Evaluating the Applicability of SWOT Satellite Data for Reservoir Surface Area and Water Level Monitoring

Global warming accelerates climate change, increasing the frequency of floods and droughts, thereby emphasizing the importance of developing monitoring technologies. Therefore, the importance of continuous water resources monitoring is essential. Satellite remote sensing data is an effective tool for water resources monitoring. Monitoring water resources using conventional satellite imagery requires complex calculations and data preprocessing. The recently launched Surface Water and Ocean Topography (SWOT) satellite provides information on the height and distribution of inland water bodies without extra computational effort. In this study, validation of SWOT water surface data using Sentinel-1 imagery-based water mask and in-situ water level. For validation, a confusion matrix-based metric was used (accuracy, precision, recall, IoU). As a result, SWOT satellite data demonstrated high performance, achieving an accuracy of over 0.90 in monitoring reservoir surface areas and detecting water level changes. These findings indicate that strengths of SWOT data have potential to efficiently monitoring water resources. Furthermore, the results provide valuable insights into advancing hydrological research.

 

Keywords: SWOT, Water Body Detection, Water Level, Confusion Matrix

 

Acknowledgment

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Environment (MOE)(RS-2024-00332300). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2010266).