Inversion method for large-scale dynamic evolution of dust-type aerosols

Abstract

The invention discloses a large-scale dynamic evolution dust-type aerosol inversion method, comprising: S1 training classifier: obtaining source domain samples, cloud samples and dust-type aerosol layers of dust-type aerosol layers according to passive satellite sensor remote sensing data The target domain samples of the aerosol layer; the feature vectors of various samples are obtained according to the active satellite sensor lidar profile data; the support vector machine classifier is trained with the feature vectors of the source domain samples and the cloud layer samples; combined with the feature vectors of the target domain samples and transfer learning theory to optimize the classifier; S2 uses the optimized classifier to invert dust-type aerosols. The invention can improve the classification accuracy of cloud-sand after long-distance transmission of sand and dust, and can effectively reverse the current situation where only low-precision data provided by NASA can only be used passively; Bottleneck problems such as poor usability meet the observation requirements of dust aerosol source areas and diffusion transmission areas in my country and even the world.

Description

technical field [0001] The invention belongs to the technical field of satellite remote sensing data processing, and in particular relates to a large-scale dynamic evolution dust type aerosol inversion method. Background technique [0002] my country suffers from many sandstorms every year. Under the influence of this extreme weather, my country's industrial and agricultural production, transportation and human life safety have suffered serious losses and harms. In recent years, the scope of dust influence has gradually expanded, and has even spread to southeastern regions such as Fuzhou and Taiwan. Therefore, it is of great scientific significance to carry out basic research on large-scale sand and dust observation and inversion methods, and will play an active role in major events of the national economy and people's livelihood such as sand and dust early warning and disaster reduction. [0003] Lidar is the best way to obtain the vertical distribution profile of aerosols...

AI Technical Summary

Problems solved by technology

CALIPSO is the best way to obtain the vertical distribution profile information of large-scale sand and dust in my country. Its hardware can detect dust information, but the defect of its data processing algorithm prevents it from being applied to the research and forecast of dust in my country.

For example, NASA's Version2 algorithm has a high dust classification error rate, and the Version3 algorithm is complex, has many parameters, and has poor adaptability to aerosol model areas, etc.

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