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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]

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]

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]

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]

The results of the development and field testing of a compact high-speed deck spectroradiometer for remote sensing reflectance measurements are presented. Validation using data obtained with hydrooptical equipment showed that the new device makes it possible to measure data on remote sensing reflectance with an accuracy sufficient for calculating bio-optical characteristics. Based on the data obtained during field tests of the new device, processed using regional algorithms, the values of the bio-optical characteristics of the Kara and Black seas surface waters were quantitatively assessed. [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.

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.

Abstract The East China Sea (ECS) has experienced severe harmful algal blooms (HABs) that have deleterious ecological effects on marine organisms. Recent studies indicated that deploying of a second geostationary ocean color imager (GOCI-II) can significantly improve ocean monitoring. This study systematically assessed GOCI-II and its ability to detect HABs and distinguish between dinoflagellates and diatoms in the ECS. First, the remote-sensing reflectance ( $${R}_{rs}\left(\lambda \right),$$ R rs λ , $$\lambda$$ λ represents the wavelength) obtained from GOCI-II was compared to the local measurement data. Compared to the bands at 412 and 443 nm, the bands at 490, 510, and 620 nm exhibited excellent consistency, which is important for HAB detection. Second, four different methods were employed to extract bloom areas in the ECS: red tide index (RI), spectral shape (SS), red band line height ratio (LHR), and algal bloom ratio ( $${R}_{AB}$$ R AB ). The SS (510) algorithm was the most applicable for detecting blooms from GOCI-II imagery. Finally, the classification capability of GOCI-II for dinoflagellates and diatoms was evaluated using three existing algorithms: the bloom index (BI), combined $$Prorocentrum donghaiens$$ Prorocentrumdonghaiens index (PDI) and diatom index (DI), and the spectral slope ( $${R}_{\_slope}$$ R _ s l o p e ). The BI algorithm yielded more satisfactory results than the other algorithms.

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.

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.

The East China Sea (ECS) has experienced severe harmful algal blooms (HABs) that have deleterious ecological effects on marine organisms. Recent studies indicated that deploying of a second geostationary ocean color imager (GOCI-II) can significantly improve ocean monitoring. This study systematically assessed GOCI-II and its ability to detect HABs and distinguish between dinoflagellates and diatoms in the ECS. First, the remote-sensing reflectance ( R rs λ , λ represents the wavelength) obtained from GOCI-II was compared to the local measurement data. Compared to the bands at 412 and 443 nm, the bands at 490, 510, and 620 nm exhibited excellent consistency, which is important for HAB detection. Second, four different methods were employed to extract bloom areas in the ECS: red tide index (RI), spectral shape (SS), red band line height ratio (LHR), and algal bloom ratio ( R AB ). The SS (510) algorithm was the most applicable for detecting blooms from GOCI-II imagery. Finally, the classification capability of GOCI-II for dinoflagellates and diatoms was evaluated using three existing algorithms: the bloom index (BI), combined Prorocentrumdonghaiens index (PDI) and diatom index (DI), and the spectral slope ( R _ s l o p e ). The BI algorithm yielded more satisfactory results than the other algorithms. [ABSTRACT FROM AUTHOR]

The results of the development and field testing of a compact high-speed deck spectroradiometer for remote sensing reflectance measurements are presented. Validation using data obtained with hydrooptical equipment showed that the new device makes it possible to measure data on remote sensing reflectance with an accuracy sufficient for calculating bio-optical characteristics. Based on the data obtained during field tests of the new device, processed using regional algorithms, the values of the bio-optical characteristics of the Kara and Black seas surface waters were quantitatively assessed. [ABSTRACT FROM AUTHOR]

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]

Abstract 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.

Abstract 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

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]

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.

Abstract 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.

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.

Remote sensing reflectance (Rrs) and inherent optical properties (IOPs) conversions are fundamental in accurate satellite measurements to guarantee the semi-analytical retrieval quality of IOPs and the biogeochemical products. Traditionally, the Rrs-IOPs conversions are determined by a quadratic polynomial function with two model parameters (Gx = 0,1). However, Gx values vary in time and location, which are attributed to the spatial and temporal variability inherent to illumination conditions, sea surface properties, and meteorological states. To improve the performance of three classical existing models used for Rrs-IOPs conversions, we designed two novel neural network models (NNGx = 1,2) to quantitatively calculate Gx from the Rrs spectrum pixel by pixel without requirement of any auxiliary illumination and meteorological data, and then proposed for Rrs-IOPs conversions. We evaluated these approaches with numerical simulations and field measurements, and the results show that the NNGx models are more effective in semi-analytically converting Rrs into IOPs than the three existing models. Furthermore, we applied the NNGx models to satellite images to understand the downstream influence of the Gx values on IOPs estimates for the global oceans. We further confirm that the Gx values dramatically change for the global ocean, which is especially true for very oligotrophic gyres, coastal waters, and high latitude oceans. When we use a constant Gx for the Rrs-IOPs conversions, it leads to substantial uncertainty of up to 30% in the IOPs retrievals for China's coastal regions. Our results suggest that it is possible to improve the data quality of IOPs for the global oceans by providing accurate pixel-level Gx values using NNGx models. [ABSTRACT FROM AUTHOR]

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]

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The Geostationary Ocean Color Imager (GOCI) is widely employed in tracking diurnal dynamics of oceanic conditions. However, the current atmospheric correction (AC) algorithms for GOCI often mask pixels around cloud edges to exclude pixels contaminated by cloud edge effects (CEEs; including stray light, cloud shadows, and cloud adjacent effects (AEs)), which results in massive data loss. In this paper, we propose a novel AC algorithm to correct these CEE-affected pixels based on deep learning (namely, DLACC) to achieve a comparable quality level to those pixels far away from cloud edges. We used the standard near-infrared iterative AC algorithm in SeaDAS (namely, NIR) to obtain Rayleigh-corrected reflectance (R rc) and remote sensing reflectance (R rs). Then, high-quality R rs values were extracted using a quality assurance (QA) algorithm. Next, we obtained 2,821,668 matchups by matching CEE-free noontime R rs values with CEE-affected R rc values in a time window of 1 h. These matchups were used to develop DLACC. Validations using in situ data from Aerosol Robotic Network-Ocean Color (AERONET-OC) stations showed that more matchups were obtained with the DLACC model than with the NIR algorithm and Korea Ocean Satellite Center standard AC algorithm in GDPS 2.0 (KOSC), and the accuracies were similar. More importantly, the DLACC algorithm is more tolerant of AEs and reduces AEs in a 2-pixel distance from cloud edges by 10% in the blue bands and by 50% in the green band. As a result, more valid observations are obtained with the DLACC algorithm, with the daily percentage of valid observations (DPVOs) increasing by 71% and 62% over those with the NIR algorithm and the KOSC algorithm, respectively. These increases in valid observations could further result in more consistent patterns in terms of space and time. Our DLACC algorithm can not only be used to process GOCI images but also provide an open framework to develop corresponding deep-learning models for other geostationary satellites. [ABSTRACT FROM AUTHOR]

Classification of water bodies is an effective means of understanding the optical properties of aquatic environments. However, most existing water classification systems are mainly based on the visible domain, and the ultraviolet (UV) wavelength, which has important impacts on the growth of phytoplankton, has received less focus. In this study, we developed a novel water classification and spectral scoring system (denoted OC_3S) using 26,709 global in situ hyperspectral data. These spectral data range from the UV to visible domain (380 nm to 750 nm), covering waters from clear to turbid. OC_3S classified water types into 30 classes, providing detailed information on the inherent optical properties and apparent optical properties of different water classes and quality assurance of remote sensing reflectance (R rs) spectra for multiple sensor data. The results show that OC_3S can capture high-quality R rs data with little loss of data volume. For example, the uncertainty of R rs (4 4 3) can be reduced by ∼1.33 % at the cost of 5 % of observations in oligotrophic oceans. In addition, OC_3S performs more stably in turbid water classes compared to the QA system (Wei, J., Lee, Z., & Shang, S., 2016b. A system to measure the data quality of spectral remote‐sensing reflectance of aquatic environments. Journal of Geophysical Research: Oceans , 8189–8207) because the coefficient of variation of OC_3S scores in turbid waters can be reduced by ∼44 % compared to that of QA scores. Moreover, good consistency was observed when OC_3S was applied to multiple current satellite images due to its superior tolerance to potential errors in atmospheric correction and radiometric calibration. Our OC_3S is capable of reducing the uncertainty of satellite products and could be applied to future sensors with UV bands (e.g., PACE). [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]

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

Green Noctiluca scintillans (NSG) is a mixotrophic dinoflagellate that frequently forms intense blooms in the north Indian Ocean, especially in the northeastern Arabian Sea during winter. This study investigates the conducive conditions and drivers associated with NSG blooms and proposes significant models for estimating NSG based on in situ (time-series) study during the bloom cycles. Two critical factors with regard to the blooms, i.e., phytoplankton abundance and sea surface temperature (SST), were examined. The first phase of heterotrophy dominance was when moderate blooms up to ~ 2.26 × 104 cells 1–1 occurred and, when NSG cells per unit chlorophyll-a (chl-a) increased, SST decreased up to ~ 24.5 ºC. The bloom intensity was proportional to the feed (diatoms/phytoplankton) availability and the degree of cooling (by the winter convection, i.e., nutrient enrichment). In the second phase of autotrophy dominance, intense blooms up to 1.9 × 105 cells l−1 occurred and NSG cells per unit chl-a fell, when the SST increased. During this period, bloom intensity was proportional to the degree of warming, i.e., nutrient and physiological stress. Phytoplankton are related to NSG by a single linear model through this SST cycle and is likely the NSG's essential biotic precursor. Attention is then focused on developing a remote sensing reflectance (Rrs) model for efficient synoptic monitoring of NSG using ocean color satellites. The Rrs band product ratio, a new metric, in combination with SST, notably modelled NSG abundance, which may be of potential routine application. [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.

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]

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]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water clarity is an important indicator for the water quality of aquatic ecosystems and measured as Secchi disk depth (Z SD). In this study, images from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Landsat Imager (OLI) over the period from 1984 to 2022 are used to study the spatiotemporal variation in Jiaozhou Bay, a typical semi-enclosed bay in China, and to gain the insights into the drivers for the variation during the study period. The satellite images are first processed to obtain Z SD , and corrected against the field measurements to ensure the Z SD to be high-quality, and then the spatial and temporal distributions of Z SD are analysed. The results show that the regional monthly average Z SD over the study period are the highest in May and lowest in January. From long-term seasonal observation, it declines most significantly in Summer. In terms of inter-annual variation, the results clearly indicate that 2015 was a turning point, as the regional average Z SD decreased significantly from 1984 to 2014 at a rate of approximately 0.022 m/year, but significantly increased from 2015 to 2022 with a rate of about 0.049 m/year. The turning trend of the water clarity in Jiaozhou Bay can be mainly attributed to the increasing sunshine duration during 2015–2022, as well as rapidly reduction emissions of industrial waste from Qingdao city during 2015–2016. • Landsat observations during 1984–2022 were used for water clarity monitoring. • The consistency of R rs products from Landsat TM/ETM+/OLI was tested. • A regional Z SD estimation model was proposed to ensure consistency with in-situ data. • Uncertainties of Z SD retrieval in coastal waters were revealed. • Drivers of Z SD variability in JZB with environmental factors and human activities. [ABSTRACT FROM AUTHOR]

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]

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 (Rrsλ) products. Firstly, the GOCI Rrsλ 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.

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.

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.