Particulate matter optical thickness remote sensing monitoring method used in urban area

Abstract

The invention discloses a particulate matter optical thickness remote sensing monitoring method used in an urban area. The method includes the following steps that preprocessing, including geometric correction, cloud recognition and atmospheric correction of a clear image, of MODIS data is carried out; surface reflectance of an image to be inversed is obtained according to geometry parameters, obtained after preprocessing, of a clear image and the image to be inversed and according to a BRDF model built in a coupled mode in the urban area; an improved structure function method is put forward according to the special surface structure of the urban area, and a structure function value of the surface reflectance and a structure function vale of apparent reflectance of the image to be inversed are calculated; finally, according to a geometry condition of the image to be inversed, a look-up table of inversion particulate matter optical thicknesses is built, and an aerosol optical thickness is looked up according to the structure function value of the surface reflectance and the structure function value of the apparent reflectance of the image. According to the improved structure function calculation method, influences of matching errors of multiple images on a calculation result can be effectively reduced, higher stability is achieved in the urban area, and inversion accuracy of the particulate matter optical thickness is improved.

Description

technical field [0001] The invention relates to a remote sensing monitoring method for particle optical thickness used in urban areas. Background technique [0002] Cities are places where people gather, and particulate matter in urban areas has an important impact on people's lives. At present, the most widely used remote sensing method to monitor particulate matter is to monitor the optical thickness of particulate matter. For the inversion of particle optical depth on land, the dense vegetation algorithm is relatively mature, but it can only be applied to dense vegetation areas with low surface reflectivity in the red and blue bands. In urban areas, most of the surface has relatively high reflectance in the visible light band. For images with low spatial resolution, it is difficult to find dense vegetation pixels in cities, which limits the application of dense vegetation methods in urban areas. For the inversion of the optical thickness of particles in areas with high ...

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Problems solved by technology

However, the value of d in these two methods is fixed in all directions. For special areas such as cities, the difference of the surface is very obvious. The setting of different d values ​​has a large difference in the calculation results of the structure function, and the error higher rate

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