Search

Estimation of chlorophyll concentration in the marine biosphere has been the central topic of ocean color remote sensing since its advent. While various algorithms were proposed in the literature so far and tested for oceanic waters of diverse constituent composition, an independent algorithm evaluation is needed for local ocean waters that have dynamic variation in optically active water constituents such as colored dissolved organic matters (CDOM) and suspended particulate matter (SPM). This paper evaluates the performance of chlorophyll algorithms for Geostationary Ocean Color Imager (GOCI) radiometric data, using in situ measurements collected at 491 stations around Korea Peninsula during 2010–2014 from which there were 130 match-ups with GOCI data. For the evaluation in areas with high variation in SPM, water samples were first classified into three levels of SPM, and then the coefficients of candidate algorithms were newly derived for the turbidity cases using the in situ and GOCI remote sensing reflectance ( R rs ) data. Functional forms of traditional band ratio algorithms ( e.g. OC algorithms (O′Reilly et al., 1998) and Tassan's algorithm (Tassan, 1994)), fluorescence line height algorithm, and near-infrared-to-red band ratio approach were tested. The evaluation results for the coincident in situ pairs of R rs and chlorophyll measurements showed that the mean uncertainty was

The paper “A neural network model for remote sensing of diffuse attenuation coefficient in global oceanic and coastal waters: Exemplifying the applicability of the model to the coastal regions in Eastern China Seas” uses the ratio of water-leaving radiance to downward irradiance multiple by π as the remote sensing reflectance for NOMAD dataset and the implement routine of diffuse attenuation coefficient retrieval models. Our results indicate that the quasi-analytical algorithm-based semi-analytical model (QSM) and spectrum-based neural network model (SNNM) produce a superior performance to these mentioned in the original paper, but these results do not affect the main conclusions in the original paper.

For global oceanic and coastal waters, a multilayer back propagation neural network (MBPNN) is developed to retrieve the diffuse attenuation coefficient for the downwelling spectral irradiance at the wavelength 490 nm (Kd(490)). The applicability of Lee's quasi-analytical algorithm-based semi-analytical model, Wang's switching model, Chen's semi-analytical model, Jamet's neural network model, and the MBPNN model is evaluated using the NASA bio-optical marine algorithm dataset (NOMAD) and the Eastern China Seas dataset. Based on the comparison of Kd(490) predicted by these five models, with field measurements taken in global oceanic and coastal waters, it is found that the MBPNN model provides a stronger performance than the Lee, Wang, Chen, and Jamet's models. The atmospheric effects on the MODIS data are eliminated using near-infrared band-based and shortwave infrared band-based combined models, and the Kd(490) is quantified from the MODIS data after atmospheric correction using the MBPNN model. The study results indicate that the MBPNN model produces ~ 28% uncertainty in estimating Kd(490) from the MODIS data. Finally, an exemplification of the applicability of the model to the coastal regions in the Eastern China Seas is carried out. Our results suggest that the Kd(490) shows a large variation in the Eastern China Seas, ranging from 0.02 to 4.0 m− 1, with an average value of ~ 0.17 m− 1.

The end of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission makes harmful algal bloom (HAB) detection with moderate resolution satellite data now relies on Moderate Resolution Imaging Spectroradiometer (MODIS). Based on MODIS and in situ data collected in the coastal region of the western part of Seto-Inland Sea, Japan with HAB-forming algae Karenia mikimotoi, a simpler new satellite remote sensing-based HAB detection method was developed. The strength of this method is, although it does not require indigenous atmospheric correction scheme, it is expected to be able to classify K. mikimotoi blooms, diatom blooms, TSM-dominated waters, gelbstoff-dominated waters, and mixed waters in the optically complex coastal waters. We anticipate that our satellite remote sensing-based HAB detection method can operate as a valuable complementary tool assisting in situ HAB monitoring and as an integrated part of HAB early warning systems to mitigate HAB negative impacts not only in the coastal waters of the western part of Seto-Inland Sea, Japan, but also in the other coastal waters with different HAB-forming algae, providing that backscattering signature and pigment packaging of other HAB-forming algae are similar to those of K. mikimotoi.

The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) has three bands in the visible and near-infrared (VNIR) with 15-m spatial resolution. The high spatial resolution has advantages for studying small aquatic areas, such as bay and lakes. Coastal areas are optically characterized by high concentrations of colored suspended matter, various phytoplankton pigments and colored dissolved organic matter (CDOM). The color ratio bio-optical algorithms often used for open sea are very difficult to apply in optically complex coastal water, since it is assumed that the constituents of seawater are mainly phytoplankton pigments. The Neural Network (NN) method, which is one of inverse modeling, has the potential to estimate chlorophyll a , suspended matter and CDOM from remotely sensed data. In the present investigation, we implemented the NN method in the analysis of ASTER data of Tokyo Bay, as a case study in the coastal waters in order to demonstrate the usefulness of remote sensing with high spatial resolution. After validation of the NN using simulated data sets and a field data set observed from a ship, estimation of the concentration of TSS and Chl-a was reasonably accurate. However, in the case of CDOM, the result is not reliable. Disadvantages of the NN method are discussed in this paper.