Large-scale winter wheat extraction based on GEE platform and automatic statistical allocation algorithm

doi:10.14088/j.cnki.issn0439-8114.2022.19.026

Abstract

Abstract: In order to improve the accuracy of large-scale crop remote sensing mapping, the Sentinel-2 data was preprocessed by Google Earth Engine （GEE） platform, and the maximum images of NDVI, EVI and EVI2 of the whole Hebei Province in 2019 were synthesized. Combined with the data of geographical conditions, the automatic statistical distribution algorithm was input to select the optimal vegetation index. The optimal image was used to extract the winter wheat information of Hebei Province with a resolution of 30 m from 2016 to 2019. The results showed that compared with the validation samples, the user accuracy of wheat distribution of NDVI, EVI and EVI2 was greater than 94%, the mapping accuracy was greater than 91%, the overall accuracy was greater than 98%, and the Kappa coefficient was greater than 0.96, of which EVI had the highest accuracy. Compared with the statistical data, the average precision of all wheat planting counties in 2016—2019 was greater than 97%. The automatic statistical allocation algorithm does not need to rely on training samples, and can be quickly copied and used in other years to improve the efficiency of large-scale crop distribution mapping, and the obtained wheat information has good consistency with the statistical data. It can provide some technical methods and ideas for rapid mapping of large-scale wheat distribution.

Key words: winter wheat, GEE platform, automatic statistical allocation algorithm, Sentinel data, remote sensing mapping

CLC Number:

S127

ZHAO Liang, LIU Li, SI Li-li, ZHAO Tei-song, HUANG Jing-feng. Large-scale winter wheat extraction based on GEE platform and automatic statistical allocation algorithm[J]. HUBEI AGRICULTURAL SCIENCES, 2022, 61(19): 132-140.

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