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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 that amoA and nir gene abundances strongly correlated with SPM (all P  < 0.01) and the ratio of amoA / nir strongly correlated with nitrate ( rho  = −0.454, P  = 0.003). Furthermore, aggregates consisting of nitrifiers and denitrifiers on SPM were also detected by fluorescence in situ hybridization. Illumina MiSeq sequencing further showed that ammonia oxidizers mainly belonged to the genus Nitrosomonas , while the potential denitrifying genera Bradyrhizobium , Comamonas , Thauera , Stenotrophomonas , Acinetobacter , Anaeromyxobacter , Sulfurimonas , Paenibacillus and Sphingobacterium showed significant correlations with SPM (all P  < 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.

The backscattering coefficient of aquatic particles (bbp(λ)) is one of the most important inherent optical properties in remote sensing. Due to the practical difficulties associated with measurements of the volume scattering function (VSF) over the whole backward hemisphere (90°–180°), bbp(λ) is estimated using either a single-angle approach, which employs the VSF at a fixed angle multiplied by a conversion factor χp(θ;λ), or a multi-angle approach, which uses the VSF at multiple angles with polynomial fitting. The angular variation in the VSF in the backward angles introduces uncertainties into bbp(λ) estimation. In this study, 178 VSF datasets from the global ocean were investigated. χp exhibited wavelength, regional, and angular variations. Although χp exhibited the lowest variability, at 120° (χp(120°;λ)), the single-angle approach exhibited a 12.71% mean absolute percent difference (MAPD) and a root mean squared error (RMSE) of approximately 4.02×10−3m−1. χp(140°;λ) exhibited larger variations at different wavelengths and in coastal regions. The three-angle approach exhibits wavelength independence and lower uncertainties, but the uncertainty of the polynomial fitting results at angles greater than 150° is relatively large, and the MAPD is still up to 10.92%. A better four-angle approach (100°, 120°, 140°, and 160°) was proposed, which could accurately determine bbp(λ) with the lowest MAPD (3.12%) and RMSE (0.86×10−3m−1). Notably, expanding to five angles provided minimal additional improvements, with the reduction in the MAPD being less than 1% compared to that under the four-angle approach. This study provides valuable insights into developing advanced optical sensors with better angular configurations for measuring bbp(λ).

Conventional bathymetric inversion approaches require bathymetric data as ground truth to obtain shallow water depth from high spatial resolution remote sensing imagery. Thus, bathymetric mapping methods that do not require inputs from in situ measurements are highly desirable. In this paper, we propose a dual-band model improvement method and evaluate the performance of this novel dual-band model approach to obtain the underwater terrain around a coastal island by using four WorldView-2/3 imageries. Then, we validate the results through changing water column properties with the Kd multiple linear regression model simulated by Hydrolight. We multiply the best coefficient and blue–green band value with different substrates on the pixels, which sample along the coastal line and isobath. The results show that the mean bias of inversed depth ranges from 1.73 to 2.96 m in the four imageries. The overall accuracy of root mean square errors (RMSEs) is better for depths shallower than 10 m, and the average relative error is 11.89%. The inversion accuracy of this new model is higher than Lee’s classical Kd model and has a wider range of applications than Chen’s dual-band model. The no-ground-truth dual-band algorithm has higher accuracy than the other log-ratio methods mentioned in this paper.

The sea surface skin temperature (SSTskin) is of critical importance with regard to air–sea interactions and marine carbon circulation. At present, no single remote sensor is capable of providing a gap-free SSTskin. The use of data fusion techniques is therefore essential for the purpose of filling these gaps. The extant fusion methodologies frequently fail to account for the influence of depth disparities and the diurnal variability of sea surface temperatures (SSTs) retrieved from multi-sensors. We have developed a novel approach that integrates depth and diurnal corrections and employs advanced data fusion techniques to generate hourly gap-free SST datasets. The General Ocean Turbulence Model (GOTM) is employed to model the diurnal variability of the SST profile, incorporating depth and diurnal corrections. Subsequently, the corrected SSTs at the same observed time and depth are blended using the Markov method and the remaining data gaps are filled with optimal interpolation. The overall precision of the hourly gap-free SSTskin generated demonstrates a mean bias of −0.14 °C and a root mean square error of 0.57 °C, which is comparable to the precision of satellite observations. The hourly gap-free SSTskin is vital for improving our comprehension of air–sea interactions and monitoring critical oceanographic processes with high-frequency variability.

Accurate and consistent observations of diurnal variability of sea surface temperature (SST DV) and its impact on air–sea heat fluxes over large areas for extended periods are challenging due to their short time scale and wide coverage. The hourly gap-free SSTs generated from Japan Aerospace Exploration Agency-Japan Agency for Marine–Earth Science and Technology (JAXA-JAMSTEC) are input to the COARE3.5 bulk flux algorithm to investigate the impact of SST DV on air–sea heat fluxes over the Northwest Pacific Ocean (NWPO). The main results are as follows. (1) The JAXA-JAMSTEC SSTs were found to be in good agreement with the buoy observations on SST DV with a very slight negative bias of −0.007 °C and a root mean square error of 0.018 °C. (2) The case study conducted on 26 June 2020 showed that the fluxes’ diurnal amplitudes were about 30–50 W m−2, and evolution was in agreement with SST DV. (3) The average impact of SST DV on heat fluxes was 2.93 W m−2 over the subtropical NWPO, decreasing from southeast to northwest and from low to high latitudes, and showing a clear seasonal cycle during 2019–2022. This research highlights the need to consider SST DV for accurate estimation of heat fluxes, which is crucial for climate and atmospheric studies.

Sun glint is commonly observed as interference in the imaging process of ocean color satellite sensors, making the extraction of water color information in sun glint-affected areas challenging and often leading to significant data gaps. The remote sensing baseline indices, calculated based on Rayleigh-corrected reflectance (Rrc), are recognized as effective in reflecting water color variability in sun glint-affected regions. However, the accurate extraction of the Rrc baseline indices requires sun glint correction. The determination of sun glint correction coefficients for different bands lacks a clear methodology, and the currently available correction coefficients are not applicable to different sea regions. Therefore, this study focuses on the South China Sea, where VIIRS imagery is significantly affected by sun glint. Based on paired datasets comprising sun glint-affected and -unaffected images acquired over the same region on adjacent dates, sun glint correction coefficients for each spectral band were derived by maximizing the cosine similarity of histograms constructed from three baseline indices: SS486 (Spectral Shape index at 486 nm), CI551 (Color Index at 551 nm), and SS671 (Spectral Shape index at 671 nm). To further evaluate the effectiveness of the proposed correction, chlorophyll-a concentrations were retrieved using a Random Forest regression model trained with baseline indices derived from sun glint-free Rrc data and subsequently applied to baseline indices after sun glint correction. Comparative analyses of both baseline index extraction and chlorophyll-a retrieval demonstrate that the proposed optimal-value and mean-value correction approaches effectively mitigate sun glint effects. The mean sun glint correction coefficients α(443), α(486), α(551), α(671) and α(745) were determined to be 0.75, 0.83, 0.89, 0.95 and 0.94, respectively. These coefficients can be applied as sun glint correction coefficients for the VIIRS Rrc data in the South China Sea region. Furthermore, the proposed method for determining sun glint correction coefficients offers a transferable framework that can be extended to other sea areas.

The coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager (SGLI) conducted by Global Change Observation Mission-Climate (GCOM-C) (Japan), the accuracy of satellite measurements was initially validated using matched pairs of satellite and ground data relating to the ECS. Additionally, using SGLI data from the coast of the ECS, we compared the applicability of three bloom extraction methods: spectral shape, red tide index, and algal bloom ratio. With an RMSE of less than 25%, satellite data at 490 nm, 565 nm, and 670 nm showed good consistency with locally measured remote sensing reflectance data. However, there was unexpected overestimation at 443 nm of SGLI data. By using a linear correction method, the RMSE at 443 nm was decreased from 27% to 17%. Based on the linear corrected SGLI data, the spectral shape at 490 nm was found to provide the most satisfactory results in separating bloom and non-bloom waters among the three bloom detection methods. In addition, the capability in harmful algae distinguished using SGLI data was discussed. Both of the Bloom Index method and the green-red Spectral Slope method were found to be applicable for phytoplankton classification using SGLI data. Overall, the SGLI data provided by GCOM-C are consistent with local data and can be used to identify bloom water bodies in the ECS, thereby providing new satellite data to support monitoring of bloom changes in the ECS.

This study used GOCI-II data to systematically evaluate the feasibility of Rayleigh-corrected reflectance (Rrc) to detect algal blooms in the complex optical environment of the East China Sea (ECS). Based on long-term in situ remote sensing reflectance (Rrs), Rrc spectra demonstrated the similar capability of reflecting the water condition under various atmospheric conditions, and the baseline indices (BLIs) derived from Rrc and Rrs showed good consistency (R2 > 0.98). The effectiveness of five Rrc-based BLIs (SS490, CI, DI, FLH, and MCI) for algal bloom detection was assessed, among which SS490 and MCI showed better performances. A synthetic bloom detection algorithm based on the BLIs of Rrc was then developed to avoid the impact of turbid water. The validation of the BLI algorithm was carried out based on the in situ algal abundance data from 2021 to 2023. Specifically, SS490 showed the best bloom detection result (F-measure coefficient, FM = 0.97), followed by MCI (FM = 0.88). Since the 709 nm bands used in MCI were missing in many ocean color satellites, the SS490 algorithm was more useful in application. Compared to Rrs based bloom detection algorithms, synthetical Rrc BLI proposed in this paper provides more effective observation results and even better algal bloom detection performance. In conclusion, the study confirmed the feasibility of utilizing Rrc for algal bloom detection in the coastal areas of the ECS, and recognized the satisfactory performance of synthetical SS490 by comparing with the other BLIs.

The data quality of the remote sensing reflectance (Rrs) from the two ocean color satellites HaiYang-1C (HY-1C) and HaiYang-1D (HY-1D) and the consistency with other satellites are critical for the products. The Layer Removal Scheme for Atmospheric Correction (LRSAC) has been applied to process the data of the Chinese Ocean Color and Temperature Scanner (COCTS) on HY-1C/1D. The accuracy of the Rrs products was evaluated by the in situ dataset from the Marine Optical BuoY (MOBY) with a mean relative error (MRE) of −1.56% and a mean absolute relative error (MAE) of 17.31% for HY-1C. The MRE and MAE of HY-1D are 1.05% and 15.68%, respectively. The comparisons of the global daily Rrs imagery with the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra show an MRE of 10.94% and an MAE of 21.38%. The comparisons between HY-1D and Aqua exhibit similar results, with an MRE of 13.31% and an MAE of 21.46%. The percentages of valid pixels of the global daily images of HY-1C and HY-1D are 32.3% and 32.6%, much higher than that of Terra (11.9%) and Aqua (11.9%). The gaps in the 8-day composite images have been significantly reduced, with 83.9% of valid pixels for HY-1C and 85.4% for HY-1D, which are also much higher than that of Terra (52.9%) and Aqua (50.9%). The gaps due to the contamination of sun glint have been almost removed from the 3-day composite imagery, with valid pixels of 63.5% for HY-1C and 65.6% for HY-1D, which are higher than that of the 8-day imagery of Terra and Aqua. The patterns of HY-1C imagery exhibit a similarity with those of HY-1D, but they are different on a pixel scale, mainly due to the changes in the ocean dynamic features within 3 h. The evaluations of the COCTS indicate that the imagery of HY-1C/1D can be used as a kind of standard product.

We found that significant errors occurred when diurnal data instead of diurnal–nocturnal data were used to calculate the daily sea-air CO2 flux (F). As the errors were mainly associated with the partial pressure of CO2 in seawater (pCO2w) and the sea surface temperature (SST) in the control experiment, pCO2w and SST equations were established, which are called the nocturnal effect of the CO2 flux. The root-mean-square error between the real daily CO2 flux (Freal) and the daily CO2 flux corrected for the nocturnal effect (Fcom) was 11.93 mmol m−2 d−1, which was significantly lower than that between the Freal value and the diurnal CO2 flux (Fday) (46.32 mmol m−2 d−1). Thus, the errors associated with using diurnal data to calculate the CO2 flux can be reduced by accounting for the nocturnal effect. The mean global daily CO2 flux estimated based on the nocturnal effect and the sub-regional pCO2w algorithm (cor_Fcom) was −6.86 mol m−2 y−1 (September 2020–August 2021), which was greater by 0.75 mol m−2 y−1 than that based solely on the sub-regional pCO2w algorithm (day_Fcom = −7.61 mol m−2 y−1). That is, compared with cor_Fcom, the global day_Fcom value overestimated the CO2 sink of the global ocean by 10.89%.