This paper presents methods of monitoring river basin development and water variability for the transboundary river in North and South Korea. River basin development, such as dams and water infrastructure in transboundary rivers, can be a potential factor of tensions between upstream and downstream countries since dams constructed upstream can adversely affect downstream riparians. However, because most of the information related to North Korea has been limited to the public, the information about dams constructed and their locations were inaccurate in many previous studies. In addition, water resources in transboundary rivers can be exploited as a political tool. Specifically, due to the unexpected water release from the Hwanggang Dam, upstream of the transboundary Imjin River in North and South Korea, six South Koreans died on 6 September 2009. The Imjin River can be used as a political tool by North Korea, and seven events were reported as water conflicts in the Imjin River from 2001 to 2016. In this paper, firstly, we have updated the information about the dams constructed over the Imjin River in North Korea using multi-temporal images with a high spatial resolution (15–30 cm) obtained from Google Earth. Secondly, we analyzed inter- and intra-water variability over the Hwanggang Reservoir using open-source images obtained from the Global Surface Water Explorer. We found a considerable change in water surface variability before and after 2008, which might result from the construction of the Hwanggang Dam. Thirdly, in order to further investigate intra-annual water variability, we present a method monitoring water storage changes of the Hwanggang Reservoir using the area-elevation curve (AEC), which was derived from multi-sensor Synthetic Aperture Radar (SAR) images (Sentinel-1A and -1B) and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). Since many previous studies for estimating water storage change have depended on satellite altimetry dataset and optical images for deriving AEC, the method adopted in this study is the only application for such inaccessible areas since no altimetry ground track exists for the Hwanggang Reservoir and because clouds can block the study area for wet seasons. Moreover, this study has newly proven that unexpected water release can occur in dry seasons because the water storage in the Hwanggang Reservoir can be high enough to conduct a release that can be used as a geopolitical tool. Using our method, potential risks can be mitigated, not in response to a water release, but based on pre-event water storage changes in the Hwanggang Reservoir.

Estimating river discharge (Q) is critical for ecosystems and water resource management. Traditionally, estimating Q has depended on a single rating curve or the Manning equation. In contrast to the single rating curve, several rating curves at different locations have been linearly combined in an ensemble learning regression method to estimate Q (ELQ) at the Brazzaville gauge station in the central Congo River in a previous study. In this study, we further tested the proposed ELQ and apply it to the Lower Mekong River Basin (LMRB) with three locations: Stung Treng, Kratie, and Tan Chau. Two major advancements for estimating Q with ELQ are presented. First, ELQ successfully estimated Q at Tan Chau, downstream of Kratie, where hydrodynamic complexities exist. Since the hydrologic characteristics downstream of Kratie are extremely diverse and complex in time and space, most previous studies have estimated Q only upstream from Kratie with hydrologic models and statistical methods. Second, we estimated Q over the LMRB using ELQ with water levels (H) obtained from two radar altimetry missions, Envisat and Jason-2, which made it possible to estimate Q seamlessly from 2003 to 2016. Owing to ELQ with multi-mission radar altimetry data, we have overcome the problems of a single rating curve: Locations for estimating Q have to be close to virtual stations, e.g., a few tens of kilometers, because the performance of the single rating curve degrades as the distance between the location of Q estimation and a virtual station increases. Therefore, most previous studies had not used Jason-2 data whose cross-track interval is about 315 km at the equator. On the contrary, several H obtained from Jason-2 altimetry were used in this study regardless of distances from in-situ Q stations since the ELQ method compensates for degradation in the performance for Q estimation due to the poor rating curve with virtual stations away from in-situ Q stations. In general, the ELQ-estimated Q ( ${\widehat{Q}}_{ELQ}$ ) showed more accurate results compared to those obtained from a single rating curve. In the case of Tan Chau, the root mean square error (RMSE) of ${\widehat{Q}}_{ELQ}$ decreased by 1504/1338 m³/s using Envisat-derived H for the training/validation datasets. We successfully applied ELQ to the LMRB, which is one of the most complex basins to estimate Q with multi-mission radar altimetry data. Furthermore, our method can be used to obtain finer temporal resolution and enhance the performance of Q estimation with the current altimetry missions, such as Sentinel-3A/B and Jason-3.

Soyeon Park, Geun-Ho Kwak, Eui Ho Hwang, No-Wook Park. Korean J. Remote Sens. 2024;40:881-93. https://doi.org/10.7780/kjrs.2024.40.6.1.1

Eui-Ho Hwang, Jin-Gyeom Kim, Jang-Yong Sung, Ki-Mook Kang. Korean J. Remote Sens. -0001;0:. https://doi.org/10.7780/kjrs.2024.40.5.2.10

Young Jun Kim, Gibeom Nam, Euiho Hwang. Korean J. Remote Sens. 2025;41:829-41. https://doi.org/10.7780/kjrs.2025.41.5.10

Due to the recent anomalous climatic conditions, the mean summer temperature in Korea for the current year reached 25.6°C, marking the highest temperature recorded since 1973. Concurrently, the incidence of large-scale green algal blooms has escalated beyond the typical frequency in the four principal river systems. The Ministry of Environment has instituted a water quality monitoring network aimed at gathering data pertinent to water quality assessment and regulatory measures; however, the reliance on discrete point data imposes constraints on comprehending the spatial distribution and proliferation of green algae. Techniques for remote monitoring of green algae utilizing satellite imagery may serve as an alternative or complementary resource to conventional point-based water quality assessments. In the present study, we devised and critically assessed an algorithm for the remote estimation of freshwater chlorophyll-a concentrations through the analysis of Sentinel-2 satellite data. Given the challenges associated with field data collection, the GLORIA dataset was employed, and three variations of the CatBoost algorithm were developed based on different input variables, with their effectiveness concerning Sentinel-2 satellite data subjected to evaluation. Among the three algorithms, the variant that was trained utilizing variables such as band ratio and spectral shape was contrasted with 76 field measurements of surface chlorophyll-a, demonstrating a commendable accuracy with an R value of 0.79, root mean square error of 10.1 mg/m3 when applied to the GLORIA dataset. The chlorophyll-a quantification algorithm predicated on Sentinel-2 satellite data, which was developed in this study, holds the potential to be utilized effectively for collaborative management and responsive measures regarding green algae by providing objective insights into the prevalence of green algae across extensive areas, temporal trends, and localized hotspots.