Crop yield estimation method based on dual-polarized synthetic aperture radar and crop model data assimilation

A technology of aperture radar and model data, applied in the field of agricultural remote sensing, can solve problems such as high-precision estimation of crop yield in difficult areas

Active Publication Date: 2018-09-07
金智农(北京)风险管理科技有限公司
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Problems solved by technology

These methods are difficult to achieve high-precision estimat

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  • Crop yield estimation method based on dual-polarized synthetic aperture radar and crop model data assimilation
  • Crop yield estimation method based on dual-polarized synthetic aperture radar and crop model data assimilation
  • Crop yield estimation method based on dual-polarized synthetic aperture radar and crop model data assimilation

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Embodiment 1

[0053] For the flow chart of the crop yield estimation method for corn yield estimation using the dual-polarization synthetic aperture radar and crop model data assimilation of the present invention, please refer to the attached figure 1 .

[0054] S1 collects Sentinel-1 data during the maize growth period in the study area, performs preprocessing such as terrain correction, and obtains the backscatter coefficients of the two polarizations (VH, VV) of the time series C band, that is, dual-polarization SAR data;

[0055] Hengshui City, Hebei Province was selected as the research area, which is located between 115°10′-116°34′ east longitude and 37°03′-38°23′ north latitude. The total area of ​​the study area is 8815km2, the terrain is mainly plains, and the cultivated land accounts for more than 60% of the total area. It belongs to the warm temperate semi-humid monsoon climate, with annual sunshine hours of 2400-3100h and annual average precipitation of 300-800mm.

[0056] The ...

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Abstract

The invention belongs to the agricultural remote sensing field and relates to a crop yield estimation method based on dual-polarized synthetic aperture radar and crop model data assimilation. The method includes the following specific steps that: S1, the satellite data of a dual-polarized synthetic aperture radar are acquired; S2, polarization decomposition is performed on the pre-processed data of the dual-polarized synthetic aperture radar; S3, an LAI inversion model of a scattering component relation combination with the highest precision is selected, so that a remote sensing observation LAI can be obtained; S4, the WOFOST model of crops in a study area is calibrated, so that a WOFOST model simulation LAI is obtained; S5, the two kinds of LAIs are assimilated with a particle filter algorithm; and S6, the S5 is executed on crop grids one by one, an optimized crop growth period LAI trajectory is adopted to re-drive the WOFOST model, so that spatial mapping can be carried out. According to the method of the invention, the advantages of the remote sensing data of the SAR and the crop model are combined, and abundant information provided by the data of the multi-polarized SAR is fully utilized, and therefore, the problem of the missing of optical remote sensing data in the key growth period of corns can be solved, the yield simulation of the crop model is improved, the LAI trajectory during the crop growth period is optimized accurately, and crop yields can be estimated on a regional scale.

Description

technical field [0001] The invention belongs to the field of agricultural remote sensing, and in particular relates to a method for estimating crop yield by assimilating dual-polarization synthetic aperture radar and crop model data. Background technique [0002] Traditional crop yield estimation methods mainly include statistical survey methods, crop model-based forecast methods, and agrometeorological forecast methods. These methods are difficult to achieve high-precision estimation of regional crop yield due to their inherent limitations. The estimation method based on satellite remote sensing technology has unique advantages in regional crop yield estimation by virtue of its spatial continuity and temporal dynamic characteristics. At the same time, the combination of remote sensing technology and crop growth models based on crop photosynthesis, respiration, transpiration, nutrition and other mechanism processes can achieve the purpose of regional high-precision yield es...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06F2218/20G06F2218/04
Inventor 黄健熙李俐卓文朱德海张晓东苏伟刘峻明刘哲
Owner 金智农(北京)风险管理科技有限公司
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