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A soil moisture prediction method based on Bayesian maximum entropy

A technology of soil water content and prediction method, applied in the field of soil water content prediction based on Bayesian maximum entropy, can solve problems such as lack of solutions, and achieve the effects of avoiding smoothing effects, advanced technical methods, and high prediction accuracy

Active Publication Date: 2018-12-14
INST OF S&T INFORMATION SHANDONG ACADEMY OFAGRI SCI +1
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Problems solved by technology

[0010] To sum up, in the existing technology, for measuring the uncertainty of soil water content prediction, combined with soil water content sampling point data to realize the prediction of other point data on the spatial area, the definition of soft data information and the construction of soft data system, etc. Many problems, there is still no effective solution

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  • A soil moisture prediction method based on Bayesian maximum entropy
  • A soil moisture prediction method based on Bayesian maximum entropy
  • A soil moisture prediction method based on Bayesian maximum entropy

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Embodiment Construction

[0068] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0069] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0070] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinatio...

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Abstract

The invention discloses a soil moisture prediction method based on Bayesian maximum entropy, which comprises the following steps: selecting an exemplary soil plot and setting a plurality of random sampling points on the soil plot; constructing a hard database and soft database of soil moisture content; using the hard and soft databases to deal with soil water content of random sampling points, including de-trend and exploratory analysis of soil water content, data normality test and spatio-temporal covariance estimation. Bayesian maximum entropy method and the hard database and soft database are combined to predict the soil water content after treatment. The invention integrates the hard data and the soft data together, predicts and estimates the posterior condition probability of the soilmoisture content, and improves the prediction accuracy of the soil moisture content.

Description

technical field [0001] The invention relates to the field of soil water content prediction, in particular to a soil water content prediction method based on Bayesian maximum entropy. Background technique [0002] Soil moisture content plays an important role in the growth and development of crops. For example, there are different growth stages in the growth and development of winter wheat. Generally, it goes through six stages of growth and development: sowing stage, greening stage, jointing stage, heading stage, milking stage and harvesting stage. Soil water content is an important state variable of crop-soil model , plays an important role in the model representing the different growth processes of winter wheat. It runs through the growth period of winter wheat. The jointing period is an important growth period for the growth and development of winter wheat. Studies have shown that this growth period is the most sensitive period to water in the growth period of winter whea...

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 杨玉建仝雪芹王清华杜振宇葛忠强
Owner INST OF S&T INFORMATION SHANDONG ACADEMY OFAGRI SCI
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