Land-based remote sensing machine learning algorithm for chlorophyll and phycocyanobilin in a water body in complex scenes

Abstract

The invention provides a land-based remote sensing machine learning algorithm for chlorophyll and phycocyanobilin in a water body in complex scenes. The algorithm comprises the steps: erecting an imaging spectrometer on a shoreside water body to carry out high-frequency automatic continuous observation on the remote sensing reflectance ratio of the water body under complex scenes of different weather conditions and water conditions; performing synchronous high-frequency automatic continuous observation on chlorophyll and phycocyanobilin on the surface layer of the same water body by using a multi-parameter water quality instrument; matching the synchronously observed remote sensing reflectance ratio and pigment concentration data, and constructing a synchronous sample data set covering different observation scenes; and establishing an inversion model by using a machine learning model and applying the inversion model to an imaging spectrometer to realize rapid real-time high-frequency automatic continuous monitoring of chlorophyll and phycocyanobilin in the water body under unattended operation. Based on shore-based remote sensing, the concentrations of chlorophyll and phycocyanobilin in the water body can be accurately and automatically inverted for the large sample data set under complex scenes of different weather conditions and water conditions, and when the algorithm is applied to an imaging spectrometer, rapid, real-time, high-frequency, automatic and continuous monitoring of chlorophyll and phycocyanobilin on the surface of the water body under unattended operation can be realized.

Description

technical field [0001] The invention relates to the rapid, accurate and automatic high-frequency remote sensing extraction of water quality by using a shore-based spectrometer in the process of monitoring the water body environment, in particular to a machine learning algorithm for chlorophyll and phycocyanin based on shore-based remote sensing means for different weather conditions and water conditions . Background technique [0002] With the rapid development of my country's economy, water environment and water pollution problems are becoming increasingly serious. Accurate and rapid water environment monitoring is an important cornerstone to realize the characteristics of water quality and water environment changes, the mechanism of formation, evaluation and assessment, governance and restoration, and management assessment. Whether it is scientific research, environmental management, or government decision-making, it depends on the results of water environment monitoring t...

AI Technical Summary

Problems solved by technology

[0005] However, so far, there is no relatively mature shore-based spectral imager and remote sensing observation system and shore-based remote sensing inversion algorithm for key water quality parameters in the market. Therefore, it is urgent to develop related instruments and remote sensing algorithms to meet the growing river Requirements for high-temporal and spatial continuous dynamic observation of key water quality parameters in cross-sections and nearshore waters

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