Qualitative and quantitative combination water quality monitoring method

Abstract

The invention relates to a qualitative and quantitative combination water quality monitoring method which can quickly and effectively analyze the reflectivity of a remote sensing image spectrum. By the use of an abnormity detection method for a support vector data description, pixel points of polluted water in a high-resolution image can be quickly identified to judge the distribution of the polluted water from the qualitative angle so as to obtain a polluted water quality analysis result; furthermore, compared with a conventional empirical method, a band difference value Gaussian process regression method is higher in model forecasting precision; by the use of the method, overproof water quality parameters can be automatically analyzed from the qualitative angle to supply reliable basis to water pollution treatment; meanwhile, homemade GF-1 WFV data and HJ-1A HSI data which are used in the monitoring method disclosed by the invention are low in cost and short in interval period and can meet the requirement for continuous and stable operation of development of environmental remote sensing service.

Description

technical field [0001] The invention relates to a qualitative and quantitative water quality monitoring method, which belongs to the cross field of remote sensing image analysis and machine learning technology. Background technique [0002] my country is a country with many lakes. Due to economic development, population expansion and over-exploitation, more and more inland water bodies have become eutrophic, the structure of water ecosystems has been destroyed, and blue-green algae blooms have occurred frequently, resulting in Huge economic loss needs to be dealt with urgently. Water quality monitoring is the main basis for water quality evaluation and water pollution prevention and control, and is one of the core links of water ecological governance. For a long time, my country's water quality monitoring has adopted the method of ground sampling and laboratory analysis. The analysis process is complicated and the cycle is long. The frequency and timeliness of data lag far b...

AI Technical Summary

Problems solved by technology

At present, the inversion of trace water quality parameters of inland water bodies is mostly based on empirical and semi-empirical methods. These methods need to collect a large amount of field data, and there are many uncertain factors between spectral reflectance and water quality parameters. Traditional empirical methods, The semi-empirical linear model cannot express the nonlinear relationship between water quality parameters and reflectance spectrum well

In addition, data from foreign satellites lack continuity, and some older satellites have ceased to operate

Most of the existing remote sensing inversion is based on satellite images with low spatial resolution or low spectral resolution. The sensor parameters are different, the inversion effect is quite different, and it has a large space-time limitation. It cannot adaptively process different imaging. The images under these conditions are difficult to meet the needs of continuous and stable operation of environmental remote sensing operations

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