Combining Satellite Imagery and a Deep Learning Algorithm to Retrieve the Water Levels of Small Reservoirs

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 24; p. 5740
• Main Authors: Wu, Jiarui, Huang, Xiao, Xu, Nan, Zhu, Qishuai, Zorn, Conrad, Guo, Wenzhou, Wang, Jiangnan, Wang, Beibei, Shao, Shuaibo, Yu, Chaoqing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2023
• Subjects: Algorithms; Aquatic resources; Artificial neural networks; attention mechanism; Bathymetry; California; China; Climate change; convolutional neural network; Data mining; Deep learning; Global temperature changes; Hydrology; Machine learning; Management; Neural networks; Precipitation; Random sampling; Remote sensing; Reservoirs; Resource management; Retrieval; Satellite imagery; Satellites; small reservoirs; Statistical sampling; Surface water; Water; water level; Water levels; Water resources management; Water use; China; California; United States > US
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15245740

There are an estimated 800,000 small reservoirs globally with a range of uses. Given the collective importance of these reservoirs to water resource management and wider society, it is essential that we can monitor and understand the hydrological dynamics of ungauged reservoirs, particularly in a changing climate. However, unlike large reservoirs, continuous and systematic hydrological observations of small reservoirs are often unavailable. In response, this study has developed a retrieval framework for water levels of small reservoirs using a deep learning algorithm and remotely sensed satellite data. Demonstrated at four reservoirs in California, satellite imagery from both Sentinel-1 and Sentinel-2 along with corresponding water level field measurements was collected. Post-processed images were fed into a water level inversion convolutional neural network model for water level inversion, while different combinations of these satellite images, sampling approaches for training/testing data, and attention modules were used to train the model and evaluated for accuracy. The results show that random sampling of training data coupled with Sentinel-2 satellite imagery was generally the most accurate initially. Performance is improved by incorporating a channel attention mechanism, with the average R2 increasing by 8.6% and the average RMSE and MAE decreasing by 15.5% and 36.4%, respectively. The proposed framework was further validated on three additional reservoirs in different regions. In conclusion, the retrieval framework proposed in this study provides a stable and accurate methodology for water level estimation of small reservoirs and can be a powerful tool for small reservoir monitoring over large spatial scales.