Winter wheat growth monitoring and analyzing method based on GEE cloud platform

Abstract

The invention relates to a winter wheat growth monitoring and analysis method based on a GEE cloud platform, and the method comprises the following steps: firstly constructing training features on theGEE cloud platform through combining Landsat8 image data based on the advantages of Landsat8 and MODIS images; extracting the winter wheat seeding areas and spatial distribution of X-year-old measured farming regions through employing a machine learning method; and further monitoring and analyzing the growth vigor of the winter wheat in the period from the reviving period to the heading period ofthe winter wheat with the tested age limit by using MODIS image data. According to the invention, feature extraction is performed by using Landsat8 image data, so that the influence of mixed pixels on the winter wheat seeding area extraction precision is reduced, and meanwhile, the winter wheat growth vigor is continuously tracked by using the characteristic of high time resolution of MODIS imagedata, so that the accuracy of a monitoring result is greatly improved, and a scientific basis is provided for arranging and guiding farming activities.

Description

technical field [0001] The invention relates to the technical field of spatial information processing, in particular to a monitoring and analysis method for winter wheat growth based on a GEE cloud platform. Background technique [0002] Satellite remote sensing data has become one of the important means to monitor the distribution and growth of winter wheat due to its wide coverage, rich spectral information, and strong periodicity. At present, the commonly used methods to extract the sowing area of ​​winter wheat mainly include: 1. Combining with remote sensing images, using the time-series changes of vegetation indices such as NDVI to set an appropriate threshold to extract the spatial distribution information of winter wheat; 2. Using machine learning methods to analyze various types of remote sensing images The ground objects were classified to extract the sown area of ​​winter wheat. Among the machine learning methods, the random forest algorithm has a high degree of ...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that there are many mixed pixels or insufficient time resolution in the extracted data when the winter wheat is extracted with high precision in a large area and the growth condition is monitored at a high frequency, the invention provides a winter wheat growth condition based on the GEE cloud platform The monitoring and analysis method is based on the respective advantages of Landsat8 and MODIS images. On the GEE cloud platform, the Landsat8 image data is first used to construct the training features, and the machine learning method is used to extract the sown area of ​​winter wheat in the measured agricultural area of ​​X years. and its spatial distribution, and then use MODIS image data to monitor and analyze the growth of winter wheat during the period from turning green to heading.

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