The particle beam attenuation coefficient (cp, m-1) is a core parameter in bio-optical research, and its three-dimensional variability has attracted increasing attention in the South China Sea (SCS). However, satellite remote sensing algorithms for retrieving localcpat 660 nm (cp660) profiles remain underdeveloped. In this study, based on field observations, we identified three representativecp660 vertical structures in this area: uniform, Gauss-like, and exponential decay types. We found the ratio of water depth to mixed layer depth can serve as a reliable indicator for distinguishing these profile types. Based on the above finding, we further developed satellite-based inversion algorithms to derive three-dimensionalcp660 distributions in the upper SCS, achieving accuracies within 35%. These include a Bayesian optimization algorithm for the Gauss-like type, an empirical constant algorithm for the exponential decay type, and a machine learning algorithm applicable to both. Unlike the Bayesian and empirical constant algorithms, which primarily rely on sea surface information, the machine learning approach—integrating temperature and salinity profiles—could offer improved accuracy and robustness. The algorithms we proposed in this research would provide valuable tools for advancing the understanding of vertical distributions of phytoplankton biomass, functional groups, and primary productivity in the SCS.

The mesoscale eddy structure in the upper ocean is difficult to observe with altimeter satellite data due to their coarse spatiotemporal resolution. However, the world’s first Geostationary Ocean Color Imager (GOCI) makes it possible to refine the eddy structure inversion with observations of high spatiotemporal resolution (500 m and 1 h, respectively). In this study, we proposed a new flow field retrieval algorithm recurrent all-pairs field transforms in mesoscale eddy flow retrieval (RAFT-MesoEddy) based on deep current optical flow architecture in the mesoscale eddy surface flow field for GOCI data. The validation results showed that the flow field matched the particle image velocimetry (PIV) data in both quality and spatial distribution, with mean end-to-end point error (MEPE) and mean average angle error (MAAE) of less than 0.07 pixel and 2.57°, respectively. The GOCI-derived flow field showed good consistency with drifting buoy trajectory data, with MEPE and MAAE values less than 0.34 m/s and 18.78°, respectively. The GOCI-retrieved flow field showed agreement with satellite altimeter geostrophic flow data, with the MEPE and MAAE being 0.28 m/s and 10.12°, respectively. The GOCI-retrieved flow field showed an agreement with Ocean Surface Current Analyses Real-time (OSCAR) surface flow field, with the MEPE and MAAE are 0.28 m/s and 9.62°, respectively. In addition, a specific mesoscale eddy to discuss the hourly dynamics of flow field and eddy kinetic energy (EKE) is taken. The significant diurnal dynamics revealed in the hourly GOCI observations suggest caution in mapping mesoscale eddy flow using conventional altimeter satellite data, as the temporal resolution is insufficient to capture diurnal variations.

Atmospheric correction (AC) of satellite-measured polarized radiances is crucial due to the intricate radiative transfer processes within the atmospheric-ocean system and the diverse applications of polarimetric ocean color remote sensing. This study presents the intelligent polarization AC (IPAC) algorithm that efficiently handles multiangle, multispectral, and polarimetric satellite observations to derive polarized water-leaving reflectance, as well as aerosol properties (coarse-mode and fine-mode) and water inherent optical properties in open-ocean waters. To develop the IPAC algorithm, we simulated top-of-atmosphere (TOA) vector apparent reflectances over the open ocean using a vector radiative transfer simulation (VRTS) model. These simulations employed statistical results from satellite Level-3 products and precalculated lookup tables of polarization water-leaving radiance transmittances. Generating approximately 60 million TOA vector apparent reflectances and 89 inversion submodels facilitated the construction of the IPAC model. Performance assessment of IPAC included three wavelengths (490, 670, and 865 nm), three polarization states, and 13 solar-sensor geometries for each satellite pixel. Validation analysis revealed that the IPAC algorithm significantly improved the accuracy of retrieved ocean color products compared to standard AC algorithms. The mean absolute percentage error in the retrieved polarized apparent water-leaving reflectance and chlorophyll concentration relative to the measurement data was below 34.43% and 37.66%, respectively. Overall, the IPAC model proves its capability to deal with multiangle, multispectral, and polarimetric satellite data and advance the current ocean color work for many applications.

Multiangle polarization measurements in the near-infrared band from space were illustrated as suitable for the inversion of aerosol vertical distribution (AVD) information. In this study, we reported the interference of the AVD to linearly polarized radiances (${\rho }_{Q}$and${\rho }_{U}$) and scalar radiance (${\rho }_{I}$) under different simulation configurations, and the sensitivity discrepancies of${\rho }_{I}$,${\rho }_{Q}$, and${\rho }_{U}$to the aerosol scale height were analyzed by theoretical calculation of Mie scattering theory. Furthermore, the high correlation between polarization measurements in the near-infrared band and AVD inspired to perform polarization measurement inversion of the aerosol layer height (ALH). The constructed model was based on nonlinear optimization and the total-absorption ocean assumption. By fitting the linearly polarized radiances obtained by polarization observations in the near-infrared band, the AVD information were retrieved. The evaluation indicators showed that the root mean square error (RMSE) of the retrievals is less than 1 km for three typical sea areas, which demonstrates that polarization inversion is a valuable addition to light dectection and ranging (Lidar) data for AVD measurement.

The adjacency effect (AE) caused by the surrounding land cannot be ignored for satellite remote sensing of coastal and inland waters, especially for rivers with a narrow width. However, there is currently a lack of comprehensive understanding of AE for rivers, much less the precise correction methods for these effects. Here, a 3-D vector radiative transfer model for a 3-D radiative transfer model for a nonuniform underlying surface (NUS-MC) was developed. The ability of the NUS-MC to accurately simulate AEs in the case of Rayleigh scattering was validated by comparing its results with those from existing models for both uniform and nonuniform underlying surface cases. The mean absolute percentage deviations (MAPDs) for the simulated radiance are within 0.14% for a uniform water underlying and within 0.34% for a uniform land underlying surface. Based on the NUS-MC, AEs on Rayleigh scattering radiance for river waters under various conditions were systematically quantified. The results show that$\rho _{\mathrm {AE}}$decreases with increasing solar zenith angle (SZA) but increases with increasing view zenith angle (VZA). Even for a large river, 10 km across, at a wavelength of 443 nm, the mean${\rho }_{\mathrm {AE}}$at the river center is 3.97% at an SZA of 0° when the land albedo is 0.1, increasing to 18.19% and 33.37% at land albedos of 0.3 and 0.5, respectively. Overall, AEs significantly affect Rayleigh scattering even in rivers with widths of up to 10 km. In addition, the angle between the river channel and the observation plane as well as the distance from the riverbank to the point of view in the river can also affect the AEs. These findings suggest that the impact of land AEs on Rayleigh scattering must be thoroughly considered to retrieve water-leaving radiance in rivers accurately, and it is essential to develop atmospheric correction methods capable of removing AEs.

We present a novel method using satellite and biogeochemical Argo (BGC-Argo) data to retrieve the 3-D structure of chlorophyll$a$(Chla) in the northern Indian Ocean (NIO). The random forest (RF)-based method infers the vertical distribution of Chla using the near-surface and vertical features. The input variables can be divided into three categories: 1) near-surface features acquired by satellite products; 2) vertical physical properties obtained from temperature and salinity profiles collected by BGC-Argo floats; and 3) the temporal and spatial features, i.e., day of the year, longitude, and latitude. The RF-model is trained and evaluated using a large database including 9738 profiles of Chla and temperature-salinity properties measured by BGC-Argo floats from 2011 to 2021, with synchronous satellite-derived products. The retrieved Chla values and the validation dataset (including 1948 Chla profiles) agree fairly well, with$R^{2} = 0.962$, root-mean-square error (RMSE) = 0.012, and mean absolute percent difference (MAPD) = 11.31%. The vertical Chla profile in the NIO retrieved from the RF-model is more accurate and robust compared to the operational Chla profile datasets derived from the neural network and numerical modeling. A major application of the RF-retrieved Chla profiles is to obtain the 3-D Chla structure with high vertical resolution. This will help to quantify phytoplankton productivity and carbon fluxes in the NIO more accurately. We expect that RF-model can be used to develop long-time series products to understand the variability of 3-D Chla in future climate change scenarios.

Mariculture is an important offshore economic activity, and excessive farming can lead to the deterioration of sea ecology. The concentration of nutrients (mainly DIN (dissolved inorganic nitrogen) and PO4 (orthophosphate-phosphorous)) is the main factor characterizing the health condition of farmed seas. Conventional field monitoring methods are spatiotemporally limited, and remote sensing technology has the advantages of high spatial coverage and long time series monitoring. Thus, the Sentinel-3 reflectance data and the in situ measured data for the offshore waters of Wenzhou were matched simultaneously. Then, the matched dataset between the Sentinel-2 band and the in situ measured data were obtained through spectral correspondence conversion between Sentinel-2 and Sentinel-3, and a machine learning algorithm was used to build the inversion model with an independent validation process. The correlations between the concentration of nutrients, area of rafts and precipitation were assessed, and a strong positive correlation was found between the concentration of nutrients and the area of rafts, and a weak negative correlation was found between the former and precipitation.

L-band passive microwave remote sensing of ocean surfaces is hampered by uncertainties due to the contribution of precipitation. However, modeling and correcting rain effect are complicated because all the precipitation contributions from the atmosphere and sea surface, including the rain effect in the atmosphere, water refreshing, rain-perturbed sea surface, and rain-induced local wind, are coupled. These rain effects significantly alter L-band microwave satellite measurements. This study investigates the impact of precipitation on the satellite measured brightness temperature (TB) and proposes a correction method. The results show that the rain-induced TB increase is approximately 0.5–1.4 K in the atmosphere, depending on the incidence angle and polarization. Moreover, the rain effect on sea surface emissions is more significant than that in the atmosphere. The result shows that rain effects on sea surface emissions are higher than 3 K for both polarizations when the rain rate is higher than 20 mm/h. We validate the rain effect correction model based on Soil Moisture Active Passive (SMAP) observations. The results show that the TBs at the top of the atmosphere (TOA) simulated by the model are in a good agreement with the SMAP observations, with root-mean-square errors (RMSEs) of 1.137 and 1.519 K for the horizontal and vertical polarizations, respectively, indicating a relatively high accuracy of the established model. Then, a correction model is applied to sea surface salinity (SSS) retrieval for analysis, and the results show that the developed model corrects the underestimation in SSS retrieval. Finally, the rain effect correction model is validated with Aquarius observations in three regions, and it is found that the RMSEs of the corrected TOA TBs range from 1.013 to 1.608 K, which is higher than those without rain effect correction (RMSEs range from 2.078 to 3.894 K). Overall, the model developed in this study provides relatively a good accuracy for rain effect correction.

The South China Sea (SCS) is one of the largest marginal seas in the world. It includes a river-dominated, highly productive ocean margin on the northern shelf and an oligotrophic ocean-dominated basin along with other sub-regions with various features. It was challenge to estimate the air-sea CO2 flux in this area. We developed a retrieval algorithm for sea surfacepCO2by a combination of our previously established semi-mechanistic approach (MeSAA) and machine learning (ML) method, named MeSAA-ML-SCS, built upon a large dataset of sea surface partial pressure of CO2(pCO2) collected fromin situmeasurements during 44 cruises/legs to the SCS in the last two decades. We set several semi-analytical parameters, includes:pCO2_thermrepresented the combined effect of thermodynamics and the atmospheric CO2forcing on seawaterpCO2; upwelling index (UISST) and mixing layer depth (MLD) to characterize the mixing processes; chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm (Rrs(443) and Rrs(555)), which were proxies of biological effects and other characteristics for distinguishing shelf, basin, and sub-regions. We set the difference between seawaterpCO2and atmosphericpCO2(ΔpCO2Sea-Air) as the output, and the seawaterpCO2was finally obtained by summing atmosphericpCO2and ΔpCO2Sea-Air. We compared several ML models, and the XGBoost model was confirmed as the best. Independent cruise-based datasets that are not involved in the model training were used to validate the satellite products, with low root mean square error (RMSE = 11.69 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of time-series satellite-derivedpCO2(2.44 ± 0.24 μatm/yr) were validated by thein situdata at the Southeastern Asia Time-series Study (SEATS) station, showing good consistency. Results indicate that the SCS as a whole is a source of atmospheric CO2, releasing an average of 12.34 ± 3.11 Tg C/yr from a total area of 2.87 × 106km2, while the northern shelf act as a sink (2.02 ± 0.64 Tg C/yr). With the forcing of increasing atmospheric CO2, the area-integrated CO2efflux over the entire SCS is decreasing with a rate of 0.41 Tg C/yr during 2003–2019. This shared long time series, high-accuracy dataset (1 km) can be helpful to further improve our understanding of the air-sea CO2exchange dynamics in the SCS.

In this letter, we propose a downscaling approach to improve the spatial resolution of sea surface salinity (SSS) in estuarine areas using combined microwave and ocean color data. The model established a relationship between SSS and normalized sea surface emissivity and colored dissolved organic matter (CDOM). The model was validated byin situmeasurements conducted in the East China Sea (ECS) and Mississippi River Estuary (MRE). The model showed relatively good agreement within situSSS measurements and illustrated enhanced SSS at high (4 km) resolution compared with low (40 km) resolution, with root mean square errors (RMSEs) of 1.55 versus 2.58 psu in the ECS and 0.39 versus 1.49 psu in the MRE. Overall, the proposed downscaling approach enhances the spatial resolution and accuracy of satellite SSS observations over estuarine areas, which should be helpful for ocean dynamic and biogeochemical studies in these regions.

We present a statistical analysis of how residual error from satellite remote sensing reflectance ($R_{\mathrm {rs}}$) depends on the environmental factors. Our results indicate that image coverage affects the data quality, and the residual error in high-quality data correlates significantly with the residual error in data heavily contaminated with stray light. Due to imperfectly corrected bidirectional reflectance, we found many residual error anomalies in the covarying relationship between residual error and illumination–observation geometry even though high-quality$R_{\mathrm {rs}}$data appeared more homogeneous than low-quality data. Additionally, we found underestimates of$R_{\mathrm {rs}}$in very oligotrophic tropical waters, and these residual errors positively covaried with sensor zenith angles at the edge of the scans because of strong atmospheric multi-scattering effects. Furthermore, we found that due to their strong correlation with$R_{\mathrm {rs}}$, the spectral relationship of residual errors should be dynamically reinitialized with the image, which is key to an inherent optical properties (IOPs) data processing system (such as IDAS) removing the residual errors from the$R_{\mathrm {rs}}$data. Doing so, and applying the IDAS algorithm, stray-light-contaminated$R_{\mathrm {rs}}$was recovered, obtaining comparable results to those achieved considering high-quality measurements. This permits to relax the corresponding quality check for generation of the Level-3 global area coverage$R_{\mathrm {rs}}$, largely improving the satellite spatiotemporal coverage and consequently the product accuracy.

Satellite ocean color products have been widely used to monitor spatiotemporal variations in marine ecological environments from regional to global oceans. However, current satellite ocean color products fail to provide effective records during the winter in high–latitude oceans, limiting understanding of the marine ecological environment during the winter season. In this study, we proposed an atmospheric correction model, namely, the Neural Network Atmospheric Correction algorithm for the Southern Hemisphere (NN–AC–SH), and recover winter satellite ocean color products for the high–latitude oceans of the Southern Hemisphere from 2003 to 2020. The accuracy of the NN–AC–SH model was verified based on the in situ data from the Aerosol Robotic Network–Ocean Color (AERONET–OC). The results indicate that the NN–AC–SH model performed better than the traditional near–infrared (NIR) iterative atmospheric correction algorithm, e.g., in the 443, 488 and 531 nm bands, the relative deviations of the NN–AC–SH model were 23.11%, 20.96% and 23.14%, respectively, while the values of the NIR model were 30.72%, 22.85%, and 24.81%, respectively. Under the observation condition of a high solar zenith angle (SZA), the NN–AC–SH model performed better than the NIR model in terms of the amount of effective data (94 vs. 78 data points) and model inversion accuracy (a relative deviation 32.83% vs. 43.60%). Moreover, in situ chlorophyll concentration data from the NASA bio–Optical Marine Algorithm Dataset (NOMAD) and Chinese Antarctic Research Expedition (CHINARE) were used to verify the accuracy of the restored chlorophyll concentration products, and the results reveal that the NN–AC–SH model resulted in more effective records of higher accuracy than those obtained with the NASA–distributed chlorophyll concentration products. Overall, for the first time, this study recovered long time–series (2003–2020) ocean color products for the high–latitude oceans of the Southern Hemisphere (≥50°S) in the winter, which could provide unique satellite products for investigating the marine ecological environment of the Antarctic and sub–Antarctic oceans in the winter.

The Ocean and Land Color Instrument (OLCI) on Sentinel-3 is one of the most advanced ocean color satellite sensors for aquatic environment monitoring. However, limited studies have been focused on a comprehensive assessment of atmospheric correction (AC) methods for OLCI. In an attempt to fill the gap, this study evaluated seven different AC methods for OLCI using global automaticin situobservations from Aerosol Robotic Network-Ocean Color (AERONET-OC). Results showed that the POLYnomial-based algorithm applied to MERIS (POLYMER) had the best performance for bands with wavelength ≤ 443 nm, and the SeaDAS method based on 779 and 865 nm was the best for longer spectral bands; however, SeaDAS (SeaWiFS Data Analysis System) processing algorithm based on 779 and 1020 nm, as well as 865 and 1020 nm, obtained degraded AC performance; Case 2 Regional CoastColor (C2RCC) also produced large uncertainties; Baseline AC (BAC) method might be better than SeaDAS method; and simple subtraction method was the worst except for turbid waters. POLYMER and C2RCC underestimated high remote sensing reflectance (Rrs) at red and green bands; SeaDAS method based on 779 and 865 nm held an advantage for clear waters over the other two band combinations, while their difference turned small for turbid waters. AC uncertainties generally impacted the performance of chlorophyll retrievals. POLYMER outperformed other methods for chlorophyll retrieval. This study provides a good reference for selecting a suitable AC method for aquatic environment monitoring with Sentinel-3 OLCI.

The polarization characteristics of water-leaving radiation, which contain rich information on oceanic constituents, have often been neglected. Due to the lack of suitable instruments and practical difficulties in removing strong contamination by polarized skylight, direct measurement of the polarization of water-leaving radiation remains a challenge. In this study, we designed an above-water instrument (named POLWR) to directly measure the polarization of water-leaving radiation and examined its field application in Qiandao Lake, China. Results showed that the Stokes components of water-leaving radiance ($L_{w}$) measured by POLWR were consistent with the radiative transfer (RT) simulations, with a determination coefficient ($R^{2}$) and mean relative error of 0.67 and 18.86%, respectively. The Qiandao Lake results revealed that the degree of polarization (DOP) of$L_{w}$varied from 0.05 to 0.5 within the 412–865-nm range. Moreover, a good relationship between the polarized remote sensing reflectance ($R_{\mathrm {rsp}}$), and DOP and chlorophyll-a (Chla) concentration was found at 368 nm in this productive lake, indicating great potential for the inversion of oceanic constituents from polarization signals. With its small size and direct measurement ability, the POLWR instrument should be widely applicable and could help improve our understanding of the polarization characteristics of water-leaving radiation and the underwater light field.

The vertical distribution of absorbing aerosols has nonnegligible impact on the atmospheric correction of satellite ocean color remote sensing, especially for the water-leaving radiance retrieval at blue and ultraviolet bands. In this study, we investigated the impact of the vertical distribution of absorbing aerosols on the satellite-measured radiance at the top of the atmosphere (TOA) and the retrieved water-leaving radiance. First, the global occurrence frequency of absorbing aerosols was mapped, and it was found that the annual averaged occurrence frequencies of absorbing aerosols were >30% over the coasts of the Sahara and Arabian Desert, China, South-Central Africa, and the Indian Peninsula. Second, a new aerosol classification algorithm was developed to establish absorbing aerosol optical models based on AERosol RObotic NETwork (AERONET) site observations. Finally, the effects of the vertical distribution of absorbing aerosols on the upward radiance at the TOA at 412 nm and the retrieved water-leaving radiance were evaluated. The results showed that the influence of the vertical distribution on the TOA radiance could be up to 8% for dust and 10% for fine-dominated absorbing aerosols (FDAs), which was comparable with the influence of aerosol optical depth. Not considering absorbing aerosols during atmospheric correction might produce$\sim 4$%–10% errors in the water-leaving radiance retrieval at 412 nm over turbid waters under the assumption of a Gaussian distribution. Simplified exponential and two-layer atmospheric vertical distribution models can lead to errors of water-leaving radiance retrieval up to$\sim 12$%–15% and$\sim 30$%–40%, respectively. Overall, the imperfect vertical distribution assumption and aerosol model selection might induce uncertainty in water-leaving radiance retrieval from 10% to 80% under absorbing aerosol conditions.

Sea surface salinity (SSS) retrieval from satellite-based microwave radiometer is hampered by uncertainties due to atmospheric and surface scattering contributions. This study presents a comprehensive vector radiative transfer model (VRTM) for estimation of brightness temperature (TB) (for SSS retrieval) from L-band radiometry based on a matrix-operator method. It includes an efficient two-scale model (TSM) that combines a geometrical optics (GO) model and a small-perturbation model (SPM) for computing both the small- and large-scale scattering components of the sea surface. Moreover, it considers the influence of rain effects on TB by including the radiation extinction term (scattering and attenuation). The simulation results using the VRTM were validated with those obtained from the RT4 model for flat sea surface conditions and the RTTOV model for rough sea surface conditions. The relative difference in the estimated TB among the models was small (<; 1%) for low wind speeds (<; 1 m/s) and increased up to 3% for high wind speeds and observation angles. Simulations on the influence of wind speed on TB with various parameterizations were further examined. Compared with SMOS-MIRAS and Aquarius measurements, the VRTM simulations agreed well with satellite measurements for both vertically and horizontally polarized TBs with biases of less than 2.2 K for observation angles from 20° to 65°. The binned TBs showed even better results, with a standard deviation of less than 1.45 K and an absolute mean error of less than 1.2 K.

New generation ocean color imagers on geostationary orbits are designed to provide a much higher temporal resolution along with enhanced spatial and spectral resolutions that will open up obvious opportunities for improving the sampling frequency and resolving diurnal variability of phytoplankton and other biogeochemical properties in dynamic coastal waters. Despite the capabilities of such new generation sensors to detect the diurnal cycles of various ocean phenomena, there is a lack of knowledge on their radiometric sensitivity and signal detectability for observing the ocean color at morning or evening hours. This paper aims to explore the capability of geostationary satellite ocean color sensor for detecting ocean biogeochemical properties [chlorophyll (CHL); total suspended matter (TSM); colored dissolved organic matter (CDOM)] under high solar zenith angles (SZAs). The analysis is based upon simulations from the vector radiative transfer model for the coupled ocean-atmosphere system (PCOART-SA), which considers the earth curvature effects. The unitless differential signal-to-noise ratio (ASNR) is used as a discriminant parameter to indicate the radiometric sensitivity to variation of different biogeochemical properties. The results showed that the SZAs have a significant impact on the signal detectability for CHL variation. For typical shelf water (CHL = 1 μg/L, TSM = 1 mg/L, CDOM = 0.15 m-1), with the typical observation zenith angle (OZA) = 30°, changes on theorder of ΔCHL = 0.024 μg/L (2.4% to background CHL) were detectable when SZA = 30°; when SZA > 75°, the detectable minimal ΔCHL increased to 0.77 μg/L (77%), indicating the difficulty of detecting CHL under high SZA. For CDOM, the detectability of changes (ΔCDOM) was also found to be closely related to the SZAs, i.e., changes on the order of ten times depending on the SZA conditions. However, even under extremely high SZA conditions (SZA = 80°, OZA = 30°), ACDOM = 0.007 m-1which is about 4.7% of the background CDOM was still detectable at 412 nm. On the other hand, under high SZA conditions (SZA = 80°, OZA = 30°), ΔTSM = 0.211 mg/L (2.1% to the background TSM) was also detectable. Overall, our results indicate that under high SZAs conditions, the geostationary satellite ocean color sensor may experience difficulty in detecting a slight change in CHL variation in productive waters, but it still can detect small changes in TSM and CDOM contents despite a reduced sensitivity at the steeper SZAs.

High-precision measurement of sea surface temperature (SST) requires an accurate knowledge of sea surface emissivity (SSE). Many studies have found that the SST estimations in the coastal areas are less accurate than that in open seas, where water turbidity is negligible. Previous works regard SSE as a function of surface roughness and observation angle; however, works have rarely focused on the internal characteristic of water, such as its turbidity. Thus, this paper presents thermal infrared measurements of the emissivity of turbid water carried out under controlled conditions with a multichannel radiometer working in the 8-14-μm region. The results showed that measured emissivity values decreased with the increase of water turbidity. The decrease was tiny for lower suspended particulate matter (SPM) concentrations but a significance emissivity decrease with higher concentrations, especially at large observation angles. For instance, the difference between concentrations of 0 and 5000 mg/L manifested an average emissivity variation of 2.93% with 5000 mg/L at a viewing angle of 55°, depending on the radiometer spectral channels. And, a parametric relationship of emissivity in terms of SPM concentration was established in this paper. The impact of ignoring water turbidity on SST, using SST algorithms and a case of satellite retrievals, was analyzed. It was indicated that there would be an SST error lower than 0.2 °C at SPM concentrations less than 1000 mg/L and that SST deviations would reach values up to almost 0.5 °C-0.6 °C at 5000 mg/L, even higher than 1 °C at large angles for a given atmospheric water vapor content less than 4 g/cm2.

Polarization response is an important factor influencing the accuracy of radiance measurement for satellite ocean color sensors, which would change with on-orbit time. In this paper, a practical method is proposed for on-orbit estimation of polarization response. First, the linear polarization components of the Stokes vector entering the sensor are estimated by a vector radiative transfer model of the coupled ocean-atmosphere system. Second, the real radiance entering the sensor is estimated by another high-accuracy ocean color sensor using the cross-calibration method. Finally, based on the estimated linear polarization components and real radiance, the polarization response coefficients are derived by the least squares method. The proposed method is tested by applying it to the Moderate Resolution Imaging Spectroradiometer on board the Aqua satellite, and the derived polarization factors are consistent with the prelaunch values, indicating the reliability of the proposed method. In addition, our results reveal that the contribution of aerosol scattering should be included in the estimation of the linear polarization components of the Stokes vector at the top of atmosphere, particularly for long wavelengths.