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globally. However, the combination of the limited temporal-spatial ranges of traditional manual sampling, poor spectral resolutions, and weather-vulnerable satellite observations, have yielded great data gaps in TP dynamics in short-lived, extreme episodic, or unpredictable pollution. Hence, a novel ground-based hyperspectral proximal sensing system (GHPSs) with a maximum observation frequency of 20 s and a spectral resolution of 1 nm between 400 and 900 nm was developed for automatic, real-time and continuous observation of TP. Focusing on the GHRSs, a TP machine learning model was developed and validated with ideal accuracy (R2 = 0.97, RMSE = 0.017 mg\u00b7L\u22121, MAPE = 12.8%) using 377 pairs of in situ TP measurements collected from Fuchunjiang Reservoir (FR), Liangxi River (LR), and Lake Taihu (LT). Second-scale TP results showed a low-value stable period followed by a sharp change period in LT during 29\u201331 October and 1\u20133 November, respectively. The exponential increase (R2 = 0.65, p &lt; 0.05) on 1 November and the two complete variations with peak values of 0.32 mg\u00b7L\u22121 and 0.42 mg\u00b7L\u22121 were recorded in LT on 2 and 3 November, respectively. Simultaneously, a significant decrease (R2 = 0.57, p &lt; 0.05) over the observation days was observed in LR and no obvious change was observed in FR. High consistency between the GHPSs spectrum data standardized at 574 nm and the measured reflectance in different weather demonstrated the accuracy of the GHPSs spectrum data (R2 &gt; 0.99, slop = 0.98). Short and rapid TP changes were observed within one day in LT and LR based on GHPSs minute scale monitoring, which highlighted the importance of high frequency observations of TP. Several advantages of real-time, high accuracy and wide applicability to complex weather were highlighted for the GHPSs for TP monitoring compared to traditional equipment. Therefore, there are potential applications of the GHPSs in the integrated space-air-ground TP monitoring, as well as emergency monitoring and early-warning systems in the future, and it can raise our awareness of the dynamics and driving mechanisms of water quality for inland waters.<\/jats:p>","DOI":"10.3390\/rs15020507","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T04:31:32Z","timestamp":1673843492000},"page":"507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Real-Time and Continuous Tracking of Total Phosphorus Using a Ground-Based Hyperspectral Proximal Sensing System"],"prefix":"10.3390","volume":"15","author":[{"given":"Na","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3382-4570","authenticated-orcid":false,"given":"Yunlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"}]},{"given":"Kun","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"Nanjing Zhongke Deep Insight Technology Research Institute Co., Ltd., Nanjing 211899, China"}]},{"given":"Yibo","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"}]},{"given":"Xiao","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Weijia","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Haiming","family":"Qian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"School of Environmental & Safety Engineering, Changzhou University, Changzhou 213164, China"}]},{"given":"Huayin","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yongkang","family":"Niu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"364","DOI":"10.4319\/lo.2006.51.1_part_2.0364","article-title":"Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades","volume":"51","author":"Howarth","year":"2006","journal-title":"Limnol. 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