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.

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.

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 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.

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.

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.

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.

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.

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.

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.

New algorithm for computation of the Rayleigh-scattering radiance for remote sensing of water color from space.

Rayleigh-scattering radiance (Lr) calculations based on the standard algorithm are often associated with significant uncertainties leading to inconsistent water-leaving radiance retrievals, both spatially and temporally across latitudes and altitudes. The uncertainty could result from the use of Rayleigh lookup tables generated for the standard surface atmospheric pressure and hence the Rayleigh optical thickness (ROT) at the specific atmospheric pressure regardless of its daily and seasonal variations. This study presents a new algorithm (hereafter referred to as the refined algorithm) to compute the Rayleigh-scattering radiance that relies on accurate calculations of the ROT as a function of the composition of air (CO2 volume concentration), surface atmospheric pressure and relative air mass for given sun-sensor geometries. As CO2 is well mixed throughout the atmospheric column, the CO2 volume concentrations derived from this study agree well with measurements in different seasons across studied latitudes. Relative air mass has a significant effect on the ROT and that is calculated as a function of apparent sun-sensor zenith angles with the variations in pressure and thermal characteristics of the atmosphere. Thus, the results indicate significant variations of ROT and air mass with location on the earth's surface and their influence on the Lr, particularly in the UV-Blue region of the spectrum. The refined algorithm for calculating the Lr is tested on several MODIS-Aqua Level 1A data and the relative errors in Rayleigh-scattering radiance and normalized water-leaving radiance (Lwn) retrievals between the refined algorithm and standard (SeaDAS) algorithm are compared using in-situ measurement data collected at MOBY (clear ocean), AERONET (turbid coastal ocean), and NOMAD (clear ocean) sites. The results indicate that the Lr calculated using the SeaDAS algorithm are mostly underestimated and show significant departures with the Lr calculated using the refined algorithm. This departure induced by the SeaDAS algorithm to Lr becomes larger with decreasing wavelength (ΔLr from -2.38% at 412 nm to 1.69% at 678 nm), which causes errors in Lwn retrievals (ΔLwn) of up to 26.48% at 412 nm and 13.34% at 678 nm. The overall improvements in the retrieved Lwn values achieved vary from 56% at 412 nm to 29% at 678nm, which yield similar improvements in Lwn retrievals with lower errors and higher slopes and correlation coefficients when compared with the in-situ Lwn data. These results indicate that the refined algorithm for computation of the Lr can yield more accurate Lwn retrievals and produce spatially and temporally consistent biogeochemical products at different latitudes and altitudes as desired by the scientific community.

UV-NIR approach with non-zero water-leaving radiance approximation for atmospheric correction of satellite imagery in inland and coastal zones.

In the atmospheric correction process of the satellite ocean color data, the removal of the aerosol scattering contribution over the coastal and inland water bodies has been a major challenge with the standard algorithms. In this work, a practical method is proposed based on a combination of NIR and ultraviolet (UV) bands (named as UVNIR-ex) for the succeeding generation of space borne multispectral and hyperspectral sensors. This scheme replaces the black-ocean assumption and accounts for non-zero water-leaving radiance contributions in the NIR and UV bands. The aerosol contributions are thus deduced for these two bands and used to select the appropriate aerosol models to retrieve aerosol optical properties and hence, water-leaving radiances in the UV, Visible and NIR bands. The performance of the UVNIR-ex algorithm was tested and evaluated based on match-ups between HICO and in-situ observations in optically complex coastal and inland waters and by comparison with three alternative aerosol correction methods based on UV-NIR, Spectral Shape Parameter (SSP) and iterative NIR (INIR) approaches. A preliminary comparison with in-situ aerosol optical thickness (AOT) measurements from AERONET-OC sites revealed that the UVNIR-ex algorithm significantly improved the AOT retrievals with a mean relative error (MRE) around 25%, while the UVNIR, SSP and INIR algorithms showed performance degradation with a MRE of 27%, 34%, and 42%, respectively. The comparison with AERONET-OC and regional in-situ measurements from turbid and productive waters further showed that the INIR algorithm underestimated the nLw retrievals in blue bands in turbid waters (MRE > 100%) and negligible nLw in red-NIR bands and high anomalous radiances in UV-Blue bands in productive waters (MRE 53%). The SSP and UVNIR algorithms performed better in retrieving the nLw in green-NIR bands but showed significant errors in UV-blue bands in both turbid and productive waters. Based on these match-up analyses, the UVNIR-ex algorithm yielded best nLw retrievals across all the UV-NIR bands in terms of accuracy and performance. The highest accuracy and consistency of the UVNIR-ex algorithm indicates that it is more suited for estimating the aerosol optical properties and water-leaving radiance and has a significant advantage over the requirement of shortwave infrared bands for turbid and productive waters.

Semi-analytical algorithms of ocean color remote sensing under high solar zenith angles.

With the increasing interest in ocean color remote sensing in polar oceans and geostationary ocean color satellite with diurnal observations, it is unavoidable to encounter ocean color retrievals under high solar zenith angles. Under these scenarios, the capability of current remote sensing algorithms is poorly known. In this study, the performance of the two widely used semi-analytical algorithms for the water inherent optical properties (QAA and GSM01) under high solar zenith angle conditions were firstly evaluated based on global in situ data set (SeaBASS-NOMAD). The results showed that the performances of both QAA and GSM01 degraded significantly with the increasing in solar zenith angle (SZA), and the biases increased about 1.3-fold when SZA varied from 30° to 80°. The high uncertainties at high SZA was mainly induced by the systematic overestimation of the key parameter u (ratio of backscattering coefficient to the sum of absorption and backscattering coefficients) at high solar zenith angles. Based on the Hydrolight-simulated data set, a new model (NN-algorithm) for retrieving u from remote sensing reflectance was developed for high solar zenith angle conditions using the neural network method. The validation results revealed that the NN-algorithm could improve the estimation of parameter u and further ocean color products. In addition, our results indicate that a more accurate atmosphere correction is needed to deal with ocean color remote sensing data acquired under large solar zenith angle conditions.

Ocean color retrieval from MWI onboard the Tiangong-2 Space Lab: preliminary results.

The Moderate-resolution Wide-wavelengths Imager (MWI) is the ocean color sensor onboard the Chinese Tiangong-2 Space Lab, which was launched on Sept. 15, 2016. The MWI is also an experimental satellite sensor for the Chinese next generation ocean color satellites, HY-1E and HY-1F, which are scheduled for launch around 2021. With 100m spatial resolution and 18 bands in the visible light and infrared wavelengths, MWI provides high quality ocean color observations especially over coastal and inland waters. For the first time, this study presents some important results on water color products generated from the MWI for the oceanic and inland waters. Preliminary validation in turbid coastal and inland waters showed good agreement between the MWI-retrieved normalized water-leaving radiances (Lwn) and in situ data. Further, the MWI-retrieved Lwn values compared well with the GOCI-retrieved Lwn values, with the correlation coefficient greater than 0.90 and mean relative differences smaller than 26.63% (413 nm), 4.72% (443 nm), 3.69% (490 nm), 7.15% (565 nm), 9.45% (665 nm), 8.11% (682.5 nm), 14.68% (750 nm) and 18.55% (865 nm). As for the Level 2 product (e.g, total suspended matter TSM) in turbid Yangtze River Estuary and Hangzhou Bay waters, the relative difference between MWI and GOCI-derived TSM values was ~18.59% with the correlation coefficient of 0.956. In open-oceanic waters, the retrieved MWI-Chla distributions were well consistent with the MODIS/Aqua and VIIRS Chla values products and resolved finer spatial structures of phytoplankton blooms. This study provides encouraging results for the MWI's performance and operational applications in oceanic and inland regions.

Polarization-based enhancement of ocean color signal for estimating suspended particulate matter: radiative transfer simulations and laboratory measurements.

Absorption and scattering by molecules, aerosols and hydrosols, and the reflection and transmission over the sea surface can modify the original polarization state of sunlight. However, water-leaving radiance polarization, containing embedded water constituent information, has largely been neglected. Here, the efficiency of the parallel polarization radiance (PPR) for enhancing ocean color signal of suspended particulate matter is examined via vector radiative transfer simulations and laboratory experiments. The simulation results demonstrate that the PPR has a slightly higher ocean color signal at the top-of-atmosphere as compared with that of the total radiance. Moreover, both the simulations and laboratory measurements reveal that, compared with total radiance, PPR can effectively enhance the normalized ocean color signal for a large range of observation geometries, wavelengths, and suspended particle concentrations. Thus, PPR has great potential for improving the ocean color signal detection from satellite.

[Impact of Light Polarization on the Measurement of Water Particulate Backscattering Coefficient].

Particulate backscattering coefficient is a main inherent optical properties (IOPs) of water, which is also a determining factor of ocean color and a basic parameter for inversion of satellite ocean color remote sensing. In-situ measurement with optical instruments is currently the main method for obtaining the particulate backscattering coefficient of water. Due to reflection and refraction by the mirrors in the instrument optical path, the emergent light source from the instrument may be partly polarized, thus to impact the measurement accuracy of water backscattering coefficient. At present, the light polarization of measuring instruments and its impact on the measurement accuracy of particulate backscattering coefficient are still poorly known. For this reason, taking a widely used backscattering coefficient measuring instrument HydroScat6 (HS-6) as an example in this paper, the polarization characteristic of the emergent light from the instrument was systematically measured, and further experimental study on the impact of the light polarization on the measurement accuracy of the particulate backscattering coefficient of water was carried out. The results show that the degree of polarization(DOP) of the central wavelength of emergent light ranges from 20% to 30% for all of the six channels of the HS-6, except the 590 nm channel from which the DOP of the emergent light is slightly low (-15%). Therefore, the emergent light from the HS-6 has significant polarization. Light polarization has non-neglectable impact on the measurement of particulate backscattering coefficient, and the impact degree varies with the wave band, linear polarization angle and suspended particulate matter (SPM) concentration. At different SPM concentrations, the mean difference caused by light polarization can reach 15.49%, 11.27%, 12.79%, 14.43%, 13.76%, and 12.46% in six bands, 420, 442, 470, 510, 590, and 670 nm, respectively. Consequently, the impact of light polarization on the measurement of particulate backscattering coefficient with an optical instrument should be taken into account, and the DOP of the emergent light should be reduced as much as possible.

High-Frequency Observation of Water Spectrum and Its Application in Monitoring of Dynamic Variation of Suspended Materials in the Hangzhou Bay.

In situ measurement of water spectrum is the basis of the validation of the ocean color remote sensing. The traditional method to obtain the water spectrum is based on the shipboard measurement at limited stations, which is difficult to meet the requirement of validation of ocean color remote sensing in the highly dynamic coastal waters. To overcome this shortage, continuously observing systems of water spectrum have been developed in the world. However, so far, there are still few high-frequency observation systems of the water spectrum in coastal waters, especially in the highly turbid and high-dynamic waters. Here, we established a high-frequency water-spectrum observing system based on tower in the Hangzhou Bay. The system measures the water spectrum at a step of 3 minutes, which can fully match the satellite observation. In this paper, we primarily developed a data processing method for the tower-based high-frequency water spectrum data, to realize automatic judgment of clear sky, sun glint, platform shadow, and weak illumination, etc. , and verified the processing results. The results show that the normalized water-leaving radiance spectra obtained through tower observation have relatively high consistency with the shipboard measurement results, with correlation coefficient of more than 0. 99, and average relative error of 9.96%. In addition, the long-term observation capability of the tower-based high-frequency water-spectrum observing system was evaluated, and the results show that although the system has run for one year, the normalized water-leaving radiance obtained by this system have good consistency with the synchronously measurement by Portable spectrometer ASD in respect of spectral shape and value, with correlation coefficient of more than 0.90 and average relative error of 6.48%. Moreover, the water spectra from high-frequency observation by the system can be used to effectively monitor the rapid dynamic variation in concentration of suspended materials with tide. The tower-based high-frequency water-spectrum observing system provided rich in situ spectral data for the validation of ocean color remote sensing in turbid waters, especially for validation of the high temporal-resolution geostationary satellite ocean color remote sensing.

Satellite retrieval of oceanic particulate organic carbon: Towards an accurate and seamless dataset for the global ocean.

Particulate organic carbon (POC) plays crucial roles in the global ocean carbon cycle and the oceanic biological pump. Satellite remote sensing has been demonstrated to be an effective technique for the retrieval of surface oceanic POC concentration. However, the complex spatiotemporal variations of the relationships between POC and oceanic optical properties across different waters posed challenges for accurate retrieval of POC concentration from satellite observations. Additionally, interference factors, such as cloud cover and sun glint, resulted in severe data missing problems and impeding daily coverage of the global ocean. With an attempt to generate accurate, seamless and readily available POC products for the global ocean, this study aimed to develop accurate satellite POC retrieval models for the Moderate Resolution Imaging Spectroradiometer (MODIS) data from both Terra and Aqua satellites, and to explore the possibility of using the empirical orthogonal function interpolation technique (DINEOF) to reconstruct satellite-retrieved POC data to generate gap-free global oceanic POC products. Results showed that the eXtreme Gradient Boosting (XGBoost) method could accurately retrieve POC with R2 approximately 0.80 and RMSE about 0.20 in log10 scale, obviously outperforming the operational blue-to-green band ratio algorithm and the hybrid polynomial algorithm based on two multi-band indices; and the DINEOF method, which could reconstruct approximately 88 % missing pixels for the global ocean, contributed to better revealing the global oceanic POC variations at a daily scale than the satellite-retrieved POC products. Based on the developed models, a suit of long time-series accurate and seamless POC products of the global surface ocean were generated, which is readily available for other applications and should be helpful to investigate the spatiotemporal variations of POC concentrations over global ocean and its roles in the global carbon cycle. The generated seamless products are openly accessible via the DOIs listed in the data availability section.