Long-term spatiotemporal mapping in lacustrine environment by remote sensing:Review with case study, challenges, and future directions

期限(时间) 遥感 环境科学 环境资源管理 地图学 地理 计算机科学 环境规划 地质学 物理 量子力学
作者
Lai Lai,Yuchen Liu,Yuchao Zhang,Z. Cao,Yuepeng Yin,Xi Chen,Jiale Jin,Shui-mu Wu
出处
期刊:Water Research [Elsevier BV]
卷期号:267: 122457-122457 被引量:2
标识
DOI:10.1016/j.watres.2024.122457
摘要

Satellite remote sensing, unlike traditional ship-based sampling, possess the advantage of revisit capabilities and provides over 40 years of data support for observing lake environments at local, regional, and global scales. In recent years, global freshwater and coastal waters have faced adverse environmental issues, including harmful phytoplankton blooms, eutrophication, and extreme temperatures. To comprehensively address the goal of 'reviewing the past, assessing the present, and predicting the future', research increasingly focuses on developing and producing algorithms and products for long-term and large-scale mapping. This paper provides a comprehensive review of related research, evaluating the current status, shortcomings, and future trends of remote sensing datasets, monitoring targets, technical methods, and data processing platforms. The analysis demonstrated that the long-term spatiotemporal dynamic lake monitoring transition is thriving: (i) evolving from single data sources to satellite collaborative observations to keep a trade-off between temporal and spatial resolutions, (ii) shifting from single research targets to diversified and multidimensional objectives, (iii) progressing from empirical/mechanism models to machine/deep/transfer learning algorithms, (iv) moving from local processing to cloud-based platforms and parallel computing. Future directions include, but are not limited to: (i) establishing a global sampling data-sharing platform, (ii) developing precise atmospheric correction algorithms, (iii) building next-generation ocean color sensors and virtual constellation networks, (iv) introducing Interpretable Machine Learning (IML) and Explainable Artificial Intelligence (XAI) models, (v) integrating cloud computing, big data/model/computer, and Internet of Things (IoT) technologies, (vi) crossing disciplines with earth sciences, hydrology, computer science, and human geography, etc. In summary, this work offers valuable references and insights for academic research and government decision-making, which are crucial for enhancing the long-term tracking of aquatic ecological environment and achieving the Sustainable Development Goals (SDGs).
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