Regional crop yield estimation method based on ensemble Kalman filter assimilation

A Kalman filter and crop technology, applied in the field of agricultural remote sensing, can solve problems such as huge workload, simple and rough mechanism, and difficulty in reflecting the formation process of yield mechanically

Inactive Publication Date: 2014-07-30
CHINA AGRI UNIV
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Problems solved by technology

The index correlation method is based on the correlation analysis of remote sensing vegetation index, meteorological elements and other data and yield to estimate, so the mechanism is simple and rough, and it is difficult to reflect the formation process of yield mechanically
The assimilation method can couple remote sensing observations and crop models, and can realize the complementary advantages of the two. However, the variables to be assimilated by the current assimilation method are the leaf area index (LAI), and it is difficult to obtain high-precision LAI data. If you use high-resolution image reflection Acting requires rich technical accumulation and a huge workload, and at the same time introduces errors

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  • Regional crop yield estimation method based on ensemble Kalman filter assimilation
  • Regional crop yield estimation method based on ensemble Kalman filter assimilation
  • Regional crop yield estimation method based on ensemble Kalman filter assimilation

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Embodiment 1

[0047] Flow chart of the present invention sees figure 1 .

[0048] Step S1, select the main winter wheat production area in Baoding, Hebei as the research area. This area is located at 115°10′E–116°20′E, 38°15′N–39°40′N. Accounting for more than 60% of the total area, most areas are suitable for wheat growth, and it is also the main winter wheat producing area in Hebei Province. The climate is a temperate monsoon climate, with annual sunshine hours of 2400-3100h and average annual precipitation of 300-800mm. Obtain the following data: According to the enveloping range outside the study area, select 6 meteorological elements required by the daily maximum / minimum temperature, total sunshine radiation, water vapor pressure, wind speed, and precipitation model of 21 national meteorological stations; agricultural meteorology in the study area Soil parameters and crop parameters collected by the test station; control parameters such as longitude, latitude, and elevation; agricult...

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Abstract

The invention provides a regional crop yield estimation method based on ensemble Kalman filter assimilation. The advantages of remote sensing data and crop models are combined, EVIs generally used in vegetation remote sensing are used as observational variables, LAIs are used as assimilation variables, optimization adjustment is carried out on the model LAIs through an ensemble Kalman filter algorithm, a PROSAIL model is used as an observational operator, the problem that the observational variables and state variables are inconsistent is solved, assimilation of remote sensing information and the models is achieved, and errors due to the fact that inversion is carried out on the LAIs through reflectivity are avoided. Compared with the yield of crop with unassimilated EVIs, the RMSE of the yield of crop with the assimilated EVIs is reduced, the determination coefficient R2 is improved obviously, estimation precision of the crop model yield is obviously improved after assimilation, and the yield space distribution trend is consistent with the statistical yield.

Description

technical field [0001] The invention belongs to the field of agricultural remote sensing, in particular to a regional crop yield estimation method based on ensemble Kalman filter assimilation. Background technique [0002] Remote sensing technology is today's cutting-edge technology, which can help to quickly and accurately collect information on agricultural resources and agricultural production. Combined with other modern high-tech technologies such as geographic information systems and global positioning systems, it can achieve timing, quantification, positioning, and objectiveness of information collection and analysis. Strong, free from human interference, convenient for decision-making. At present, the use of remote sensing technology to carry out agricultural monitoring work has raised the scientific decision-making of agriculture to a new level, and at the same time provided high-quality services for agricultural production. Now in business, it is possible to carry ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/02
Inventor 黄健熙陈英义马鸿元刘峻明苏伟张晓东朱德海张超
Owner CHINA AGRI UNIV
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