Crop Yield Estimation Model Reconstructed Vi Time Series Curve Based on Extreme Mathematical Model

Abstract

The invention relates to a crop yield estimation model for reconstructing VI time series curves based on an Extreme mathematical model, and belongs to the technical field of crop yield forecasting. The purpose of the invention is to solve the problem that the acquisition rate of remote sensing images used in the existing crop yield estimation is low, resulting in poor versatility of the established crop yield estimation model. It first collects remote sensing images of the entire production period of the crops in the production area to be estimated, and performs preprocessing; then cuts according to the range of the vector map of the plot in the production area to be estimated, and obtains the vegetation index VI time series curve of each pixel in the cropped image; then The VI time series curves are sorted according to the behavioral pixels to obtain the time series of cropped images; the mathematical model Extreme is used to fit the vegetation index VI time series curves of all pixels; the best fitting VI is determined, and the vegetation index VI time series curves are extracted The characteristic parameters; finally obtain the production estimation model. The invention is used for the prediction of crop yield.

Description

technical field [0001] The invention relates to a crop yield estimation model for reconstructing VI time series curves based on an Extreme mathematical model, and belongs to the technical field of crop yield prediction. Background technique [0002] In the process of obtaining remote sensing images, due to the influence of meteorology, soil, hydrology, etc., it is generally difficult to obtain all remote sensing images within the range of the annual crop growth period in the study area. Therefore, it is difficult to establish a general Appropriate production estimation model. [0003] At present, the main data source of most crop yield estimation model research is the time series MODIS vegetation index product of remote sensing data. However, although the MODIS data covers a large area, the data of a single time phase is easily affected by clouds and fog, resulting in abnormal situations. It is necessary to use multi-day VI (NDVI and EVI) synthesis technology. The MODIS dat...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the acquisition rate of remote sensing images used in the existing crop yield estimation is low, resulting in poor versatility of the established crop yield estimation model, and provides a crop yield estimation model based on the Extreme mathematical model to reconstruct the VI time series curve

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