Method for predicting soil moisture by utilizing surface reflection signals and random forest regression algorithm

A technology of ground reflection signal and random forest algorithm, which is applied in the direction of prediction, calculation, calculation model, etc., can solve the problems of high cost, limited temporal and spatial resolution, and inability to obtain soil moisture on the spot, so as to achieve continuous prediction and enrich scientific research data Effect

Inactive Publication Date: 2020-02-14
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, due to the need for multiple sites and high-cost monitoring equipment, it is usually impractical to continuously monitor the soil moisture content of large areas through on-site observations, and due to the limitations of the surface and geographical environment, there are still a large number of soils that cannot be obtained in the field. In the unknown region of humidity, it is still a major challenge to continuously measure it on a global scale or a large scale with a certain temporal and spatial resolution.
Although traditional measurement methods (weight-moisture method, electrical resistanc

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  • Method for predicting soil moisture by utilizing surface reflection signals and random forest regression algorithm
  • Method for predicting soil moisture by utilizing surface reflection signals and random forest regression algorithm
  • Method for predicting soil moisture by utilizing surface reflection signals and random forest regression algorithm

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Embodiment 1

[0043] A method for predicting soil moisture based on satellite surface reflection signals and a random forest regression algorithm, comprising the following steps:

[0044] Step 1: Obtain the surface reflectance and satellite elevation angle of different sample areas, where the surface reflectance is the ratio of the maximum power correlation value of the reflected signal to the maximum power correlation value of the direct signal. The satellite altitude angle is the vertical angle between the antenna of the receiver and the satellite connection direction and the horizontal plane of the station.

[0045] Step 2: According to the obtained surface reflectance and satellite elevation angle of different sample areas, as well as the soil moisture data of the sample areas, train and establish an optimal random forest algorithm model. Wherein, the surface reflectance of the sample area and the satellite elevation angle are used as the input samples of the training optimal random for...

Embodiment 2

[0071] For the establishment of the soil simulation database in the implementation example, the soil moisture detection method based on the satellite reflection signal and the random forest regression algorithm, such as figure 1 , including the following steps:

[0072] Step 1: The ratio of the signal-to-noise ratio of the received bistatic radar direct signal to the signal-to-noise ratio of the reflected signal is obtained to obtain the soil reflectance . In this study, there is only specular reflection by default, and the soil reflectance can be obtained , , is the soil reflection coefficient, is the satellite altitude angle, is the left-handed polarization surface reflection coefficient, is the horizontal polarization reflection coefficient, is the vertical polarization reflection coefficient. Therefore, the surface reflectivity of the received bistatic radar signal can be expressed as . is the correction parameter of the system, which can be obtained by...

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Abstract

The invention belongs to the technical field of soil water content prediction and relates to a method for predicting soil moisture by utilizing surface reflection signals and a random forest regression algorithm. The method comprises the following steps: training and establishing a random forest algorithm model according to obtained surface reflectance and satellite elevation angles of different soil sample areas and soil humidity data of the sample areas; obtaining the soil type, the surface reflectance and the satellite elevation angle of the to-be-detected area, inputting the surface reflectance and the satellite elevation angle of the to-be-detected area into the random forest algorithm model, and obtaining the predicted soil humidity of the to-be-detected area. According to the method, through an all-weather, wide-coverage, continuous and stable signal source provided by a satellite, and in combination with a random forest algorithm in machine learning, relatively accurate soil humidity prediction can be carried out on a large area and an unknown area lacking remote sensing data.

Description

technical field [0001] The invention belongs to the technical field of soil water content prediction, and in particular relates to a method for predicting soil water content by using surface reflection signals and a random forest regression algorithm. Background technique [0002] Soil moisture is an important basic parameter in the study of climate, hydrology, ecology and agriculture. It directly controls the transport and balance of water and heat between the land and the atmosphere. At present, remote sensing technology can obtain regional large-scale land soil moisture change information, and apply it to various fields such as land hydrology research, waterlogging and drought detection, crop growth situation assessment, and natural and ecological environment research. However, due to the need for multiple sites and high-cost monitoring equipment, it is usually impractical to continuously monitor the soil moisture content of large areas through on-site observations, and d...

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06N20/00G06Q10/04
CPCG06N20/00G06Q10/04G06F18/214G06F18/24323
Inventor 贾燕王杰袁媛陈一祥
Owner NANJING UNIV OF POSTS & TELECOMM
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