Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS
The total phosphorus (TP) concentration is a key water quality parameter for water monitoring and a major indicator of the state of eutrophication in inland lakes. Using remote-sensing to estimate TP concentration is useful, as it provides a synoptic view of the entire water region; however, the wea...
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| Format: | Article |
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MDPI AG
2020-08-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/9/2372 |
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| author | Chujiang Ding Fangling Pu Caoyu Li Xin Xu Tongyuan Zou Xiangxiang Li |
| author_facet | Chujiang Ding Fangling Pu Caoyu Li Xin Xu Tongyuan Zou Xiangxiang Li |
| author_sort | Chujiang Ding |
| collection | DOAJ |
| description | The total phosphorus (TP) concentration is a key water quality parameter for water monitoring and a major indicator of the state of eutrophication in inland lakes. Using remote-sensing to estimate TP concentration is useful, as it provides a synoptic view of the entire water region; however, the weak optical characteristics of TP lead to difficulty in accurately estimating TP concentration. The differences in water characteristics and components between lakes mean that most TP estimation methods are not applicable to all lakes. An artificial neural network (ANN) model was created to represent the correlation between TP concentration and the spectral bands of Moderate Resolution Imaging Spectroradiometer (MODIS) images in different research areas. We investigated the causal inference under the potential outcome framework to analyze the sensitivity of each band with regard to the TP concentration of different lakes for the research of water characteristics. Our results show that the accuracy of the ANN-based TP concentration estimation, with <i>R</i><sup>2</sup> > 0.73, root mean squared error (RMSE) < 0.037 mg/L in Lake Okeechobee and <i>R</i><sup>2</sup> > 0.73, RMSE < 4.1 μg/L in Lake Erie, respectively, is much higher than traditional empirical methods, e.g., linear regression. We found that the sensitive bands of TP concentration in Lake Erie are blue bands, whereas the sensitive bands in Lake Okeechobee are green bands. Various TP concentration maps were drawn to indicate the distribution of TP concentration and its tendency to change. The maps show that the distribution of TP concentration closely corresponds to the shore land-use, and a high TP concentration corresponds to the latest algal blooms breakout. Our proposed approach shows good potential for the remote-sensing estimation of TP concentration for inland lakes. Identifying the sensitive bands not only help characterize the lakes, but will also help the researchers to further observe the TP concentration of specific lakes in an efficient way. |
| first_indexed | 2024-03-10T16:54:23Z |
| format | Article |
| id | doaj.art-d1171360ecb844f8bc3263fb4d3a67d0 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T16:54:23Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-d1171360ecb844f8bc3263fb4d3a67d02023-11-20T11:12:09ZengMDPI AGWater2073-44412020-08-01129237210.3390/w12092372Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODISChujiang Ding0Fangling Pu1Caoyu Li2Xin Xu3Tongyuan Zou4Xiangxiang Li5School of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaSchool of Electronic Information, Wuhan University, Wuhan 430072, ChinaSpace Star Technology Co., Ltd., Beijing 100000, ChinaSpace Star Technology Co., Ltd., Beijing 100000, ChinaThe total phosphorus (TP) concentration is a key water quality parameter for water monitoring and a major indicator of the state of eutrophication in inland lakes. Using remote-sensing to estimate TP concentration is useful, as it provides a synoptic view of the entire water region; however, the weak optical characteristics of TP lead to difficulty in accurately estimating TP concentration. The differences in water characteristics and components between lakes mean that most TP estimation methods are not applicable to all lakes. An artificial neural network (ANN) model was created to represent the correlation between TP concentration and the spectral bands of Moderate Resolution Imaging Spectroradiometer (MODIS) images in different research areas. We investigated the causal inference under the potential outcome framework to analyze the sensitivity of each band with regard to the TP concentration of different lakes for the research of water characteristics. Our results show that the accuracy of the ANN-based TP concentration estimation, with <i>R</i><sup>2</sup> > 0.73, root mean squared error (RMSE) < 0.037 mg/L in Lake Okeechobee and <i>R</i><sup>2</sup> > 0.73, RMSE < 4.1 μg/L in Lake Erie, respectively, is much higher than traditional empirical methods, e.g., linear regression. We found that the sensitive bands of TP concentration in Lake Erie are blue bands, whereas the sensitive bands in Lake Okeechobee are green bands. Various TP concentration maps were drawn to indicate the distribution of TP concentration and its tendency to change. The maps show that the distribution of TP concentration closely corresponds to the shore land-use, and a high TP concentration corresponds to the latest algal blooms breakout. Our proposed approach shows good potential for the remote-sensing estimation of TP concentration for inland lakes. Identifying the sensitive bands not only help characterize the lakes, but will also help the researchers to further observe the TP concentration of specific lakes in an efficient way.https://www.mdpi.com/2073-4441/12/9/2372total phosphorus concentration estimationsensitive spectral bandsartificial neural networkcausal inferenceMODIS |
| spellingShingle | Chujiang Ding Fangling Pu Caoyu Li Xin Xu Tongyuan Zou Xiangxiang Li Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS Water total phosphorus concentration estimation sensitive spectral bands artificial neural network causal inference MODIS |
| title | Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS |
| title_full | Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS |
| title_fullStr | Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS |
| title_full_unstemmed | Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS |
| title_short | Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS |
| title_sort | combining artificial neural networks with causal inference for total phosphorus concentration estimation and sensitive spectral bands exploration using modis |
| topic | total phosphorus concentration estimation sensitive spectral bands artificial neural network causal inference MODIS |
| url | https://www.mdpi.com/2073-4441/12/9/2372 |
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