Land Colour Instrument Research Articles

Evaluation of twelve algorithms to estimate suspended particulate matter from OLCI over contrasted coastal waters

Remote sensing of suspended particulate matter (SPM) is crucial for water-quality monitoring, as it influences turbidity, light availability, or nutrient transport. This study aims to provide a comprehensive evaluation of twelve common and well-used SPM models for the Ocean and Land Color Instrument (OLCI) on-board Sentinel-3 satellite, based on different methods and assumptions, including estimation from water-leaving reflectance or proxies, a combination of semi-analytical equations, and machine learning algorithms. The models are tested in three stages: 1) performance assessment on in-situ measurements, 2) matchup exercise with OLCI and 3) visual assessment of satellite SPM products. The models are first tested on the GLORIA dataset (n = 767, 0.21 g.m−3 <SPM <2,626.82 g.m−3). The matchup analysis is then conducted in French coastal waters using the SOMLIT dataset (n = 71, 0.2 g.m−3 <SPM <722 g.m−3), based on the standard OLCI L2 remote sensing reflectance product. Finally, the visual assessment of the SPM maps provided by the twelve models is conducted for two French coastal sites. Results show that the algorithms proposed by Jiang et al. [ Remote. Sens. Environ. 258, 112386 (2021)10.1016/j.rse.2021.112386 ] and Novoa et al. [ Remote. Sens. 9, 61 (2017)10.3390/rs9010061 ] exhibit the highest score and the most accurate retrievals when compared to in-situ measurements. However, the matchup exercise shows that the method from Jiang et al. demonstrates more overall accurate SPM retrievals (Error = 49.85%, Bias = 0.55%, RMSLE = 0.35, Slope = 1.06). The visual assessment of SPM maps reveals that this model displays a larger dynamic range, making it suitable for applications in regions with a wide range of SPM concentrations. The sensitivity of these models to the atmospheric correction procedure is further explored. When all OLCI spectra are taken into account for the matchup exercise, the performance of the algorithms from Han et al. [ Remote. Sens. 8, 211 (2016)10.3390/rs8030211 ] improve, relative to the other one. Finally, the standard OLCI SPM product is evaluated, and the advantages of using the OLCI standard product over the MODIS one for studying coastal waters are discussed.

Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll a concentration in the Black Sea

Abstract. Satellite observations provide a global or near-global coverage of the World Ocean. They are however affected by clouds (among others), which severely reduce their spatial coverage. Different methods have been proposed in the literature to reconstruct missing data in satellite observations. For many applications of satellite observations, it has been increasingly important to accurately reflect the underlying uncertainty of the reconstructed observations. In this paper, we investigate the use of a denoising diffusion model to reconstruct missing observations. Such methods can naturally provide an ensemble of reconstructions where each member is spatially coherent with the scales of variability and with the available data. Rather than providing a single reconstruction, an ensemble of possible reconstructions can be computed, and the ensemble spread reflects the underlying uncertainty. We show how this method can be trained from a collection of satellite data without requiring a prior interpolation of missing data and without resorting to data from a numerical model. The reconstruction method is tested with chlorophyll a concentration from the Ocean and Land Colour Instrument (OLCI) sensor (aboard the satellites Sentinel-3A and Sentinel-3B) on a small area of the Black Sea and compared with the neural network DINCAE (Data-INterpolating Convolutional Auto-Encoder). The spatial scales of the reconstructed data are assessed via a variogram, and the accuracy and statistical validity of the reconstructed ensemble are quantified using the continuous ranked probability score and its decomposition into reliability, resolution, and uncertainty.

Spatio-temporal Variation of Chlorophyll-a around the Flores Sea, Java Sea, and Makassar Strait and its Relationship with Temperature and Salinity

Abstract The Flores Sea, Java, and Makassar Strait are areas where water masses meet as well as one of the entry points for the Indonesian Through Flow. Freshwater mass from the Pacific Ocean enters Indonesian waters through the Makassar Strait. Not only that, Indonesia’s maritime region is influenced by the Asian-Australian Monsoon, which also causes seasonal changes in temperature and salinity. This phenomenon affects primary productivity in Indonesian waters as indicated by variations in chlorophyll-a concentrations in surface waters. Therefore, it is important to study the dynamics of chlorophyll-a in time series to better understand the ecosystem and the phenomena that occur. This study aimed to analyze the spatio-temporal variation of chlorophyll-a in the waters surrounding the Flores Sea, Java, and Makassar Strait, and its correlation with temperature and salinity over a period of seven years, from 2016 to 2023. This reasearch uses chlorophyll-a data collected from sensor, named Ocean and Land Color Instrument carried by the Sentinel-3 satellite. The chlorophyll-a data used is Ocean Color 4 for MERIS data. This study found that chlorophyll-a concentrations in the Flores Sea, Java, and Makassar Strait sample areas tend to be high during the Northwest Monsoon period, along with the arrival of the rainy season which increases river water runoff. On the other hand, chlorophyll-a concentrations in the southern waters of Java to Nusa Tenggara exhibit a notable increase during the Southeast Monsoon, which is subsequently accompanied by a decline in temperature of sea surface and a rise in salinity of sea surface. In 2018 and 2019, the occurrence of the El Niño phenomenon, along with the positive phase of the IOD, led to an increase in chlorophyll-a. From 2018 to 2019, the average chlorophyll-a reached 0.71 mg/m3 in the Flores Sea and Makassar Strait and 0.73 mg/m3 in the Java Sea. This study analyzes chl-a based on satellite observation data that may have errors due to cloud cover. Further research with in situ observation methods is needed for better results and a longer temporal range so that the variability of chl-a against the global phenomena of ENSO and IOD can be seen more clearly.

Seamless observations of chlorophyll-a from OLCI and VIIRS measurements in inland lakes

The Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) are two main instruments for the ocean color community to observe the global lake environment in the following decades. Despite their applications to retrieve various water optical parameters, the spatial and temporal resolutions of individual sensors cannot meet the requirements for lake monitoring effectively. To date, the possibility of complementary observations through the OLCI-VIIRS data to lake aquatic environments remains unclear. Here, we evaluated the agreement between OLCI and VIIRS-derived remote sensing reflectance (Rrs(λ)) and chlorophyll-a (Chl-a) in Chinese lakes spanning a variety of lake characteristics. We find that OLCI Rrs(λ) data generated by the NOAA Multi-Sensor Level-1 to Level-2 (MSL12) system perform satisfactory accuracy in 20 Chinese lakes with less than 30 % uncertainty from 490 nm to 865 nm and show good agreements with VIIRS Rrs(λ) in more than 200 large lakes in China (> 0.90 correlation). The deep neural network algorithm outperformed several state-of-the-art algorithms in Chl-a estimates from OLCI images (23 % bias). The spatial and temporal patterns of OLCI and VIIRS-derived Chl-a presented an excellent consistency with ∼20 % difference, suggesting the feasibility of seamless OLCI-VIIRS observations in Chl-a for lakes. With the OLCI data and well-validated algorithm, we revealed the high-resolution maps of Chl-a in 681 lakes of larger than 10 km2 in China, which significantly filled the results in small-medium lakes where VIIRS did not observe before. This study demonstrates the reasonable agreement of OLCI-VIIRS observations in lakes and proposes an initiative to generate seamless data records in inland lakes through OLCI-VIIRS data.

Development of VIIRS-OLCI chlorophyll-a product for the coastal estuaries

Coastal waters require monitoring of chlorophyll-a concentration (Chl-a) in a wide range of Chl-a from a few mg/m3 to hundreds of mg/m3, which is of interest to the fisheries industry, evaluation of climate change effects, ecological modeling and detection of Harmful Algal Blooms (HABs). Monitoring can be carried out from the Visible Infrared Imaging Radiometer Suite (VIIRS) and Ocean and Land Colour Instrument (OLCI) Ocean Color (OC) satellite sensors, which are currently on orbit and are expected to be the main operational OC sensors at least for the next decade. A Neural Network (NN) algorithm, which uses VIIRS M3-M5 reflectance bands and an I1 imaging band, was developed to estimate Chl-a in the Chesapeake Bay, for the whole range of Chl-a from clear waters in the Lower Bay to extreme bloom conditions in the Upper Bay and the Potomac River, where Chl-a can be used for bloom detection. The NN algorithm demonstrated a significant improvement in the Chl-a retrieval capabilities in comparison with other algorithms, which utilize only reflectance bands. OLCI NIR/red 709/665 nm bands red edge 2010 algorithm denoted as RE10 was also explored with several atmospheric corrections from EUMETSAT, NOAA and NASA. Good consistency between the two types of algorithms is shown for the bloom conditions and the whole range of waters in the Chesapeake Bay (with RE10 switch to OC4 for lower Chl-a) and these algorithms are recommended for the combined VIIRS-OLCI product for the estimation of Chl-a and bloom monitoring. The algorithms were expanded to the waters in Long Island Sound, demonstrating good performance.

An Observed Relationship Between Satellite‐Estimated Transmittance and Ground‐Estimated Water Vapor: Implications for High‐Temporal‐Resolution Water Vapor Retrieval From Non‐Geostationary Satellite Measurements

AbstractThe magnitude of integrated water vapor (IWV) varies considerably in the spatial‐temporal domain, which demonstrates the significance of high‐spatiotemporal‐resolution IWV observations for atmospheric water vapor distribution monitoring, both locally and globally. Unlike previously published algorithms based on data fusion, an empirical retrieval algorithm is for the first time proposed to directly retrieve high‐temporal‐resolution IWV estimates from near‐infrared radiance observations of the non‐geostationary Ocean and Land Color Instrument (OLCI). The retrieval algorithm is developed based on an observed regression relationship between satellite‐based OLCI‐estimated transmittance and ground‐based Global Navigation Satellite System (GNSS)‐estimated IWV in the temporal domain. The results show that all newly retrieved IWV estimates have an overall good consistency with ground‐based IWV from additional GNSS‐sensed measurements, indicating the feasibility of the retrieval approach. The performance of the retrieval algorithm is acceptable and satisfactory when compared with that of IWV retrievals listed in previous studies.

Suitability of different in-water algorithms for eutrophic and absorbing waters applied to Sentinel-2 MSI and Sentinel-3 OLCI data

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%).

Validation and Conformity Testing of Sentinel-3 Green Instantaneous FAPAR and Canopy Chlorophyll Content Products

This article presents validation and conformity testing of the Sentinel-3 Ocean Land Colour Instrument (OLCI) green instantaneous fraction of absorbed photosynthetically active radiation (FAPAR) and OLCI terrestrial chlorophyll index (OTCI) canopy chlorophyll content (CCC) products with fiducial reference measurements (FRM) collected in 2018 and 2021 over two sites (Las Tiesas—Barrax, Spain, and Wytham Woods, UK) in the context of the European Space Agency (ESA) Fiducial Reference Measurement for Vegetation (FRM4Veg) initiative. Following metrological principles, an end-to-end uncertainty evaluation framework developed in the project is used to account for the uncertainty of reference data based on a two-stage validation approach. The process involves quantifying uncertainties at the elementary sampling unit (ESU) level and incorporating these uncertainties in the upscaling procedures using orthogonal distance regression (ODR) between FRM and vegetation indices derived from Sentinel-2 data. Uncertainties in the Sentinel-2 data are also accounted for. FRM-based high spatial resolution reference maps and their uncertainties were aggregated to OLCI’s native spatial resolution using its apparent point spread function (PSF). The Sentinel-3 mission requirements, which give an uncertainty of 5% (goal) and 10% (threshold), were considered for conformity testing. GIFAPAR validation results revealed correlations > 0.95, RMSD ~0.1, and a slight negative bias (~−0.06) for both sites. This bias could be partly explained by the differences in the FAPAR definitions between the satellite product and the FRM-based reference. For the OTCI-based CCC, leave-one-out cross-validation demonstrated correlations > 0.8 and RMSDcv ~0.28 g·m−2. Despite the encouraging validation results, conclusive conformity with the strict mission requirements was low, with most cases providing inconclusive results (driven by large uncertainties in the satellite products as well as by the uncertainties in the upscaling approach). It is recommended that mission requirements for bio-geophysical products are reviewed, at least at the threshold level. It is also suggested that the large uncertainties associated with the two-stage validation approach may be avoided by directly comparing with spatially representative FRM.

Developing a novel index for detection of optically shallow waters using multispectral satellite imagery and radiative transfer modelling

ABSTRACT Optical remote sensing of water quality has been problematic due to contamination of remote sensing observations by the sea-bottom effect over complex shallow or very clear waters. This has resulted in providing misleading information on the estimation of Water Constituent Concentrations (WCCs) retrieved from satellite images. In this research, we used Radiative Transfer (RT) Modelling to develop a simple and innovative index named the Sea-Bottom Effect Index (SEBEI) to readily determine water pixels contaminated by the sea-bottom effect, (hereafter named Optically Shallow Waters, (OSWs)) from remote sensing observations. To define the SEBEI, we initially assessed the influence of the sea-bottom on simulated water-leaving reflectance (R rs ) using RT Water-Sea-Bottom (WSB) modelling. This evaluation encompassed a range of water depths, seabed albedos, and WCCs (i.e. Chlorophyll-a (Chla) and Suspended Particulate Matter (SPM)). Next, we detected the most sensitive wavelengths (i.e. 750 nm, 810 nm, and 900 nm) to the seabed contribution on the simulated R rs spectra at the water surface level and developed the SEBEI. We validated the accuracy of the proposed SEBEI over a time series of MEdium Resolution Imaging Spectrometer (MERIS) and Ocean and Land Colour Instrument (OLCI) images captured during low and high tidal phases over shallow waters of the Dutch Wadden Sea, the Netherlands. The results indicate that the SEBEI effectively distinguishes between OSWs and Optically Deep Waters (ODWs) using satellite images (with an R2 value of ≥ 0.97 and a Root Mean Square Error (RMSE) of ≤ 0.65). The SEBEI can serve as an intermediate solution for detecting OSWs in multispectral and hyperspectral satellite images worldwide. It eliminates the need for ancillary datasets like bathymetry maps and significantly enhances the reliability of WCC maps produced over complex shallow coastal waters.

Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced Arctic spring sea ice surface classification

The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution Ku-band radar altimetry data over the polar regions up to 81° North. The combination of synthetic aperture radar (SAR) mode altimetry (SRAL instrument) from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise this synergy between altimetry and imagery to demonstrate a novel application of deep learning to distinguish sea ice from leads in spring. We use SRAL classified leads as training input for pan-Arctic lead detection from OLCI imagery. This surface classification is an important step for estimating sea ice thickness and to predict future sea ice changes in the Arctic and Antarctic regions. We propose the use of Vision Transformers (ViT), an approach adapting the popular deep learning algorithm Transformer, for this task. Their effectiveness, in terms of both quantitative metric including accuracy and qualitative metric including model roll-out, on several entire OLCI images is demonstrated and we show improved skill compared to previous machine learning and empirical approaches. We show the potential for this method to provide lead fraction retrievals at improved accuracy and spatial resolution for sunlit periods before melt onset.

Continuous Sargassum monitoring across the Caribbean Sea and Central Atlantic using multi-sensor satellite observations

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.

Retrieval of particulate organic carbon concentration in Erhai Lake using sentinel-3 remote sensing data

ABSTRACT Particulate organic carbon (POC) plays a crucial role in the carbon cycle of inland lake ecosystems. The utilization of remote sensing satellite data provides an effective approach for monitoring the temporal and spatial variations in POC concentration within inland water. However, Erhai Lake, situated in the unique natural environment of the Yunnan-Kweichow Plateau, poses distinct challenges due to the complex and diverse origin of its water quality elements. Existing methods face difficulties in accurately detecting POC concentration in Erhai Lake. In this study, 112 water samples and 574 in-situ remote sensing reflectance curves were collected from Erhai Lake during April, May, and June 2023. The analysis of the optical characteristics of the water in Erhai Lake revealed the closest relationship between the concentration of total particulate matter and POC. Consequently, a POC concentration inversion algorithm was developed, utilizing bands 8, 12, and 16 of Sentinel-3 Ocean and Land Colour Instrument (OLCI). Various POC concentration inversion algorithms were evaluated utilizing a separate dataset. The results indicate that the three-band method offers superior accuracy in POC concentration inversion for Erhai Lake, with a root mean square error (RMSE) of 0.13 mg/L and a mean absolute percentage error (MAPE) of 28.80%. The three-band method was effectively applied to OLCI images from April, May, and June 2023, enabling the analysis of the spatiotemporal distribution of POC in Erhai Lake.

How Representative Are European AERONET-OC Sites of European Marine Waters?

Data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) have been extensively used to assess Ocean Color radiometric products from various satellite sensors. This study, focusing on Ocean Color radiometric operational products from the Sentinel-3 Ocean and Land Colour Instrument (OLCI), aims at investigating where in the European seas the results of match-up analyses at the European marine AERONET-OC sites could be applicable. Data clustering is applied to OLCI remote sensing reflectance RRS(λ) from the various sites to define different sets of optical classes, which are later used to identify class-based uncertainties. A set of fifteen classes grants medium-to-high classification levels to most European seas, with exceptions in the South-East Mediterranean Sea, the Atlantic Ocean, or the Gulf of Bothnia. In these areas, RRS(λ) spectra are very often identified as novel with respect to the generated set of classes, suggesting their under-representation in AERONET-OC data. Uncertainties are finally mapped onto European seas according to class membership. The largest uncertainty values are obtained in the blue spectral region for almost all classes. In clear waters, larger values are obtained in the blue bands. Conversely, larger values are shown in the green and red bands in coastal and turbid waters.

Estimates of Crop Yield Anomalies for 2022 in Ukraine Based on Copernicus Sentinel-1, Sentinel-3 Satellite Data, and ERA-5 Agrometeorological Indicators.

The study explores the feasibility of adapting the EOStat crop monitoring system, originally designed for monitoring crop growth conditions in Poland, to fulfill the requirements of a similar system in Ukraine. The system utilizes satellite data and agrometeorological information provided by the Copernicus program, which offers these resources free of charge. To predict crop yields, the system uses several factors, such as vegetation condition indices obtained from Sentinel-3 Ocean and Land Color Instrument (OLCI) optical and Sea and Land Surface Temperature Radiometer (SLSTR). It also incorporates climate information, including air temperature, total precipitation, surface radiation, and soil moisture. To identify the best predictors for each administrative unit, the study utilizes a recursive feature elimination method and employs the Extreme Gradient Boosting regressor, a machine learning algorithm, to forecast crop yields. The analysis indicates a noticeable decrease in crop losses in 2022 in certain regions of Ukraine, compared to the previous year (2021) and the 5-year average (2017-2021), specifically for winter crops and maize. Considering the reduction in yield, it is estimated that the decline in production of winter crops in 2022 was up to 20%, while for maize, it was up to 50% compared to the decline in production.

Analysis of Chlorophyll-a Variability in the Eastern Indonesian Waters Using Sentinel-3 OLCI from 2020-2021

The Eastern Indonesian waters are significant in influencing the global climate system and oceanic connectivity. However, the Indonesian Through Flow (ITF) facilitates the movement of waters from the Pacific Ocean to the Indian Ocean. This flow vertically mixes water masses in the Eastern regions, leading to the concentration of phytoplankton. In addition, the distribution of phytoplankton, indicative of chlorophyll-a concentration, is influenced by upwelling and downwelling phenomena. Chlorophyll-a, responsible for capturing carbon and producing oxygen in marine ecosystems, is important in regulating climate change. Moreover, oceanographic conditions play a significant role in the dispersion of chlorophyll-a concentration. Therefore, this study adopted ocean colour remote sensing technology to assess chlorophyll-a distribution. Monthly ocean colour data was collected by the multi-temporal Sentinel-3 Ocean and Land Colour Instrument (OLCI). The analysis output included chlorophyll-a concentration associated with currents and the El Nino Southern Oscillation (ENSO). Data processing using the Case-2 Regional Coast Colour (C2RCC) processor resulted in an average chlorophyll-a concentration in the Eastern Indonesian waters ranging from 0.16 to 0.52. The results showed higher chlorophyll-a levels during the southeast monsoon (July to September) and lower levels during the northwest monsoon (January to March).

Nanoparticles in terrestrial sediments and the behavior of the spectral optics of Sentinel-3B OLCI Satellite images in a river basin of UNESCO World Cultural and Natural Heritage.

The dangerous chemical elements associated with nanoparticles (NPs) and ultra-fine sediment particles in hydrological bays have the capacity to move contaminants to large oceanic regions. The general objective of this study is to quantify the major chemical elements present in NPs and ultra-fine particles in aquatic sediments sampled from Guanabara Bay and compare these data to values determined through spectral optics using the Sentinel-3B Satellite OLCI (Ocean Land Color Instrument) during the winter and summer seasons of 2018, 2019, 2020, 2021, and 2022. This is done to highlight the impacts anthropogenic environmental hazards have on the marine ecosystem and human beings. Ten aquatic sediment field collection points were selected by triangulated irregular network (TIN). Samples were subjected to analysis by X-ray diffraction (XRD), scanning electron microscopy (SEM), electron dispersion spectroscopy (EDS), and transmission electron microscopy (TEM), which enabled a detailed analysis using scanning transmission electron microscopy (STEM). Geospatial analyses using Sentinel-3B OLCI Satellite images considered Water Full Resolution (WFR) at 300m resolution, in neural network (NN), normalized at 0.83µg/mg. A maximum average spectral error of 6.62% was utilized for the identification of the levels of Absorption Coefficient of Detritus and Gelbstoff (ADG443_NN) at 443m-1, Chlorophyll-a (CHL_NN) (m-3), and Total Suspended Matter (TSM_NN) (g m-3) at 581 sample points. The results showed high levels of ADG443_NN, with average values as high as of 4444m-1 (summer 2021). When related to the analyses of nanoparticulate sediments and ultrafine particles collected in the field, they showed the presence of major chemical elements such as Ge, As, Cr, and others, highly toxic to human health and the aquatic environment. The application of satellite and terrestrial surveys proved to be efficient, in addition to the possibility of this study being applied to other hydrological systems on a global scale.

Evaluation and Refinement of Chlorophyll-a Algorithms for High-Biomass Blooms in San Francisco Bay (USA)

A massive bloom of the raphidophyte Heterosigma akashiwo occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll a is typically <30 mg m−3, while also capturing bloom periods of >100 mg m−3 chlorophyll a.

Deconstructing co-occurring marine heatwave and phytoplankton bloom events in the Arkona Sea in 2018

Between May and August 2018, two separate marine heatwaves (MHWs) occurred in the Arkona Sea in the western Baltic Sea. These heatwaves bookended an extended period of phytoplankton growth in the region. Data from the Ocean and Land Colour Instrument (OLCI) on board the European Sentinel-3 satellite revealed an eddy-like structure containing high chlorophyll a (Chl-a) concentrations (ca. 25 mg.m-3) persisting for several days at the end of May in the Arkona Sea. Combining ocean colour observations, a coupled bio-optical ocean model and a particle tracking model, we examined the three dimensional relationship between these co-occurring MHW and phytoplankton bloom events. We find that the onset of the MHW in May provided the optimal conditions for phytoplankton growth, i.e. sufficient light and nutrients. Wind-driven surface eddy circulation, geostrophic eddy stirring and transient submesoscale dynamics along the edges of the eddy provided a transport path for nutrient fluxes and carbon export, and helped to sustain the phytoplankton bloom. The bloom may have indirectly had an enhancing effect on the MHW, through the impact of water constituent-induced heating rates on air-sea energy fluxes. The subsurface signature of the MHW plays a critical role in de-coupling surface and subsurface dynamics and terminating the phytoplankton bloom. Subsurface temperature anomalies of up to 8°C between 15 and 20 m depth are found to persist up to 15 days after the surface signature of the MHW has disappeared. The study reveals how surface and subsurface dynamics of MHWs and phytoplankton blooms are connected under different environmental conditions. It extends our knowledge on surface layer processes obtained from satellite data.

A novel water optical types framework for Chinese inland waters with the application of multitype satellite sensor

ABSTRACT Optical Water Type (OWT) analysis is crucial for comprehending water composition and quality, key factors in assessing water quality over extensive areas. However, China’s inland waters lack a standardized system for such analysis. To quantitatively analyze the classification results, our study compared three K-means clustering methods, for analyzing 1310 spectral data from various Chinese lakes and reservoirs, thereby addressing this gap. The innovative split-merge K-means method identified 13 distinct OWTs that more closely adhere to the principles of minimizing intra-class distance and maximizing inter-class distance. These were categorized into four groups: clear water, turbid water, eutrophic water, and special type water. Additionally, we developed a method based on Spectral Angle Distance (SAD) to evaluate the classification capabilities of 12 satellite sensors. The results show that Sentinel-3 OLCI (Ocean and Land Color Instrument), MERIS (Medium Resolution Imaging Spectrometer), and Sentinel-2 MSI (Multispectral Instrument) have the best water classification capabilities, making them well-suited for large-scale monitoring of OWT changes. Conversely, other sensors, such as the Sustainable Development Scientific Satellite-1 (SDGSAT-1), Landsat-8, GaoFen-6, GaoFen-1, GaoFen-2, Landsat-5, Landsat-7, Moderate Resolution Imaging Spectroradiometer (MODIS), and HuanJing-1, necessitate the consolidation of water types for effective categorization, indicative of their more limited classification capabilities.

Impacts of Hydrology and Extreme Events on Dissolved Organic Carbon Dynamics in a Heavily Urbanized Estuary and Its Major Tributaries: A View From Space

AbstractDissolved organic matter and its colored component, Colored Dissolved Organic Matter (CDOM), play a major role in global carbon budgets, and their fluxes provide an essential link between terrestrial and aquatic biogeochemical cycles. Satellite observations can uniquely capture the hydro‐biogeochemical connectivity of terrestrial and aquatic landscapes, across scales. Yet, accurate satellite retrievals of CDOM and dissolved organic carbon (DOC) dynamics remain challenging in urbanized estuaries and coasts. Here, we present an advanced unified algorithm for space‐based retrieval of coastal CDOM and DOC dynamics and its application in Long Island Sound—one of the world's most heavily urbanized estuaries that is becoming increasingly vulnerable to climate change stressors. A rich bio‐optical data set, encompassing a wide range of environmental conditions, was integrated into the algorithm training to retrieve DOC concentrations and CDOM spectral shape (i.e., spectral slope S275–295)—a proxy for DOC quality. The new algorithms were applied to full‐resolution satellite imagery from the Sentinel‐3 Ocean and Land Color Instrument (OLCI) after thoroughly evaluating the performance of six ocean color atmospheric correction approaches (ACOLITE, BAC, C2RCC, MUMM, l2gen, and Polymer). Evaluation of the algorithms yielded mean absolute percent differences of 28%, 12%, and 10% for aCDOM(300), S275–295, and DOC, respectively. Application of the algorithms to multi‐year satellite OLCI imagery captured, for the first time, the coupled impact of seasonal transitions, wind regimes, freshwater inputs, anthropogenic disturbances, and hydrological extremes (both intense precipitation and droughts) on DOC fluxes and CDOM quality at the ecosystem scale. Results have important implications for improved predictions of coastal biogeochemical fluxes in complex urban−estuary systems.