaufort Sea on a large spatial scales and high frequency throughout the entire open water period. Highest DOC concentrations and largest lateral extent of the plume were observed in spring right after the Mackenzie River ice break-up indicating that the freshet was the main driver of plume propagation and DOC distribution on the shelf. Satellite-derived images of surface water DOC concentration placed the in situ observations into a larger temporal and spatial context and revealed a strong seasonal variability in transport pathways of DOC in the Mackenzie- Beaufort Sea region.

Evaluation of seven atmospheric correction algorithms for OLCI images over the coastal waters of Qinhuangdao in Bohai Sea

Atmospheric correction (AC) over coastal waters is always critical in ocean color remote sensing. The performance of the AC algorithms determines the reliability of the retrieval of the water-leaving radiance and subsequent bio-optical parameters. In this study, the seven AC algorithms for the Ocean and Land Color Instrument (OLCI) were evaluated with an in situ dataset over the coastal waters of Qinhuangdao in the Bohai Sea, China: the Baseline Atmospheric Correction (BAC), the Case 2 Regional Coast Color Atmospheric Correction (C2RCC) and its alternative version (C2RCC-AltNets), the Management Unit Mathematical Models (MUMM), the polynomial-based algorithm applied to MERIS (POLYMER), the image correction for atmospheric effects (iCOR), and ACOLITE for Dark Spectrum Fitting (ACOLITE-DSF). For OLCI-derived remote sensing reflectance, Rrs(λ), where λ is light wavelength, on the respective match-ups, all algorithms except ACOLITE-DSF had higher accuracy in the wavelength range from 490 nm to 560 nm, with the absolute percentage difference (APD) < 43%. On the common match-ups of all algorithms, the performance of seven AC algorithms was better than that of the respective match-ups. POLYMER obtained the best performance in visible bands, with APD less than 24.04% and 17.13% for respective and common match-ups, respectively. In terms of blue/green band ratios, Rrs(490)/Rrs(560) obtained by all algorithms except C2RCC performed better than Rrs(442)/Rrs(560). POLYMER performed best in blue/green band ratios, with the APD of 10.38% and 5.51% for Rrs(442)/Rrs(560) and Rrs(490)/Rrs(560), respectively. Using a scoring scheme based on all statistical parameters, POLYMER scored the highest. ACOLITE-DSF represented the worst accuracy for all evaluations over our in situ dataset.

Optical classification of an urbanized estuary using hyperspectral remote sensing reflectance.

Optical water classification based on remote sensing reflectance (Rrs(λ)) data can provide insight into water components driving optical variability and inform the development and application of bio-optical algorithms in complex aquatic systems. In this study, we use an in situ dataset consisting of hyperspectral Rrs(λ) and other biogeochemical and optical parameters collected over nearly five years across a heavily urbanized estuary, the Long Island Sound (LIS), east of New York City, USA, to optically classify LIS waters based on Rrs(λ) spectral shape. We investigate the similarities and differences of discrete groupings (k-means clustering) and continuous spectral indexing using the Apparent Visible Wavelength (AVW) in relation to system biogeochemistry and water properties. Our Rrs(λ) dataset in LIS was best described by three spectral clusters, the first two accounting for the majority (89%) of Rrs(λ) observations and primarily driven by phytoplankton dynamics, with the third confined to measurements in river and river plume waters. We found AVW effective at tracking subtle changes in Rrs(λ) spectral shape and fine-scale water quality features along river-to-ocean gradients. The recently developed Quality Water Index Polynomial (QWIP) was applied to evaluate three different atmospheric correction approaches for satellite-derived Rrs(λ) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor in LIS, finding Polymer to be the preferred approach. Our results suggest that integrative, continuous indices such as AVW can be effective indicators to assess nearshore biogeochemical variability and evaluate the quality of both in situ and satellite bio-optical datasets, as needed for improved ecosystem and water resource management in LIS and similar regions.

Impact of cloud cover on local remote sensing – Piaśnica River case study

New satellite-based techniques open up new horizons to researchers and local communities. Concurrently, however, requirements and expectations with regard to satel-lite-based remote sensing products are increasingly higher. By relying on satellite-derived information, environmental observations can cover areas of a few to several metres resolution. Here we are dealing with freeof-charge and generally available sources of satellite-based information. The Piaśnica River mouth area was selected as an observation site representing a highly dynamic morphological transect. The paper compares products of cloud cover detection, supplied with data and available in the Copernicus database for a local area in the coastal zone of the Baltic Sea. The absolute difference did not exceed 5%, which confirms a high efficiency of the solutions offered. More than 96% of the clouded area determined for the Sentinel-2/MSI (Multispectral Instrument) was correctly identified when compared with supervised observations. The rate was lower (92%) for the Sentinel-3/OLCI (Ocean and Land Colour Instrument). It was eventually concluded that, at the local level, successful observations can be conducted using the cloud cover map supplied with the satellite data. At the same time, the analyses presented do not rule out further efforts to, e.g., increase the accuracy and speed of the analyses.

The Approximate Analytical Solution for the Top-of-Atmosphere Spectral Reflectance of Atmosphere—Underlying Snow System over Antarctica

The analytical solutions of the radiative transfer equation are needed for the solution of various applied atmospheric and snow optics problems. In this paper, we propose a simple analytical equation for the top-of-atmosphere (TOA) spectral reflectance. To simplify the problem under study we consider the case of Antarctica, where both snow and atmosphere are almost free of pollutants. This work is focused on the simulation of the moderate spectral resolution TOA measurements (1 nm or so) and the spectral range 400–1000 nm. The values of the coefficient of variance (CV) between the measured by the Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A and modelled spectra are smaller than 10% for most cases in Antarctica. There are regions in Eastern Antarctica, where the values of CV are smaller than 5%. The areas with larger deviations between measured and retrieved spectra could be due to the presence of clouds or structures on the snow surface not captured by the proposed model.

A hybrid remote sensing approach for estimating chemical oxygen demand concentration in optically complex waters: A case study in inland lake waters in eastern China

Chemical oxygen demand concentration (CCOD) is widely used to indicate the degree of organic pollution of lakes, reservoirs and rivers. Mastering the spatiotemporal distribution of CCOD is imperative for understanding the variation mechanism and controlling of organic pollution in water. In this study, a hybrid approach suitable for Sentinel 3A/Ocean and Land Colour Instrument (OLCI) data was developed to estimate CCOD in inland optically complex waters embedding the interaction between CCOD and the absorption coefficients of optically active constituents (OACs). Based on in-situ sampling in different waters, the independent validations of the proposed model performed satisfactorily in Lake Taihu (MAPE = 23.52 %, RMSE = 0.95 mg/L, and R2 = 0.81), Lake Qiandaohu (MAPE = 21.63 %, RMSE = 0.50 mg/L and R2 = 0.69), and Yangtze River (MAPE = 29.34 %, RMSE = 0.83 mg/L, and R2 = 0.64). In addition, the approach not only showed significant superiority compared with previous algorithms, but also was suitable for other common satellite sensors equipped same or similar bands. The hybrid approach was applied to OLCI images to retrieve CCOD of Lake Taihu from 2016 to 2020 and reveals substantial interannual and seasonal variations. The above results indicate that the proposed approach is effective and stable for studying spatiotemporal dynamic of CCOD in optically complex waters, and that satellite-derived products can provide reliable information for lake water quality management.

Extended validation and evaluation of the OLCI–SLSTR SYNERGY aerosol product (SY_2_AOD) on Sentinel-3

Abstract. We present the first extended validation of a new SYNERGY global aerosol product (SY_2_AOD), which is based on synergistic use of data from the Ocean and Land Color Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR) sensors aboard the Copernicus Sentinel-3A (S3A) and Sentinel-3B (S3B) satellites. Validation covers period from 14 January 2020 to 30 September 2021. Several approaches, including statistical analysis, time series analysis, and comparison with similar aerosol products from the other spaceborne sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), were applied for validation and evaluation of S3A and S3B SY_2 aerosol products, including aerosol optical depth (AOD) provided at different wavelengths, AOD pixel-level uncertainties, fine-mode AOD, and Angström exponent. Over ocean, the performance of SY_2 AOD (syAOD) retrieved at 550 nm is good: for S3A and S3B, Pearson correlation coefficients with the Maritime Aerosol Network (MAN) component of the AErosol RObotic NETwork (AERONET) are 0.88 and 0.85, respectively; 88.6 % and 89.5 % of pixels fit into the MODIS error envelope (EE) of ±0.05 ± 0.2 × AOD. Over land, correlation coefficients with AERONET AOD (aAOD) are 0.60 and 0.63 for S3A and S3B, respectively; 51.4 % and 57.9 % of pixels fit into MODIS EE. Reduced performance over land is expected since the surface reflectance and angular distribution of scattering are higher and more difficult to predict over land than over ocean. The results are affected by a large number of outliers. Evaluation of the per-retrieval uncertainty with the χ2 test indicates that syAOD prognostic uncertainties (PU) are slightly underestimated (χ2 = 3.1); if outliers are removed, PU describes the syAOD error well (χ2 = 1.6). The regional analysis of the Angström exponent, which relates to the aerosol size distribution, shows spatial correlation with expected sources. For 40 % of the matchups with AERONET in the Northern Hemisphere (NH) and for 60 % of the matchups in the Southern Hemisphere (SH), which fit into the AE size range of [1, 1.8], an offset between SY_2 AE (syAE) and AERONET AE (aAE) is within ±0.25. General overestimation of low (&lt; 0.5) syAE and underestimation of high (&gt; 1.8) syAE results in high (0.94, globally) overall bias. Good agreement (bias &lt; 0.03) was observed between Sy_2 fine-mode AOD (syFMAOD) and AERONET fine-mode AOD (aFMAOD) for aFMAOD &lt; 1. At aFMAOD &gt; 1, syFMAOD is considerably underestimated (by 0.3–0.5 in different aFMAOD ranges) in the NH. In the SH, only a few aFMAOD values above 1 are measured. The fine-mode fraction (FMF) in the SY_2 AOD product (syFMF) in the range of [0, 0.7] is overestimated; the positive offset of 0.3–0.5 for low (&lt; 0.25) FMF gradually decreases. Differences between the annual and seasonal AOD values from SY_2 and MODIS (mod) Dark Target and Deep Blue products are within 0.02 for the study area (30∘ S–60∘ N, 80∘ W–45∘ E). The agreement is better over ocean; however, a difference up to 0.6 exists between syFMF and modFMF. Over bright land surface (Saharan desert) the difference in AOD between the two products is highest (up to 0.11); the sign of the difference varies over time and space. For both S3A and S3B AOD products, validation statistics are often slightly better in the Southern Hemisphere. In general, the performance of S3B is slightly better.

Validation of Copernicus Sentinel-3/OLCI Level 2 Land Integrated Water Vapour product

Abstract. 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 Estimation of the Particulate Phosphorus Concentrations in Inland Water Bodies: A Case Study in Hongze Lake

Phosphorus is the most important nutrient associated with lake eutrophication and changes in cyanobacterial blooms, and particulate phosphorus (PP) is the main form of phosphorus found in highly turbid inland waters. Therefore, it is urgent to monitor PP concentrations in inland water bodies. In this study, we take Hongze Lake as the research area and establish a semianalytical model to estimate PP concentrations based on the total particle absorption coefficient (ap); the mean absolute percentage error (MAPE) and root-mean-square error (RMSE) values, which indicate the model accuracy, were 14.90% and 0.009 mg/L, respectively. In addition, the construction process and parameter selection criteria of the remote sensing-based PP concentration estimation model were derived using remote sensing data obtained at different spectral resolutions. Sentinel 3 Ocean and Land Color Instrument (OLCI) and Landsat 9 Operational Land Imager version 2 (OLI-2) data were selected as representatives to verify the accuracy of the model; compared to these two datasets, the MAPE values of the models were 16.32% and 26.84%, respectively, while the RMSE values were 0.010 mg/L and 0.014 mg/L, respectively. Finally, the models were applied to Sentinel 3 OLCI and Landsat 9 OLI-2 images obtained on 16 January 2022. The results show that the spatiotemporal distributions of PP concentrations in Hongze Lake estimated from these two images were relatively consistent, but the OLI data reflected overestimations and underestimations in some areas. These research results provide a new methodology for estimating PP concentrations through remote sensing methods and help to further improve the accuracy of remotely sensed PP concentration estimations in inland water bodies.

Data-Free Area Detection and Evaluation for Marine Satellite Data Products

The uncertainty verification of satellite ocean color products and the bias analysis of multiple data are both indispensable in the evaluation of ocean color products. Incidentally, ocean color products often have missing information that causes the methods mentioned above to be difficult to evaluate these data effectively. We propose an analysis and evaluation method based on data-free area. The objective of this study is to evaluate the quality of ocean color products with respect to information integrity and continuity. First, we use an improved Spectral Angle Mapper, also called ISAM. It can automatically obtain the optimal threshold value for each class of objects. Then, based on ISAM, we perform spectral information mining on first-level Yellow Sea and Bohai Sea data obtained from the Geostationary Ocean Color Imager (GOCI), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean and Land Color Instrument (OLCI). In this manner, quantitative results of information related to data-free areas of ocean data products are obtained. The findings indicate that the product data of OLCI are optimal with respect to both completeness and continuity. GOCI and MODIS have striking similarities in their quantitative or visualization results for both evaluation metrics. Moreover, a concomitant phenomenon of ocean-covered objects is apparent in the data-free area with temporal and spatial distribution characteristics. The two characteristics are subsequently explored for further analysis. The evaluation method adopted in this study can help to enrich the content of ocean color product evaluation, facilitate the research of cloud detection algorithms and further understand the composition of the data-free regional information of marine data products. The method proposed in this study has a wide application value.

Geospatial Analysis with Landsat Series and Sentinel-3B OLCI Satellites to Assess Changes in Land Use and Water Quality over Time in Brazil

Geospatial analyses have gained fundamental importance on a global scale following emphasis on sustainability. Here we geospatially analyze images from Landsat 2/5/7/8 satellites captured during 1975 to 2020 in order to determine changes in land use. Sentinel-3B OLCI (Ocean Land Color Instrument) images obtained in 2019 and 2021 were utilized to assess water resources, based on water turbidity levels (TSM_NN), suspended pollution potential (ADG_443_NN) and the presence of chlorophyll-a (CHL_NN) in order to temporally monitor the effectiveness of Brazilian legislation currently in force. This work on sustainability standards was applied to a hydrographic basin dedicated to agricultural production located in southern Brazil. Satellite images from Landsat 2/5/7/8 (1975 to 2020) and Sentinel-3B OLCI (2019 and 2021) revealed that changes in land use, vegetation cover and water in the Capinguí Dam reservoir detected high concentrations of ADG_443_NN (3830 m−1), CHL_NN (20,290 mg m−3) and TSM_NN (100 gm−3). These results can alert the population to the risks to public health and harm to hydrographic preservation, capable of covering large regions.

Sentinel-3B OLCI satellite imagery and advanced electron microscopy of nanoparticle analysis in a marine estuary and watershed: Robust multi-analytics and geospatial assessment of key contaminants

Globally, hydrographic basins are routinely contaminated by anthropogenic contaminants originating from coal mining, swine farming, urban sewage, pesticide use, and other various sources. These contaminants not only spread pollution but also nanoparticles (NPs) capable of contaminating the fauna and flora of estuaries in addition to being harmful to human health. The use of images from the Sentinel-3B OLCI (Ocean Land Color Instrument) satellite makes it possible to obtain spectral data aimed at identifying the presence of possible contamination in large regions, which can be complemented by advanced electron microscopies. The general objective of this study is to analyze the dispersion of NPs and ultrafine particles (UFPs) in a region that contains multiple contaminants through the use of images from the Sentinel-3B OLCI satellite. The authors examined images taken after two particularly intense tropical rain events in December 2018 and 2021, in the region of the Laguna estuary located in the Brazilian state of Santa Catarina. Suspended sediments (SSs) were collected from seventeen points on the Tubarão River in 2018. These samples were then submitted to an analytical laboratory for various analyses such as X-ray Diffraction (XRD), and transmission and scanning electron microscopes. Additionally, images from the Sentinel-3B satellite OLCI in the Laguna estuary region from 2018 and 2021 were analyzed based on the following variables: water turbidity (TSM_NN), chlorophyll-a (CHL_NN) and suspended pollution potential (ADG_443_NN). The SSs demonstrated the presence of massive crystals of ultrafine pyrite in a pseudomorph crystal of jarosite containing nanospheres with hazardous elements (e.g. As, Hg, Cd and Pb), which represent important pollutants. The Sentinel-3B OLCI images in relation to the variables TSM_NN, CHL_NN and ADG_443_NN (2018 and 2021), identified high turbidity in the waters of the Tubarão River. Analysis of SSs and satellite images are suitable for identifying contaminants toxic to the marine ecosystem and human health on a global scale.

Convolutional neural network model for discrimination of harmful algal bloom (HAB) from non-HABs using Sentinel-3 OLCI imagery

Harmful algal bloom (HAB) caused by Magalefidinium polykrikoides becomes frequent in Korean coastal waters during the mid-1990s and is now annual events on the southern coast of Korea. HAB often leads to high rates of fish mortality and subsequent economic losses in aquaculture. In addition, non-harmful algal blooms (non-HABs) caused by the dinoflagellate Alexandrium sp., Mesodinium rubrum, and the diatom Skeletonema sp. occur simultaneously in time and space. Because HAB and non-HABs are difficult to discriminate using multi-band satellite data, most previous studies have attempted only detection or qualitative classification with limited data. In contrast, in this current study, we aimed to quantitatively discriminate M. polykrikoides bloom associated HAB from non-HABs around the southern coast of Korea using a convolutional neural network (CNN) model with Sentinel-3 Ocean and Land Colour Instrument (OLCI) imagery with a spatial resolution of 300 m and 16 spectral bands for the first time. To identify the effect of non-HAB patches on the performance of the CNN model, five CNN models were trained with OLCI images as input and ground-truth HAB maps as output data. The appropriate figure-of-merits values (FOMs) with sensitivity of 0.53, precision of 0.92, and F-measure of 0.67 were reasonable when the CNN model trained using the dataset with the highest ratio of non-HABs patches was applied to HAB images. Even when non-HAB images were applied to the models, the CNN model exhibited the lowest error pixel count. Therefore, we confirmed that the CNN model, which can discriminate red tide blooms with subtle differences between the spectrum bands and spatial characteristics, helps solve the complexity and ambiguity in discriminating HAB from non-HABs.

First record of red-tide in Elba protectorate coast using Sentinel-3 and its impacts on ecosystem

This study is the first to document an algal bloom caused by Noctiluca scintillans, which was detected using remote sensing techniques and confirmed in the field along the coast of Elba National Park. Noctiluca scintillans is a phytoplankton species that accumulate toxic ammonia that endangers various organisms, including fish, birds, mammals, and human. Therefore, monitoring red tide is essential for water pollution control and management. This study aims to evaluate the potential of the recent launch of the Ocean and Land Color Instrument (OLCI) onboard the Sentinel-3 satellite to identify chlorophyll-a (Chl-a) as a proxy for an algal bloom event and study its impacts on the ecosystem. To fulfill the aim, OLCI images were collected over the study area from the 25th of May to the 13th of June 2017. Satellite images were converted from level 1 to level 2 through radiometric and atmospheric correction, and Chl-a maps were produced from the level 3 data during product development using standard Ocean Color 4 for MERIS (OC4Me) model. The time-series analyses of the Sentinel-3 satellite images indicated that the algal blooms (Red tide) event occurred four days at the studied sites (7th to 10th of June). The current findings reveal a strong relationship between the deterioration in water quality criteria and Noctiluca scintillans cell counts (×104) /L (0.05 > P ≤ 0.01). This study strongly urges routine monitoring of the physicochemical characteristics of coastal waters and using remote sensing techniques for early rid tide detection to control the phenomenon.

Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam

This paper aims to assess the potential of Ocean Land Colour Instrument (OLCI) for the retrieval of chlorophyll-a (chl-a) over southern coastal waters of Vietnam. For that purpose, four chlorophyll-a ocean color (OC) algorithms (OC4ME and three new OC version 7 OC4, OC5, OC6) were applied based on water-leaving reflectance obtained from two atmospheric correction processors (C2RCC and DSF). To overcome high cloud coverage in the area of interest, full spatial data reconstruction was implemented using Data Interpolating Empirical Orthogonal Functions (DINEOF). Numerical error metrics of in situ measurements (n = 49) collected in different ship-based campaigns has been assessed for Sentinel-3A (S-3A) and 3B (S-3B) as well as on the combined products built from these two later satellites. Results showed that products based on C2RRC significantly outperformed DSF. For chl-a algorithms, C2RCC-based OC5 gave the most accurate retrieval while applied to S-3A (R2: 0.58, RMSE: 1.018 mg m−3, MAPE: 49.4 %), S-3B (R2: 0.75, RMSE: 0.776 mg m−3, MAPE: 37.3 %), and synergy datasets (R2: 0.70, RMSE: 0.844 mg m−3, MAPE: 42.5 %). With>50 % of observations missing due to cloud cover, DINEOF provides a promising solution to reconstruct the full spatial information. The successfully demonstrated retrieval of chl-a in our study presents potential for daily monitoring when combining observations from S-3A/B to further improve our understanding of the spatio-temporal dynamics of coastal ecosystems.

Comparison of phycocyanin concentrations in Chaohu Lake, China, retrieved using MODIS and OLCI images

Phycocyanin (PC) concentration is used as an indicator to characterize cyanobacteria biomass while monitoring eutrophication in inland water. Remote sensing provides useful methods for quantifying PC concentration; however, there is a shortage of datasets for the long-term monitoring of PC concentration when only a single remote sensing data is used. Therefore, PC concentrations obtained from multisource remote sensing images should be compared before integrating them for long-term monitoring. In this study, machine learning (ML) regression algorithms are used to develop PC concentration retrieval models suitable for Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-3 Ocean and Land Colour Instrument (OLCI) images, and their accuracies are compared. The two optimal retrieval models are applied to satellite images acquired on the same days to compare the spatial consistency of the two PC concentration retrieval results. The results show that the sensitive spectral range of PC concentration is 560–680 nm. Among the ML regression algorithms, gradient boosted tree (GBT) regression exhibits the highest PC retrieval accuracy for both the MODIS images (R2 = 0.82, RMSE = 61.9 μg/L) and OLCI images (R2 = 0.86, RMSE = 45.44 μg/L). The PC concentrations retrieved from the MODIS and OLCI images acquired in bloom and no-bloom periods have a high spatial consistency in most areas of Chaohu Lake. Their correlation coefficient also exceeds 0.7, and the average relative error reaches 0.293 μg/L. However, a large difference exists in areas with high PC concentrations, which may cause by the poor applicability of atmospheric correction algorithms and PC retrieval models in these areas. The proposed PC concentration retrieval models developed using GBT regression in this paper can expend the idea for the quantitative retrieval of other inland water quality parameters in inland water, and the conclusions should enable the effective integration of MODIS and OLCI images for the time series monitoring of PC concentrations in reservoirs and lakes.

Properties of aerosol and surface derived from OLCI/Sentinel-3A using GRASP approach: Retrieval development and preliminary validation

The Ocean and Land Color Instrument (OLCI) onboard the Copernicus Sentinel-3A satellite is a medium-resolution and multi-spectral push-broom imager acquiring radiance in 21 spectral bands covering from the visible to the far near-infrared. These measurements are primary dedicated to land & ocean color applications, but actually include also reliable information for atmospheric aerosol and surface brightness characterization. In the framework of the EUMETSAT funded study to support the Copernicus Program, we describe the retrieval of aerosol and surface properties from OLCI single-viewing multi-spectral Top-Of-Atmosphere (TOA) radiances based on the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm. The high potential of the OLCI/GRASP configuration stems from the attempt to retrieve both aerosol load and surface reflectance simultaneously using a globally consistent high-level approach. For example, both over land and ocean surfaces OLCI/GRASP uses 9 spectral channels (albeit with different weights), strictly the same prescribed aerosol models and globally the same a priori constraints (though with some differences for observations over land and ocean). Due to the lack of angular multi-viewing information, the directional properties of underlying surface are strongly constrained in the retrieval: over ocean the Fresnel reflection together with foam/whitecap albedo are exclusively computed using a priori wind speed; over land, the Bidirectional Reflectance Distribution Function (BRDF) is slightly adjusted from a priori values of climatological Ross-Li volumetric and geometric terms. Meanwhile, the isotropic reflectance is retrieved globally under mild spectral smoothness constraints. It should be noticed that OLCI/GRASP configuration employs innovative multi-pixel concept (Dubovik et al., 2011) that enhance retrieval by simultaneously inverting large group of pixels. The concept allows for benefiting from knowledge about natural variability of the retrieved parameters.The obtained OLCI/GRASP products were validated with the Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) and intercompared with the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol and surface products. The overall performance is quite comparable to the community-referenced MODIS. Over ocean the OLCI/GRASP results are encouraging with 67% of the AOD (550 nm) satisfying the Global Climate Observing System (GCOS) requirement using AERONET coastal sites and 74% using MAN deep ocean measurements, and an AOD (550 nm) bias 0.01 with AERONET and nearly zero bias with MAN. Over land, 48% of OLCI/GRASP AOD (550 nm) satisfy the GCOS requirement and a bias within ±0.01 for total and AOD < 0.2. Key challenges are identified and discussed: adequate screening of cloud contaminations, retrieval of aerosol over bright surfaces and in the regions containing complex mixtures of aerosol.

Characterising retrieval uncertainty of chlorophyll-a algorithms in oligotrophic and mesotrophic lakes and reservoirs

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.