Distribution, migration and transformation of chromophoric dissolved organic matter (CDOM) in coastal waters are closely related to marine biogeochemical cycle. Ocean color remote sensing retrieval of CDOM absorption coefficient (a_g(λ)) can be used as an indicator to trace the distribution and variation characteristics of the Changjiang diluted water, and further to help understand estuarine and coastal biogeochemical processes in large spatial and temporal scales. The quasi-analytical algorithm (QAA) has been widely applied to remote sensing inversions of optical and biogeochemical parameters in water bodies such as oceanic and coastal waters, however, whether the algorithm can be applicable to highly turbid waters (i.e., Changjiang estuarine and coastal waters) is still unknown. In this study, large amounts of in situ data accumulated in the Changjiang estuarine and coastal waters from 9 cruise campaigns during 2011 and 2015 are used to verify and calibrate the QAA. Furthermore, the QAA is remodified for CDOM retrieval by employing a CDOM algorithm (QAA_CDOM). Consequently, based on the QAA and the QAA_CDOM, we developed a new version of algorithm, named QAA_cj, which is more suitable for highly turbid waters, e.g., Changjiang estuarine and coastal waters, to decompose a_g from a_dg (CDOM and non-pigmented particles absorption coefficient). By comparison of matchups between Geostationary Ocean Color Imager (GOCI) retrievals and in situ data, it reveals that the accuracy of retrievals from calibrated QAA is significantly improved. The root mean square error (RMSE), mean absolute relative error (MARE) and bias of total absorption coefficients (a(λ)) are lower than 1.17, 0.52 and 0.66 m⁻¹, and a_g(λ) at 443 nm are lower than 0.07, 0.42 and 0.018 m⁻¹. These results indicate that the calibrated algorithm has a better applicability and prospect for highly turbid coastal waters with extremely complicated optical properties. Thus, reliable CDOM products from the improved QAA_cj can advance our understanding of the land-ocean interaction process by earth observations in monitoring spatial-temporal distribution of the river plume into sea.

Optical closure exercises are pivotal for evaluating the accuracy of water quality remote-sensing techniques. The agreement between radiometrically derived and inherent optical property (IOP)-derived above-water spectral remote-sensing reflectance R_rs(λ) is necessary for resolving IOPs, the diffuse attenuation coefficient, and biogeochemical parameters from space. We combined spectral radiometric and IOP measurements to perform an optical closure exercise for two optically contrasting Chinese waters – the Changjiang (Yangtze) River Estuary and its adjacent coastal area in the East China Sea. The final aim of our investigation was to compare two derivations of R_rs(λ): R_rs(λ), derived from radiometric measurements; and R_rs(λ), derived from simultaneous IOP measurements. Five subsequent steps have been taken to achieve this goal, including (1) estimation of the R_rs(λ) from radiometric measurements; (2) scattering correction for the non-water spectral absorption coefficient a_pd(λ); (3) estimation of the below-water spectral remote-sensing reflectance r_rs(λ) from IOPs measurements; (4) the estimation of the R_rs(λ) from the r_rs(λ) values; and (5) the comparison between the R_rs(λ) derived from radiometric and IOP measurements. All steps were realized by using both direct measurements and different models based on radiative transfer theory. Results demonstrated that the impact of the errors caused by the scattering correction procedure and conversion of radiometric quantities into R_rs(λ) may be rather significant, especially in the long-wavelength spectrum range. Nevertheless, spectral features were similar between these R_rs(λ) sets for all waters – from relatively clear to very turbid. Exploiting this fact allows use of the spectral reflectance ratios for remote sensing of the estuarine and coastal Chinese waters.

Development and validation of the surface suspended sediment concentration (SSC) models derived from the surface remote-sensing reflectance spectra [R _rs (λ)] are important in satellite monitoring of estuarine and coastal waters. Seven empirical and seven semianalytical spectral reflectance models for evaluation of the surface SSC were compared with one another and with laboratory tank (one dataset) and in situ measurements (two datasets) performed in different natural waters of East China. All models were presented in the form of Rrs spectral ratios, in which wavelengths were selected from the list of NASA's satellite sensor, MODIS unsaturated central wavelengths. A statistical analysis has been performed to find the best models and spectral ratios for remote-sensing monitoring purposes. Analysis has shown that empirical models are generally superior to the semianalytical models for solution existence, prediction accuracy, and correlation with the observed SSC values. However, all semianalytical models using the red to green spectral ratio have demonstrated approximately the same accuracy and correlation as empirical models, what provides an additional support for using more simple easily calculated empirical models. Additionally, relationships between SSC and inherent optical properties (IOPs) (absorption and backscattering coefficients) and between IOPs and R _rs (λ) provided by the semianalytical models have their own benefits for aquatic optics and remote sensing purposes.

Two remote-sensing optical algorithms for the retrieval of the water quality components (WQCs) in the Albemarle-Pamlico Estuarine System (APES) were developed and validated for chlorophyll a (Chl). Both algorithms were semi-empirical because they incorporated some elements of optical processes in the atmosphere, water, and air/water interface. One incorporated a very simple atmospheric correction and modified quasi-single-scattering approximation (QSSA) for estimating the spectral Gordon’s parameter, and the second estimated WQCs directly from the top of atmosphere satellite radiance without atmospheric corrections. A modified version of the Global Meteorological Database for Solar Energy and Applied Meteorology (METEONORM) was used to estimate directional atmospheric transmittances. The study incorporated in situ Chl data from the Ferry-Based Monitoring (FerryMon) program collected in the Neuse River Estuary (n = 633) and Pamlico Sound (n = 362), along with Medium Resolution Imaging Spectrometer (MERIS) satellite imagery collected (2006–2009) across the APES; providing quasi-coinciding samples for Chl algorithm development and validation. Results indicated a coefficient of determination (R²) of 0.70 and mean-normalized root-mean-squares errors (NRMSE) of 52% in the Neuse River Estuary and R² = 0.44 (NRMSE = 75 %) in the Pamlico Sound—without atmospheric corrections. The simple atmospheric correction tested provided on performance improvements. Algorithm performance demonstrated the potential for supporting long-term operational WQCs satellite monitoring in the APES.

We present an analysis of a number of different approximations for the plane transmittance $T_p$ and diffuse attenuation coefficient $K_d$ of a semi-infinite, unbounded, plane-parallel, and optically homogeneous layer. The maximally wide optical conditions (from the full absorption to the full scattering and from the fully forward to the fully backward scattering) were considered. The approximations were analyzed from the point of view of their physical limitations and closeness to the numerical solution of the radiative transfer equation for the plane transmittance. The main criterion for inclusion of the models for analysis was the possibility of practical use, i.e., approximations were well parameterized and included only easily measured or estimated parameters. A detailed analysis of errors for different $T_p$ and $K_d$ models showed that the two-stream radiative transfer Ben-David model yields the best results over all optical conditions and depths. However, the quasi-single-scattering and polynomial Gordon’s approximations proved to be the best for the depths close to zero.