Robust remote sensing retrieval of key eutrophication indicators in coastal waters based on explainable machine learning

梯度升压 可解释性 富营养化 环境科学 随机森林 遥感 计算机科学 Boosting(机器学习) 化学需氧量 卫星 决策树 营养物 机器学习 生态学 地理 生物 环境工程 航空航天工程 废水 工程类
作者
Liudi Zhu,Tingwei Cui,A Runa,Xinliang Pan,Wenjing Zhao,Jinzhao Xiang,Mengmeng Cao
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing 卷期号:211: 262-280 被引量:16
标识
DOI:10.1016/j.isprsjprs.2024.04.007
摘要

Excessive discharges of nitrogen and phosphorus nutrients lead to eutrophication in coastal waters. Optical remote sensing retrieval of the key eutrophication indicators, namely dissolved inorganic nitrogen concentration (DIN), soluble reactive phosphate concentration (SRP), and chemical oxygen demand (COD), remains challenging due to lack of distinct spectral features. Although machine learning (ML) has shown the potential, the retrieval accuracy is limited, and the interpretability is insufficient in terms of the black-box characteristics. To address these limitations, based on robust and explainable ML algorithms, we constructed models for retrieving DIN, SRP, and COD over coastal waters of Northern South China Sea (NSCS), which is experiencing prominent eutrophication. Retrieval models based on classification and regression trees (CART) ML algorithms were developed using 4038 groups of in situ observations and quasi-synchronous satellite images. A comparison of CART algorithms, including Random Forest, Gradient Boosting Decision Tree, and eXtreme Gradient Boosting (XGBoost), indicated the highest retrieval accuracy of XGBoost for DIN (R2 = 0.88, MRE = 24.39 %), SRP (R2 = 0.92, MRE = 33.27 %), and COD (R2 = 0.75, MRE = 18.58 %) for validation dataset. On the basis of spectral remote sensing reflectance, further inputs of ocean physio-chemical properties, spatio-temporal information, and inherent optical properties may reduce retrieval errors by 30.16 %, 19.85 %, and 3.95 %, respectively, and their combined use reduced errors by 54.71 %. Besides, explainable ML analysis characterized the contribution of input features and enhanced the transparency of ML black-box models. Based on the proposed models, 27,278 satellite images and spatio-temporal reconstruction method, 1-km resolution gap-free daily DIN, SRP, and COD products were constructed from 2002 to 2022 for the coastal waters of NSCS. Under the influence of urbanization and river discharge, nitrogen and phosphorus concentrations in this area were found to have increased by 6.09 % and 11.04 %, respectively, over the past 21 years, with the fastest rise in the Pearl River Estuary, where the eutrophic water area had shown an increase rate of approximately 112.66 km2/yr. The proposed robust and explainable ML retrieval models may support ocean environment management and water quality monitoring by providing key eutrophication indicators products over coastal waters.
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
七子完成签到,获得积分10
刚刚
咳咳咳完成签到,获得积分10
1秒前
hdbys发布了新的文献求助10
1秒前
llllt完成签到,获得积分10
1秒前
Ygy发布了新的文献求助10
1秒前
1秒前
zzx396完成签到,获得积分0
2秒前
666完成签到,获得积分10
2秒前
3秒前
拉长的晓蕾发布了新的文献求助100
3秒前
3秒前
ZONG完成签到,获得积分10
3秒前
Funny完成签到,获得积分10
4秒前
健康的雁凡完成签到,获得积分10
4秒前
林小昀完成签到 ,获得积分10
4秒前
科奇应助科研通管家采纳,获得10
4秒前
领导范儿应助科研通管家采纳,获得10
4秒前
4秒前
5秒前
墨痕mohen完成签到,获得积分10
5秒前
Muhi完成签到,获得积分10
5秒前
达到完成签到,获得积分10
5秒前
薄荷小新完成签到 ,获得积分10
6秒前
caozhi完成签到,获得积分10
6秒前
何为会完成签到,获得积分10
6秒前
顾矜应助七七采纳,获得10
7秒前
小鱼儿完成签到,获得积分10
7秒前
7秒前
yyyy完成签到,获得积分10
8秒前
马东完成签到,获得积分10
8秒前
成森完成签到,获得积分10
8秒前
爱吃大米发布了新的文献求助10
9秒前
9秒前
秋秋糖xte发布了新的文献求助10
10秒前
zyshao完成签到,获得积分10
10秒前
Lynn完成签到,获得积分10
10秒前
liuchao完成签到,获得积分10
10秒前
大模型应助焓晓芈采纳,获得10
11秒前
xyzdmmm完成签到,获得积分10
11秒前
Haibara5发布了新的文献求助10
12秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
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 3968593
求助须知:如何正确求助?哪些是违规求助? 3513416
关于积分的说明 11167791
捐赠科研通 3248853
什么是DOI,文献DOI怎么找? 1794507
邀请新用户注册赠送积分活动 875170
科研通“疑难数据库(出版商)”最低求助积分说明 804671