Remote sensing data driven bathing water quality assessment using sentinel-3

<span>In this paper we are investigating the possibility of usage of remote sensing satellite data, more precisely sentinel-3 OLCI and SLSTR data, for assessment of bathing water quality. In this research we used data driven approach and analysis of data in order to pinpoint aspects of remote sensing data that can be useful for bathing water quality assessment. For this purpose we collected satellite images for period from start of June till end of September of 2019 and results of in-situ measurement for the same period. Results of in-situ measurement were correlated with satellite images bands and analyzed. We propose a simple method for rapid assessment of possible deterioration of bathing water quality to be used by public health authorities for better planning of in situ measurements. Results of implementation of predictive models based on k-nearest neighbour (KNN) and decision tree (DT) are described.</span>

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Does the bathing water classification depend on sampling strategy? A bootstrap approach for bathing water quality assessment, according to Directive 2006/7/EC requirements

Journal of Environmental Management ◽

10.1016/j.jenvman.2012.08.006 ◽

2012 ◽

Vol 111 ◽

pp. 236-242

Author(s):

Iago López ◽

César Álvarez ◽

José L. Gil ◽

José A. Revilla

Keyword(s):

Water Quality ◽

Quality Assessment ◽

Sampling Strategy ◽

Water Quality Assessment ◽

Bathing Water ◽

Water Classification ◽

Bootstrap Approach ◽

Bathing Water Quality

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Evaluation method of water quality for river based on multi-spectral remote sensing data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-1517-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 1517-1523 ◽

Cited By ~ 1

Author(s):

X. Xiao ◽

X. Jian ◽

W. Xiongfei ◽

H. Chengfang ◽

C. Xuejun ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Water Quality ◽

Quality Assessment ◽

Water Quality Index ◽

Quality Index ◽

Remote Sensing Data ◽

Water Quality Assessment ◽

Retrieval Model ◽

Han River

With the rapid development of the regional economy, water pollution has gradually become an environmental problem that cannot be ignored. As an important water source in central China, the Han River should strengthen water quality monitoring and management in order to ensure the sustainable development of watershed and related areas. Taking typical sections of middle and lower reaches of the Han River as the study area, this paper focuses on rapid river water quality assessment using multispectral remote sensing images. Based on measured water quality data and synchronous spatial high and medium-resolution remote sensing data (multi-spectral data of ZY3 and HJ1A) in 2013, neural network algorithm is used to establish water quality index retrieval model for the study area, and then water quality status is assessed accordingly. The results show that BP neural network retrieval model of water quality index that is established based on multispectral data of ZY3 satellite has higher accuracy and that its assessment results are of high credibility and strong applicability, which can really reflect changes in water quality and better achieve water quality assessment for the study area. In addition, water quality assessment results show that major excessive factors in the study area are total nitrogen and total phosphorus; the polluting type is organic pollution; water quality varies greatly with seasons.

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