Identification method and device for winter wheatear differentiation phase

Abstract

The embodiment of the invention provides an identification method and device for a winter wheatear differentiation phase. The method comprises the following steps that: according to collected parameters in a plantation area of winter wheat to be identified, obtaining the model parameter of a wheat growth simulation model, and obtaining a localized wheat growth simulation model; according to the agricultural meteorological station data of the plantation area of winter wheat to be identified, obtaining the first air temperature data time sequence of the winter wheat to be identified, and obtaining the second air temperature data time sequence of the winter wheat to be identified according to a multi-temporal remote sensing image in the plantation area of winter wheat to be identified; utilizing a data assimilation method, carrying out data assimilation on the first air temperature data time sequence and the second air temperature data time sequence, and obtaining the assimilation data time sequence of the winter wheat to be identified; and according to the assimilation data time sequence and the localized wheat growth simulation model, obtaining a wheatear differentiation phase identification result in the plantation area of winter wheat to be identified. By use of the embodiment of the invention, the wheatear differentiation phase identification on a regional scale is realized.

Description

technical field [0001] The embodiments of the present invention relate to the field of agricultural technology, and in particular to a method and device for identifying the ear differentiation stage of winter wheat. Background technique [0002] As an indicator reflecting the growth and development process of crops, crop growth period plays an important role in determining potential yield. Real-time grasp of the crop development process can provide useful information and scientific basis for field management at each growth stage, and is of great significance for achieving stable and high grain yields. In recent years, with the in-depth research on the basic theory of plant physiology and ecology, crop growth models based on the mechanisms of crop photosynthesis, respiration, transpiration, and nutrition have gradually developed. The crop growth models rely on their internal physiological processes and dynamic mechanisms. It can more accurately simulate the growth and develo...

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Problems solved by technology

For a long time, the temperature data driving the operation of the crop model has been mainly obtained through fixed-point long-term observation meteorological stations distributed in the study area. This method has the following two defects: On the one hand, due to the limitation of economic and The number of observing meteorological stations is limited and the spatial distribution is uneven. The temperature data set obtained by the observations can only reflect the temperature changes within a certain range near the station, and cannot express the characteristics of temperature changes at points other than non-observing stations.

Although the temperature value of unknown points can be estimated by using spatial interpolation method, due to the influence of terrain, density and distribution of sample points and interpolation method, the error of interpolation result is relatively large

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