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dc.contributor.authorZhao, Jing
dc.contributor.authorZhang, Fujie
dc.contributor.authorChen, Shuisen
dc.contributor.authorWang, Chongyang
dc.contributor.authorChen, Jinyue
dc.contributor.authorZhou, Hui
dc.contributor.authorXue, Yong
dc.date.accessioned2021-02-01T10:51:12Z
dc.date.available2021-02-01T10:51:12Z
dc.date.issued2020-12-03
dc.identifier.citationZhao, J., Zhang, F., Chen, S., Wang, C., Chen, J., Zhou, H. and Xue, Y., (2020). 'Remote sensing evaluation of total suspended solids dynamic with markov model: a case study of inland reservoir across administrative boundary in South China'. Sensors, 20(23), p. 6911.en_US
dc.identifier.doi10.3390/s20236911
dc.identifier.urihttp://hdl.handle.net/10545/625572
dc.description.abstractAccurate and quantitative assessment of the impact of natural environmental changes and human activities on total suspended solids (TSS) concentration is one of the important components of water environment protection. Due to the limits of traditional cross-sectional point monitoring, a novel water quality evaluation method based on the Markov model and remote sensing retrieval is proposed to realize the innovation of large-scale spatial monitoring across administrative boundaries. Additionally, to explore the spatiotemporal characteristics and driving factors of TSS, a new three-band remote sensing model of TSS was built by regression analysis for the inland reservoir using the synchronous field spectral data, water quality samples and remote sensing data in the trans-provincial Hedi Reservoir in the Guangdong and Guangxi Provinces of South China. The results show that: (1) The three-band model based on the OLI sensor explained about 82% of the TSS concentration variation (R2=0.81, N=34, p value<0.01) with an acceptable validation accuracy (RMSE=6.24 mg/L,MRE=18.02%, N=15), which is basically the first model of its kind available in South China. (2) The TSS concentration has spatial distribution characteristics of high upstream and low downstream, where the average TSS at 31.54 mg/L in the upstream are 2.5 times those of the downstream (12.55 mg/L). (3) Different seasons and rainfall are important factors affecting the TSS in the upstream cross-border area, the TSS in the dry season are higher with average TSS of 33.66 mg/L and TSS are negatively correlated with rainfall from upstream mankind activity. Generally, TSS are higher in rainy seasons than those in dry seasons. However, the result shows that TSS are negatively correlated with rainfall, which means human activities have higher impacts on water quality than climate change. (4) The Markov dynamic evaluation results show that the water quality improvement in the upstream Shijiao Town is the most obvious, especially in 2018, the improvement in the water quality level crossed three levels and the TSS were the lowest. This study provided a technical method for remote sensing dynamic monitoring of water quality in a large reservoir, which is of great significance for remediation of the water environment and the effective evaluation of the river and lake chief system in China.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.urlhttps://www.mdpi.com/1424-8220/20/23/6911en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectElectrical and Electronic Engineeringen_US
dc.subjectAnalytical Chemistryen_US
dc.subjectAtomic and Molecular Physics, and Opticsen_US
dc.subjectBiochemistryen_US
dc.subjectData Scienceen_US
dc.titleRemote sensing evaluation of total suspended solids dynamic with markov model: a case study of inland reservoir across administrative boundary in south Chinaen_US
dc.typeArticleen_US
dc.identifier.eissn1424-8220
dc.contributor.departmentGuangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Chinaen_US
dc.contributor.departmentUniversity of Science and Technology, Kunming, Chinaen_US
dc.contributor.departmentChina Agricultural University, Beijing, Chinaen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journalSensorsen_US
dc.source.journaltitleSensors
dc.source.volume20
dc.source.issue23
dc.source.beginpage6911
dcterms.dateAccepted2020-11-30
dc.author.detail785299en_US


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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International