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基于多物候特征指数的冬小麦分布信息提取
引用本文:吴喜芳,化仕浩,张莎,谷玲霄,马春艳,李长春. 基于多物候特征指数的冬小麦分布信息提取[J]. 农业机械学报, 2023, 54(12): 207-216
作者姓名:吴喜芳  化仕浩  张莎  谷玲霄  马春艳  李长春
作者单位:河南理工大学;青岛大学
基金项目:国家自然科学基金项目(42101382)、河南省科技攻关项目(222102110038、232102210093)、河南省博士后基金项目(202103072)、河南理工大学博士基金项目(B2021-19)和河南理工大学测绘科学与技术“双一流”学科创建项目(JXSFZXKFJJ202308、JXSFZXKFJJ202305)
摘    要:以往的冬小麦分布信息提取研究大多基于单个物候期或单个植被指数,未考虑不同物候期特征及不同物候期之间的联系导致分类精度较低。为提高提取精度,本文基于冬小麦播种期、越冬期、生长期及成熟期选取相应特征指数,提出一种多物候特征指数的冬小麦识别方法,并对2020年焦作市的冬小麦面积进行提取。通过对不同物候期、不同分类方法下的结果进行对比,结果表明:在物候期的选择上,加入越冬期后,随机森林与支持向量机分类的总体精度与Kappa系数呈现不同程度的提升,均方根误差(RMSE)分别减小19.3%和9.8%,提取冬小麦面积的误差百分比分别降低8.64、4.42个百分点。在不同分类方法上,随机森林相较于支持向量机、最小距离,分类的总体精度与Kappa系数更高。相较于支持向量机,随机森林分类的RMSE减小19.6%。相较于单一特征指数,基于随机森林的多物候特征指数分类的总体精度,Kappa系数更高,RMSE为1.84×103 hm2,比单一特征指数减小33.6%,提取冬小麦面积的误差百分比减小7.14个百分点。

关 键 词:冬小麦  Sentinel-2  多物候特征  支持向量机  随机森林  最小距离
收稿时间:2023-07-25

Extraction of Winter Wheat Distribution Information Based on Multi-phenological Feature Indices Derived from Sentinel-2 Data
WU Xifang,HUA Shihao,ZHANG Sh,GU Lingxiao,MA Chunyan,LI Changchun. Extraction of Winter Wheat Distribution Information Based on Multi-phenological Feature Indices Derived from Sentinel-2 Data[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(12): 207-216
Authors:WU Xifang  HUA Shihao  ZHANG Sh  GU Lingxiao  MA Chunyan  LI Changchun
Affiliation:Henan Polytechnic University;Qingdao University
Abstract:Previous research on the extraction of winter wheat distribution information has mostly relied on single phenological periods or individual vegetation indices, neglecting the characteristics of different phenological periods and their interconnections, which has limited the classification accuracy. To enhance the extraction accuracy, a method for winter wheat identification was proposed based on corresponding feature indices for the sowing period, overwintering period, growth period, and maturation period. The method was applied to extract the winter wheat area in Jiaozuo City in 2020. By comparing the results under different phenological periods and classification methods, the findings indicated that the inclusion of the overwintering period led to varying degrees of improvement in overall accuracy and Kappa coefficients for both random forest and support vector machine classification methods, with respective reductions in root mean square error (RMSE) by 19.3% and 9.8%. The error percentage in winter wheat area extraction was reduced by 8.64 percentage points and 4.42 percentage points, respectively. Among different classification methods, random forest outperforms support vector machine and minimum distance in terms of overall accuracy and Kappa coefficient. Compared with support vector machine, random forest classification reduced RMSE by 19.6%. When compared with single feature indices, the overall accuracy and Kappa coefficient of the multi-phenological feature index based on random forest were higher, with RMSE of 1.84×103hm2, representing 33.6% reduction compared with single feature indices and 7.14 percentage points decrease in the error percentage for winter wheat area extraction.
Keywords:
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