Long-term spatiotemporal mapping in lacustrine environment by remote sensing:Review with case study, challenges, and
Lai Lai1, Yuchen Liu2, Yuchao Zhang3
1Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Water Research
|September 23, 2024
Summary
Satellite remote sensing offers 40+ years of lake data for environmental monitoring. This review highlights advancements in data, methods, and platforms for long-term aquatic ecosystem tracking and sustainable development.
Area of Science:
- Earth and Ocean Sciences
- Environmental Monitoring
- Data Science
Background:
- Global freshwater and coastal waters face threats like harmful algal blooms and eutrophication.
- Satellite remote sensing provides extensive historical data (40+ years) for observing lake environments at various scales.
- Traditional ship-based sampling lacks the revisit capabilities and broad coverage of satellite data.
Purpose of the Study:
- To comprehensively review the current status, shortcomings, and future trends in satellite remote sensing for lake environment monitoring.
- To analyze the evolution of remote sensing datasets, monitoring targets, technical methods, and data processing platforms.
- To provide insights for academic research and governmental decision-making for aquatic ecosystem management and Sustainable Development Goals (SDGs).
Main Methods:
- Review of existing literature on satellite remote sensing for lake monitoring.
- Analysis of trends in data sources, research objectives, algorithms, and processing platforms.
- Evaluation of current challenges and future directions in the field.
Main Results:
- Long-term lake monitoring is advancing with collaborative satellite observations, diversified objectives, machine/deep/transfer learning algorithms, and cloud-based processing.
- Key trends include moving from single data sources to integrated observations and from empirical models to advanced AI.
- Progress is evident in temporal and spatial resolution trade-offs and the shift to cloud computing.
Conclusions:
- Future directions include global data-sharing platforms, improved atmospheric correction, next-generation sensors, and integration of AI (IML, XAI) and IoT technologies.
- Interdisciplinary collaboration across earth sciences, hydrology, computer science, and human geography is crucial.
- This work provides valuable references for enhancing aquatic ecological environment tracking and achieving SDGs.
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