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通过遥感在湖泊环境中进行长期时空测绘:用案例研究,挑战和未来方向进行回顾

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.

|September 23, 2024

概括

卫星遥感为环境监测提供了40多年的湖泊数据. 本次审查强调了水生生态系统长期跟踪和可持续发展的数据,方法和平台的进步.

科学领域:

地球和海洋科学地球和海洋科学
环境监测环境监测
数据科学数据科学数据科学

背景情况:

全球淡水和沿海水域面临着有害的藻类繁殖和肥胖等威胁.
卫星遥感提供了广泛的历史数据 (超过40年) 用于在各种尺度上观察湖泊环境.
传统的基于船舶的采样缺乏卫星数据的复查能力和广泛覆盖.

研究的目的:

综合审查湖泊环境监测的卫星遥感现状,缺陷和未来趋势.
分析遥感数据集,监测目标,技术方法和数据处理平台的演变.
为学术研究和政府决策提供关于水生生态系统管理和可持续发展目标 (SDGs) 的见解.

主要方法:

审查现有关于卫星遥感用于湖泊监测的文献.
分析数据源,研究目标,算法和处理平台的趋势.
评估当前的挑战和该领域的未来方向.

主要成果:

长期湖泊监测正在通过协作卫星观测,多样化的目标,机器/深度/转移学习算法和基于云的处理进行.
关键趋势包括从单个数据源转向综合观测,从经验模型转向先进的人工智能.
在时间和空间分辨率的权衡以及向云计算的转变方面,进展显而易见.

结论:

未来的方向包括全球数据共享平台,改善大气校正,下一代传感器,以及人工智能 (IML,XAI) 和物联网技术的整合.
地球科学,水文学,计算机科学和人类地理学之间的跨学科合作至关重要.
这项工作为加强水生生态环境跟踪和实现可持续发展目标提供了有价值的参考资料.

关键词:

人工智能的人工智能是人工智能.长期的时间 - 长期的时间.卫星协调卫星协调时间空间绘制地图.水的质量水的质量

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