Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China
Total suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sens...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2021/5957376 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567705370099712 |
|---|---|
| author | Xiuyu Liu Zhen Zhang Tao Jiang Xuehua Li Yanyi Li |
| author_facet | Xiuyu Liu Zhen Zhang Tao Jiang Xuehua Li Yanyi Li |
| author_sort | Xiuyu Liu |
| collection | DOAJ |
| description | Total suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sensing data and field-measured TSM concentration as data sources. Firstly, the characteristic variables with high correlation were selected based on spectral analysis. Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. Among them, the GA_RF model retrieves the suspended solids concentration with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.98, a root mean square error (RMSE) of 1.715 mg/L, and an average relative error (ARE) of 6.83%. Additionally, the spatial distribution of TSM concentration was inversed by Hyperion remote sensing image. The results showed that the concentration of TSM was lower in the northwest and higher in the southeast, and the concentration distribution was uneven, showing the characteristics of a typical shallow macrophytic lake. This study provides an effective method for monitoring TSM concentration and other water quality parameters in the shallow macrophytic lake and further proves the advantages of machine learning in ocean color inversion. All in all, this research provides some useful methods and suggestions for quantitative inversion of TSM concentration in shallow macrophytic lakes. |
| format | Article |
| id | doaj-art-11414fdddd004eb49389d6e3c4dceea2 |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-11414fdddd004eb49389d6e3c4dceea22025-02-03T01:00:47ZengWileyJournal of Spectroscopy2314-49202314-49392021-01-01202110.1155/2021/59573765957376Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North ChinaXiuyu Liu0Zhen Zhang1Tao Jiang2Xuehua Li3Yanyi Li4College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266500, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266500, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266500, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266500, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, ChinaTotal suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sensing data and field-measured TSM concentration as data sources. Firstly, the characteristic variables with high correlation were selected based on spectral analysis. Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. Among them, the GA_RF model retrieves the suspended solids concentration with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.98, a root mean square error (RMSE) of 1.715 mg/L, and an average relative error (ARE) of 6.83%. Additionally, the spatial distribution of TSM concentration was inversed by Hyperion remote sensing image. The results showed that the concentration of TSM was lower in the northwest and higher in the southeast, and the concentration distribution was uneven, showing the characteristics of a typical shallow macrophytic lake. This study provides an effective method for monitoring TSM concentration and other water quality parameters in the shallow macrophytic lake and further proves the advantages of machine learning in ocean color inversion. All in all, this research provides some useful methods and suggestions for quantitative inversion of TSM concentration in shallow macrophytic lakes.http://dx.doi.org/10.1155/2021/5957376 |
| spellingShingle | Xiuyu Liu Zhen Zhang Tao Jiang Xuehua Li Yanyi Li Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China Journal of Spectroscopy |
| title | Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China |
| title_full | Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China |
| title_fullStr | Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China |
| title_full_unstemmed | Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China |
| title_short | Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China |
| title_sort | evaluation of the effectiveness of multiple machine learning methods in remote sensing quantitative retrieval of suspended matter concentrations a case study of nansi lake in north china |
| url | http://dx.doi.org/10.1155/2021/5957376 |
| work_keys_str_mv | AT xiuyuliu evaluationoftheeffectivenessofmultiplemachinelearningmethodsinremotesensingquantitativeretrievalofsuspendedmatterconcentrationsacasestudyofnansilakeinnorthchina AT zhenzhang evaluationoftheeffectivenessofmultiplemachinelearningmethodsinremotesensingquantitativeretrievalofsuspendedmatterconcentrationsacasestudyofnansilakeinnorthchina AT taojiang evaluationoftheeffectivenessofmultiplemachinelearningmethodsinremotesensingquantitativeretrievalofsuspendedmatterconcentrationsacasestudyofnansilakeinnorthchina AT xuehuali evaluationoftheeffectivenessofmultiplemachinelearningmethodsinremotesensingquantitativeretrievalofsuspendedmatterconcentrationsacasestudyofnansilakeinnorthchina AT yanyili evaluationoftheeffectivenessofmultiplemachinelearningmethodsinremotesensingquantitativeretrievalofsuspendedmatterconcentrationsacasestudyofnansilakeinnorthchina |