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.

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 data quality of the satellite-retrieved water-leaving reflectance (Rrs) depends on the accuracy of radiometric calibration and the performance of atmospheric correction. A radiometric calibration scheme (RCS) has been developed to ensure the accuracy of Rrs through the gain adjustment factors (GAFs) to adjust the satellite calibrated data. The GAF is obtained from the ratio of the simulated reflectance at the top of atmosphere to the calibrated values. The simulated reflectance is computed by a satellite image simulation model (SISM) based on a dataset of climatological global Rrs images according to the same geometric angles of the image pixels. The dataset, taken as a kind of the pseudo-invariant calibration sites for in situ measurements, is generated from the average of standard satellite-retrieved Rrs during more than two decades (1997–2019). The SISM inputs the aerosol properties retrieved from the satellite level 1B data (L1B) and uses the same algorithms of the data-processing system. The results show that the accuracy of the calibration of the website downloaded Chinese Ocean Color and Temperature Scanner on the Haiyang-1C satellite (COCTS/HY-1C) is beyond the requirement of the operational data-processing system (higher than 10%). The daily GAFs can be used to recalibrate the L1B data and monitor the daily sensor degradations. The influences of GAFs are assessed on different meteorological conditions, indicating that the values decrease with the increase of the aerosol optical depths (AODs) but the average of the GAF image is little affected by the meteorological conditions. The uncertainty of GAFs was tested by the different inputs of Rrs values and the results show that they are actually little affected by errors of the Rrs inputs. Therefore, the RCS, taking the advantage of vicarious calibration, offers a tool to recalibrate the COCTS/HY-1C L1B data for the data reprocessing system.

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.

The radiance received by satellite sensors viewing the ocean is a mixed signal of the atmosphere and ocean. Accurate decomposition of the radiance components is crucial because any inclusion of atmospheric signal in the water-leaving radiance leads to an incorrect estimation of the oceanic parameters. This is especially true over the turbid coastal waters, where the estimation of the radiance components is difficult. A layer removal scheme for atmospheric correction (LRSAC) has been developed to take the atmospheric and oceanic components as the layer structure according to the sunlight passing in the Sun-Earth-satellite system. Compared with the normal coupled atmospheric column, the uncertainty of the layer structure of Rayleigh and aerosols has a relatively small error with a mean relative error (MRE) of 0.063%. As the aerosol layer was put between Rayleigh and ocean, a new Rayleigh lookup table (LUT) was regenerated using 6SV (Second Simulation of a Satellite Signal in the Solar Spectrum, Vector version 3.2) based on the zero reflectance at the ground to produce the pure Rayleigh reflectance without the Rayleigh-ocean interaction. The accuracy of the LRSAC was validated by in situ water-leaving reflectance, obtaining an MRE of 6.3%, a root-mean-square error (RMSE) of 0.0028, and the mean correlation coefficient of 0.86 based on 430 matchup pairs over the East China Sea. Results show that the LRSAC can be used to decompose the reflectance at the top of each layer for the atmospheric correction over turbid coastal waters.

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.

Information diffusion through various types of networks, such as social networks and media networks, is a very common phenomenon on the Internet nowadays. In many scenarios, we can track only the time when the information reaches a node. However, the source infecting this node is usually unobserved. Inferring the underlying diffusion network based on cascade data (observed sequence of infected nodes with timestamp) without additional information is an essential and challenging task in information diffusion. Many studies have focused on constructing complex models to infer the underlying diffusion network in a parametric way. However, the diffusion process in the real world is very complex and hard to be captured by a parametric model. Even worse, inferring the parameters of a complex model is impractical under a large data volume.

This paper presents a new approach to multiscale segmentation of satellite multispectral imagery using edge information. The Canny edge detector is applied to perform multispectral edge detection. The detected edge features are then utilized in a multiscale segmentation loop, and the merge procedure for adjacent image objects is controlled by a separability criterion that combines edge information with segmentation scale. The significance of the edge is measured by adjacent partitioned regions to perform edge assessment. The present method is based on a half-partition structure, which is composed of three steps: single edge detection, separated pixel grouping, and significant feature calculation. The spectral distance of the half-partitions separated by the edge is calculated, compared, and integrated into the edge information. The results show that the proposed approach works well on satellite multispectral images of a coastal area.