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; Artificial intelligence; Artificial neural networks; attention mechanism; Bathymetry; California; climate; Climate change; Comparative analysis; convolutional neural network; Deep learning; Hydrology; Machine learning; Measurement; Neural networks; Precipitation; Random sampling; Remote sensing; Reservoirs; Resource management; Retrieval; Satellite imagery; Satellite imaging; Satellites; small reservoirs; society; Statistical sampling; Surface water; water level; Water levels; water management; Water resources management; China; United States > US; California

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 ch...