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]
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).
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
lilac完成签到,获得积分10
7秒前
852应助David采纳,获得10
11秒前
唠叨的天亦完成签到 ,获得积分10
14秒前
Ray完成签到 ,获得积分10
16秒前
17秒前
18秒前
香蕉大侠完成签到 ,获得积分10
18秒前
21秒前
fantasy发布了新的文献求助10
23秒前
CC发布了新的文献求助10
24秒前
26秒前
27秒前
SN完成签到 ,获得积分10
31秒前
32秒前
科研通AI6.3应助fantasy采纳,获得10
33秒前
34秒前
Michael完成签到 ,获得积分10
38秒前
英吉利25发布了新的文献求助10
39秒前
Ava应助科研通管家采纳,获得10
40秒前
今后应助科研通管家采纳,获得10
40秒前
orixero应助科研通管家采纳,获得30
40秒前
研友_VZG7GZ应助科研通管家采纳,获得10
40秒前
ding应助科研通管家采纳,获得10
40秒前
HJJHJH应助科研通管家采纳,获得30
40秒前
CipherSage应助科研通管家采纳,获得10
40秒前
桐桐应助科研通管家采纳,获得10
40秒前
无花果应助科研通管家采纳,获得10
41秒前
41秒前
liljie完成签到,获得积分10
41秒前
45秒前
50秒前
55秒前
屈煜彬完成签到 ,获得积分10
58秒前
shenshi完成签到,获得积分10
58秒前
benlaron完成签到,获得积分10
1分钟前
英吉利25发布了新的文献求助10
1分钟前
1分钟前
FashionBoy应助雪山飞龙采纳,获得10
1分钟前
淘淘完成签到 ,获得积分10
1分钟前
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Handbook of pharmaceutical excipients, Ninth edition 5000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Polymorphism and polytypism in crystals 1000
Social Cognition: Understanding People and Events 800
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6028342
求助须知:如何正确求助?哪些是违规求助? 7689068
关于积分的说明 16186417
捐赠科研通 5175543
什么是DOI,文献DOI怎么找? 2769540
邀请新用户注册赠送积分活动 1752998
关于科研通互助平台的介绍 1638784