Interpretation of remote sensing reflectance from coastal waters at different wavelengths of light yields valuable information about water column constituents, which in turn, gives information on a variety of processes occurring in coastal waters, such as primary production, biogeochemical cycles, sediment transport, coastal erosion, and harmful algal blooms. The Hyperspectral Infrared Imager (HyspIRI) is well suited to produce global, seasonal maps and specialized observations of coastal ecosystems and to improve our understanding of how phytoplankton communities are spatially distributed and structured, and how they function in coastal and inland waters. This paper draws from previously published studies on high-resolution, hyperspectral remote sensing of coastal and inland waters and provides an overview of how the HyspIRI mission could enable the retrieval of new aquatic biophysical products or improve the retrieval accuracy of existing satellite-derived products (e.g., inherent optical properties, phytoplankton functional types, pigment composition, chlorophyll-a concentration, etc.). The intent of this paper is to introduce the development of the HyspIRI mission to the coastal and inland remote sensing community and to provide information regarding several potential data products that were not originally part of the HyspIRI mission objectives but could be applicable to research related to coastal and inland waters. Further work toward quantitatively determining the extent and quality of these products, given the instrument and mission characteristics, is recommended.

The most problematic issue in the ocean color application is the presence of heavy clouds, especially in polar regions. For that reason, the demand for the ocean color application in polar regions is increased. As a way to overcome such issues, we conducted the reconstruction of the chlorophyll-a concentration (CHL) data using the machine learning-based models to raise the usability of CHL data. This analysis was first conducted on a regional scale and focused on the biologically-valued Cape Hallett, Ross Sea, Antarctica. Environmental factors and geographical information associated with phytoplankton dynamics were considered as predictors for the CHL reconstruction, which were obtained from cloud-free microwave and reanalysis data. As the machine learning models used in the present study, the ensemble-based models such as Random forest (RF) and Extremely randomized tree (ET) were selected with 10-fold cross-validation. As a result, both CHL reconstructions from the two models showed significant agreement with the standard satellite-derived CHL data. In addition, the reconstructed CHLs were close to the actual CHL value even where it was not observed by the satellites. However, there is a slight difference between the CHL reconstruction results from the RF and the ET, which is likely caused by the difference in the contribution of each predictor. In addition, we examined the variable importance for the CHL reconstruction quantitatively. As such, the sea surface and atmospheric temperature, and the photosynthetically available radiation have high contributions to the model developments. Mostly, geographic information appears to have a lower contribution relative to environmental predictors. Lastly, we estimated the partial dependences for the predictors for further study on the variable contribution and investigated the contributions to the CHL reconstruction with changes in the predictors.

The mixed layer depth (MLD) is generally estimated using in situ or model data. However, MLD analyses have limitations due to the sparse resolution of the observed data. Therefore, this study reconstructs three-dimensional (3D) ocean thermal structures using only satellite sea surface measurements for a higher spatial and longer temporal resolution than that of Argo and diagnoses the decadal variation of global MLD variability. To simulate the ocean thermal structures, the relationship between the ocean subsurface temperature and the sea surface fields was computed based on gridded Argo data. Based on this relationship, high spatial resolution and extended periods of satellite-derived altimeter, sea surface temperature (SST), and wind stress data were used to estimate the 3D ocean thermal structures with 0.25° spatial resolution and 26 standard depth levels (5–2000 m) for 24 years (1993–2016). Then, the MLD was calculated using a temperature threshold method (∆T = 0.2 °C) and correlated reasonably well (>0.9) with other MLD datasets. The extended 24-year data enabled us to analyze the decadal variability of the MLD. The global linear trend of the 24-year MLD is −0.110 m yr⁻¹; however, from 1998 to 2012, the linear trend is −0.003 m yr⁻¹ which is an order of magnitude smaller than that of other periods and corresponds to a global warming hiatus period. Via comparisons between the trends of the SST anomalies and the MLD anomalies, we tracked how the MLD trend changes in response to the global warming hiatus.

Sea surface salinity (SSS) is an important tracer for monitoring the Changjiang Diluted Water (CDW) extension into Korean coastal regions; however, observing the SSS distribution in near real time is a difficult task. In this study, SSS detection algorithm was developed based on the ocean color measurements by Geostationary Ocean Color Imager (GOCI) in high spatial and temporal resolution using multilayer perceptron neural network (MPNN). Among the various combinations of input parameters, combinations with three to six bands of GOCI remote sensing reflectance (Rrs), sea surface temperature (SST), longitude, and latitude were most appropriate for estimating the SSS. According to model validations with the Soil Moisture Active Passive (SMAP) and Ieodo Ocean Research Station (I-ORS) SSS measurements, the coefficient of determination (R²) were 0.81 and 0.92 and the root mean square errors (RMSEs) were 1.30 psu and 0.30 psu, respectively. In addition, a sensitivity analysis revealed the importance of SST and the red-wavelength spectral signal for estimating the SSS. Finally, hourly estimated SSS images were used to illustrate the hourly CDW distribution. With the model developed in this study, the near real-time SSS distribution in the East China Sea (ECS) can be monitored using GOCI and SST data.

A record-breaking agglomeration of Sargassum was packed along the northern Jeju coast in Korea in 2021, and laborers suffered from removing them from the beach. If remote sensing can be used to detect the locations at which Sargassum accumulated in a timely and accurate manner, we could remove them before their arrival and reduce the damage caused by Sargassum. This study aims to detect Sargassum distribution on the coast of Jeju Island using the Geostationary KOMPSAT 2B (GK2B) Geostationary Ocean Color Imager-II (GOCI-II) imagery that was launched in February 2020, with measurements available since October 2020. For this, we used GOCI-II imagery during the first 6 months and machine learning models including Fine Tree, a Fine Gaussian support vector machine (SVM), and Gentle adaptive boosting (GentleBoost). We trained the models with the GOCI-II Rayleigh-corrected reflectance (R_hoC) image and a ground truth map extracted from high-resolution images as input and output, respectively. Qualitative and quantitative assessments were carried out using the three machine learning models and traditional methods such as Sargassum indexes. We found that GentleBoost showed a lower false positive (6.2%) and a high F-measure level (0.82), and a more appropriate Sargassum distribution compared to other methods. The application of the machine learning model to GOCI-II images in various atmospheric conditions is therefore considered successful for mapping Sargassum extent quickly, enabling reduction of laborers’ efforts to remove them.