Remote sensing estimates of global sea surface nitrate: Methodology and validation.

Information about sea surface nitrate (SSN) concentrations is crucial for estimating oceanic new productivity and for carbon cycle studies. Due to the absence of optical properties in SSN and the intricate relationships with environmental factors affecting spatiotemporal dynamics, developing a more representative and widely applicable remote sensing inversion algorithm for SSN is challenging. Most methods for the remote estimation of SSN are based on data-driven neural networks or deep learning and lack mechanistic descriptions. Since fitting functions between the SSN and sea surface temperature (SST), mixed layer depth (MLD), and chlorophyll (Chl) content have been established for the open ocean, it is important to include the remote sensing indicator photosynthetically active radiation (PAR), which is critical in nitrate biogeochemical processes. In this study, we employed an algorithm for estimating the monthly average SSN on a global 1° by 1° resolution grid; this algorithm relies on the empirical relationship between the World Ocean Atlas 2018 (WOA18) monthly interpolated climatology of nitrate in each 1° × 1° grid and the estimated monthly SST and PAR datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and MLD from the Hybrid Coordinate Ocean Model (HYCOM). These results indicated that PAR potentially affects SSN. Furthermore, validation of the SSN model with measured nitrate data from different months and locations for the years 2018-2023 yielded a high prediction accuracy (N = 12,846, R2 = 0.93, root mean square difference (RMSE) = 3.12 µmol/L, and mean absolute error (MAE) = 2.22 µmol/L). Further independent validation and sensitivity tests demonstrated the validity of the algorithm for retrieving SSN.

Effects of the Earth curvature on Mie-scattering radiances at high solar-sensor geometries based on Monte Carlo simulations.

Given the importance of vector radiative transfer models in ocean color remote sensing and a lack of suitable models capable of analyzing the Earth curvature effects on Mie-scattering radiances, this study presents an enhanced vector radiative transfer model for a spherical shell atmosphere geometry by the Monte Carlo method (MC-SRTM), considering the effects of Earth curvature, different atmospheric conditions, flat sea surface reflectance, polarization, high solar and sensor geometries, altitudes and wavelengths. A Monte Carlo photon transport model was employed to simulate the vector radiative transfer processes and their effects on the top-of-atmosphere (TOA) radiances. The accuracy of the MC-SRTM was verified by comparing its scalar model outputs from Henyey-Greenstein (HG) phase function with the Kattawar-Adams model results, and the mean relative differences were less than 2.75% and 4.33% for asymmetry factors (g-values) of 0.5 and 0.7, respectively. The vector mode results of MC-SRTM for a spherical shell geometry with the Mie-scattering phase matrix were compared with the PCOART-SA model results (from Polarized Coupled Ocean-Atmosphere Radiative Transfer model based on the pseudo-spherical assumption), and the mean relative differences were less than 2.67% when solar zenith angles (SZAs) > 70 ∘ and sensor viewing zenith angles (VZAs) < 60 ∘ for two aerosol models (coastal and tropospheric models). Based on the MC-SRTM, the effects of Earth curvature on TOA radiances at high SZAs and VZAs were analyzed. For pure aerosol atmosphere, the effects of Earth curvature on TOA radiances reached up to 5.36% for SZAs > 70 ∘ and VZAs < 60 ∘ and reduced to less than 2.60% for SZAs < 70 ∘ and VZAs > 60 ∘. The maximum Earth curvature effect of pure aerosol atmosphere was nearly same (10.06%) as that of the ideal molecule atmosphere. The results also showed no statistically significant differences for the aerosol-molecule mixed and pure aerosol atmospheres. Our study demonstrates that there is a need to consider the Earth curvature effects in the atmospheric correction of satellite ocean color data at high solar and sensor geometries.

Enhanced POLYMER atmospheric correction algorithm for water-leaving radiance retrievals from hyperspectral/multispectral remote sensing data in inland and coastal waters.

Accurate retrieval of the water-leaving radiance from hyperspectral/multispectral remote sensing data in optically complex inland and coastal waters remains a challenge due to the excessive concentrations of phytoplankton and suspended sediments as well as the inaccurate estimation and extrapolation of aerosol radiance over the visible wavelengths. In recent years, reasonably accurate methods were established to estimate the enhanced contribution of suspended sediments in the near-infrared (NIR) and shortwave infrared (SWIR) bands to enable atmospheric correction in coastal waters, but solutions to derive the dominant phytoplankton contribution in the NIR and SWIR bands are less generalizable and subject to large uncertainties in the remotely-derived water color products. These issues are not only associated with the standard atmospheric correction algorithm in the SeaDAS processing system but with the non-traditional algorithms such as POLYMER (POLYnomial-based approach established for the atmospheric correction of MERIS data). This study attempts to enhance the POLYMER algorithm to enable atmospheric correction of hyperspectral and multispectral remote sensing data over a wide range of inland and ocean waters. The original POLYMER algorithm is less suitable owing to its complete reliance on a polynomial approach to model the atmospheric reflectance as a function of the wavelength and retrieve the water-leaving reflectance using two semi-analytical models (MM01 and PR05). The polynomial functions calculate the bulk atmospheric contribution instead of using an explicit method to estimate aerosol radiance separately, resulting the erroneous water color products in inland and coastal waters. The modified POLYMER algorithm (mPOLYMER) employs more realistic approaches to estimate aerosol contributions with a combination of UV and Visible-NIR bands and enables accurate retrievals of water-leaving radiance from both hyperspectral and multispectral remote sensing data. To assess the relative performance and wider applicability of mPOLYMER, the original and enhanced algorithms were tested on a variety of HICO, MSI and MODIS-Aqua data and the retrieved Lwn products were compared with AERONET-OC and OOIL-regional in-situ data. Expectedly, the mPOLYMER algorithm greatly improved the accuracy of Lwn (in terms of magnitude and spectral shape) when applied to MODIS-Aqua and HICO data in highly turbid productive waters (with higher concentrations of phytoplankton or with dense algal blooms) in Muttukadu Lagoon, Lake Erie, Yangtze River Estuary, Baltic Sea and Arabian Sea. In contrast, the original POLYMER algorithm overestimated Lwn in the visible and NIR bands and produced unphysical negative Lwn or distorted Lwn spectra in turbid productive waters. The mPOLYMER yielded a relative mean error reduction of more than 50% (i.e., from 79% to 34%) in Lwn for a large number of matchup data. The improved accuracy and data quality is because the mPOLYMER algorithm's funio and coefficients sufficiently accounted for the enhanced backscattering contribution of phytoplankton and suspended sediments in optically complex waters.

Neural network spectral relationship to improve an inherent optical properties data processing system for residual error correction.

The residual error was a critical indicator to measure the data quality of ocean color products, which allows a user to decide the valuable envisioned application of these data. To effectively remove the residual errors from satellite remote sensing reflectance (Rrs) using the inherent optical data processing system (IDAS), we expressed the residual error spectrum as an exponential plus linear function, and then we developed neural network models to derive the corresponding spectral slope coefficients from satellite Rrs data. Coupled with the neural network models-based spectral relationship, the IDAS algorithm (IDASnn) was more effective than an invariant spectral relationship-based IDAS algorithm (IDAScw) in reducing the effects of residual errors in Rrs on IOPs retrieval for our synthetic, field, and Chinese Ocean Color and Temperature Scanner (COCTS) data. Particularly, due to the improved spectral relationship of the residual errors, the IDASnn algorithm provided more accurate and smoother spatiotemporal ocean color product than the IDAScw algorithm for the open ocean. Furthermore, we could monitor the data quality with the IDASnn algorithm, suggesting that the residual error was exceptionally large for COCTS images with low effective coverage. The product effective coverage should be rigorously controlled, or the residual error should be accurately corrected before temporal and spatial analysis of the COCTS data. Our results suggest that an accurate spectral relationship of residual errors is critical to determine how well the IDAS algorithm corrects for residual error.

Satellite-estimated air-sea CO2 fluxes in the Bohai Sea, Yellow Sea, and East China Sea: Patterns and variations during 2003-2019.

The Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS) together form one of the largest marginal sea systems in the world, including enclosed and semi-enclosed ocean margins and a wide continental shelf influenced by the Changjiang River and the strong western boundary current (Kuroshio). Based on in situ seawater pCO2 data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO2 was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML-ECS). MeSAA-ML-ECS introduced semi-analytical parameters, including the temperature-dependent seawater pCO2 (pCO2,therm) and upwelling index (UISST), to characterise the combined effect of atmospheric CO2 forcing, thermodynamic effects, and multiple mixing processes on seawater pCO2. The best-selected machine learning algorithm is XGBoost. The satellite-derived pCO2 achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE = 20 µatm) and mean absolute percentage deviation (APD = 4.12 %) for independent in situ validation dataset. During 2003-2019, the annual average CO2 sinks in the BS, YS, ECS, and entire study area were 0.16 ± 0.26, 3.85 ± 0.68, 14.80 ± 3.09, and 18.81 ± 3.81 Tg C/yr, respectively. Under continuously increasing atmospheric CO2 concentration, the BS changed from a weak source to a weak sink, the YS experienced interannual fluctuations but did not show significant trend, while the ECS acted as a strong sink with CO2 absorption increased from ∼10 Tg C in 2003 to ∼19 Tg C in 2019. In total, CO2 uptake in the entire study area increased by 85 % in 17 years. For the first time, we present the most refined variation in the satellite-derived pCO2 and air-sea CO2 flux dataset. These complete ocean carbon sink statistics and new insights will benefit further research on carbon fixation and its potential capacity.

Monitoring and spatial traceability of river water quality using Sentinel-2 satellite images.

Due to limited monitoring stations along rivers, it is difficult to trace the specific locations of high pollution areas along the whole river by traditionally in situ measurement. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of river water quality if reliable retrieval algorithms are available. This study took seven major rivers (Qiantang River (QTR), Cao'e River (CEJ), Yongjiang River (YJ), Jiaojiang River (JJ), Oujiang River (OJ), Feiyun River (FYR), and Aojiang River (AJ)) in Zhejiang Province, China, as examples to illustrate the spatial traceability of river water quality parameters (permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN)) from Sentinel-2 satellite images. The regional retrieval models established for these parameters (CODMn, TP and TN) provided correlation coefficients (R) of 0.68, 0.82, and 0.7, respectively. Based on these models, time-series CODMn, TP, and TN products were obtained for the seven rivers from 2016 to 2021 from Sentinel-2 satellite images, and the results show that the CODMn, TP and TN were high downstream and low upstream; exceptions the CEJ, which was slightly higher in the middle reach than other reaches, and the TN in YJ, which was higher upstream than downstream. The downstream reaches were the main areas suffering from relatively high values in most seasons. Except for the springtime TN level in CEJ, the high value areas were located along the middle reaches. In summer and autumn, the high TN areas in JJ, OJ, and AJ were located along the middle and lower reaches, and the TN in YJ was highest in the upstream. More importantly, this study revealed that the specific locations of high pollution areas along rivers can be effectively traced using Sentinel-2 satellite images, which would be helpful for precise water quality control of rivers.

Spherical vector radiative transfer model for satellite ocean color remote sensing.

Atmospheric correction is the key step for satellite ocean color remote sensing. However, most of the existing atmospheric correction algorithms do not consider the effects of Earth curvature. In fact, Earth curvature has a significant impact on satellite observation signals under large solar zenith angles or large viewing zenith angles. In this study, based on the Monte Carlo method, a vector radiative transfer model with spherical shell atmosphere geometry (hereafter our SSA-MC model) considering the influence of Earth curvature was established, which can be applied to conditions with high solar zenith angles or high viewing zenith angles. Our SSA-MC model was first compared with the Adams&Kattawar model, and the results show that the mean relative differences are 1.72%, 1.36% and 1.28% for solar zenith angles of 0 ∘, 70.47 ∘ and 84.26 ∘, respectively. Moreover, our SSA-MC model was further validated by more recently benchmarks from Korkin's scalar and vector models, and the results show that the relative differences are mostly less than 0.5% even at extremely high solar zenith angles (84.26 ∘). Then, our SSA-MC model was verified with the Rayleigh scattering radiance calculated by the look-up tables (LUTs) in SeaDAS under low-to-moderate solar or viewing zenith angles, and the results show that the relative differences are less than 1.42% when solar zenith angles are less than 70 ∘ and viewing zenith angles are less than 60 ∘. Our SSA-MC model was also compared with the Polarized Coupled Ocean-Atmosphere Radiative Transfer model based on the pseudo-spherical assumption (PCOART-SA), and the results show that the relative differences are mostly less than 2%. At last, based on our SSA-MC model, the effects of Earth curvature on Rayleigh scattering radiance were analyzed for both high solar zenith angles and high viewing zenith angles. The result shows that the mean relative error between the plane-parallel (PP) geometry and spherical shell atmosphere (SSA) geometry is 0.90% when the solar zenith angle is 60 ∘ and the viewing zenith angle is 60.15 ∘. However, the mean relative error increases with increasing solar zenith angle or viewing zenith angle. When the solar zenith angle is 84 ∘ and the viewing zenith angle is 84.02 ∘, the mean relative error is 4.63%. Thus, Earth curvature should be considered in atmospheric corrections at large solar or viewing zenith angles.

Satellite retrieval of the linear polarization components of the water-leaving radiance in open oceans.

Atmospheric correction (AC) of polarized radiances acquired by polarization satellite sensors, remains a challenge due to the complex radiative transfer processes of the coupled ocean-atmosphere system. In this study, we proposed an innovative polarized AC algorithm built on the near-infrared band (PACNIR) with an emphasis on the retrieval of the linear polarization components of the water-leaving radiance in clear open oceans. This algorithm was based on the black ocean assumption in the near-infrared band and fitted polarized radiance measurements along multiple observation directions with nonlinear optimized processing. Our retrieval algorithm notably inverted the linearly polarized components of the water-leaving radiance and aerosol parameters. Compared with that of the simulated linear polarization components of the water-leaving radiance via the vector radiative transfer model for the studied sea regions, the mean absolute error of the PACNIR-retrieved linearly polarized components (nQw and nUw) exhibited a magnitude of 10-4, while the magnitude of that of the simulated nQw and nUw data was 10-3. Moreover, the PACNIR-retrieved aerosol optical thicknesses at 865 nm exhibited a mean absolute percentage error of approximately 30% relative to in situ values obtained from Aerosol Robotic Network-Ocean Color (AERONET-OC) sites. The PACNIR algorithm could facilitate AC of the polarized data provided by the next generation of multiangle polarization satellite ocean color sensors.

Atmospheric correction algorithm based on the interpolation of ultraviolet and shortwave infrared bands.

Traditional atmospheric correction algorithms of ocean color remote sensing are mostly based on the extrapolation of aerosol scattering radiance from a reference band (near infrared, shortwave infrared, or ultraviolet bands), which inevitably leads to the problem of extrapolation error amplification with the increase of extrapolation spectral distance. In this study, we propose a practical interpolation-based algorithm (named the UV-SWIR-AC algorithm) using three reference bands (one ultraviolet and two shortwave infrared bands) for turbid waters. According to 6SV radiative transfer simulations with 15 customized aerosol types, we establish a fitting function framework for the aerosol scattering radiance in the wavelength range of 322-1643 nm. We apply the UV-SWIR-AC algorithm to the real satellite ocean color data observed by the Second-Generation Global Imager aboard the Global Change Observation Mission-Climate (SGLI/GCOM-C) and compare the retrieved remote sensing reflectance with the in-situ data from the observation platform of Hangzhou Bay in the East China Sea and typical bays. The results show that the UV-SWIR-AC algorithm can achieve a better performance than the traditional, extrapolation-based algorithm in turbid waters. Moreover, in the typical regional analysis, this new algorithm also demonstrates a high applicability. The UV-SWIR-AC algorithm should be helpful to improve the atmospheric correction accuracy for next-generation ocean color missions (e.g., NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission and China's Haiyang-1E/F (HY-1E/F) mission) with wider spectral ranges from the ultraviolet to shortwave infrared wavelengths.

Three stages in the variation of the depth of hypoxia in the California Current System 2003-2020 by satellite estimation.

The depth of hypoxia (DOH) is the shallowest depth at which the waters become hypoxic (oxygen concentration < 60 µmol kg-1), is a crucial indicator of the formation and expansion of oxygen minimum zones (OMZs). In this study, a nonlinear polynomial regression inversion model was developed to estimate the DOH in the California Current System (CCS), based on the dissolved oxygen profile detected by the Biogeochemical-Argo (BGC-Argo) float and remote sensing data. Satellite-derived net community production was used in the algorithm development, to denote the combined effect of phytoplankton photosynthesis and O2 consumption. Our model performs well, with a coefficient of determination of 0.82 and a root mean square error of 37.69 m (n = 80) from November 2012 to August 2016. Then, it was used to reconstruct the variation in satellite-derived DOH in the CCS from 2003 to 2020, and three stages of the DOH variation trend were identified. From 2003 to 2013, the DOH showed a significant shallowing trend due to the intense subsurface O2 consumption caused by strong phytoplankton production in the CCS coastal region. The trend was interrupted by two successive strong climate oscillation events from 2014 to 2016, which led to a significant deepening of the DOH and a slowing, or even reversal, of the variations in other environmental parameters. After 2017, the effects of climate oscillation events gradually disappeared, and the shallowing pattern in the DOH recovered slightly. However, by 2020, the DOH had not returned to the pre-2014 shallowing characteristic, which would lead to continuing complex ecosystem responses in the context of global warming. Based on the satellite inversion model of DOH in the CCS, we provide a new insight on the high-resolution spatiotemporal OMZ variations during an 18-year period in the CCS, which will aid in the evaluation and prediction of local ecosystems variation.

Satellite estimation of suspended particle types using a backscattering efficiency-based model in the marginal seas.

The particle composition of suspended matter provides crucial information for a deeper understanding of marine biogeochemical processes and environmental changes. Particulate backscattering efficiency (Qbbe(λ)) is critical to understand particle composition, and a Qbbe(λ)-based model for classifying particle types was proposed. In this study, we evaluated the applicability of the Qbbe(λ)-based model to satellite observations in the shallow marginal Bohai and Yellow Seas. Spatiotemporal variations of the particle types and their potential driving factors were studied. The results showed that the Qbbe(λ) products generated from Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellite Aqua agreed well with the in situ measured values, with determination coefficient, root mean square error, bias, and mean absolute percentage error of 0.76, 0.007, 16.5%, and 31.0%, respectively. This result verifies the satellite applicability of the Qbbe(λ)-based model. Based on long-term MODIS data, we observed evident spatiotemporal variations of the Qbbe(λ), from which distinct particle types were identified. Coastal waters were often dominated by minerals, with high Qbbe(λ) values, though their temporal changes were also observed. In contrast, waters in the offshore regions showed clear changes in particle types, which shifted from organic-dominated with low Qbbe(λ) levels in summer to mineral-dominated with high Qbbe(λ) values in winter. We also observed long-term increasing and decreasing trends in Qbbe(λ) in some regions, indicating a relative increase in the proportions of mineral and organic particles in the past decades, respectively. These spatiotemporal variations of Qbbe(λ) and particle types were probably attributed to sediment re-suspension related to water mixing driven by wind and tidal forcing, and to sediment load associated with river discharge. Overall, the findings of this study may provide valuable proxies for better studying marine biogeochemical processes, material exchanges, and sediment flux.

The carbon sink of the Coral Sea, the world's second largest marginal sea, weakened during 2006-2018.

The latest reports show that the ocean absorbs approximately 26 % of anthropogenic CO2 and that the carbon sink of the global ocean (air-sea CO2 flux) is continually increasing, while variations in different marginal seas are complicated. The Coral Sea, the second largest marginal sea in the world, is characterized by a generally oligotrophic basin and borders the biodiversity hotspot of Great Barrier Reef. In this study, we proposed a semianalytical method and reconstructed the first high-resolution satellite-based pCO2 and air-sea CO2 flux dataset from 2006 to 2018 for the Coral Sea. This dataset performed well in the basin (RMSE<10 µatm, R2 > 0.72) and coral reef areas (RMSE<12 µatm, R2 > 0.8) based on validation by a massive independent dataset. We found that sea surface pCO2 is increasing (1.8 to 2.7 µatm/year) under the forcing of increasing atmospheric CO2, and the pCO2 growth rate in water is faster than that in the atmosphere. The combination of increasing sea surface pCO2, high pCO2 seawater from coral reef areas, and the low depletion capacity of the oligotrophic basin led to a gradual weakening of the carbon sink in the Coral Sea, with the 2016 carbon sink being 52 % of that in 2006. This weakening was more pronounced after strong El Niño events (e.g., 2007, 2010, and 2016), with the corresponding high SST and low wind speed further weakening the carbon sink. This understanding of the long-term change in the Coral Sea provides new insight on the carbonate system variation and carbon sink capacity evolution in seawater under increasing atmospheric CO2.

Atmospheric diffuse transmittance of the linear polarization component of water-leaving radiation.

The polarization characteristics of water-leaving radiation contain rich information on oceanic constituents. Determining the atmospheric diffuse transmittance is crucial for extracting the polarization information of water-leaving radiation from the radiation acquired by polarimetry satellites at the top of the atmosphere. However, there is still a lack of understanding of the atmospheric diffuse transmittance of the linear polarization component of water-leaving radiation. Here, we first evaluated the difference between the atmospheric diffuse transmittance of the linear polarization component (TQ, TU) and the intensity component (TI) of the water-leaving radiation based on the Ocean Successive Orders with Atmosphere Advanced radiative transfer model. As a consequence, there were apparent differences between TQ, TU and TI. In the case of a large solar zenith angle and a large viewing zenith angle, the difference between TQ, TU and TI will exceed 1. Meanwhile, compared with TI, the oceanic constituents had a prominent interference with TQ and TU, and the sediment concentration had little interference with TQ and TU in low- and medium-turbidity water with respect to the aerosol model, optical thickness, observation geometry, and phytoplankton. Moreover, TQ and TU lookup tables were generated for medium- and low-turbidity water, which laid the foundation for extracting the water-leaving radiation polarization information from the satellite observation polarization signal.

Automatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery.

Terrestrial pollution has a great impact on the coastal ecological environment, and widely distributed coastal outfalls act as the final gate through which pollutants flow into rivers and oceans. Thus, effectively monitoring the water quality of coastal outfalls is the key to protecting the ecological environment. Satellite remote sensing provides an attractive way to monitor sewage discharge. Selecting the coastal areas of Zhejiang Province, China, as an example, this study proposes an innovative method for automatically detecting suspected sewage discharge from coastal outfalls based on high spatial resolution satellite imageries from Sentinel-2. According to the accumulated in situ observations, we established a training dataset of water spectra covering various optical water types from satellite-retrieved remote sensing reflectance (Rrs). Based on the clustering results from unsupervised classification and different spectral indices, a random forest (RF) classification model was established for the optical water type classification and detection of suspected sewage. The final classification covers 14 optical water types, with type 12 and type 14 corresponding to the high eutrophication water type and suspected sewage water type, respectively. The classification result of model training datasets exhibited high accuracy with only one misclassified sample. This model was evaluated by historical sewage discharge events that were verified by on-site observations and demonstrated that it could successfully recognize sewage discharge from coastal outfalls. In addition, this model has been operationally applied to automatically detect suspected sewage discharge in the coastal area of Zhejiang Province, China, and shows broad application value for coastal pollution supervision, management, and source analysis.

Experimental Study on Bottom-Up Detection of Underwater Targets Based on Polarization Imaging.

Previous studies on the polarization imaging of underwater targets mainly focused on top-down detection; however, the capacities of bottom-up detection were poorly known. Based on in situ experiments, the capability of bottom-up detection of underwater targets using polarization imaging was investigated. First, to realize the objective of bottom-up polarization imaging, a SALSA polarization camera was integrated into our Underwater Polarization Imaging System (UPIS), which was integrated with an attitude sensor. At Qiandao Lake, where the water is relatively clear, experiments were conducted to examine the capacity of the UPIS to detect objects from the bottom up. Simultaneously, entropy, clarity, and contrast were adopted to compare the imaging performance with different radiation parameters. The results show that among all the used imaging parameters, the angle of polarization is the optimal parameter for bottom-up detection of underwater targets based on polarization imaging, which may result from the different diffused reflectance of the target surface to the linear polarization components of the Stokes vector.

Requirement on the diurnal observation frequency for satellite remote sensing of photosynthetically available radiation.

Photosynthetically available radiation (PAR) is essential for the photosynthesis processes of land plants and aquatic phytoplankton. Satellite observation with different diurnal frequencies (e.g., high frequency from geostationary satellites and low frequency from polar-orbit satellites) provides a unique technique to monitor PAR variation on large tempo-spatial scales. Owing to different climatic characteristics, different regions may require different observation frequencies to obtain accurate PAR estimation, but such requirements are still poorly known. Here, based on Advanced Himawari Imager (AHI) high-frequency (10-min) observation data from the geostationary satellite Himawari-8, we investigated the influence of diurnal observation frequency on the accuracy of PAR estimation and provided the minimal observing frequency to get high accurate PAR estimation in the AHI coverage area. Our results revealed a remarkable difference in the requirements for the diurnal observation frequency in both spatial and temporal distributions. Overall, high-latitude regions need a higher observing frequency than low-latitude areas, and winter half-years need higher observing frequency than summer half-years. These results provide a basis for designing satellites to accurately remote sensing of PAR in different regions.

Southward spreading of the Changjiang Diluted Water in the La Niña spring of 2008.

The La Niña of 2007/2008 was particularly strong, so was the southward flow of the cold, nutrient-rich Changjiang (Yangtze River) Diluted Water (CDW) when the winter monsoon started to blow in the fall. Here we use shipboard data in 2008 in two transects, one in the southwestern East China Sea and one in the southern Taiwan Strait, to show that as late as April in 2008 the CDW was still clearly identifiable when the winter monsoon had weakened. Waters as cold as 16 °C with a salinity lower than 30 still occupied the southwestern East China Sea. Waters of 17 °C and S < 32 could also be found off the coast of China in the central Taiwan Strait. The concentration of NO3 + NO2 was higher than 18 µmol L-1 at both places, which was as much as 40 times higher than the northward moving South China Sea (SCS) water to the east. As a result, the Changjiang River plume may be a significant source of nutrients, particularly N, to the oligotrophic, N-poor SCS, especially in the La Niña years. Indeed, colder and more turbid CDW was more intense and went farther south in 2008 compared with the normal springs of 2006, 2007 and 2009.

Characteristics of water leaving reflectance at ultraviolet wavelengths: radiative transfer simulations.

Ultraviolet (UV) bands have attracted considerable attention in regard to satellite ocean color remote sensing due to their potential application in atmospheric correction, oil spill detection, and water organic matter retrieval. However, the characteristics of the water spectrum in the UV bands are still poorly understood. In this study, by extending the bio-optical model from traditional visible light wavelengths to UV light wavelengths, the water spectrum in UV bands under different water types was simulated by using the HydroLight water radiative transfer model, and influences of ocean color components on the water spectrum in UV bands were investigated. Results showed that remote sensing reflectance (Rrs) in the UV bands decreased rapidly with the increase in chlorophyll concentration (Chl) and colored dissolved organic matter (CDOM). In clean waters, Rrs in the UV bands was relatively large and sensitive to changes in Chl and CDOM, which could be of benefit for satellite retrieval of water organic matter. In eutrophic water, Rrs in the UV bands was quite low, and thence the UV bands could be used as a reference band for atmospheric correction. Compared to the monotonic decreasing effects of Chl and CDOM, concentration of non-algal particles (NAP) had a complex effect on Rrs in the UV bands, i.e., increase and decrease in Rrs in low-moderately and highly turbid waters, respectively. Thus, the traditional model for the relationship between Rrs and inherent optical properties (IOPs) could be applied to the UV bands in clean waters; in highly turbid waters, however, its deviation increases and empirical coefficients in the model should be improved.

High-frequency and tidal period observations of suspended particulate matter in coastal waters by AHI/Himawari-8.

Driven by tidal forcing and terrestrial inputs, suspended particulate matter (SPM) in shallow coastal waters usually shows high-frequency dynamics. Although specific geostationary satellite ocean color sensors such as the geostationary ocean color imager (GOCI) can observe SPM hourly eight times in a day from morning to afternoon, it cannot cover the whole semi-diurnal tidal period (∼12 h), and an hourly frequency may be insufficient to witness rapid changes in SPM in highly dynamic coastal waters. In this study, taking the Yangtze River Estuary as an example, we examined the ability of the geostationary meteorological satellite sensor AHI/Himawari-8 to monitor tidal period SPM dynamics with 10-min frequency. Results showed that the normalized water-leaving radiance (Lwn) retrieved by the AHI was consistent with the in-situ data from both cruise- and tower-based measurements. Specifically, AHI-retrieved Lwn was consistent with the in-situ cruise values, with mean relative errors (MREs) of 19.58%, 16.43%, 18.74%, and 26.64% for the 460, 510, 640, and 860 nm bands, respectively, and determination coefficients (R2) larger than 0.89. Both AHI-retrieved and tower-measured Lwn also showed good agreement, with R2 values larger than 0.75 and MERs of 14.38%, 12.42%, 18.16%, and 18.89% for 460, 510, 640, and 860 nm, respectively. Moreover, AHI-retrieved Lwn values were consistent with the GOCI hourly results in both magnitude and spatial distribution patterns, indicating that the AHI can monitor ocean color in coastal waters, despite not being a dedicated ocean color sensor. Compared to the 8 h of SPM observations by the GOCI, the AHI was able to monitor SPM dynamics for up to 12 h from early morning to late afternoon covering the whole semi-diurnal tidal period. In addition, the high-frequency 10-min monitoring by the AHI revealed the minute-level dynamics of SPM in the Yangtze River Estuary (with SPM variation amplitude found to double over 1 h), which were impossible to capture based on the hourly GOCI observations.

Elevated primary productivity triggered by mixing in the quasi-cul-de-sac Taiwan Strait during the NE monsoon.

The Taiwan Strait (TS) connects two of the largest marginal seas in the world, namely the East China Sea (ECS) and the South China Sea (SCS). When the NE monsoon prevails, the fresh, nutrient-rich but P-limited China Coastal Current (CCC) flows southward. Yet, part of the CCC turns eastward after entering the TS and then turns back toward the ECS. In the southern TS, part of the salty, N-limited, northward TS current (TSC) in the eastern part of the strait turns westward and eventually returns to the SCS. That is, the TS acts like a quasi-cul-de-sac during the NE monsoon season. Based on 822 samples from 28 cruises, the highest Chl. a concentration occurs at a salinity around 32 even though the nutrient concentration is not the highest. Mixing the cold-fresh-eutrophic CCC water and the warm-salty-oligotrophic TSC water results in a more suitable condition for biological uptake in both the southern ECS and the northern SCS.