Monitoring Terrestrial Water Resources Using Multiple Satellite Sensors (Second Edition)

Dear Colleagues,

In recent decades, climate change and population growth have increased global demands for water resources, especially in arid and densely populated regions. In order to better implement water resource management in the future, it is critical to accurately evaluate terrestrial water resources (such as lakes, reservoirs, and rivers) and track their changes over time. Satellite technology (such as satellite altimeters, gravity satellites, optical remote sensing, and microwave remote sensing) provides an unprecedented tool to quantitatively monitor terrestrial water resources from local to global scales.

In this Special Issue, our focus is on the pioneering applications of multi-satellite techniques for the detailed observation and management of terrestrial water resources across scales, from local to global. We are particularly interested in the integration of multiple satellite sensors to enhance the monitoring and assessment of water resources. Submissions to this issue may cover a wide range of topics, including, but not limited to, tracking lake/reservoir water levels and storage, monitoring river water levels and discharge, map** shallow water bathymetry, and observing lake ice dynamics. This Special Issue aims to showcase the innovative use of multi-satellite strategies—especially the synergistic combination of different satellite sensors—in the fields of hydrology and limnology. We also invite submissions that present novel theories and methodologies in satellite technology applications for hydrology and related areas.

• Monitoring surface water environments;
• Assessing water resources and security;
• Evaluating drought and flood risk;
• Advancing water-related Sustainable Development Goals (SDGs);
• Tracking lake/reservoir levels and storage;
• Observing river levels and discharge;
• Map** the bathymetry of inland waters;
• Surveying lake ice and snow cover;
• Leveraging big data and machine learning in water resource monitoring;
• Exploring other applications of satellite technology in hydrology and limnology.

Prof. Dr. Yao Li
Dr. Nan Xu
Dr. Yue Ma
Prof. Dr. Yi Ma
Guest Editors

Manuscript Submission Information

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• satellite sensors
• terrestrial water resources
• lake/reservoir/river/wetland
• coast and ocean
• hydrology
• drought/flooding risk
• water environment and security
• sustainable development goals

• Monitoring Terrestrial Water Resource Using Multiple Satellite Sensors in Remote Sensing (16 articles)

Title: Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities
Authors: Zhen Liu; Hao Liu; Yue Ma; **n Ma; Jian Yang; Yang Jiang; Shaohui Li
Affiliation: State Key Laboratory of Information Engineering in Surveying, Map** and Remote Sensing, Wuhan University
Abstract: Satellite-Derived Bathymetry (SDB) is an effective means of obtaining global shallow water depths. However, the effect of inherent optical properties (IOPs) on the accuracy of SDB under different water quality conditions has not been clearly clarified. To enhance the accuracy of machine learning SDB models, this study aims to assess the performance improvement of integrating the Quasi-Analytical Algorithm (QAA) derived IOPs using Sentinel-2 and ICESat-2 datasets. In different water quality experiments, the results indicate that four SDB models (the Gaussian process regression, neural networks, random forests, and support vector regression) incorporating QAA-IOP parameters equal to or outperform those solely based on the remote sensing reflectance (Rrs) datasets, especially in turbid waters. By analyzing information gains in SDB, the most effective inputs are identified and prioritized under different water qualities. Also, we find that incorporating an excessive number of redundant bands into machine learning models not only increases the demand of computing resources but also leads to worse accuracy in SDB. In conclusion, the integration of QAA-IOPs offers promising improvements in obtaining bathymetry and the optimal feature selection should be carefully considered in diverse aquatic environments.