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Atmospheric Correction (AC) aims to restore surface reflectance from the top of the atmosphere (TOA) reflectance. In this study, seven highly established state-of-the-art AC (OC-SMART, ACOLITE, Polymer, Sen2Cor, iCOR, C2RCC, and L2gen from SeaDAS) approaches were employed on Sentinel-2 MSI high-resolution imageries. The performance of the AC algorithms was validated by comparing the satellite-derived remote sensing reflectance ($R_{rs}(\lambda)$) at four visible wavelengths (443, 460, 590, and 665 nm) with the co-located in-situ hyperspectral measurements acquired within a temporal window of $\pm $3 hours across various water zones located in the Atlantic Ocean. 75 optimal match-up pairs were obtained from six sites between December 2018 and August 2020. An unsupervised learning technique was used to classify the in-situ hyperspectral $R_{rs}(\lambda)$ measurements into three optical water types encompassing nearly clear to moderately turbid coastal and deep water zones. Upon general analysis, OC-SMART produced the most precise $R_{rs}(\lambda)$ with and $S_{tot}$ score and $\bar{\chi }^{2}$ value of 20.54/24, and 0.11, respectively. L2gen AC produced $R_{rs}(\lambda)$ that show the highest resemblance with the spectral shape in terms of Spectral Angle and Quality Assurance score with the in-situ $R_{rs}(\lambda)$ that are 10.4 and 0.84, respectively. The performance of ACs varies across the water types and wavelengths. Using existing bio-optical algorithms, the validation is further extended by obtaining downstream water-quality parameters, such as $Chl_{a}$, TSS, and $a_{cdom}(440)$, from the in-situ measured and atmospherically corrected $R_{rs}(\lambda)$. The expected reasons that affect the performance of ACs across designated water types were discussed.

Surface glint significantly reduces the measurement accuracy of remote sensing reflectance of water, Rrs, making it difficult to effectively use field measurements for studying water optical properties, accurately retrieving water quality parameters, and validating satellite remote sensing products. To accurately assess the effectiveness of various glint removal methods and enhance the accuracy of water reflectance measurements, a portable multiprobe high-resolution System (PMHRS) is designed. The system is composed of a spectrometer, fiber bundles, an irradiance probe, and three radiance probes. The reliability and measurement accuracy of the PMHRS are ensured through rigorous laboratory radiometric calibration and temperature correction. The comprehensive uncertainty of laboratory calibration ranges from 1.29% to 1.43% for irradiance calibration and from 1.47% to 1.59% for radiance calibration. Field measurement results show a strong correlation with both synchronous ASD data, and Sen2Cor-atmospherically corrected Sentinel-2B data (R2 = 0.949, RMSE = 0.013; R2 = 0.926, RMSE = 0.0105). The water-leaving radiance measurements obtained under different solar elevation angles using three methods (M99 method, polarization method, and SBA) demonstrate that the improved narrow field-of-view polarization probe effectively removes surface glint across various solar elevation angles (with overall better performance than the traditional M99 method). At a solar elevation angle of 69.7°, the MAPD and MAD between the measurements of this method and those of the SBA are 5.8% and 1.4 × 10−4, respectively. The results demonstrate that the PMHRS system outperforms traditional methods in sun glint removal, significantly enhancing the accuracy of water remote sensing reflectance measurements and improving the validation quality of satellite data. This work provides a crucial technical foundation for the development of high-resolution continuous observation platforms in complex aquatic environments. It holds significant implications for improving the accuracy of field-based water remote sensing reflectance measurements and for enhancing the quality of water ecological monitoring data and satellite validation data. [ABSTRACT FROM AUTHOR]

Ocean colour (OC) remote sensing benefits society by providing continuous biological and ecological parameters relevant to sustainable marine resource exploitation. It enhances our understanding of climate change and allows us to monitor oceanographic phenomena over various scales of variability. However, significant data gaps occur daily due to cloud cover, atmospheric correction failures, sun-glint contamination, and satellite coverage limitations. Level 4 (L4) gap-free images are generally created by averaging over specific periods (e.g., weekly, monthly, seasonal) or re-gridding data with coarser resolution to overcome these limitations. These approaches, however, often fail to capture anomalous events or fine-scale resolution processes, calling for more advanced methods. The Data Interpolating Empirical Orthogonal Function (DINEOF) method has proved effective in reconstructing missing OC data and capturing smaller-scale features in noisy fields. To the best of authors knowledge, DINEOF is here used for the first time to interpolate multispectral Remote Sensing Reflectance (Rrs) to produce a consistent and gap-free L4 Rrs dataset, minimizing errors in inferred ocean products, such as Chlorophyll-a (Chl), the most widely used proxy for phytoplankton biomass. Specifically, using a multivariate approach, we assessed the DINEOF technique’s capability to reconstruct Rrs, focusing on six bands (412, 443, 490, 510, 555, and 670 nm) and validating the results using extensive in situ datasets. Our outcomes show that this “upstream interpolation” method can generate a consistent Rrs dataset, thereby improving the accuracy of L4 Chl predictions when used as input in algorithms for remote Chl estimation. We anticipate further improvements in L4 Rrs accuracy using richer spectral information from upcoming hyperspectral satellite missions. This study highlights the effectiveness of using Rrs as a standalone dataset for DINEOF interpolation. Operationally, it can derivate various gap-free and consistent biogeochemical parameters with reduced uncertainty, thus providing a more reliable and versatile method.

ABSTRACTOptical 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.

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 R R S (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, R R S (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. [ABSTRACT FROM AUTHOR]

The multi‐to hyperspectral evolution of satellite ocean color sensors is anticipated to enable satellite‐based identification of phytoplankton biodiversity, a key factor in aquatic ecosystem functioning and upper ocean biogeochemistry. In this work the bio‐optical Phytoplankton Detection with Optics (PHYDOTax) approach for deriving taxonomic (class‐level) phytoplankton community composition (PCC, e.g. diatoms, dinoflagellates) from hyperspectral information is evaluated in the Chesapeake Bay estuary on the U.S. East Coast. PHYDOTax is among relatively few optical‐based PCC differentiation approaches available for optically complex waters, but it has not yet been evaluated beyond the California coastal regime where it was developed. Study goals include: (a) testing the approach in a turbid estuary including novel incorporation of colored dissolved organic matter (CDOM) and non‐algal particles (NAP), and (b) performance assessment with both synthetic mixture and field data sets. Algorithm skill was robust on synthetic mixtures. Using field data, cryptophyte and/or cyanophyte phytoplankton groups were predicted, but diatom and dinoflagellate detection was not conclusive. For one field data set, small but significant improvements were observed in predicted PCC groups when tested with incorporation of CDOM and NAP into the algorithm, but not for the second field data set. Sensitivity to three hyperspectral‐relevant spectral resolutions (1, 5, 10 nm) was low for all field and synthetic data. PHYDOTax can identify some phytoplankton groups in the estuary using hyperspectral, field‐collected measurements, but validation‐quality data with broad temporospatial coverage are needed to determine whether the approach is robust enough for science applications. Key Points: A hyperspectral, bio‐optical approach to identifying phytoplankton types from remote sensing reflectance is tested in a turbid estuaryIn field data, cyanophytes and cryptophytes were typically detected but diatom and dinoflagellate detection was not conclusiveWork adds support to further testing this approach in optically complex waters to assess its potential for science applications [ABSTRACT FROM AUTHOR]

This paper is the first to examine the operation of the HawkEye satellite in the presence of dust aerosol. The study region is the Black Sea. Dust transport dates were identified using visual inspection of satellite imagery, back-kinematic HYSPLIT trajectory analysis, CALIPSO aerosol stratification and typing maps, and the global forecasting model SILAM. In a comparative analysis of in-situ and satellite measurements of the remote sensing reflectance, an error in the atmospheric correction of HawkEye measurements was found both for a clean atmosphere and in the presence of an absorbing aerosol. It is shown that, on average, the dependence of the atmospheric correction error on wavelength has the form of a power function of the form from λ−3 to λ−9. The largest errors are in the short-wavelength region of the spectrum (412–443 nm) for the dust and dusty marine aerosol domination dates. A comparative analysis of satellite and in situ measurements of the optical characteristics of the atmosphere, namely the AOD and the Ångström parameter, was carried out. It is shown that the aerosol model used by HawkEye underestimates the Angström parameter and, most likely, large errors and outliers in satellite measurements are associated with this. [ABSTRACT FROM AUTHOR]

This study exploits the potential of satellite remote sensing reflectance spectra (Rrs(λ)) for detecting ocean surface algal blooms. Three types of floating surface algae are examined: Sargassum, Ulva, and Trichodesmium. The satellite images are processed with a shortwave infrared (SWIR)-based atmospheric correction processor from the Visible Infrared Imaging Radiometer Suite (VIIRS). We calculate the red-edge reflectance anomaly from the Rrs(λ) data to delineate the notable spectral difference at 671 and 862 nm. The new data have generated floating algal maps comparable with historical methods relying on Rayleigh-corrected reflectance data. The Rrs(λ) spectra are found to have high quality scores over Sargassum and Trichodesmium waters, suggesting less uncertainty from atmospheric correction. With problems to be addressed, this preliminary study finds it promising to use the VIIRS reflectance products for floating algae detection and mapping. Continuous efforts are highly recommended as the new data and approach can not only facilitate a retrospective analysis over global oceans but also benefit a greater application to next-generation hyperspectral satellites.

The machine learning (ML) model provides an alternative method for estimating inherent optical properties (IOPs) in clear and coastal waters. This study introduces an effective approach by employing ensemble machine learning techniques, such as random forest, gradient boosting, extra tree, adaboost, bagging, and voting model, to predict phytoplankton absorption coefficient ( $\text{a}_{\mathrm {ph}}(\lambda)$ , $\text{m}^{-1}$ ) at selected key wavebands of 443, 489, 510, 555, and 670 nm in clear and coastal waters. The optimization of the hyperparameters of these models through Bayesian techniques ensured high predictive accuracy. Furthermore, this research highlights the critical importance of wavelengths 670, 489, and 510 nm through feature importance analysis. The models exhibit excellent performance in terms of the coefficient of determination (R2) value when predicting phytoplankton at various wavelengths (e.g., 443, 489, 510, 555, and 670 nm). The R2 value of around 0.9033 is obtained for the absorption coefficient of phytoplankton aph at the wavelength 510 nm. The lowest mean squared error (MSE) of 0.0001 was achieved at the green waveband (i.e., 555 nm). Other statistical matrices, such as mean absolute percentage error (MAPE) and mean absolute error (MAE), have shown a low error across the selected wavelengths. It is found that the predicted phytoplankton absorption coefficients are in close agreement with actual values. This study shows the success of optimized ensemble models for both global and selected regional datasets that can accurately derive $\text{a}_{\mathrm {ph}}(\lambda)$ , which will contribute to the improvement of ocean primary productivity modelling and understanding the distribution of phytoplankton blooms.

Bubble clouds produced by wind-induced breaking surface waves are highly reflective features that significantly influence the spectral shape and magnitude of remote sensing reflectance in sea surface waters and introduce uncertainties in the water colour products derived from airborne and satellite data. Under windy conditions, the uncertainty can extend over several orders of magnitude due to the spectral enhancement effects of bubble clouds on remote sensing reflectance ( $R_{rs}$ ). In this study, the effects of bubbles on the spectral properties of $R_{rs}$ and water colour products are investigated using radiative transfer simulations and field measurements of bubble clouds. Radiative transfer (RT) simulations with HydroLight were performed with the inputs of the inherent optical properties (IOPs) of different waters and the scattering coefficients of bubbles to generate $R_{rs}$ , which in turn became the input for the retrieval algorithms of chlorophyll (Chl), suspended sediments (SS) and coloured dissolved organic matter (CDOM), and for planning of our field experiments. The experiment data were obtained from the Chennai harbour on 15 February 2020 (from 11 am to 3 pm, local time, IST) using a set of RAMSES TriOS radiometric sensors. These measurements were made over the time period less than two minutes to capture the wave formation, breaking and dissipation conditions. HydroLight simulations and field measurement data showed that the $R_{rs}$ spectra in the visible and near-infrared (NIR) wavelengths are significantly enhanced in the presence of bubble clouds. The effect of bubble clouds ( $b^{bub}$ ) on the water-leaving reflectance was well pronounced in clear waters than in turbid waters, particularly in the green-NIR wavelengths due to the strong backscattering of bubbles and weak backscattering of water molecules. The $R_{rs}$ bubble cloud ratio of different water types showed more variation with the increasing effect toward the longer wavelengths. In clear ocean waters, when Chl $\lt =1$ mg m3, the $R_{rs}$ bubble cloud ratio was increased from 1.5 to 2.5 (across the visible-NIR spectrum) in the case of less bubble clouds ( $b^{bub} =0.1$ m1) and 4 to 15 in the case of more bubble clouds ( $b^{bub} \gt 0.9$ m1). More than 50% changes were observed at higher bubble populations as confirmed by our field experiments and earlier studies. In turbid coastal waters, the effect of bubbles on the $R_{rs}$ was less pronounced due to the strong influence of water IOPs and the weak effect of bubbles. The magnitude of the $R_{rs}$ spectra obtained from the field experiments also increased with increasing bubble fraction/bubble density. Consequently, the error in the water colour products retrieved from the $R_{rs}$ data was magnified due to the overestimation of Chl and SS and underestimation of CDOM in the presence of bubbles. The results presented will have significant implications for further studies on investigating the spatial effects of bubble clouds on $R_{rs}$ data and improving the accuracy of the water colour products retrieved from satellite data.

Atmospheric correction is one of the major challenges in ocean color remote sensing, thus threatening comprehensive evaluation of water quality within aquatic environments. In this study, five state-of-the-art atmospheric correction (AC) processors (i.e. Acolite, C2RCC, iCOR, L2gen, and Polymer) were applied to Operational Land Imager (OLI) Landsat-8 scenes and evaluated against in situ measurements across various types of waters worldwide. A total of 262 matchups between in situ measured and satellite-derived remote sensing reflectance (Rrs) at 20 sites were obtained between August 2013 and August 2021. Classification of optical water types (OWTs) was carried out using in situ measurements with matched satellite observations. OWT-specific analysis demonstrated that L2gen produced the most accurate Rrs with R2 ≥ 0.74 and root mean squared error (RMSE) ≤ 0.0018 sr–1 for the four visible bands of OLI, followed by Polymer, C2RCC, iCOR, and Acolite. In terms of Rrs spectral similarity, C2RCC yielded the lowest spectral angle (SA) of 8.55°, followed by L2gen (SA = 9.20°). The advantage and disadvantage of each AC scheme were discussed. Recommendations to improve the accuracy for atmospheric correction were made, such as polarization observations and concurrent aerosol and ocean color measurements.

Surface glint significantly reduces the measurement accuracy of remote sensing reflectance of water, Rrs, making it difficult to effectively use field measurements for studying water optical properties, accurately retrieving water quality parameters, and validating satellite remote sensing products. To accurately assess the effectiveness of various glint removal methods and enhance the accuracy of water reflectance measurements, a portable multiprobe high-resolution System (PMHRS) is designed. The system is composed of a spectrometer, fiber bundles, an irradiance probe, and three radiance probes. The reliability and measurement accuracy of the PMHRS are ensured through rigorous laboratory radiometric calibration and temperature correction. The comprehensive uncertainty of laboratory calibration ranges from 1.29% to 1.43% for irradiance calibration and from 1.47% to 1.59% for radiance calibration. Field measurement results show a strong correlation with both synchronous ASD data, and Sen2Cor-atmospherically corrected Sentinel-2B data (R2 = 0.949, RMSE = 0.013; R2 = 0.926, RMSE = 0.0105). The water-leaving radiance measurements obtained under different solar elevation angles using three methods (M99 method, polarization method, and SBA) demonstrate that the improved narrow field-of-view polarization probe effectively removes surface glint across various solar elevation angles (with overall better performance than the traditional M99 method). At a solar elevation angle of 69.7°, the MAPD and MAD between the measurements of this method and those of the SBA are 5.8% and 1.4 × 10−4, respectively. The results demonstrate that the PMHRS system outperforms traditional methods in sun glint removal, significantly enhancing the accuracy of water remote sensing reflectance measurements and improving the validation quality of satellite data. This work provides a crucial technical foundation for the development of high-resolution continuous observation platforms in complex aquatic environments. It holds significant implications for improving the accuracy of field-based water remote sensing reflectance measurements and for enhancing the quality of water ecological monitoring data and satellite validation data.

This work is devoted to estimating the spectral course of validation error of satellite and in situ measurements of remote sensing reflectance for various atmospheric conditions. During data validation, a number of systematic errors of standard algorithms were noted, for example, negative values of remote sensing reflectance in the short-wavelength region at 412 and 443 nm in the presence of dust in the atmosphere. It is shown that the modern approach to determining aerosol light scattering in the short-wavelength part of the visible range by extrapolating the signal from the near-IR region is not sufficiently correct from a physical point of view, and similar solutions by the interpolation method have more accurate estimates. The obtained results show that in the presence of an absorbing aerosol, the spectral law of atmospheric correction errors is close to the function λ–4. This effect is explained by the fact that dust aerosol is determined by remote sensing methods using the Gordon and Wang algorithms using the infrared channel, but arid aerosol has the main effect on the ratio of the aerosol and molecular components (shortwave range). This paper presents trends for further interpolation of satellite data not only under the condition of a clean atmosphere, but also in the presence of an absorbing aerosol. Experimental patterns of validation error for Aqua MODIS have been obtained. [ABSTRACT FROM AUTHOR]

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

The European Space Agency (ESA) developed the Sentinel-2 Multispectral Imager (MSI), which offers a higher spatial resolution and shorter repeat coverage, making it an important source for the remote-sensing monitoring of water bodies. Atmospheric correction is crucial for the monitoring of water quality. To compare the applicability of seven publicly available atmospheric correction processors (ACOLITE, C2RCC, C2XC, iCOR, POLYMER, SeaDAS, and Sen2Cor), we chose complex and diverse lakes in Qinghai Province, China, as the research area. The lakes were divided into three types based on the waveform characteristics of Rrs: turbid water bodies (class I lakes) represented by the Dabusun Lake (DBX), clean water bodies (class II lakes) represented by the Qinghai Lake (QHH), and relatively clean water bodies (class III lakes) represented by the Longyangxia Reservoir (LYX). Compared with the in situ Rrs, it was found that for the DBX, the Sen2Cor processor performed best. The POLYMER processor exhibited a good performance in the QHH. The C2XC processor performed well with the LYX. Using the Sen2Cor, POLYMER, and C2XC processors for classes I, II, and III, respectively, compared with the Sentinel-3 OLCI Level-2 Water Full Resolution (L2-WFR) products, it was found that the estimated Rrs from the POLYMER had the highest consistency. Slight deviations were observed in the estimation results for both the Sen2Cor and C2XC. [ABSTRACT FROM AUTHOR]

As an environmental parameter, the chlorophyll-a concentration (Chl-a) is essential for monitoring water quality and managing the marine ecosystem. However, current mainstream Chl-a inversion algorithms have limited accuracy and poor spatial and temporal generalization in Case II waters. In this study, we constructed a quantitative model for retrieving the spatial and temporal distribution of Chl-a in the Bohai–Yellow Sea area using Geostationary Ocean Color Imager (GOCI) spectral remote sensing reflectance ( R r s λ ) products. Firstly, the GOCI  R r s λ  correction model based on measured spectral data was proposed and evaluated. Then, the feature variables of the band combinations with the highest correlation with Chl-a were selected. Subsequently, Chl-a inversion models were developed using three empirical ocean color algorithms (OC4, OC5, and YOC) and four machine learning methods: BP neural network (BPNN), random forest (RF), AdaBoost, and support vector regression (SVR). The retrieval results showed that the machine learning methods were much more accurate than the empirical algorithms and that the RF model retrieved Chl-a with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.916, a root mean square error (RMSE) of 0.212 mg · m−3, and a mean absolute percentage error (MAPE) of 14.27%. Finally, the Chl-a distribution in the Bohai–Yellow Sea using the selected RF model was derived and analyzed. Spatially, Chl-a was high in the Bohai Sea, including in Laizhou Bay, Bohai Bay, and Liaodong Bay, with a value higher than 4 mg · m−3. Chl-a in the Bohai Strait and northern Yellow Sea was relatively low, with a value of less than 3 mg · m−3. Temporally, the inversion results showed that Chl-a was considerably higher in winter and spring compared to autumn and summer. Diurnal variation retrieval effectively demonstrated GOCI's potential as a capable tool for monitoring intraday changes in chlorophyll-a concentrations. [ABSTRACT FROM AUTHOR]

Sea surface salinity (SSS) is a master variable in oceanography and important to understand marine biogeochemical and physical processes. In the East China Sea (ECS), a random forest based regression ensemble model (RF) was developed to estimate the SSS with a spatial resolution of ∼1 km based on a large synchronous data set of in situ SSS observations, MODIS‐derived remote sensing reflectance (Rrs) and sea surface temperature (SST). The model showed the best performance when the Rrs(412), Rrs(488), Rrs(555), Rrs(667), SST and Julian day (JD) were used as inputs, with a root mean square error (RMSE) of 0.84, mean absolute error (MAE) of 0.31 and coefficient of determination (R2) of 0.81 for model training (N = 4,504), and a RMSE of 0.77, MAE of 0.30 and R2 of 0.86 for the model test (N = 1,153). The accuracy of the SSS model was examined using an independent data set during the period of 2020–2022 with a RMSE of 0.66 and MAE of 0.39 (N = 2,151). The interannual and seasonal signal of modeled SSS of the ECS, showed that important drivers of variability are the Changjiang discharge and the East‐Asian monsoon. Applications of the model to other Chinese marginal seas (Yellow and Bohai seas) showed good agreement in distribution patterns when compared with the estimated SSS from NASA Soil Moisture Active Passive. Once more empirical oceanographic data is made available, this robust model can be applied to other regions retraining the model with informed local data sets. Key Points: A random forest based regression ensemble model is developed to estimate the sea surface salinity for the East China Sea from MODIS productsThe model is evaluated using an independent data set from the East China Sea and its sensitivity to input parameter errors are quantifiedThe model is successfully applied in the Chinese marginal seas, indicating it is an efficient tool to monitor the spatiotemporal variations in SSS [ABSTRACT FROM AUTHOR]

The proliferation of algal blooms can lead to environmental issues. The phytoplankton responsible for these blooms are diverse. Different species of bloom-forming algae have distinct characteristics and hazards, and therefore need different treatment methods. An accurate and quick determination of the spatial and temporal distribution characteristics of different algal species is crucial for lake ecological restoration. Based on the differences in remote sensing reflectance (Rrs) of various typical algae species in eutrophic lakes (including Microcystis aeruginosa, Aphanizomenon sp., and Pseudanabaena sp. in Cyanobacteria and Chlorella sp. and Scenedesmus quadricauda in Chlorophytes), difference index and algae distinguishing index were developed to differentiate algae species. A validation, using an independent dataset from an indoor experiment and in-situ-measured and satellite-image-derived Rrs, showed that the algorithm can provide reliable results (overall accuracies of 81.97%, 81.25%, and 60.42%, respectively). According to Ocean and Land Color Instrument images of Lake Taihu in the period of 2016 to 2020, Microcystis was the dominant algae, followed by Pseudanabaena and Aphanizomenon. The dominance of the two types of Chlorophytes was less pronounced. The proportion of Microcystis as the dominant algae was highest in summer, while the proportion of Pseudanabaena peaked in winter. The proportion of Aphanizomenon varied slightly throughout the year, while the proportion of the two Chlorophytes peaked in winter. In terms of spatial distribution, the patterns in spring and autumn were relatively similar. In summer, approximately 80% of the lake was dominated by Microcystis. In winter, Chlorella and Scenedesmus were more prevalent along the southeastern shore of Lake Taihu. The construction and application of this model can provide a technical support for prediction and prevention of blooms in inland lakes.

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

In this paper, we propose to analyze the values of the "blue" color index for further use in additional atmospheric correction of Level 2 remote sensing reflectance data for the waters of the Black Sea. Regardless of seasonal phenomena, atmospheric conditions, and the type of water, the average color index in the short-wave region, according to in situ measurements CI(412/443), varies from 0.77 to 0.83. The most frequently observed value is 0.8. In turn, the values of the "blue" color index CI(412/443) according to the satellite data of MODIS Aqua/Terra, VIIRS SNPP, and OLCI Sentinel 3A scanners showed a large scatter in values based on the standard deviation of the sample. The paper proposes to introduce the value of the minimum allowable threshold CI(412/443) > 0.59 based on the small variance found from in situ measurements, as well as on the basis of a theoretical estimate of the possible values of the index CI(412/443) when varying the backscattering exponent and the exponent for the absorption approximation. The quality check of the remote sensing data showed that, according to this selection criterion, 15% of data are physically incorrect for MODIS Aqua, 30% for MODIS Terra, 20% for Sentinel 3A, and 26% for VIIRS SNPP. In the course of the work, it was shown that the MODIS Aqua satellite provides the most high-quality and reliable information about the optical characteristics of the Black Sea. [ABSTRACT FROM AUTHOR]

Inaccuracies in the atmospheric correction (AC) of data on coastal waters significantly limit the ability to quantify the parameters of water quality. Many studies have compared the effects of the atmospheric correction of data provided by the Sentinel−2 satellites, but few have investigated this issue for coastal waters in China owing to a limited amount of in situ spectral data. The authors of this study compared four processors for the atmospheric correction of data provided by Sentinel−2—the Atmospheric Correction for OLI 'lite'(ACOLITE), Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System (SeaDAS), Polynomial-based algorithm applied to MERIS (POLYMER), and Case 2 Regional Coast Colour (C2RCC)—to identify the most suitable one for water bodies with different turbidities along the coast of China. We tested the algorithms used in these processors for turbid waters and compared the resulting inversion of the remote sensing reflectance (Rrs) using in situ reflectance data from three stations with varying levels of coastal turbidity (HTYZ, DONG'OU, and MUPING). All processors significantly underestimated the results on data from the HTYZ station, which is located along waters with high turbidity, with the SeaDAS delivering the best performance, with an average band R M S E of 0.0146 and an average M A P E of 29.80%. It was followed by ACOLITE, with an average band R M S E of 0.0213 and an average M A P E of 43.43%. The performance of two AC algorithms used in ACOLITE, dark spectrum fitting (DSF) and exponential extrapolation (EXP), was also evaluated by comparing their results with in situ measurements at the HTYZ site. The ACOLITE-EXP algorithm delivered a slight improvement in results for the blue band compared with the DSF algorithm in highly turbid water, but led to no significant improvement in the green and red bands. C2RCC delivered the best performance on data from the DONG'OU station, which is located along water with medium turbidity, and from the MUPING station (water with low turbidity), with values of the M A P E of 18.58% and 28.41%, respectively. [ABSTRACT FROM AUTHOR]

A modified regional algorithm to quantify the coccolithophore concentration in the northeastern part of the Black Sea under conditions of intense bloom is presented. To modify the algorithm, the data of in situ measurements of coccolithophore Emiliania huxleyi abundance performed in June 2017 and 2022 (when the maximum values were 9 × 106 and 13 × 106 Cells L−1, respectively), as well as the data from hydro-optical and satellite measurements, were used. In addition, the ratio between the number of detached coccoliths and coccolithophore cells was taken into account. Based on the expanded array of in situ data, the optimal values of the regional algorithm parameters were obtained. The modified algorithm makes it possible to obtain more accurate results in areas of high coccolithophore concentrations and takes into account the contribution of coccoliths. To test the sensitivity of the algorithm to variations in bio-optical characteristics, model calculations were performed using Hydrolight software. The updated algorithm is significantly less sensitive to variations in chlorophyll concentration and CDOM absorption coefficient than its previous version. [ABSTRACT FROM AUTHOR]

Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher uncertainty in retrieval accuracy exist. In this paper, a hybrid Chl-a estimation method based on spectral characteristics (i.e., remote sensing reflectance (Rrs)) classification was developed for oligotrophic and mesotrophic waters using high-resolution satellite Sentinel-2 (A and B) data. First, 99 samples and quasi-synchronous Sentinel-2 satellite data were collected from four small- and medium-sized reservoirs in central China, and the usability of the Sentinel-2 Rrs data in inland oligotrophic and mesotrophic waters was verified by accurate atmospheric correction. Second, a new optical classification method was constructed based on different water characteristics to classify waters into clear water, phytoplankton-dominated water, and water dominated by phytoplankton and suspended matter together using the thresholds of Rrs490/Rrs560 and Rrs665/Rrs560. The proposed method has a higher classification accuracy compared to other classification methods, and the band-ratio algorithm is simpler and more effective for satellite sensors without NIR bands. Third, given the sensitivity of the empirical method to water variability and the ease of development and implementation, a nonlinear least squares fitted one-dimensional nonlinear function was established based on the selection of the best-fitting spectral indices for different optical water types (OWTs) and compared with other Chl-a estimation algorithms. The validation results showed that the hybrid two-band method had the highest accuracy with squared correlation coefficient, root mean squared difference, mean absolute percentage error, and bias of 0.85, 2.93, 32.42%, and −0.75 mg/m3, respectively, and the results of the residual values further validated the applicability and reliability of the model. Finally, the performance of the classification and estimation algorithms on the four reservoirs was evaluated to obtain images mapping the Chl-a in the reservoirs. In conclusion, this study improves the accuracy of Chl-a estimation for oligotrophic and mesotrophic waters by combining a new classification algorithm with a two-band hybrid model, which is an important contribution to solving the problem of low resolution and high uncertainty in the retrieval of Chl-a in oligotrophic and mesotrophic waters in small- and medium-sized reservoirs and has the potential to be applied to other optically similar oligotrophic and mesotrophic lakes and reservoirs using similar spectrally satellite sensors. [ABSTRACT FROM AUTHOR]

Remote sensing reflectance (Rrs), which is currently measured mainly using the above-water approach, is the most crucial parameter in the remote sensing inversion of plateau inland water colors. It is very difficult to measure the Rrs of plateau inland unmanned areas; thus, we provide a measurement solution using a micro-spectrometer. Currently, commercial micro-spectrometers are not factory calibrated for radiation, and thus, a radiometric calibration of the micro-spectrometer is an essential step. This article uses an Ocean Optics micro-spectrometer (STS-VIS) and a traditional water spectrometer (Trios) to simultaneously measure the irradiance and radiance of diffuse reflectance plates with different reflectance values for field calibration. The results show the following: (1) different fiber types have different calibration coefficients, and the integration time is determined according to the diameter of the fiber and the type of fiber, and (2) by comparing the simultaneous measurement results of STS-VIS with Trios, the mean absolute percentage difference (MAPD) of both reached 18.64% and 5.11% for Qinghai Lake and Golmud River, respectively, which are accurate Rrs measurements of water bodies. The Rrs of the Hoh Xil and Qarhan Salt Lake water bodies in unmanned areas of China was measured, and this was the first collection of in situ spectral information with a micro-spectrometer. This article shows that the micro-spectrometer can perform the in situ measurement of water Rrs in unmanned inland areas. With this breakthrough in the radiometric performance of the micro-spectrometer, we are able to obtain more accurate remote sensing reflectance results of unmanned water bodies. [ABSTRACT FROM AUTHOR]

Uncertainties in remote sensing reflectance Rrs for the Ocean Color sensors strongly affect the quality of the retrieval of concentrations of chlorophyll-a and water properties. By comparison of data from SNPP VIIRS and several AERONET-OC stations and MOBY, it was recently shown that the main uncertainties come from the Rayleigh-type spectral component (Gilerson et al., 2022), which was associated with small variability in the Rayleigh optical thickness in the atmosphere and/or its calculation. In addition, water variability spectra proportional to Rrs were found to play a significant role in coastal waters, while other components including radiances from aerosols and glint were small. This work expands on the previous study, following a similar procedure and applying the same model for the characterization of uncertainties to the Sentinel-3A and B OLCI sensors. It is shown that the primary sources of uncertainties are the same as for VIIRS, i.e., dominated by the Rayleigh-type component, with the total uncertainties for OLCI sensors typically higher in coastal areas than for VIIRS.

Atmospheric correction is one of the major challenges in ocean color remote sensing, thus threatening comprehensive evaluation of water quality within aquatic environments. In this study, five state-of-the-art atmospheric correction (AC) processors (i.e. Acolite, C2RCC, iCOR, L2gen, and Polymer) were applied to Operational Land Imager (OLI) Landsat-8 scenes and evaluated against in situ measurements across various types of waters worldwide. A total of 262 matchups between in situ measured and satellite-derived remote sensing reflectance (Rrs) at 20 sites were obtained between August 2013 and August 2021. Classification of optical water types (OWTs) was carried out using in situ measurements with matched satellite observations. OWT-specific analysis demonstrated that L2gen produced the most accurate Rrs with R2 ≥ 0.74 and root mean squared error (RMSE) ≤ 0.0018 sr–1 for the four visible bands of OLI, followed by Polymer, C2RCC, iCOR, and Acolite. In terms of Rrs spectral similarity, C2RCC yielded the lowest spectral angle (SA) of 8.55°, followed by L2gen (SA = 9.20°). The advantage and disadvantage of each AC scheme were discussed. Recommendations to improve the accuracy for atmospheric correction were made, such as polarization observations and concurrent aerosol and ocean color measurements. [ABSTRACT FROM AUTHOR]

Turbidity is a good indicator to determine the quality of the water; it is a measure of the number of particles in suspension in the water. Using ocean color data, we analyzed the seasonal variability of turbidity in front of the Campeche Bank and part of the confluence zone of the coastal currents coming from the Tamaulipas-Veracruz and Campeche platforms [94°W-87.6°W and 18°N-24°N]. The extended platform has several river discharges that are strongly influenced by winds and the loop current coming from the Strait of Yucatan. 2003-2020 was analyzed using satellite images (reflectance, Chlorophyll, sea surface temperature, and altimetry), reanalyzed winds and discharges from the grijalva-usumacinta river system. We used seasonal climatologies, Hovmoller diagrams, and Orthogonal and Complex Empirical Functions to describe the data. In addition, through the “Coastal and Regional Ocean Community model-CROCO) the surface fields of Salinity and velocity were incorporated into the analysis. The results indicated that the seasonal turbidity and its intensification in autumn and winter on the western side of the Campeche platform are due mainly to the contribution of intense flows from the Grijalva-Usumacinta which is further dynamically mix in autumn by the southwest winds, the cyclonic gyres, and the return currents. In spring-summer, the same forcing persists; however, the turbidity decreases due to the ineffectiveness of the intense zonal winds to produce Ekman transport.

Monitoring the spatial distribution and seasonal dynamics of phytoplankton functional types (PFTs) in coastal oceans is essential for understanding fisheries production, changes in water quality, and carbon export to the deep ocean. The launch of new generation ocean color sensors such as OLCI (Ocean Land Color Instrument) onboard Sentinel 3A provides an unprecedented opportunity to study the surface dynamics of PFTs at high spatial (300 m) and temporal (daily) resolution. Here we characterize the seasonal dynamics of the major PFTs over the surface waters of the west coast of Canada using OLCI imagery and Chemical Taxonomy (CHEMTAX, v1.95) software. The satellite-based approach was adapted from a previously proven Empirical Orthogonal Function (EOF)-based algorithm by using a local matchup dataset comprising CHEMTAX model output and EOF scores derived from OLCI remote sensing reflectance. The algorithm was developed for the following PFTs: diatoms, dinoflagellates, dictyochophytes, haptophytes, green algae, cryptophytes, cyanobacteria, raphidophytes, and total chlorophyll-a (TChla) concentration. Of these PFTs, first level evaluation of the OLCI-derived retrievals showed reliable performance for diatoms and raphidophytes. The second level of validation showed that TChla had the best performance, and green algae, cryptophytes, and diatoms followed seasonal trends of a high temporal resolution in situ CHEMTAX time-series. Somewhat reduced correspondence was observed for raphidophytes. Due to their low contribution to the phytoplankton community (26%) and low range of variation, weak performance was noted for haptophytes, dictyochophytes, cyanobacteria, and dinoflagellates. The EOF-based PFT maps from daily OLCI imagery showed seasonal spring and fall diatom blooms with succession from spring blooms to high diversity flagellate dominated summer conditions. Furthermore, strong localized summer raphidophyte blooms (Heterosigma akashiwo) were observed, which are a regionally important harmful species. Overall, this study demonstrates the potential of the OLCI in deriving the surface dynamics of major PFTs of the Strait of Georgia (SoG), a critical habitat for the juvenile Pacific Salmon.

Surface water quality is an important factor affecting the ecological environment and human living environment. The monitoring of surface water quality by remote sensing monitoring technology can provide important research significance for water resources protection and water quality evaluation. Finding the optimal spectral index sensitive to water quality for remote sensing monitoring of water quality is extremely important for surface water quality analysis and treatment in the Ebinur Lake Basin in arid areas. This study used Sentinel-2MSI data at 10 m resolution to quickly monitor the water quality of the watershed. Through laboratory experiments and measurement data from the Ebinur Lake Basin, 22 water quality parameters (WQPs) were obtained. Through Z-score and redundancy analysis, 9 WQPs with significant contributions were extracted. Based on the remote sensing spectral band, four water indexes (NDWI, NWI, EWI, AWEI-nsh) and 2D modeling spectral index (DI, RI, NDI), the correlation analysis between WQPs and two kinds of spectral band indexes is carried out, and it is concluded that the overall correlation between WQP and 2D spectral modeling is more relevant. This paper calculates the evaluation and models the 2D spectrum of the Water Quality Index (WQI). The WQI is predicted and modeled through four machine learning algorithms (RF, SVM, PLSR, PLSR-SVM).The results show that the inversion effect of the two-dimensional spectral modeling index on water quality parameters (WQPs) is superior to that of the water index, and the correlation coefficient of the DI (R12-R1) SWIR-2 and BLUE band interpolation index reaches 0.787. On this basis, three kinds of two-dimensional spectral modeling indexes are used to inversely synthesize the WQI, and the correlation coefficient of the ratio index of the RI (R11/R8) SWIR-1 and near-infrared (NIR) bands is preferably 0.69. In the WQI prediction, the partial least squares regression support vector machine (PLSR-SVM) model in machine learning algorithms has good modeling and prediction effects (R2c = 0.873, R2v = 0.87), which can provide a good basis. The research results provide references for remote monitoring of surface water in arid areas, and provide a basis for water quality prediction and safety evaluation. HIGHLIGHTS Inversion of water quality in the Ebinur Lake Basin by Sentinel-2 MSI data.; Analysis of the contribution rate of different water quality parameters in water by Z-score and PCA.; Comparison of classical water quality index, correlation between spectral modeling and water quality parameters.; Modeling and predicting water quality index (WQI) using machine learning and linear correlation methods.;

The precise correction of atmospheric effects is essential for the accurate analysis of inland water color from remote sensing (RS) data. This study systematically evaluates the efficacy of five atmospheric correction (AC) algorithms—Acolite, C2RCC, FLAASH, iCOR, and L2gen—when applied to Sentinel-2 Multi Spectral Instrument (MSI) and Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery. The performance of these algorithms is scrutinized across different spectral bands and spatial resolutions, with a focus on their suitability for water body AC. Our validation approach primarily focuses on the spatial arrangement of sampling points and the timeliness of the collected data. The results reveal that Acolite demonstrates superior performance in the red band of MSI data, whereas C2RCC exhibits inconsistencies in the blue and green bands. FLAASH stands out in its handling of OLCI data, although iCOR exhibits sensitivity to resolution, resulting in over-correction in lower resolution scenarios. L2gen is noted for its consistent provision of concentrated data amplitude across the board. The findings, graphically represented through a radar chart, are pivotal for guiding the selection of optimal AC algorithms for the visible spectrum, thereby enhancing the accuracy of RS applications in environmental monitoring and research. Additionally, our study highlights the impact of sampling time differences on the AC spectrum, with variations of approximately 3% observed for a single sensor. This underscores the critical need for temporal consistency in field measurements. • Emphasizes differences in atmospheric correction results from satellites with varying spatial resolution. • Quantifies the impact of time differences between field measurements and satellite overpass on atmospheric correction. • Evaluates the performance of five atmospheric correction algorithms over plateau lake. [ABSTRACT FROM AUTHOR]

Following the Aerosol Robotic Network-Ocean Color (AERONET-OC) network scheme and instrument deployment protocols, two fixed platforms (Muping and Dong'ou) in the Yellow Sea and East China Sea were implemented with the support of the China National Satellite Ocean Application Service. Optical radiometry instruments were established at the two sites to autonomously determine remote sensing reflectance (Rrs) and aerosol optical depth (AOD). Details about location selection, platform design, instrument deployment, and the associated data processing procedure are reported in this study. Rrs and AOD measured by independent instruments at the Muping site were compared and results showed that they were consistent, with a median relative percentage difference (MRPD) < 0.6% for AOD and <10% for Rrs. The spectral feature and temporal pattern of Rrs and AOD at the two sites were examined and compared with data from 14 AERONET-OC sites. Rrs and AOD data measured at the two sites were used to evaluate ocean color operational products of MODIS/Aqua (MODISA), OLCI/Sentinel-3A (OLCI-3A), and OLCI/Sentinel-3B (OLCI-3B). Comparison showed that the three satellite sensor-derived Rrs agreed well with in situ measurements, with an MRPD < 25% for MODISA, <30% for OLCI-3A, and <40% for OLCI-3B, respectively. Large uncertainties were observed in the blue bands for the three satellite sensors, particularly for OLCI-3B at 400 nm. AOD at 865 nm derived from the three satellite sensors also agreed well with in situ measurements, with an MRPD of 28.1% for MODISA, 30.6% for OLCI-3A, and 39.9% for OLCI-3B. Two commonly used atmospheric correction (AC) processors, the ACOLITE and SeaDAS, were also evaluated using in situ measurements at two sites and 20 m-resolution MSI/Sentinel-2A data. Close agreements were achieved for both AC processors, while the SeaDAS performed slightly better than ACOLITE. The optimal band selection in the AC models embedded in two AC processors was a combination of one near-infrared and one short-wave infrared band such as 865 and 1609 nm, shedding light on MSI data applications in the aquatic environment. [ABSTRACT FROM AUTHOR]

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm. [ABSTRACT FROM AUTHOR]

The colors of the ocean and inland waters span clear blue to turbid brown, and the corresponding spectral shapes of the water-leaving signal are diverse depending on the various types and concentrations of phytoplankton, sediment, detritus and colored dissolved organic matter. Here we present a simple metric developed from a global dataset spanning blue, green and brown water types to assess the quality of a measured or derived aquatic spectrum. The Quality Water Index Polynomial (QWIP) is founded on the Apparent Visible Wavelength (AVW), a one-dimensional geophysical metric of color that is inherently correlated to spectral shape calculated as a weighted harmonic mean across visible wavelengths. The QWIP represents a polynomial relationship between the hyperspectral AVW and a Normalized Difference Index (NDI) using red and green wavelengths. The QWIP score represents the difference between a spectrum’s AVW and NDI and the QWIP polynomial. The approach is tested extensively with both raw and quality controlled field data to identify spectra that fall outside the general trends observed in aquatic optics. For example, QWIP scores less than or greater than 0.2 would fail an initial screening and be subject to additional quality control. Common outliers tend to have spectral features related to: 1) incorrect removal of surface reflected skylight or 2) optically shallow water. The approach was applied to hyperspectral imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), as well as to multispectral imagery from the Visual Infrared Imaging Radiometer Suite (VIIRS) using sensor-specific extrapolations to approximate AVW. This simple approach can be rapidly implemented in ocean color processing chains to provide a level of uncertainty about a measured or retrieved spectrum and flag questionable or unusual spectra for further analysis.

Remote sensing reflectance (Rrs) is a fundamental data of ocean color remote sensing that is used as an input data for algorithm development. In this study, the Rrs spectra acquired from the coast of Korea and California, on the opposite side of the Pacific Ocean, were analyzed and compared. The waters of Gyeonggi Bay and Mokpo had a similar spectrum to those of the waters inside of the San Francisco Bay, although the waters of each region had different characteristics. The South Sea in Korea showed similar spectral characteristics on the waters of Monterey Bay and outside of the San Francisco Bay in California. The upward slope of the Rrs spectra in the range of 400 to 600 nm obtained from the inside of the San Francisco Bay was higher than the coastal waters on the Gyeonggi Bay and Mokpo in Korea. The Rrs spectra showing peaks on 580 nm and 680 ~ 700 nm due to chlorophyll were similarly observed in the South and East Seas of Korea, and the coastal waters of Monterey Bay and the outside of the San Francisco Bay in California.

Uncertainties in the retrieval of the remote sensing reflectance, Rrs, from Ocean Color (OC) satellite sensors have a strong impact on the performance of algorithms for the estimation of chlorophyll-a, mineral concentrations, and inherent optical properties (IOPs). The uncertainties are highest in the blue bands. The total radiance measured at the top of the atmosphere captures the instantaneous state of the atmosphere-ocean system: the in-water conditions, sky and Sun glint reflected from the wind-roughened ocean surface, as well as light scattered from molecules and aerosols in the atmosphere. Each of these components has associated uncertainties, and when combined with the additional uncertainties from the instrument noise and the atmospheric correction process, they contribute to the total uncertainty budget for the retrieved Rrs. We analyzed the contribution of each component uncertainties to the total Rrs uncertainties in SNPP-VIIRS level 2 products, taking advantage of the spectral differences between the components. We examined multiple scenes in the open ocean and coastal waters at spatial resolutions ranging from 2250 to 5250 m by comparing the retrieved Rrs to in situ measurements made at several AERONET-OC sites and at the MOBY site. It was shown that uncertainties associated with the molecular (Rayleigh) scattering play the most significant role, while the contributions of other components are usually smaller. Uncertainties in Rayleigh scattering are primarily attributed to the variability of Rayleigh optical thickness (ROT) with a standard deviation of approximately 1.5% of ROT, which can largely explain the frequency of negative Rrs retrievals as observed using the current standard atmospheric correction process employed by NASA. Variability of the sky light reflected from the ocean surface in some conditions also contributed to uncertainties in the blue; water variability proportional to Rrs had a very pronounced peak in the green at coastal sites.

Remote sensing reflectance (Rrs), which is currently measured mainly using the above-water approach, is the most crucial parameter in the remote sensing inversion of plateau inland water colors. It is very difficult to measure the Rrs of plateau inland unmanned areas; thus, we provide a measurement solution using a micro-spectrometer. Currently, commercial micro-spectrometers are not factory calibrated for radiation, and thus, a radiometric calibration of the micro-spectrometer is an essential step. This article uses an Ocean Optics micro-spectrometer (STS-VIS) and a traditional water spectrometer (Trios) to simultaneously measure the irradiance and radiance of diffuse reflectance plates with different reflectance values for field calibration. The results show the following: (1) different fiber types have different calibration coefficients, and the integration time is determined according to the diameter of the fiber and the type of fiber, and (2) by comparing the simultaneous measurement results of STS-VIS with Trios, the mean absolute percentage difference (MAPD) of both reached 18.64% and 5.11% for Qinghai Lake and Golmud River, respectively, which are accurate Rrs measurements of water bodies. The Rrs of the Hoh Xil and Qarhan Salt Lake water bodies in unmanned areas of China was measured, and this was the first collection of in situ spectral information with a micro-spectrometer. This article shows that the micro-spectrometer can perform the in situ measurement of water Rrs in unmanned inland areas. With this breakthrough in the radiometric performance of the micro-spectrometer, we are able to obtain more accurate remote sensing reflectance results of unmanned water bodies.

Coral reefs are fragile and endangered ecosystems in the tropical marine and coastal environment. Thermal stress due to marine heat waves (MHW) could cause significantly negative impacts on the health conditions, i.e., bleaching of the coral ecosystem. The current study is an attempt to quantify the intensity of coral bleaching in the Andaman region in recent decades using the intensity of marine heat wave (IMHW) estimated from satellite measured sea surface temperature (SST). A linear regression model was developed between IMHW and in situ observations of percent coral bleaching (PCB) which has the slope 7.767 (of IMHW unit) and intercept (− 141.7). Further, an attempt was also made to establish the relationship between PCB and the ratio between the remote sensing reflectance (Rrs) at 443 and 531 nm to upscale the percentage of coral bleaching at synoptic scales. A significant positive correlation between the PCB and band ratio index was found (R2 = 0.72). This approach can be used for the operational monitoring of coral reef beaching in this region. [ABSTRACT FROM AUTHOR]

The chlorophyll and total suspended materials concentrations and remote sensing reflectance data were observed for red tides occurring every summer in waters around the Korean Peninsula. In observation area and date, the field survey were performed (1) in the Jinhae and Geoje coasts during August 1998, August 1999, August 2001, and August 2003, (2) in East Sea coast during August 2013. The remote sensing reflectance data were obtained from portable spectroradiometer. The chlorophyll concentration data were obtained from spectrophotometric method and the total suspended materials concentration data were obtained from filter-weight difference method. The remote sensing reflectance data were validated using Moon et al.(2012). The chlorophyll concentration data were validated using baseline correction and subtraction of 750 nm value, and the total suspended materials concentration data were validated using variation of humidity.

With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations.

Atmospheric correction of satellite optical data is based on an assessment of the optical characteristics of the atmosphere, such as the aerosol optical depth of the atmosphere and the spectral slope of its spectrum, the so-called Angstrom parameter. Inaccurate determination of these parameters is one of the causes of errors in the retrieval of the remote sensing reflectance spectra. In this work, the obtained large array of field and satellite data for the northeastern part of the Black Sea is used, including ship-based measurements of atmospheric characteristics and sea reflectance, MODIS Aqua/Terra and OLCI Sentinel-3 A/B Level 2 remote sensing reflectance and atmospheric data. The purpose of this study is to show the numerical differences between the atmospheric parameters measured from the surface level and from the satellite and demonstrate their relationship with the differences between in situ and satellite remote sensing reflectance. Based on the information received, we propose an algorithm for the additional correction of satellite Level 2 data that uses a two-parametric model of the Black Sea remote sensing reflectance as a first approximation. This method does not require any in situ information. It is shown that additional correction significantly reduces the discrepancy between in situ and retrieved remote sensing reflectance, especially in short-wave spectral bands.

In the application of ocean color remote sensing, remote sensing reflectance spectral (Rrs(λ)) is the most important and basic parameter for the development of bio-optical algorithms. Atmospheric correction of ocean color data is a key factor in obtaining accurate water Rrs(λ) data. Based on the QA (quality assurance) score spectral quality evaluation system, the quality of Rrs(λ) spectral of GOCI (Geostationary Ocean Color Imager) obtained from four atmospheric-correction algorithms in the Bohai Sea were evaluated and analyzed in this paper. The four atmospheric-correction algorithms are the NASA (National Aeronautics and Space Administration) standard near-infrared atmospheric-correction algorithm (denoted as Seadas—Default), MUMM (Management Unit of the North Sea Mathematical Models, denoted as Seadas—MUMM), and the standard atmospheric-correction algorithms of KOSC GOCI GDPS2.0 (denoted as GDPS2.0) and GDPS1.3 (denoted as GDPS1.3). It is shown that over 90% of the Rrs(λ) data are in good quality with a score ≥4/6 for the GDPS1.3 algorithm. The probability of Rrs(λ) with a QA score of 1 is significantly higher for the GDPS1.3 algorithm (57.36%), compared with Seadas—Default (37.91%), Seadas—MUMM (35.96%), and GDPS2.0 (33.05%). The field and MODIS measurements of Rrs(λ) were compared with simultaneous GOCI Rrs(λ), and they demonstrate that the QA score system is useful in evaluating the spectral shape of Rrs(λ). The comparison results indicate that higher QA scores have higher accuracy of the Rrs band ratio. The QA score system is helpful to develop and evaluate bio-optical algorithms based on the band ratio. The hourly variation of QA score from UTC 00:16 to 07:16 was investigated as well, and it demonstrates that the data quality of GOCI Rrs(λ) can vary in an hour scale. The GOCI data with high quality should be selected with caution when studying the hourly variation of biogeochemical properties in the Bohai Sea from GOCI measurements.

The Sentinel-2A and Sentinel-2B satellites, with on-board Multi-Spectral Instrument (MSI), and launched on 23 June 2015 and 7 March 2017, respectively, are very useful tools for studying ocean color, even if they were designed for land and vegetation applications. However, the use of these satellites requires a process called “atmospheric correction”. This process aims to remove the contribution of the atmosphere from the total top of atmosphere reflectance measured by the remote sensors. For the purpose of assessing this processing, seven atmospheric correction algorithms have been compared over two French coastal regions (English Channel and French Guiana): Image correction for atmospheric effects (iCOR), Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (C2RCC), Sentinel 2 Correction (Sen2Cor), Polynomial-based algorithm applied to MERIS (Polymer), the standard NASA atmospheric correction (NASA-AC) and the Ocean Color Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART). The satellite-estimated remote-sensing reflectances were spatially and temporally matched with in situ measurements collected by an ASD FieldSpec4 spectrophotometer. Results, based on 28 potential individual match-ups, showed that the best performance processor is OC-SMART with the highest values for the total score Stot (16.89) and for the coefficient of correlation R2 (ranging from 0.69 at 443 nm to 0.92 at 665 nm). iCOR and Sen2Cor show the less accurate performances with total score Stot values of 2.01 and 7.70, respectively. Since the size of the in situ observation platform can be significant compared to the pixel resolution of MSI onboard Sentinel-2, it can create bias in the pixel extraction process. Thus, to study this impact, we used different methods of pixel extraction. However, there are no significant changes in results; some future research may be necessary.

Airborne hyperspectral data play an important role in remote sensing of coastal waters. However, before their application, atmospheric correction is required to remove or reduce the atmospheric effects caused by molecular and aerosol scattering and absorption. In this study, we first processed airborne hyperspectral CASI-1500 data acquired on 4 May 2019 over the Uljin coast of Korea with Polymer and then compared the performance with the other two widely used atmospheric correction approaches, i.e., 6S and FLAASH, to determine the most appropriate correction technique for CASI-1500 data in coastal waters. Our results show the superiority of Polymer over 6S and FLAASH in deriving the Rrs spectral shape and magnitude. The performance of Polymer was further evaluated by comparing CASI-1500 Rrs data with those obtained from the MODIS-Aqua sensor on 3 May 2019 and processed using Polymer. The spectral shapes of the derived Rrs from CASI-1500 and MODIS-Aqua matched well, but the magnitude of CASI-1500 Rrs was approximately 0.8 times lower than MODIS Rrs. The possible reasons for this difference were time difference (1 day) between CASI-1500 and MODIS data, higher land adjacency effect for MODIS-Aqua than for CASI-1500, and possible errors in MODIS Rrs from Polymer.

Ocean colour (OC) remote sensing is an important tool for monitoring phytoplankton in the global ocean. In optically complex waters such as the Baltic Sea, relatively efficient light absorption by substances other than phytoplankton increases product uncertainty. Sentinel-3 OLCI-A, Suomi-NPP VIIRS and MODIS-Aqua OC radiometric products were assessed using Baltic Sea in situ remote sensing reflectance (Rrs) from ferry tracks (Alg@line) and at two Aerosol Robotic Network for Ocean Colour (AERONET-OC) sites from April 2016 to September 2018. A range of atmospheric correction (AC) processors for OLCI-A were evaluated. POLYMER performed best with Rrs and 28% at 665 nm, suggesting that using this AC for deriving Chl a will be the most accurate. Suomi-VIIRS and MODIS-Aqua underestimated Rrs by 35, 29, 22 and 39% and 34, 22, 17 and 33% at 442, 486, 560 and 671 nm, respectively. The consistency between different AC processors for OLCI-A and MODIS-Aqua and VIIRS products was relatively poor. Applying the POLYMER AC to OLCI-A, MODIS-Aqua and VIIRS may produce the most accurate Rrs and Chl a products and OC time series for the Baltic Sea.

Results of unique laboratory measurements of remote sensing reflectance (Rrs) of several phytoplankton species typically occurring in high abundances in the Baltic Sea waters are presented. Reflectance spectra for diatoms: Cyclotella meneghiniana and Skeletonema marinoi and cyanobacteria: Dolichospermum sp., Nodularia spumigena and Synechococcus sp. were analysed in terms of assessment of their characteristic features and the differences between them. These species contain similar pigments, which results in general similarities of reflectance spectra, i.e. decrease of reflectance magnitude in the blue and red spectrum regions. However, hyper-spectral resolution of optical measurements let us find differences between optical signatures of diatoms and cyanobacteria groups and between species belonging to one group as well. These differences are reflected in location of local maxima and minima in the reflectance spectrum and changes in relative height of characteristic peaks with changes of phytoplankton concentration. Wide ranges of phytoplankton concentrations were analysed in order to show the persistence of Rrs characteristic features. The picoplankton species, Synechococcus sp. show the most distinct optical signature, which let to distinguish separate cluster in hierarchical cluster analysis (HCA). The results can be used to calibrate input data into radiative transfer model, e.g. phase function or to validate modelled Rrs spectra.

The composition of phytoplankton and the concentration of pigments in their cells make their absorption and specific absorption coefficients key parameters for bio-optical modeling. This study investigated whether the multispectral vertical diffuse attenuation coefficient of downward irradiance (Kd) gradients could be a good framework for accessing phytoplankton genera. In situ measurements of remote sensing reflectance (Rrs), obtained in an Amazon Floodplain Lake (Lago Grande do Curuai), were used to invert Kd, focusing on Sentinel-3/Ocean and Land Color Instrument (OLCI) sensor bands. After that, an analysis based on the organization of three-way tables (STATICO) was applied to evaluate the relationships between phytoplankton genera and Kd at different OLCI bands. Our results indicate that phytoplankton genera are organized according to their ability to use light intensity and different spectral ranges of visible light (400 to 700 nm). As the light availability changes seasonally, the structure of phytoplankton changes as well. Some genera, such as Microcystis, are adapted to low light intensity at 550–650 nm, therefore high values of Kd in this range would indicate the dominance of Microcysts. Other genera, such as Aulacoseira, are highly adapted to harvesting blue-green light with higher intensity and probably grow in lakes with lower concentrations of colored dissolved organic matter that highly absorbs blue light (405–498). These findings are an important step to describing phytoplankton communities using orbital data in tropical freshwater floodplains. Furthermore, this approach can be used with biodiversity indexes to access phytoplankton diversity in these environments.

Optical water types (OWTs) were identified from an in situ dataset of concomitant biogeochemical and optical parameters acquired in the Amazon River and its tributaries, in the Lower Amazon region, at different hydrological conditions from 2014 to 2017. A seasonal bio-optical characterization was performed. The k-means classification was applied to the in situ normalized reflectance spectra (rn(λ)), allowing the identification of four OWTs. An optical index method was also applied to the rn(λ) defining the thresholds of the OWTs. Next, level-3 Sentinel-3 Ocean and Land Color Instrument images representative of the seasonal discharge conditions were classified using the identified in situ OWTs as reference. The differences between Amazon River and clearwater tributary OWTs were dependent on the hydrological dynamics of the Amazon River, also showing a strong seasonal variability. Each OWT was associated with a specific bio-optical and biogeochemical environment assessed from the corresponding absorption coefficient values of colored dissolved organic matter (aCDOM) and particulate matter (ap), chlorophyll-a and suspended particulate matter (SPM) concentrations, and aCDOM/ap ratio. The rising water season presented a unique OWT with high SPM concentration and high relative contribution of ap to total absorption compared to the other OWTs. This bio-optical characterization of Lower Amazon River waters represents a first step for developing remote sensing inversion models adjusted to the optical complexity of this region.

High inflows of freshwater from the Mississippi and Atchafalaya rivers into the northern Gulf of Mexico during spring contribute to strong physical and biogeochemical gradients which, in turn, influence phytoplankton community composition across the river plume–ocean mixing zone. Spectral features representative of bio-optical signatures of phytoplankton size classes (PSCs) were retrieved from underway, shipboard hyperspectral measurements of above-water remote sensing reflectance using the quasi-analytical algorithm (QAA_v6) and validated against in situ pigment data and spectrophotometric analyses of phytoplankton absorption. The results shed new light on sub-km scale variability in PSCs associated with dynamic and spatially heterogeneous environmental processes in river-influenced oceanic waters. Our findings highlight the existence of localized regions of dominant picophytoplankton communities associated with river plume fronts in both the Mississippi and Atchafalaya rivers in an area of the coastal margin that is otherwise characteristically dominated by larger microphytoplankton. This study demonstrates the applicability of underway hyperspectral observations for providing insights about small-scale physical-biological dynamics in optically complex coastal waters. Fine-scale observations of phytoplankton communities in surface waters as shown here and future satellite retrievals of hyperspectral data will provide a novel means of exploring relationships between physical processes of river plume–ocean mixing and frontal dynamics on phytoplankton community composition.