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and Technology Innovation Action Plan\u201d Social Development Science and Technology Research Project","award":["2018-07"],"award-info":[{"award-number":["2018-07"]}]},{"name":"Jiangsu Provincial Water Conservancy Science and Technology Research Project","award":["2022YFB3902000"],"award-info":[{"award-number":["2022YFB3902000"]}]},{"name":"Jiangsu Provincial Water Conservancy Science and Technology Research Project","award":["D040102"],"award-info":[{"award-number":["D040102"]}]},{"name":"Jiangsu Provincial Water Conservancy Science and Technology Research Project","award":["21DZ1202500"],"award-info":[{"award-number":["21DZ1202500"]}]},{"name":"Jiangsu Provincial Water Conservancy Science and Technology Research Project","award":["2020068"],"award-info":[{"award-number":["2020068"]}]},{"name":"Jiangsu Provincial Water Conservancy Science and Technology Research Project","award":["2021-10"],"award-info":[{"award-number":["2021-10"]}]},{"name":"Jiangsu Provincial Water Conservancy Science and Technology Research Project","award":["2018-07"],"award-info":[{"award-number":["2018-07"]}]},{"name":"Science and Technology Project of the Shanghai Municipal Water Bureau","award":["2022YFB3902000"],"award-info":[{"award-number":["2022YFB3902000"]}]},{"name":"Science and Technology Project of the Shanghai Municipal Water Bureau","award":["D040102"],"award-info":[{"award-number":["D040102"]}]},{"name":"Science and Technology Project of the Shanghai Municipal Water Bureau","award":["21DZ1202500"],"award-info":[{"award-number":["21DZ1202500"]}]},{"name":"Science and Technology Project of the Shanghai Municipal Water Bureau","award":["2020068"],"award-info":[{"award-number":["2020068"]}]},{"name":"Science and Technology Project of the Shanghai Municipal Water Bureau","award":["2021-10"],"award-info":[{"award-number":["2021-10"]}]},{"name":"Science and Technology Project of the Shanghai Municipal Water Bureau","award":["2018-07"],"award-info":[{"award-number":["2018-07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg\/L for TN and 0.05 mg\/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management.<\/jats:p>","DOI":"10.3390\/rs16091614","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T08:02:22Z","timestamp":1714723342000},"page":"1614","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Seasonal Monitoring Method for TN and TP Based on Airborne Hyperspectral Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Lei","family":"Dong","sequence":"first","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9349-485X","authenticated-orcid":false,"given":"Cailan","family":"Gong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinhui","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Shanghai Municipal Institute of Surveying and Mapping, Shanghai 200063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daogang","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103187","DOI":"10.1016\/j.earscirev.2020.103187","article-title":"Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing","volume":"205","author":"Sagan","year":"2020","journal-title":"Earth-Sci. 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