RETRIEVAL OF CASE 2 WATER QUALITY PARAMETERS WITH MACHINE LEARNING

被引：0
|
作者：
Ruescas, Ana B. [1 ]
Mateo-Garcia, Gonzalo [1 ]
Camps-Valls, Gustau [1 ]
Hieronymi, Martin [2 ]
机构：
[1] Univ Valencia, IPL, Valencia, Spain
[2] Helmholtz Zentrum Geesthacht, Inst Coastal Res, Geesthacht, Germany
来源：
IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年
基金：
欧洲研究理事会;
关键词：
Remote Sensing; Water Quality Parameters; Case 2 Absorbing Waters; Machine Learning Regression;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Water quality parameters are derived applying several machine learning regression methods on the Case2eXtreme dataset (C2X). The used data are based on Hydrolight in-water radiative transfer simulations at Sentinel-3 OLCI wavebands, and the application is done exclusively for absorbing waters with high concentrations of coloured dissolved organic matter (CDOM). The regression approaches are: regularized linear, random forest, Kernel ridge, Gaussian process and support vector regressors. The validation is made with and an independent simulation dataset. A comparison with the OLCI Neural Network Swarm (ONSS) is made as well. The best approached is applied to a sample scene and compared with the standard OLCI product delivered by EUMETSAT/ESA.
引用
收藏
页码：124 / 127
页数：4
相关论文
共 50 条
  • [21] Retrieval of water quality parameters from hyperspectral images using a hybrid feedback deep factorization machine model
    Zhang, Yishan
    Wu, Lun
    Deng, Licui
    Ouyang, Bin
    [J]. WATER RESEARCH, 2021, 204
  • [22] Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China
    Xu, Jing
    Mo, Yuming
    Zhu, Senlin
    Wu, Jinran
    Jin, Guangqiu
    Wang, You-Gan
    Ji, Qingfeng
    Li, Ling
    [J]. HELIYON, 2024, 10 (13)
  • [23] Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms
    Lu, Qikai
    Si, Wei
    Wei, Lifei
    Li, Zhongqiang
    Xia, Zhihong
    Ye, Song
    Xia, Yu
    [J]. REMOTE SENSING, 2021, 13 (19)
  • [24] Ecological states of watercourses regarding water quality parameters and hydromorphological parameters: deriving empirical equations by machine learning models
    Mohammad Najafzadeh
    Elahe Sadat Ahmadi-Rad
    Daniel Gebler
    [J]. Stochastic Environmental Research and Risk Assessment, 2024, 38 : 665 - 688
  • [25] Ecological states of watercourses regarding water quality parameters and hydromorphological parameters: deriving empirical equations by machine learning models
    Najafzadeh, Mohammad
    Ahmadi-Rad, Elahe Sadat
    Gebler, Daniel
    [J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2024, 38 (02) : 665 - 688
  • [26] Predicting Coastal Water Quality with Machine Learning, a Case Study of Beibu Gulf, China
    Bai, Yucai
    Xu, Zhefeng
    Lan, Wenlu
    Peng, Xiaoyan
    Deng, Yan
    Chen, Zhibiao
    Xu, Hao
    Wang, Zhijian
    Xu, Hui
    Chen, Xinglong
    Cheng, Jinping
    [J]. WATER, 2024, 16 (16)
  • [27] Machine Learning for Information Retrieval
    Si, Luo
    Jin, Rong
    [J]. PROCEEDINGS OF THE 34TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR'11), 2011, : 1293 - 1293
  • [28] Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning
    Gao, Lingfang
    Shangguan, Yulin
    Sun, Zhong
    Shen, Qiaohui
    Shi, Zhou
    [J]. REMOTE SENSING, 2024, 16 (03)
  • [29] Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters
    Mojtaba Kadkhodazadeh
    Saeed Farzin
    [J]. Water Resources Management, 2022, 36 : 3901 - 3927
  • [30] Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters
    Kadkhodazadeh, Mojtaba
    Farzin, Saeed
    [J]. WATER RESOURCES MANAGEMENT, 2022, 36 (10) : 3901 - 3927
12345