Abstract
In this paper, through the principal component analysis of water quality survey data of Bohai Bay in 2006, 2009 and 2013, the main pollutant was selected, and the quasi-simultaneous Landsat multispectral remote sensing data are regressed to establish the quantitative inversion model of the sensitive band and the main pollutants in seawater. The accuracy of the model is determined to meet the requirements of quantitative inversion of water quality remote sensing through the significance test method of accuracy assessment, providing a basis for future multispectral remote sensing monitoring of water quality indicators. assessment, providing a basis for future multispectral remote sensing monitoring of water quality indicators.
Highlights
The Bohai Sea as a semi-enclosed inland sea, pollution buffer capacity is weak, marine biological disaster prevention and control is a long way to go[1]
Principal Component Analysis can incorporate multi-dimensional factors into the same system for quantitative research, using a small number of comprehensive indicators to reflect the information of multiple variables without losing the original variable data, avoiding the subjectivity of artificially determining weights, making the analysis more accurate and reliable[3].Multispectral remote sensing is a photoelectric detection technique that emerged in the 1980s to extract objects of interest through the spectral difference between the target and the background
Take five indicators in the remaining water pollutants to assess the accuracy of the model, as shown in Table5, it can be seen that the stepwise regression equation has a strong correlation, the error value is very small
Summary
The Bohai Sea as a semi-enclosed inland sea, pollution buffer capacity is weak, marine biological disaster prevention and control is a long way to go[1]. Principal Component Analysis can incorporate multi-dimensional factors into the same system for quantitative research, using a small number of comprehensive indicators to reflect the information of multiple variables without losing the original variable data, avoiding the subjectivity of artificially determining weights, making the analysis more accurate and reliable[3].Multispectral remote sensing is a photoelectric detection technique that emerged in the 1980s to extract objects of interest through the spectral difference between the target and the background It has been more widely used in agriculture and forestry monitoring, disaster assessment, urban planning, emergency monitoring, land and resources, etc[4].
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