Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model
Revealing the spatiotemporal variations of nutrients in coastal waters is crucial to the understanding and evaluation of coastal environment, thereby providing efficient guidance for the aquatic environmental treatment. This study proposed a spatiotemporal-incorporated deep learning model, which is...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-08-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222000991 |
| _version_ | 1798036706717335552 |
|---|---|
| author | Sensen Wu Jin Qi Zhen Yan Fangzheng Lyu Tao Lin Yuanyuan Wang Zhenhong Du |
| author_facet | Sensen Wu Jin Qi Zhen Yan Fangzheng Lyu Tao Lin Yuanyuan Wang Zhenhong Du |
| author_sort | Sensen Wu |
| collection | DOAJ |
| description | Revealing the spatiotemporal variations of nutrients in coastal waters is crucial to the understanding and evaluation of coastal environment, thereby providing efficient guidance for the aquatic environmental treatment. This study proposed a spatiotemporal-incorporated deep learning model, which is easily applicable to establish the quantitative relationships between measured environmental factors and large-scale satellite maps, and can reduce estimation errors by more than 40% compared with non-spatiotemporal-incorporated deep learning model. The spatiotemporal distributions of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphate (DIP) over 44400 km2 of the East China Sea on 8-day scale from 2010 to 2018 were obtained. Based on the spatiotemporal variations, the water quality patterns were depicted, and the fluctuation variations of the two essential nutrients were found in the harbors with complex anthropogenic influences, in the typical estuaries with multiple river inputs, and in the open seas with important fisheries. Although the concentration of DIN and DIP decreased by 24% and 19% in 9 years, respectively, the water quality level in the inshore sea has not been significantly improved, especially in autumn and winter. Further, we quantitatively analyzed the main factors of deteriorated water and provided scientific suggestions for targeted monitoring and regional cooperative governances. |
| first_indexed | 2024-04-11T21:16:38Z |
| format | Article |
| id | doaj.art-5d920bb69991468d9f1195ebac47c6ff |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-11T21:16:38Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-5d920bb69991468d9f1195ebac47c6ff2022-12-22T04:02:47ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-08-01112102897Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning modelSensen Wu0Jin Qi1Zhen Yan2Fangzheng Lyu3Tao Lin4Yuanyuan Wang5Zhenhong Du6School of Earth Sciences, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, ChinaCenter of Agricultural and Rural Development, School of Public Affairs, Zhejiang University, Hangzhou, ChinaDepartment of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, USACollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, ChinaOcean Academy, Zhejiang University, Zhoushan, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, ChinaSchool of Earth Sciences, Zhejiang University, Hangzhou, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China; Corresponding author at: School of Earth Sciences, Zhejiang University, Hangzhou, China.Revealing the spatiotemporal variations of nutrients in coastal waters is crucial to the understanding and evaluation of coastal environment, thereby providing efficient guidance for the aquatic environmental treatment. This study proposed a spatiotemporal-incorporated deep learning model, which is easily applicable to establish the quantitative relationships between measured environmental factors and large-scale satellite maps, and can reduce estimation errors by more than 40% compared with non-spatiotemporal-incorporated deep learning model. The spatiotemporal distributions of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphate (DIP) over 44400 km2 of the East China Sea on 8-day scale from 2010 to 2018 were obtained. Based on the spatiotemporal variations, the water quality patterns were depicted, and the fluctuation variations of the two essential nutrients were found in the harbors with complex anthropogenic influences, in the typical estuaries with multiple river inputs, and in the open seas with important fisheries. Although the concentration of DIN and DIP decreased by 24% and 19% in 9 years, respectively, the water quality level in the inshore sea has not been significantly improved, especially in autumn and winter. Further, we quantitatively analyzed the main factors of deteriorated water and provided scientific suggestions for targeted monitoring and regional cooperative governances.http://www.sciencedirect.com/science/article/pii/S1569843222000991Aquatic environmentSpatiotemporal deep learningWater qualityRemote sensingCoastal restoration |
| spellingShingle | Sensen Wu Jin Qi Zhen Yan Fangzheng Lyu Tao Lin Yuanyuan Wang Zhenhong Du Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model International Journal of Applied Earth Observations and Geoinformation Aquatic environment Spatiotemporal deep learning Water quality Remote sensing Coastal restoration |
| title | Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model |
| title_full | Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model |
| title_fullStr | Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model |
| title_full_unstemmed | Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model |
| title_short | Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model |
| title_sort | spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model |
| topic | Aquatic environment Spatiotemporal deep learning Water quality Remote sensing Coastal restoration |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222000991 |
| work_keys_str_mv | AT sensenwu spatiotemporalassessmentsofnutrientsandwaterqualityincoastalareasusingremotesensingandaspatiotemporaldeeplearningmodel AT jinqi spatiotemporalassessmentsofnutrientsandwaterqualityincoastalareasusingremotesensingandaspatiotemporaldeeplearningmodel AT zhenyan spatiotemporalassessmentsofnutrientsandwaterqualityincoastalareasusingremotesensingandaspatiotemporaldeeplearningmodel AT fangzhenglyu spatiotemporalassessmentsofnutrientsandwaterqualityincoastalareasusingremotesensingandaspatiotemporaldeeplearningmodel AT taolin spatiotemporalassessmentsofnutrientsandwaterqualityincoastalareasusingremotesensingandaspatiotemporaldeeplearningmodel AT yuanyuanwang spatiotemporalassessmentsofnutrientsandwaterqualityincoastalareasusingremotesensingandaspatiotemporaldeeplearningmodel AT zhenhongdu spatiotemporalassessmentsofnutrientsandwaterqualityincoastalareasusingremotesensingandaspatiotemporaldeeplearningmodel |