The climate-induced decrease in fish catches in South Korea has been a big concern over the last decades. The increase in sea surface temperature (SST) due to climate change has led to not only a decline in fishery landings but also a shift in the fishing grounds of several fish species. The habitat suitability index (HSI), a reliable indicator of the capacity of a habitant to support selected species, has been widely used to detect and forecast fishing ground formation. In this study, the catch data of the chub mackerel and satellite-derived environmental factors were used to calculate the HSI for the chub mackerel in the South Sea, South Korea. More than 80% of the total catch was found in areas with an SST of 14.72–25.72 °C, chlorophyll-a of 0.30–0.92 mg m⁻³, and primary production of 523.7–806.46 mg C m⁻² d⁻¹. Based on these results, the estimated climatological monthly HSI from 2002 to 2016 clearly showed that the wintering ground of the chub mackerel generally formed in the South Sea of South Korea, coinciding with the catch distribution during the same period. This outcome implies that our estimated HSI can yield a reliable prediction of the fishing ground for the chub mackerel in the East/Japan Sea and South Sea of South Korea.

Accurate and timely quantification of widespread harmful algal bloom (HAB) distribution is crucial to respond to the natural disaster, minimize the damage, and assess the environmental impact of the event. Although various remote sensing-based quantification approaches have been proposed for HAB since the advent of the ocean color satellite sensor, there have been no algorithms that were validated with in-situ quantitative measurements for the red tide occurring in the Korean seas. Furthermore, since the geostationary ocean color imager (GOCI) became available in June 2010, an algorithm that exploits its unprecedented observation frequency (every hour during the daytime) has been highly demanded to better track the changes in spatial distribution of red tide. This study developed a novel red tide quantification algorithm for GOCI that can estimate hourly chlorophyll-a (Chl a) concentration of Cochlodinium (Margalefidinium) polykrikoides, one of the major red tide species around Korean seas. The developed algorithm has been validated using in-situ Chl a measurements collected from a cruise campaign conducted in August 2013, when a massive C. polykrikoides bloom devastated Korean coasts. The proposed algorithm produced a high correlation (R2=0.92) with in-situ Chl a measurements with robust performance also for high Chl a concentration (300mg/m3) in East Sea areas that typically have a relatively low total suspended particle concentration (<0.5mg/m3).

Geostationary Ocean Color Imager (GOCI) observations are applied to marine fog (MF) detection in combination with Himawari-8 data based on the decision tree (DT) approach. Training and validation of the DT algorithm were conducted using match-ups between satellite observations and in situ visibility data for three Korean islands. Training using different sets of two satellite variables for fog and nonfog in 2016 finally results in an optimal algorithm that primarily uses the GOCI 412-nm Rayleigh-corrected reflectance (R_rc) and its spatial variability index. The algorithm suitably reflects the optical properties of fog by adopting lower R_rc and spatial variability levels, which results in a clear distinction from clouds. Then, cloud removal and fog edge detection in combination with Himawari-8 data enhance the performance of the algorithm, increasing the hit rate (HR) of 0.66 to 1.00 and slightly decreasing the false alarm rate (FAR) of 0.33 to 0.31 for the cloudless samples among the 2017 validation cases. Further evaluation of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data reveals the reliability of the GOCI MF algorithm under optically complex atmospheric conditions for classifying marine fog. Currently, the high-resolution (500 m) GOCI MF product is provided to decision-makers in governments and the public sector, which is beneficial to marine traffic management.

Japanese encephalitis (JE) is the major cause of viral encephalitis (VE) in most Asian-Pacific countries. In Vietnam, there is no nationwide surveillance system for JE due to lack of medical facilities and diagnoses.Culex tritaeniorhynchus,Culex vishnui, andCulex quinquefasciatushave been identified as the major JE vectors in Vietnam. The main objective of this study was to forecast a risk map ofCulexmosquitoes in Hanoi, which is one of the most densely populated cities in Vietnam. A total of 10,775 female adultCulexmosquitoes were collected from 513 trapping locations. We collected temperature and precipitation information during the study period and its preceding month. In addition, the other predictor variables (e.g., normalized difference vegetation index [NDVI], land use/land cover and human population density), were collected for our analysis. The final model selected for estimating theCulexmosquito abundance included centered rainfall, quadratic term rainfall, rice cover ratio, forest cover ratio, and human population density variables. The estimated spatial distribution ofCulexmosquito abundance ranged from 0 to more than 150 mosquitoes per 900m². Our model estimated that 87% of the Hanoi area had an abundance of mosquitoes from 0 to 50, whereas approximately 1.2% of the area showed more than 100 mosquitoes, which was mostly in the rural/peri-urban districts. Our findings provide better insight into understanding the spatial distribution ofCulexmosquitoes and its associated environmental risk factors. Such information can assist local clinicians and public health policymakers to identify potential areas of risk for JE virus. Risk maps can be an efficient way of raising public awareness about the virus and further preventive measures need to be considered in order to prevent outbreaks and onwards transmission of JE virus.

Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensor’s field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASA’s SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving “remote-sensing” reflectance (R_rs) for the 412, 443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 × 10⁻⁴, 5.49 × 10⁻⁴, 4.48 × 10⁻⁴, 2.51 × 10⁻⁴, 8.83 × 10⁻⁵, and 1.36 × 10⁻⁴ sr⁻¹, respectively, and 1.09 × 10⁻² mg m⁻³ for the chlorophyll-a concentration (Chl-a), 2.09 × 10⁻³ m⁻¹ for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (a_g (412)), and 3.7 mg m⁻³ for particulate organic carbon (POC). These R_rs values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI.