In estuarine and coastal ecosystems, the majority of previous studies have considered coupled nitrification-denitrification (CND) processes to be exclusively sediment based, with little focus on suspended particulate matter (SPM) in the water column. Here, we present evidence of CND processes in the water column of Hangzhou Bay, one of the largest macrotidal embayments in the world. Spearman’s correlation analysis showed that SPM was negatively correlated with nitrate (rho = −0.372,P = 0.018) and marker genes for nitrification and denitrification in the water column were detected by quantitative PCR analysis. The results showed thatamoAandnirgene abundances strongly correlated with SPM (allP < 0.01) and the ratio ofamoA/nirstrongly correlated with nitrate (rho = −0.454,P = 0.003). Furthermore, aggregates consisting of nitrifiers and denitrifiers on SPM were also detected by fluorescencein situhybridization. Illumina MiSeq sequencing further showed that ammonia oxidizers mainly belonged to the genusNitrosomonas, while the potential denitrifying generaBradyrhizobium,Comamonas,Thauera,Stenotrophomonas,Acinetobacter,Anaeromyxobacter,Sulfurimonas,PaenibacillusandSphingobacteriumshowed significant correlations with SPM (allP < 0.01). This study suggests that SPM may provide a niche for CND processes to occur, which has largely been missing from our understanding of nitrogen cycling in estuarine waters.

Oceanic lidar is an effective tool for detecting water’s vertical structure. Cost-effective design of an oceanic lidar system and processing algorithms requires an effective lidar simulator. In this study, an oceanic lidar simulation tool was developed, which is available to the public ( https://github.com/soedchen/OLE ), and named the Oceanic Lidar Emulator (OLE). OLE is an improved semianalytic Monte Carlo-based lidar simulator, which has the capability of dealing with the physics of light propagating through wind-driven rough air–water interface into the ocean, being scattered by subsurface phytoplankton and reflected from the sea bottom, and returning to the lidar receiver. OLE has three main features: 1) it can deal with stratified water, while most existing lidar models can only be used for homogeneous water; 2) it can be applied to arbitrary scattering phase function (SPF) (e.g., Fournier–Forand or Petzold), while most existing lidar models can only use the Henyey–Greenstein SPF due to the difficulty of solving the inverse equation for the cumulative distribution function; and 3) it takes general consideration of lidar system observation geometry (e.g., receiver field of view, receiving aperture, altitude, incident angle, and so on) and environmental parameters (e.g., wind-driven rough water surface, vertical structure of water optical properties, and sea bottom albedo). We studied lidar extinction caused by multiple scattering and the effects of the SPF, rough sea surface, and stratified water, by comparing simulation results with measurements. These results demonstrated that our model is effective for oceanic lidar simulation.

Knowledge about the spatiotemporal distribution of sea surface salinity (SSS) provides valuable and important information for understanding various marine biogeochemical processes and ecosystems, especially for those coastal waters significantly affected by human activities. Remote-sensing techniques have been used to monitor salinity in the open ocean with their advantages of wide-area surveys and real-time monitoring. However, potential challenges remain when using satellite data with coarse spatiotemporal resolutions, leading to a loss of valuable information. In the current study, based on the local dataset collected over the southern Yellow Sea (SYS), a region-customized algorithm was developed to estimate SSS by using the remote sensing reflectance. The model evaluations indicated that our algorithm yielded good SSS estimation, with a root-mean-square error (RMSE) of 0.29 psu and a mean absolute percentage error (MAPE) of 0.75%. Satellite-derived SSS results compared well with those derived from in situ observations, further suggesting the good performance of our developed algorithm for the study regions. We applied this algorithm to Geostationary Ocean Color Imager (GOCI) data for the month of August from 2011 to 2018 in the SYS, and produced the spatial distribution patterns of the SSS for August of each year. The SSS values were high in offshore waters and lower in coastal waters, especially in the Yangtze River estuary. The negative correlation between the monthly Changjiang River discharge (CRD) and SSS (R = −0.71, p < 0.001) near the Yangtze River estuary was observed, suggesting that the SSS distribution in the Yangtze River estuary was potentially influenced by the CRD. In offshore waters, the correlation between SSS and CRD was weak (R < 0.2), suggesting that the riverine discharge’s effect might be weak.

The ability to detect oil spills remotely is important in marine environmental monitoring. The optical polarization remote sensing has the unique advantage of inversion of refractive index of spilled oils which is the key parameter for calculation of sunglint reflectance. Compared to nonpolarization optical image, the degree of linear polarization (DOLP) of spilled oil's sunglint depends on the refractive index and viewing angles but not on the surface roughness. Accurate correction of sunglint reflectance can promote optical estimation of spilled oils. In this article, a polarized optical model was used to calculate equivalent refractive index of Deepwater Horizon (DWH) spilled oils using space-borne Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) images covering Gulf of Mexico (GOM) in 2010. When the angle (θm) between the direction of the flat surface specular reflection and that of observation is less than 20°, the PARASOL-derived and modeled DOLPs agree well, and the atmospheric polarization effects can be neglected. The equivalent refractive index of the spilled oil area, which implies the relative proportions of seawater and spilled oil in each pixel, could be estimated using polarized remote sensing under sunglint. Furthermore, if the relationship between the equivalent refractive index and remote sensing reflectance (Rrs) of spilled oils in the remote sensing images can be given, it might be used to correct the sunglint effect on various spilled oils, thereby leading to an improvement for optical quantifying spilled oil volume.

Islands are an important part of the marine ecosystem. Increasing impervious surfaces in the Zhoushan Islands due to new development and increased population have an ecological impact on the runoff and water quality. Based on time-series classification and the complement of vegetation fraction in urban regions, Landsat thematic mapper and other high-resolution satellite images were applied to monitor the dynamics of impervious surface area (ISA) in the Zhoushan Islands from 1986 to 2011. Landsat-derived ISA results were validated by the high-resolution Worldview-2 and aerial photographs. The validation shows that mean relative errors of these ISA maps are $<15%$. The results reveal that the ISA in the Zhoushan Islands increased from $19.2km2$ in 1986 to $86.5km2$ in 2011, and the period from 2006 to 2011 had the fastest expansion rate of $5.59km2$ per year. The major land conversions to high densities of ISA were from the tidal zone and arable lands. The expansions of ISA were unevenly distributed and most of them were located along the periphery of these islands. Time-series maps revealed that ISA expansions happened continuously over the last 25 years. Our analysis indicated that the policy and the topography were the dominant factors controlling the spatial patterns of ISA and its expansions in the Zhoushan Islands. With continuous urbanization processes, the rapid ISA expansions may not be stopped in the near feature.