Research on Peanut Planting Information Extraction Based on GEE Combined Multi‐features and Random Forest

doi:10.15933/j.cnki.1004-3268.2026.05.015

Abstract

Abstract: Peanut is one of the important oil crops in China. Using remote sensing images to classify peanut cultivated land accurately and efficiently is of great significance for maintaining food security and guiding agricultural production. This research took Kangping County，Shenyang City as the research area，used Sentinel‐2 multi temporal remote sensing image as the data source based on Google Earth Engine（GEE）platform，constructed vegetation index features，texture features and phenological features，and used pixel‐based and object‐oriented random forest classifiers to achieve peanut planting information extraction in Kangping County，Shenyang City. The experimental results showed that the pigment index，texture feature and phenology feature improved the performance of the classifiers to a certain extent，among which the texture feature had the largest improvement for the object‐oriented random forest classifier.Compared with the use of spectrum+vegetation index+pigment index feature，the overall accuracy and Kappa coefficient after adding texture feature increased by 3.85 percentage points and 0.068 4 respectively，and the pixel‐based random forest classifier also rose by 2.66 percentage points and 0.035 6.In different feature combinations，the performance of pixel‐based random forest classifier was better than that of object‐oriented random forest classifier. The optimal overall accuracy of the two classifiers was 95.28% and 92.31% respectively，and the optimal Kappa coefficients were 0.908 6 and 0.860 8 respectively.

Key words: Peanut, Planting information, Google Earth Engine, Random forest, Phenological characteristics, Pigment index, Texture feature

摘要： 花生是我国重要的油料作物之一，利用遥感影像对花生种植空间分布进行精确提取，对于维护粮油安全、指导农业生产具有重要意义。以沈阳市康平县为研究区，基于谷歌地球引擎（Google Earth Engine，GEE）平台，使用Sentinel-2多时相遥感影像，构建植被指数特征、纹理特征和物候特征，分别采用基于像元和面向对象的随机森林分类器，实现沈阳市康平县的花生种植信息提取。结果表明，色素指数、纹理特征与物候特征对于分类器的性能都有一定的提升，其中纹理特征对于面向对象的随机森林分类器提升最大，相较于使用光谱+植被指数+色素指数特征，增加纹理特征的总体精度和Kappa系数提升分别达到3.85百分点和0.068 4，基于像元的随机森林分类器也分别提升2.66百分点和0.035 6。在不同的特征组合中，基于像元的随机森林分类器表现要优于面向对象的随机森林分类器，两者取得的最佳总体精度分别为95.28%与92.31%，最优Kappa系数分别为0.908 6与0.860 8。

关键词: 花生, 种植信息, 谷歌地球引擎, 随机森林, 物候特征, 色素指数, 纹理特征

CLC Number:

S127

SHI Kaixuan, YANG Huadong. Research on Peanut Planting Information Extraction Based on GEE Combined Multi‐features and Random Forest[J]. Journal of Henan Agricultural Sciences, 2026, 55(5): 146-154.

史凯旋, 杨华东. 基于GEE联合多特征与随机森林的花生种植信息提取方法研究[J]. 河南农业科学, 2026, 55(5): 146-154.

Figures/Tables 9

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