Atmospheric correction (AC) of satellite-measured polarized radiances is crucial due to the intricate radiative transfer processes within the atmospheric-ocean system and the diverse applications of polarimetric ocean color remote sensing. This study presents the intelligent polarization AC (IPAC) algorithm that efficiently handles multiangle, multispectral, and polarimetric satellite observations to derive polarized water-leaving reflectance, as well as aerosol properties (coarse-mode and fine-mode) and water inherent optical properties in open-ocean waters. To develop the IPAC algorithm, we simulated top-of-atmosphere (TOA) vector apparent reflectances over the open ocean using a vector radiative transfer simulation (VRTS) model. These simulations employed statistical results from satellite Level-3 products and precalculated lookup tables of polarization water-leaving radiance transmittances. Generating approximately 60 million TOA vector apparent reflectances and 89 inversion submodels facilitated the construction of the IPAC model. Performance assessment of IPAC included three wavelengths (490, 670, and 865 nm), three polarization states, and 13 solar-sensor geometries for each satellite pixel. Validation analysis revealed that the IPAC algorithm significantly improved the accuracy of retrieved ocean color products compared to standard AC algorithms. The mean absolute percentage error in the retrieved polarized apparent water-leaving reflectance and chlorophyll concentration relative to the measurement data was below 34.43% and 37.66%, respectively. Overall, the IPAC model proves its capability to deal with multiangle, multispectral, and polarimetric satellite data and advance the current ocean color work for many applications.

Multiangle polarization measurements in the near-infrared band from space were illustrated as suitable for the inversion of aerosol vertical distribution (AVD) information. In this study, we reported the interference of the AVD to linearly polarized radiances (${\rho }_{Q}$and${\rho }_{U}$) and scalar radiance (${\rho }_{I}$) under different simulation configurations, and the sensitivity discrepancies of${\rho }_{I}$,${\rho }_{Q}$, and${\rho }_{U}$to the aerosol scale height were analyzed by theoretical calculation of Mie scattering theory. Furthermore, the high correlation between polarization measurements in the near-infrared band and AVD inspired to perform polarization measurement inversion of the aerosol layer height (ALH). The constructed model was based on nonlinear optimization and the total-absorption ocean assumption. By fitting the linearly polarized radiances obtained by polarization observations in the near-infrared band, the AVD information were retrieved. The evaluation indicators showed that the root mean square error (RMSE) of the retrievals is less than 1 km for three typical sea areas, which demonstrates that polarization inversion is a valuable addition to light dectection and ranging (Lidar) data for AVD measurement.

The estimation of dissolved inorganic carbon (DIC) in global surface ocean waters is crucial for understanding air-sea CO2flux rates, ocean acidification, and climate change. DIC magnitude and spatiotemporal variability are influenced by various physical and biogeochemical processes. Due to dynamic variations in ocean surface water, estimating DIC through in-situ data alone is challenging. Ocean color remote sensing offers high spatial and temporal resolution data with extensive synoptic views. Over decades, multiple DIC approaches have emerged using in-situ and satellite observations but are limited to specific regions due to improper model parameter selection and sparse in-situ measurements. To address this, we propose a novel Multi-Parametric Regression (MPR) approach that relates DIC as a function of sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chla) concentration. Utilizing in-situ data from the Global Ocean Data Analysis Project (GLODAP), trends of DIC with SST, SSS, and Chla were analyzed to develop MPR regression equations. The validation results indicated that the proposed regression approach accurately estimates DIC in global surface ocean waters. This approach offers benefits such as DIC estimates at any spatiotemporal resolutions, easy implementation, and cost-effective alternatives to in-situ measurements. Additionally, seasonal and inter-annual variations of global DIC fields were demonstrated through satellite oceanographic data, enhancing monitoring of ocean acidification and climate change scenarios.

Estimation of particulate organic carbon (POC) is essential for the studies of biological carbon export from the surface to the deep-ocean, carbon-based net primary production, phytoplankton growth rate and global carbon cycle. Despite the number of regional and global algorithms reported in earlier studies, an accurate estimation of POC and its spatiotemporal variability from satellite ocean colour data are often hampered by biases associated with the algorithm parameterizations and a lack of in-situ data for the coastal ocean associated with complex physical and biogeochemical processes (such as physical mixing, biological production, horizontal and vertical transport of POC through the ocean currents and circulations, and POC sinking fluxes). In the present study, we developed a simple maximum band ratio index (MBRI) algorithm based on the global in-situ POC and remote sensing reflectance data and validated and inter-compared with the existing algorithms using independent in-situ and MODIS-Aqua POC data in the global oceanic waters. In general, the POC products estimated by the MBRI algorithm have greater accuracy with a mean relative error of 0.218, a root mean square error of 34.07, and a correlation coefficient of 0.88. The MBRI approach was further applied to time series satellite data to analyze the spatiotemporal variations and trends in POC in regional/ global oceanic waters as well as the Arctic Ocean region. This study highlighted a substantial change and increase in POC fields in the Arctic Ocean region in response to the global change scenarios over the recent decades.

Sea surface salinity (SSS) retrieval from satellite-based microwave radiometer is hampered by uncertainties due to atmospheric and surface scattering contributions. This study presents a comprehensive vector radiative transfer model (VRTM) for estimation of brightness temperature (TB) (for SSS retrieval) from L-band radiometry based on a matrix-operator method. It includes an efficient two-scale model (TSM) that combines a geometrical optics (GO) model and a small-perturbation model (SPM) for computing both the small- and large-scale scattering components of the sea surface. Moreover, it considers the influence of rain effects on TB by including the radiation extinction term (scattering and attenuation). The simulation results using the VRTM were validated with those obtained from the RT4 model for flat sea surface conditions and the RTTOV model for rough sea surface conditions. The relative difference in the estimated TB among the models was small (<; 1%) for low wind speeds (<; 1 m/s) and increased up to 3% for high wind speeds and observation angles. Simulations on the influence of wind speed on TB with various parameterizations were further examined. Compared with SMOS-MIRAS and Aquarius measurements, the VRTM simulations agreed well with satellite measurements for both vertically and horizontally polarized TBs with biases of less than 2.2 K for observation angles from 20° to 65°. The binned TBs showed even better results, with a standard deviation of less than 1.45 K and an absolute mean error of less than 1.2 K.

Deep chlorophyll maxima (DCM) layer is the concentrated patch of phytoplankton, which occurs as a layer at a depth extending few meters vertically and spanning many kilometers horizontally. These highly productive regions of the ocean are signatures of critical physical, chemical, and biological processes in the aquatic environment. This study reports and characterizes a DCM layer observed in the Bay of Bengal during three different cruises conducted in 2015, 2016, and 2018. The relationships of the defining parameters for the DCM like the depth and maximum chlorophyll concentration are modeled with respect to the biogeochemical parameters and are validated for the region. A new approach using the density for the estimation of the vertical chlorophyll profile was formulated. The DCM characteristics and covariation with respect to the other parameters are, thus, formulated and the factors controlling the DCM in this region are characterized. The model was applied to satellite remote sensing data to derive the vertical profiles of chlorophyll layer in the Bay of Bengal waters. The results were discussed in the context of understanding of the underlying biological and physical processes as well as subsurface phytoplankton biomass and the carbon cycle in the region.

New generation ocean color imagers on geostationary orbits are designed to provide a much higher temporal resolution along with enhanced spatial and spectral resolutions that will open up obvious opportunities for improving the sampling frequency and resolving diurnal variability of phytoplankton and other biogeochemical properties in dynamic coastal waters. Despite the capabilities of such new generation sensors to detect the diurnal cycles of various ocean phenomena, there is a lack of knowledge on their radiometric sensitivity and signal detectability for observing the ocean color at morning or evening hours. This paper aims to explore the capability of geostationary satellite ocean color sensor for detecting ocean biogeochemical properties [chlorophyll (CHL); total suspended matter (TSM); colored dissolved organic matter (CDOM)] under high solar zenith angles (SZAs). The analysis is based upon simulations from the vector radiative transfer model for the coupled ocean-atmosphere system (PCOART-SA), which considers the earth curvature effects. The unitless differential signal-to-noise ratio (ASNR) is used as a discriminant parameter to indicate the radiometric sensitivity to variation of different biogeochemical properties. The results showed that the SZAs have a significant impact on the signal detectability for CHL variation. For typical shelf water (CHL = 1 μg/L, TSM = 1 mg/L, CDOM = 0.15 m-1), with the typical observation zenith angle (OZA) = 30°, changes on theorder of ΔCHL = 0.024 μg/L (2.4% to background CHL) were detectable when SZA = 30°; when SZA > 75°, the detectable minimal ΔCHL increased to 0.77 μg/L (77%), indicating the difficulty of detecting CHL under high SZA. For CDOM, the detectability of changes (ΔCDOM) was also found to be closely related to the SZAs, i.e., changes on the order of ten times depending on the SZA conditions. However, even under extremely high SZA conditions (SZA = 80°, OZA = 30°), ACDOM = 0.007 m-1which is about 4.7% of the background CDOM was still detectable at 412 nm. On the other hand, under high SZA conditions (SZA = 80°, OZA = 30°), ΔTSM = 0.211 mg/L (2.1% to the background TSM) was also detectable. Overall, our results indicate that under high SZAs conditions, the geostationary satellite ocean color sensor may experience difficulty in detecting a slight change in CHL variation in productive waters, but it still can detect small changes in TSM and CDOM contents despite a reduced sensitivity at the steeper SZAs.

A model is developed for deriving the backscattering properties of phytoplankton [bbph(λ)] and nonalgal particles [bbNAP(λ)] from the total particulate backscattering coefficient [bbp(λ)] in turbid and productive coastal and inland water environments. In situ spectral particulate backscattering data acquired from several field campaigns on the turbid and productive coastal regions of southern India are used to develop a partitioning method and establish robust relationships between the partitioned bbph(λ) and bbNAP(λ) versus chlorophyll and turbidity. The performance of this method is assessed using independent data sets from marine and inland productive waters. The bbph(λ) and bbNAP(λ) products derived from this method are found in good agreement with in situ data, with the overall percentile error of a few percent which is well within the benchmark for a validated uncertainty of ±35% endorsed for the chlorophyll-a retrieval in oceanic waters. Further comparison with other inversion methods demonstrated the relative performance of the new model, especially in productive and algal bloom-dominated waters. To illustrate the use of the new model for remote sensing applications, it was applied to both multispectral Moderate Resolution Imaging Spectroradiometer- Aqua and hyperspectral (Hyperspectral Imager for the Coastal Ocean) images from the algal bloom-dominated waters of the Arabian Sea and turbid productive lagoon waters on the coastal region of the Bay of Bengal. The results were interesting, i.e., the existence of strong spectral features and inflections in the phytoplankton backscattering spectra in bloom-dominated waters and the nearly featureless NAP backscattering spectra varying approximately according to a power law in sedimentladen waters. Corroborating the previous experimental and theoretical studies, our results for algal bloom waters show a steep slope in the blue region and enhanced backscattering in the green and near-infrared regions due to the influence of phytoplankton absorption and backscattering. These results further suggest that living algal matter in productive waters may be a source of significant backscattering. Under typical nonbloom conditions in turbid coastal waters, our analyses confirm that the minerogenic and detrital particles are likely the most important because of their high abundance relative to living organic particles and their spectral dependences have profound implications for our understanding of the relationship between the backscattering to scattering ratio and particle size distribution and related properties. The new model clearly represents as an important tool permitting the analysis of the spectral backscattering coefficients associated with phytoplankton and nonliving components in sediment-laden and algal-bloom-dominated productive waters within coastal environments.

The present work aims to investigate spatial/temporal fluctuations of vertical visibility namely Secchi disk depth (Zsd) using ocean colour remote sensing data. The Secchi depth (Zsd) obtained for clear and turbid coastal waters in the region of Point Calimere (located at east coast of Southern India) is estimated through the semi-empirical approach which is based on the the remote sensing reflectance band ratio (Rrs(443/555). This study demonstrates a strong positive correlation between the band ratio Rrs(443/555) and the independent parameters of length attenuation coefficient (1/(c+Kd)), which positively governs the rate at which Coupling Coefficient (Γ) varies under a given illumination condition and hence, the Secchi transparency. The proper parameterization of beam attenuation coefficient c(m-1) and spectral diffuse attenuation coefficient Kd(m-1) is achieved at 531nm through the band ratio technique to capture the effect of the maximum light penetration. Moreover, the secondary parameter which governs the rate at which penetration of light occurs is modeled as the function of reflectance at 551nm (Rrs551). The modeled Secchi Depth (Zsd) is found to be in good agreement with the measured values with significantly low errors and high slope and correlation coefficient.

Scientific implications and practical applications of spectral particulate backscattering (bbp(λ)) in oceanography are wide ranging, particularly in optical remote sensing as the light backscattered from various seawater constituents provides possibility to derive information about the particle properties of the water under investigation. Several inversion models have been previously developed for use with remote sensing reflectance (Rrs) data over open ocean waters; however, when applied to coastal waters, these models return bbphaving large differences with in situ bbpvalues primarily because of the improper definitions of parameters of the functions describing the spectra of bbp(λ). The present study is aimed to develop a new inversion model with appropriate definitions of parameters of the functions to provide reliable retrievals of bbpin a variety of waters covering both the coastal and ocean environments. The new model is tested using large independent in situ data sets (NOMAD and Carder data sets) and simulated data provided by the IOCCG working group. When applied to these data sets, the new model outperformed the currently existing inversion models (e.g., GSM, LM and QAA models). The percent mean relative error (MRE) and root mean square error (RMSE) were found to be MRE -5.16 ~ 0.35% and RMSE 0.114 ~ 0.146 (in the 412-555 nm range) for the simulated data, MRE -0.14 ~ 2.42% and RMSE 0.125 ~ 0.157 for the NOMAD data, MRE -0.77% ~ 2.23% and RMSE 0.124 ~ 0.15 for the Korean regional data, and MRE 6.88% and RMSE 0.218 for the Carder data (for 490 nm only). Slopes close to unity, high R2and low intercept values also indicated that the new model provides better performance over other models. The results further suggest that the new model is more robust and can be effectively applied to satellite ocean color data to retrieve the particulate backscattering coefficients in a variety of waters commonly found in the coastal and offshore domains.

An effort has been made to evaluate spectral shape matching method (SSMM) for correcting atmospheric effects in the Landsat VIS/NIR imagery, in order to estimate the desired water-leaving radiance spectra and therefore the water-constituents', particularly in optically complex Case-2 waters off the Korean coast in the southern peninsula. This method is compared with in-situ data and other classical atmospheric correction algorithms such as the 6S radiative transfer model and standard SeaWiFS atmospheric algorithm. Assuming a standard atmosphere with constant aerosol loading and a uniform, Lambertian surface carries out the atmospheric correction through 6S radiative transfer code, whereas the standard SeaWiFS atmospheric correction algorithm assumes that water-leaving radiance values at the two near-infrared (NIR) bands are negligible to enable retrieval of aerosol reflectance in the correction of ocean color imagery. The atmospheric correction through SSMM is accomplished by assuming the path signal, that is obtained from the total signal recorded at the top of the atmosphere (LTOA) by the use of match-up in-situ water-leaving radiance spectra for typical clears, to be spatially homogeneous contribution of the scattered signal from aerosols and Raleigh particles and the sea surface. The results indicate that the overall shape and magnitude of retrieved spectra with SSMM appear to be in good agreement with in-situ spectra collected from both clear and turbid waters. Because of the standard atmosphere with constant aerosols and models adopted in 6S radiative transfer code, a large error is possible between the retrieved and in-situ spectra. Further extension of SSMM to SeaWiFS ocean color imagery reveals that accurate estimation of water-leaving radiance spectra is feasible with SSMM but not with standard SeaWiFS atmospheric correction algorithm that yields unrealistic water-leaving radiance values in turbid waters, primarily because the water-column reflectance interferes with the atmospheric correction based on the 765 nm and 865 nm spectral bands.

The present study involves analyzing chlorophyll-a (Chl-a) from SeaWiFS data collected over the period 1998-2002 to better understand the spatial and temporal aspects of red tide algal blooms created by Cochlodinium polykrikoides species in the enclosed and semi-enclosed bays of the South Sea of Korea. NOAA-AVHRR data is analyzed for sea surface temperature (SST) to elucidate physical factors affecting these aspects and abundance of Cochlodinium.p blooms. The time series Chl-a give an impression that recent red tide events with higher concentrations appear to have spanned more than 8 weeks in summer and fall seasons and were widespread in most of the South Sea coastal bays and neighboring ocean waters. The Chl-a estimates from SeaWiFS data appeared to be useful in demonstrating spatial and temporal aspects of these blooms, but uniquely identification of Cochlodinium.p from non-bloom and sediment dominated waters remains ineffective with these data. Thus the classical techniques such as Forward Principal Component Analysis (FPCA) and Minimum Spectral Distance (MSD) are attempted on both low spatial resolution SeaWiFS ocean color image data and high spatial resolution Landsat-7 ETM+ data. In September 2000, when an exchange of water masses took place off the southeastern coast, dense and mixed phases of the Cochlodinium.p blooms were conspicuous in the spectrally transformed SeaWiFS image. The existence of these phases inferred from SeaWiFS imagery was verified with both the atmospherically corrected and in-situ radiance spectra collected from these regions. Findings show that the application of SeaWiFS to uniquely identify Cochlodinium.p bloom from mixed and turbid plume was not feasible, but it was very effective with Landsat-7 ETM+ imagery detecting intricate and filament-like patterns of the Cochlodinium.p blooms and its dynamics owing to numerous physical mechanisms as determined by sea surface temperature from AVHRR infrared data.