Water Level Estimation by using Sentinel-1 C-band SAR and Deep Learning approach

Recently, global climate change has led to the occurrence of intensified heavy rainfall and extreme floods. As a result, analyzing, monitoring, and predicting flood risks caused by extreme rainfall and water level fluctuations have become increasingly important. In South Korea, river water levels are continuously monitored using telemeter (TM) based water level observations. However, uneven distribution of water level gauges leads to obtain water level data over ungauged stream or basins. This phenomenon further poses a significant constraint for developing spatial monitoring and prediction systems of extreme flood events.

To address these challenges, this study proposes a Deep learning-based algorithm for estimating river water levels using Sentinel-1 Synthetic Aperture Radar (SAR), which enables to provide spatially continuous observations over ungauged basin. Sentinel-1 SAR has advantages in providing all-weather observations regardless of the weather conditions, and their data, based on surface roughness characteristics, are widely used in urban flooding and flood mapping research.

This study extends beyond water body and flood monitoring by aiming to estimate river water levels based on Sentinel-1 SAR data and the variation in backscatter intensity due to river water level changes. It seeks to overcome the limitations of existing observation systems and provide a new methodology that can contribute to flood response and water resource management.

In this study, a Long-Short Term Memory (LSTM)-based water level estimation model was developed, and σ⁰_VH, σ⁰_VV, Local Incidence angle from Sentinel-1 C-band SAR (from 2015 to 2024) and Day of Year (DOY) was considered as input variable. For the training datasets, water level observations from In order to find the optimized set of input variables, this study generated sets of input data scenarios based on the Fisher’s Chi-Square test, and the model performance was examined by using multiple statistical indices (e.g., Correlation coefficient [R], Root Mean Square Error [RMSE], Mean Absolute Error [MAE], and the Index of Agreement [IOA]). Overall results indicated that 4 out of 11 stations revealed that LSTM models with all four input variables yielded the best statistical performance. Especially, Nasan Bridge station located in Hampyeong, South Korea, yielded best statistical results with R of 0.77, RMSE of 0.30m, MAE of 0.25m, and IOA of 0.63. However, other locations yielded relatively low statistical results, which can be attributed to the relatively less dynamic water level variations. For the future study, separation of training period based on the rainfall pattern and explicit consideration of meteorological information could help to enhance the overall model performance.

Acknowledgement: This work was supported by Korea Environment Industry & Technology Institute(KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis Program, funded by Korea Ministry of Environment(MOE)(RS-2023-00218873).

This work also was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2024-00416443).