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Monitoring Lodging Extents of Maize Crop Using Multitemporal GF-1 Images

文献类型： 外文期刊

作者： Qu, Xuzhou 1 ; Shi, Dong 1 ; Gu, Xiaohe 2 ; Sun, Qian 2 ; Hu, Xueqian 2 ; Yang, Xin 2 ; Pan, Yuchun 2 ;

作者机构： 1.Yangtze Univ, Sch Geosci, Wuhan 430100, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

关键词： Crops; Monitoring; Vegetation mapping; Tropical cyclones; Rain; Remote sensing; Indexes; Lodging; maize crop; multitemporal; random forest (RF); recursive feature elimination (RFE) method based on cross-validation (RFECV)

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：4.715； 五年影响因子：4.734 ）

ISSN： 1939-1404

年卷期： 2022 年 15 卷

页码：

收录情况： SCI

摘要： Maize crop lodging is a recurrent phenomenon which results in significant reduction of grain yield and quality in addition to the impediment of mechanical harvesting. The large-scale monitoring of maize crop lodging is important for production policy adjustment and agricultural insurance compensation. In this article, we derived a variety of features from multitemporal GaoFen-1 (GF-1) images before and after maize crop lodging. We screened the most sensitive features of the spectrum, texture, and vegetation index to monitor maize crop lodging. The recursive feature elimination method based on cross-validation and mutual information were compared to obtain the optimal feature combination for monitoring the lodging extents of maize crop. The random forest classifier was used to classify the lodging extents. The results showed that the most sensitive features of the spectrum, texture, and vegetation indices of lodging extents included the difference of reflectance in blue, green, and red bands, the difference of normalized difference vegetation index, the difference of ratio vegetation index, the difference of enhanced vegetation index difference, the difference of mean value of blue band, the difference of mean value of green band, and the difference of mean value of red band. The total accuracy of lodging extents classification was 87.50%, and the Kappa coefficient was 0.83 for testing samples. Based on multiple features derived from GF-1 images before and after lodging, the lodging extents of maize crop can be monitored on a large scale.

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