Satellite image and machine learning water quality monitoring method and system

Abstract

The invention discloses a satellite image and machine learning water quality monitoring method which comprises the following steps: respectively preprocessing acquired first satellite image data and second satellite image data to generate reflectivity data; establishing radiation normalization models of different sensors according to different seasons and different ground features; establishing aspace-time fusion model based on weight filtering; establishing a water quality parameter inversion database based on the time information of the space-time fusion result and the space information ofthe water quality monitoring data of the ground monitoring station; screening a water quality parameter inversion database, extracting data of the training set and the test set, and establishing an inversion model of the water quality parameters by utilizing multiple machine learning algorithms; and outputting a water quality inversion result according to the generated reflectivity image data setand the established water quality parameter inversion model. A water quality parameter inversion model based on machine learning is established, the model precision is high, and the obtained water quality parameter inversion result can reflect the spatial distribution condition of water quality parameters.

Description

technical field [0001] The invention belongs to the technical field of urban surface water environmental water quality monitoring, and in particular relates to a long-term continuous surface water quality monitoring method and system based on satellite images and machine learning. Background technique [0002] Surface water environment water quality monitoring is one of the main contents of environmental monitoring. It is to accurately, timely and comprehensively reflect the current situation and development trend of water quality, provide scientific basis for water environment management, pollution source control, environmental planning, etc. Pollution control as well as maintaining the health of the water environment plays a vital role. At present, there are mainly the following methods for surface water environmental water quality monitoring: [0003] Traditional urban surface water environmental water quality monitoring methods are mainly manual and automatic site monit...

AI Technical Summary

Problems solved by technology

[0006] a. The transit period of the satellite load is short, and the spatial resolution of the satellite load is often low, so it is difficult to effectively monitor the tiny target objects;

[0007] b The spatial resolution of the image is high, but the revisit period of the satellite load is often long, and it is impossible to conduct short-term continuous monitoring of the target object

At the same time, satellite remote sensing images are also affected by cloud cover, which can effectively use less data, and it is difficult to realize long-term continuous monitoring of fine ground objects.

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