Mapping rice lodging severity using dual-pol Sentinel-1 SAR data: polarimetric parameters, lodging sensitivity, and fuzzy classification modelling

文献类型： 外文期刊

第一作者： Wang, Mo

作者： Wang, Mo;Chen, Li;Cui, Yunpeng;Liu, Juan;Wu, Jinming;Wang, Ting;Li, Huan;Wang, Mo;Sun, Qing;Chen, Li;Cui, Yunpeng;Liu, Juan;Wu, Jinming;Wang, Ting;Li, Huan;Sun, Qing;Sun, Qing;Che, Xianghong

作者机构：

关键词： Rice lodging; PolSAR; polarimetric parameters; lodging severity classification

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：2.6； 五年影响因子：2.9 ）

ISSN： 0143-1161

年卷期： 2025 年 46 卷 7 期

页码：

收录情况： SCI

摘要： Lodging is a major cause of crop yield loss worldwide. Remote sensing data provides unparalleled advantages for large-scale lodging monitoring. This study leverages the strengths of polarimetric synthetic aperture radar (PolSAR) data to detect changes in surface roughness and texture caused by rice lodging. Using Stokes parameters and a dual-polarization H-alpha decomposition, we extracted 14 polarimetric parameters from dual-polarization Sentinel-1 data to assess their sensitivity to rice lodging. A feature sensitivity metric was defined as the absolute value of Cohen's d for pre- and post-lodging samples. The results indicate that the Stokes parameter g2 was the most sensitive feature for discriminating rice lodging. Other top-ranked sensitive features include VH backscatter intensity (${\rm{\sigma }}_{{\rm{VH}}}<<^>>0$sigma VH0), the second eigenvalue (l2) of the coherence matrix ${C_2}$C2, and Normalized Shannon Entropy (NSE). The three least important features were Stokes parameter g1, VV backscatter intensity (${\rm{\sigma }}_{{\rm{VV}}}<<^>>0$sigma VV0), and the first eigenvalue (l1) of ${C_2}$C2. We further developed a recursive feature selection procedure based on the permutation importance of the features. Three types of classifiers - Random Forest, XGBoost, and Multilayer Perceptron - were tested for the binary classification of lodging states. The random forest classifier was identified as the most effective model for detecting severe rice lodging (Precision = 0.86 and Kappa = 0.67). The probability of severe rice lodging was mapped with a fuzzy classification strategy. This study confirms the feasibility of using polarimetric parameters from dual-pol SAR images to monitor rice lodging and offers a reference for PolSAR feature selection in related studies.

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