Effectiveness of Satellite-based Vegetation Index for Simulating Watershed Response Using an LSTM-based model in a Distributed Framework

This study developed a distributed hydrologic model based on Long Short-Term Memory (LSTM) to predict flow discharge of Joongrang stream located in a highly urbanized area in Seoul, Korea. The model inputs are the time series of 10-minute radar-gauge composite precipitation data at 239 grid cells (1km²) in the watershed and the Normalized Difference Vegetation Index (NDVI) data derived from Landsat 8 images and the model output is the 10-minute flow discharge at the watershed outlet as output. The model was trained for the calibration period of 2013-2016 and was validated for the period of 2017-2019. The NSE value over the validation period corresponding to the optimal model architecture (256 LSTM hidden layers) with and without NDVI input data was 0.68 and 0.52, respectively, which suggests that the machine can learn dynamic processes of soil infiltration and plant interception from the remotely sensed information provided by satellite.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).