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Bayesian soil moisture estimation method based on multi-source remote sensing data

A technology for soil moisture and remote sensing data, applied in electrical digital data processing, special data processing applications, calculations, etc., can solve the problems of low spatial resolution of passive microwave satellites and complicated verification of SM products.

Pending Publication Date: 2019-11-08
AEROSPACE INFORMATION RES INST CAS
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Problems solved by technology

Due to the limitations of remote sensing devices and imaging principles, the spatial resolution of passive microwave satellites is very low, within the range of tens of kilometers
This leads to internal heterogeneity of microwave pixels, which complicates the verification of SM products

Method used

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  • Bayesian soil moisture estimation method based on multi-source remote sensing data
  • Bayesian soil moisture estimation method based on multi-source remote sensing data
  • Bayesian soil moisture estimation method based on multi-source remote sensing data

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Embodiment Construction

[0039] The purpose of this study is to estimate higher resolution and higher precision SM by integrating multi-source remote sensing data into the BME algorithm, and the process method is schematically shown in the attached figure 1 As shown, the specific examples are as follows:

[0040] 1. Hard data preparation

[0041] study area ( figure 2 ) in Hengshui City, Hebei Province, in Northwest China (38°3'00"N, 115°27'54"E). This is a typical agricultural area with only a few types of land cover (bare soil, corn, orchard, and grass) with a uniform soil texture. The region has a typical continental monsoon climate, with high temperatures and abundant rainfall in summer and cold and dry winters. Therefore, SM content plays an important role in agricultural production, especially in summer. To measure SM content, field experiments were carried out in summer. Sample points such as figure 2 shown. Corn, cotton, and peanuts are the main crops at these sites. The main soil t...

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Abstract

The invention relates to a Bayesian soil moisture estimation method based on multi-source remote sensing data. According to the Bayesian soil moisture estimation method, aiming at how to obtain high-precision soil moisture weighting probability soft data, 12 kinds of multi-source data are fused for the first time to calculate the weighting probability soft data, including downscaling FY3-B soil moisture products, and obtaining albedo (A), vegetation index NDVI (V) and surface temperature LST (T) by using MODIS products; elevation data is acquired by using an ASTER product; gradient, slope direction, plane curvature, profile curvature, surface roughness, humidity index and fluctuation amplitude data are obtained through calculation according to the elevation data obtained by the ASTER product; and weighted probability soft data is obtained by adopting two weight determination methods of multivariable correlation analysis and principal component analysis. The Bayesian soil moisture estimation method also analyzes precision analysis of different soft data quantities, proposes to guarantee sufficient soft data quantities, and plays an important role in obtaining higher-precision soil moisture spatial distribution.

Description

technical field [0001] The invention relates to a method for estimating soil moisture based on multi-source data, in particular to a method for estimating Bayesian soil moisture based on multi-source remote sensing data such as downscaled soil moisture product data and topography. Background technique [0002] The lack of soil moisture (SM) data with high spatial resolution has become one of the major bottlenecks in improving the accuracy of watershed-scale ecohydrological models. The Bayesian Maximum Entropy (BME) algorithm is an estimation algorithm for modeling large-scale spatial heterogeneity, and can integrate multiple types of data with varying accuracy and quality. In theory, using BME algorithm to integrate multiple types of data related to SM space into SM space estimation can improve SM accuracy. [0003] Soil moisture (SM) is not only a key parameter in hydrological models, climate prediction models, drought monitoring models and crop yield estimation models, bu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G01N33/24G06F17/50
CPCG01N33/246G06V10/758G06F18/24155G06F18/251G06F18/2415
Inventor 王春梅顾行发谢秋霞韩乐然余涛孟庆岩占玉林杨健李娟魏香琴
Owner AEROSPACE INFORMATION RES INST CAS
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