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.

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.

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.

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 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.

Relation extraction (RE) aims to identify the semantic relations between named entities in text.Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document.In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations.Entity global representations model the semantic information of all entities in the document, entity local representations aggregate the contextual information of multiple mentions of specific entities, and context relation representations encode the topic information of other relations.Experimental results demonstrate that our model achieves superior performance on two public datasets for document-level RE.It is particularly effective in extracting relations between entities of long distance and having multiple mentions.

Knowledge bases (KBs) have gradually become a valuable asset for many AI applications. While many current KBs are quite large, they are widely acknowledged as incomplete, especially lacking facts of long-tail entities, e.g., less famous persons. Existing approaches enrich KBs mainly on completing missing links or filling missing values. However, they only tackle a part of the enrichment problem and lack specific considerations regarding long-tail entities. In this paper, we propose a full-fledged approach to knowledge enrichment, which predicts missing properties and infers true facts of long-tail entities from the open Web. Prior knowledge from popular entities is leveraged to improve every enrichment step. Our experiments on the synthetic and real-world datasets and comparison with related work demonstrate the feasibility and superiority of the approach.

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.