Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties

随机森林 支持向量机 算法 大气校正 环境科学 人工神经网络 灵敏度(控制系统) 富营养化 计算机科学 机器学习 人工智能 遥感 生态学 地理 物理 生物 卫星 工程类 营养物 电子工程 天文
作者
Ming Shen,Juhua Luo,Zhigang Cao,Kun Xue,Tianci Qi,Jinge Ma,Dong Liu,Kaishan Song,Lian Feng,Hongtao Duan
出处
期刊:Journal of Hydrology [Elsevier BV]
卷期号:615: 128685-128685 被引量:36
标识
DOI:10.1016/j.jhydrol.2022.128685
摘要

A robust and reliable chlorophyll-a (Chla) concentration algorithm is still lacking for optically complex waters due to the lack of understanding of the bio-optical process. Machine learning approaches, which excel at detecting potential complex nonlinear relationships, provide opportunities to estimate Chla accurately for optically complex waters. However, the uncertainties in atmospheric correction (AC) may be amplified in different Chla algorithms. Here, we aim to select one state-of-the-art algorithm or establish a new algorithm based on machine learning approaches that less sensitive to AC uncertainties. Firstly, nine state-of-the-art empirical, semianalytical, and optical water types (OWT) classification-based Chla algorithms were implemented. These existing algorithms showed good performance by using in situ database, however, failed in actual OLCI applications due to their sensitivity to AC uncertainties. Thus, four popular machine learning approaches (random forest regression (RFR), extreme gradient boosting (XGBoost), deep neural network (DNN), and support vector regression (SVR)) were then employed. Among them, the “RFR-Chla” model performed the best and showed less sensitivity to AC uncertainties. Finally, the Chla spatiotemporal variations in 163 major lakes across eastern China were mapped from OLCI between May 2016 and April 2020 using the proposed RFR-Chla model. Generally, the lakes in eastern China are severely eutrophic, with an average Chla concentration of 33.39 ± 6.95 μg/L. Spatially, Chla in the south of eastern China was significantly higher than those in northern lakes. Seasonally, Chla was high in the summer and autumn and low in the spring and winter. This study provides a reference for water quality monitoring in turbid inland waters suffering certain AC uncertainties and supports aquatic management and SDG 6 reporting.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
1秒前
嘟嘟发布了新的文献求助10
2秒前
陈开山完成签到,获得积分10
2秒前
梨理栗发布了新的文献求助10
2秒前
2秒前
alex发布了新的文献求助10
3秒前
3秒前
LaTeXer应助隐形夕阳采纳,获得50
3秒前
lw发布了新的文献求助10
4秒前
4秒前
英姑应助Janusfaces采纳,获得10
4秒前
4秒前
plasmid完成签到,获得积分10
5秒前
Ava应助咕噜咕噜咕嘟咕嘟采纳,获得10
5秒前
6秒前
SHAO应助一块司康饼采纳,获得100
6秒前
嗯哼发布了新的文献求助10
6秒前
Rondab应助mariawang采纳,获得10
8秒前
MchemG应助酷酷的紫南采纳,获得30
9秒前
1111发布了新的文献求助10
9秒前
9秒前
continue发布了新的文献求助10
10秒前
zhangtong发布了新的文献求助10
10秒前
嘟嘟完成签到,获得积分10
10秒前
wdy111应助葡萄味的果茶采纳,获得20
11秒前
悦耳代真完成签到,获得积分10
11秒前
ysx完成签到,获得积分10
11秒前
12秒前
Orange应助淡淡夕阳采纳,获得10
12秒前
12秒前
yar重新开启了yl文献应助
13秒前
14秒前
14秒前
zhoup完成签到,获得积分20
15秒前
宝海青完成签到,获得积分10
15秒前
李健应助缓慢的含双采纳,获得10
15秒前
yqb完成签到,获得积分10
16秒前
上官若男应助笑点低的不采纳,获得10
17秒前
量子星尘发布了新的文献求助10
18秒前
qifunongsuo1213完成签到,获得积分10
18秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988732
求助须知:如何正确求助?哪些是违规求助? 3531027
关于积分的说明 11252281
捐赠科研通 3269732
什么是DOI,文献DOI怎么找? 1804764
邀请新用户注册赠送积分活动 881869
科研通“疑难数据库(出版商)”最低求助积分说明 809021