Soil moisture site data upscaling method based on Bayesian theory

A Bayesian theory, soil moisture technology, applied in the field of upscaling of soil moisture site data based on Bayesian theory, to achieve the effect of reducing uncertainty

Inactive Publication Date: 2015-04-29
BEIJING NORMAL UNIVERSITY
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Problems solved by technology

At present, few people have constructed upscaling methods based on Bayesian data fusion theory

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  • Soil moisture site data upscaling method based on Bayesian theory
  • Soil moisture site data upscaling method based on Bayesian theory
  • Soil moisture site data upscaling method based on Bayesian theory

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Embodiment Construction

[0040] The embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0041] Study area and data

[0042] The research area of ​​the embodiment of the present invention is located in the middle of the Qinghai-Tibet Plateau. like figure 1shown. There are few species of organisms in the study area, and the variation of soil moisture is large, and there are seasonal freeze-thaw cycles. There is a dense observation network in the study area to monitor changes in soil moisture and soil temperature. The observation network includes nested networks on three scales (1.0, 0.3, 0.1degree), and each network node includes four layers of observations ( 0-5, 10, 20, and 40cm). The soil moisture data of the observation network in 2010-2011 have been relea...

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Abstract

The invention discloses a soil moisture site data upscaling method based on the Bayesian theory. The soil moisture site data upscaling method includes steps of estimating prior probability density distribution function pre_pdf of a target variable on the basis of sparse site observation data in an upscaling area; inversing MODIS ATI into SM by establishing nonlinear regression relation between the SM and the MODIS ATI, estimating an estimated confidence interval of the soil moisture nonlinear regression and probability distribution as soft data in a probability form; integrating the prior distribution of the target variable and auxiliary information of the probability form from the MODIS ATI through the Bayesian theory, and acquiring posterior probability density distribution function post_pdf of the target variable; calculating the value of the target variable in the maximum probability through maximization of the posterior probability distribution function. By the soil moisture site data upscaling method, uncertainties caused by scale difference between soil moisture remote sensing products and ground site authentication data are effectively reduced. The soil moisture site data upscaling method can be applied to upscaling application of other ground surface parameters.

Description

technical field [0001] The invention relates to the field of navigation remote sensing, in particular to a method for upscaling soil moisture site data based on Bayesian theory. Background technique [0002] The problem of scale is a huge obstacle to the full use of multi-source soil moisture data. The problem of scale mismatch not only exists in the multi-channel observation process of soil moisture, but also exists in various related fields such as soil moisture simulation and data assimilation. Low-resolution soil moisture products (SMAP is about 100km 2 , SMOS is about 1600km 2 , etc.) are also limited by scale factors, which originate from the scale difference between satellite sensor resolution and point observations from ground-based instruments. In addition, strictly speaking, the scale of point observations (support area less than 1m 2 ) also does not match the scale of the model grid (support area greater than 10m 2 ), the scale transformation between soil mois...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 高胜国潘耀忠朱忠礼朱秀芳
Owner BEIJING NORMAL UNIVERSITY
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