Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia.

Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.

Satellite Ocean Colour: Current Status and Future Perspective.

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and interannual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

Physical processes leading to the development of an anomalously large Cochlodinium polykrikoides bloom in the East sea/Japan sea.

An anomalously large Harmful Algal Bloom (HAB) was observed in the southwest coast of the East/Japan Sea (hereafter the East Sea) during the summer of 2013. During this time period, the presence of Cochlodinium polykrikoides (C. polykrikoides) was detected by the Geostationary Ocean Color Imager (GOCI) and validated by in-situ observations. GOCI observations have been available since 2011, thus allowingto examine various stages of the physical condition of the developing C. polykrikoides bloom, thereby other multi-satellite and buoy measurements obtained between 2011 and 2013. Research results indicate that this HAB is related to four processes: the transport of C. polykrikoides from the south coast of Korea to the HAB area; a relatively high insolation; continuous coastal upwelling; and a favorable Sea Surface Temperature (SST) for C.polykrikoide growth. In examination of the main transport mechanisms, geostrophic current measurements were used to estimate the flow trajectories, showing water from the south coast to the HAB area off the southeast coast of Korea. Result shows that ninety percent of the water from the south coast reached the HAB area in 2013. Furthermore, to examine the insolation mechanism, the Photosynthetically available radiation (PAR) value was derived from the Moderate Resolution Imaging Spectoradiometer (MODIS), showing that PAR values were relatively high in the HAB area during HAB period (47Einm-1day-1). Moreover, Upwelling age (UA) was calculated in order to investigate the strength of coastal upwelling events, which were found to support relatively high UA values during the HAB period. The mean UA value during the HAB period was 1.01, higher than those in 2011 and 2012 which were 0.61 and 0.76, respectively. Finally, SST in the HAB area was also analyzed to examine which conditions were most favorable for HAB growth. Therefore, the results of this study suggest that the four mechanisms can explain the relative contributions of the anomalously HAB development observed off the southeast coast of Korea.

Satellite-based observations of unexpected coastal changes due to the Saemangeum Dyke construction, Korea.

Spatial and temporal changes around an area of conventional coastal engineering can be easily observed from field surveys because of the clear cause-and-effect observable in the before and after stages of the project. However, it is more difficult to determine environmental changes in the vicinity of tidal flats and coastal areas that are a considerable distance from the project. To identify any unexpected environmental impacts of the construction of Saemangeum Dyke in the area, we examined morphological changes identified by satellite-based observations through a field survey on Gomso Bay tidal flats (15km from Saemangeum Dyke), and changes in the suspended sediment distribution identified by satellite-based observations through a hydrodynamic analysis in the Saemangeum and Gomso coastal area. We argue that hydrodynamic changes due to conventional coastal engineering can affect the sedimentation pattern in the vicinity of tidal flats. We suggest that the environmental impact assessment conducted before a conventional coastal engineering project should include a larger area than is currently considered.

GOCI image enhancement using an MTF compensation technique for coastal water applications.

The Geostationary Ocean Color Imager (GOCI) is the first optical sensor in geostationary orbit for monitoring the ocean environment around the Korean Peninsula. This paper discusses on-orbit modulation transfer function (MTF) estimation with the pulse-source method and its compensation results for the GOCI. Additionally, by analyzing the relationship between the MTF compensation effect and the accuracy of the secondary ocean product, we confirmed the optimal MTF compensation parameter for enhancing image quality without variation in the accuracy. In this study, MTF assessment was performed using a natural target because the GOCI system has a spatial resolution of 500 m. For MTF compensation with the Wiener filter, we fitted a point spread function with a Gaussian curve controlled by a standard deviation value (σ). After a parametric analysis for finding the optimal degradation model, the σ value of 0.4 was determined to be an optimal indicator. Finally, the MTF value was enhanced from 0.1645 to 0.2152 without degradation of the accuracy of the ocean color product. Enhanced GOCI images by MTF compensation are expected to recognize small-scale ocean products in coastal areas with sharpened geometric performance.

Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats.

Spatial prediction of ground subsidence susceptibility using an artificial neural network.

Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor's relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, "distance from fault" had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.

Macrobenthos habitat mapping in a tidal flat using remotely sensed data and a GIS-based probabilistic model.

This paper proposes and tests a method of producing macrofauna habitat potential maps based on a weights-of-evidence model (a probabilistic approach) for the Hwangdo tidal flat, Korea. Samples of macrobenthos were collected during field work, and we considered five mollusca species for habitat mapping. A weights-of-evidence model was used to calculate the relative weights of 10 control factors that affect the macrobenthos habitat. The control factors were compiled as a spatial database from remotely sensed data combined with GIS analysis. The relative weight of each factor was integrated as a species potential index (SPI), which produced habitat potential maps. The maps were compared with the surveyed habitat locations, revealing a strong correlation between the potential maps and species locations. The combination of a GIS-based weights-of-evidence model and remote sensing techniques is an effective method in determining areas of macrobenthos habitat potential in a tidal flat setting.