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Monitoring rice lodging grade via Sentinel-2A images based on change vector analysis

文献类型： 外文期刊

作者： Sun, Qian 1 ; Gu, Xiaohe 2 ; Chen, Liping 1 ; Xu, Xiaobin 2 ; Pan, Yuchun 2 ; Hu, Xueqian 2 ; Xu, Bo 2 ;

作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Natl Res Ctr Intelligent Agr Equipment, Beijing, Peoples R China

4.Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs PR China, Beijing, Peoples R China

关键词： rice; lodging grade; parcel; Sentinel-2A; change vector analysis (CVA)

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：3.531； 五年影响因子：3.79 ）

ISSN： 0143-1161

年卷期： 2022 年 43 卷 5 期

页码：

收录情况： SCI

摘要： Lodging stress influences the yield, quality, and mechanical harvesting ability of rice (Oryza sativa L.). It is of great significance for the quantitative and objective evaluation of rice yield loss to obtain the rice lodging distribution and grade information rapidly and nondestructively. The purpose of this paper was to establish a remote sensing monitoring method for the grade of rice lodging based on change vector analysis via Sentinel-2A images before and after lodging. Taking the lodging ratio (LR) of rice in the parcel as the characterization index, the changes in spectral reflectance and vegetation index of the rice canopy under different lodging grade stresses were analyzed. A 7-dimensional space vector was composed of a canopy spectral reflectance and vegetation index. A new angle vector was formed by the angle between each vector object and the axis of the canopy spectrum (vegetation index). Based on the difference in feature vectors before and after lodging, a lodging grade monitoring model based on rice parcels was constructed, and the accuracy of the model was verified using field observations. The results showed that the spectral reflectance of the rice canopy after lodging increased with the increase in lodging stress. The difference in vegetation index before and after lodging increased with the increase in lodging severity. Based on the change in the magnitude of the canopy spectral reflectance and its angle vector, and the vegetation index and its angle vector, the R ( 2 ) of the rice lodging model was 0.66, 0.66, 0.60, and 0.59, respectively, and the RMSE values were 21.03%, 20.37%, 22.13%, and 22.84%, respectively. Therefore, the accuracy of the model constructed using the vegetation index was the highest. The canopy spectral reflectance and difference in the vegetation index of rice changed regularly with lodging grade stress. The model based on the change magnitude of the vegetation index could effectively realize remote sensing monitoring of the grade of rice lodging in Wuchang.

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