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Bayesian maximum entropy fusion method based on active and passive remote sensing AOD data

A fusion method, active and passive technology, applied in the field of atmospheric satellite remote sensing applications, can solve the problem of "many black holes" in satellite AOD data, and achieve the effect of ensuring data accuracy and high coverage.

Inactive Publication Date: 2019-05-17
天津珞雍空间信息研究院有限公司
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Problems solved by technology

[0010] The present invention aims at the technical defects of the prior art, and provides a Bayesian maximum entropy fusion method based on active and passive remote sensing AOD data to solve the technical problem of more "black holes" in satellite AOD data obtained by conventional methods

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  • Bayesian maximum entropy fusion method based on active and passive remote sensing AOD data
  • Bayesian maximum entropy fusion method based on active and passive remote sensing AOD data
  • Bayesian maximum entropy fusion method based on active and passive remote sensing AOD data

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Embodiment 1

[0041] Combine below figure 1 , to describe this embodiment in detail.

[0042] The present invention first clarifies that the conditional Bayes model is used to synthesize the original data to fuse the AOD data, so as to reduce the "black holes" in the satellite AOD data. The characteristics of the present invention are: (1) consider the temporal and spatial correlation of data, and incorporate the influence of auxiliary information such as regional variability; Satellite remote sensing data; (3) The obtained posterior probability distribution is more complete and reasonable than the Gaussian distribution constructed using only the predicted mean and standard deviation.

[0043] Secondly, obtain large-scale, long-sequence multi-source satellite AOD (MODIS, VIIRS, and SeaWIFS) product data, and use the MODIS spatial resolution and coordinate system as the standard to perform unified resampling and re-registration of other satellite data. deal with.

[0044] Then, the large-sc...

Embodiment 2

[0059] A Bayesian maximum entropy fusion method based on active and passive remote sensing AOD data, including:

[0060] Step 1, considering the spatio-temporal information of the data, download the large-scale and long-sequence multi-source satellite AOD product data, and perform unified preprocessing such as resampling and re-registration.

[0061] Step 2, in order to simplify the variability of heterogeneity and anisotropy, first define a space-time filter window of a×b×c, and perform the space-time trend removal process of the following expression on the large-scale continuous MODIS and other satellite AOD data respectively , and then fit the empirical model parameters according to the space-time covariance.

[0062]

[0063] Step 3. Using the principle of maximum entropy, find the prior probability density function under the Lagrange constraint (contains the largest amount of auxiliary information and is closest to the real situation to the greatest extent). According...

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Abstract

The invention discloses a Bayesian maximum entropy fusion method based on active and passive remote sensing AOD data. The method comprises the following steps of firstly, considering time and space correlation of various source data in AOD fusion; secondly, performing space-time trend removal processing on large-range continuous MODIS satellite AOD data and other satellite AOD data, fitting empirical model parameters according to the space-time covariance, and taking the empirical model parameters as auxiliary information to participate in fusion; then, finding a prior probability density function which contains a maximum amount of auxiliary information and is close to a real condition to the maximum extent by utilizing a maximum entropy principle; and finally, processing satellite AOD data such as MODIS and the like into Gaussian soft data, taking CALIPSO AOD as hard data, and obtaining an AOD fusion result in combination with the conditioned Bayes model. The obtained fusion data is higher in coverage rate, the accuracy of a fusion result can be ensured, and the method has a very good application prospect in how to obtain high-quality AOD space-time distribution information and subsequent PM2.5 inversion.

Description

technical field [0001] The invention relates to the technical field of atmospheric satellite remote sensing applications, in particular to a Bayesian maximum entropy fusion method based on active and passive remote sensing AOD data. Background technique [0002] Aerosol optical depth (AOD) is defined as the integral of the extinction coefficient of aerosol in the vertical direction. It is an important physical quantity that characterizes the turbidity of the atmosphere and can reflect the pollution degree of the regional atmosphere. Obtaining the seamless AOD spatio-temporal distribution information in the area domain is the key to inversion of large-scale regional PM 2.5 The basics. At present, satellite remote sensing has become the main technical means to obtain regional aerosol information. However, due to the limitations of inversion algorithms and cloud pollution in satellite observations, there are a large number of data black holes in the data, which limits the AOD...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 龚威朱忠敏夏幸会张天浩徐宝黄雨斯
Owner 天津珞雍空间信息研究院有限公司
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