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Regional winter wheat yield assessment method based on remote sensing phenology assimilation and a particle swarm optimization algorithm

A particle swarm optimization and winter wheat technology, which is applied in the field of agricultural remote sensing, can solve the problems such as the difficulty in commercial application of agricultural yield estimation, and achieve the effect of solving the problem of phenological shift, easy acquisition of data preparation data, and saving time and cost.

Active Publication Date: 2019-05-24
BEIJING NORMAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, when the scope of research is further expanded, the demand for high-density and high-frequency surface observation in a large area will face enormous pressure in terms of time, manpower, and money costs, and it is difficult to realize the commercial application of regional agricultural production estimation

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  • Regional winter wheat yield assessment method based on remote sensing phenology assimilation and a particle swarm optimization algorithm
  • Regional winter wheat yield assessment method based on remote sensing phenology assimilation and a particle swarm optimization algorithm
  • Regional winter wheat yield assessment method based on remote sensing phenology assimilation and a particle swarm optimization algorithm

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Embodiment

[0083] The specific application of the method of the present invention is exemplified below by taking the winter wheat planting areas of Hebei, Shandong, Henan and Anhui provinces as the research area.

[0084] Step S1, select the winter wheat planting areas in Hebei, Shandong, Henan and Anhui provinces as the research area, involving a total of 159 counties. The study area is located at 111.5°E–118.2°E, 32.1°N–38.8°N, and the terrain is dominated by plains, occupying most of the main winter wheat production areas in the North China Plain. The climate is a temperate monsoon climate, with an average temperature of about 15.6°C and an average annual precipitation of about 834.5mm. Obtain the following data: Meteorological data using a method with sufficient interpolation precision (R 2 >0.8), by Yuan et al. [1](Yuan W, Xu B, Chen Z, Xia J, Xu W, Chen Y, Wu X, Fu Y. (2015). Validation of China-wide interpolated daily climate variables from 1960 to 2011. Theor Appl Climatol119:6...

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Abstract

The invention discloses a regional winter wheat yield assessment method based on remote sensing phenology assimilation and a particle swarm optimization algorithm. The regional winter wheat yield assessment method comprises the following steps: acquiring data required for operating a crop growth model; acquiring remote sensing LAI data in the whole growth period of the research area, synthesizingaccording to a time sequence, and performing spline interpolation on the LAI node data of each lattice point unit for three times to obtain a continuous LAI curve; identifying a first inflection pointand a second inflection point of the LAI curve, and taking the first inflection point and the second inflection point as marks of a winter wheat reviving period and a heading period respectively; taking lattice points capable of simultaneously identifying a reviving period and a heading period as winter wheat planting areas; optimizing crop growth and development parameters year by year by usingdichotomy lattice points; optimizing the biophysical process parameters by using a particle swarm algorithm to complete parameter localization; substituting the localized crop growth and development parameters and the biophysical process parameters into a crop growth model to operate, obtaining the yield of each lattice point, and summarizing to obtain the total yield of the winter wheat plantingarea.

Description

technical field [0001] The invention relates to the technical field of agricultural remote sensing, and more specifically relates to a regional winter wheat production estimation method based on remote sensing phenology assimilation and particle swarm optimization algorithm. Background technique [0002] The crop growth model takes light, temperature, water, soil and other conditions as environmental driving variables, and uses mathematical physics methods and computer technology to analyze important physiological and ecological processes such as photosynthesis, respiration, and transpiration during the crop growth period and their relationship with meteorological, soil, and other environmental conditions. And the relationship between technical conditions such as tillage, irrigation, fertilization, etc. is quantitatively described and predicted, and the process of crop growth and development and yield formation is reproduced. Since the crop growth model has a strong mechanis...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/02G06N3/00
Inventor 张朝张领雁陶福禄骆玉川李子悦
Owner BEIJING NORMAL UNIVERSITY
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