MODIS AOD product missing data restoration method

Abstract

The invention relates to an MODIS AOD product missing data restoration technology. The technology comprises the following steps of 1, performing preprocessing for remote sensing data of geometric correction, re-projection and the like of MODIS AOD data; 2, verifying the applicability of different levels of DT\DB AOD data; 3, on the basis of increasing spatial coverage degrees of two algorithm products through linear regression equations of DT and DB AOD respectively, performing AERONET precision weighted fusion; 4, further fusing data of two satellites Terra and Aqua by adopting linear regression; and 5, preliminarily restoring residual missing AOD data by adopting a space-time Kriging interpolation method. By utilizing obtained residual errors, a geographically weighted regression model is adopted for further correction, and finally a completely restored AOD data result is obtained. The technology has the beneficial effects that the spatial coverage rate of the AOD data is gradually increased on the premise of guaranteeing AOD data precision, the missing data is completely restored finally, and reliable support is provided for subsequent application.

Description

technical field [0001] The invention relates to the technical field of optical remote sensing image processing, in particular to a method for repairing missing data of MODIS AOD products. Background technique [0002] Atmospheric aerosol is an important uncertainty factor in global climate change and an important component of air pollution. Aerosol Optical Depth (AOD, Aerosol Optical Depth), as one of the important parameters to describe the optical characteristics of aerosol, represents the degree of extinction caused by aerosol scattering in the vertical column of cloudless atmosphere. important indicator of pollution. [0003] Satellite remote sensing has the irreplaceable space-time advantage of ground-based measurement in AOD monitoring. At present, many satellites around the world can provide AOD data, among which the AOD products of MODIS satellites are currently the most widely researched and applied data. However, due to the orbital interval of MODIS satellites, c...

AI Technical Summary

Problems solved by technology

Due to the different data sources of this method, there is a problem of matching the spatio-temporal information of the data; the spatio-temporal information fusion based on the image data itself often adopts a simple spatial interpolation method, which does not fully utilize the temporal information

without subsequent further correction

At present, most of the repair schemes for MODIS AOD data only use one or two aspects of information, and do not make full use of the characteristics of MODIS AOD data itself, and design a targeted comprehensive improvement scheme. The repair results are not comprehensive enough, the accuracy is not high enough, and the space-time promotion is not strong.

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