摘要
油菜关键物候期信息的获取对于油菜的田间管理、观赏时间预测及产量估测等具有重要意义,是精准农业实施的重要组成部分。极化合成孔径雷达技术不仅可以实现对作物全天时监测,而且对作物的结构信息敏感,在物候期提取中极具潜力。首先,以覆盖油菜整个生长期的5景时间序列全极化Radarsat-2数据为基础,基于Stokes矢量提取了平均强度g0、归一化平均强度g0m、平均极化度ρm、零度方向路线球面度Pdor、零度孔径路线倾斜度Idap和零度孔径路线弧对称度Aadap6个典型Stokes参数;然后,对比分析了这6个参数对油菜整个生长期动态变化的响应特征,并以此为基础采用决策树(decision tree,DT)算法对油菜的物候期进行了识别。研究结果表明,6个Stokes参数中,除ρm和Aadap外,其他4个参数均对油菜物候期变化敏感,在油菜物候期识别中具有极大的潜力。DT算法能有效识别油菜的各关键物候期,其分类结果与样地实测数据具有良好的一致性,总体分类精度为87.4%;在单个物候期的识别中,识别精度最高达到了94.3%。
Objectives:The key phenological information of oilseed rapeseed(Brassica napus L.)plays an important role in field management,viewing time prediction and yield estimation of the oilseed rape.It is al⁃so an important part of precision agriculture.Polarimetric synthetic aperture radar technology shows great potential in phenological phase identification with its all-weather monitoring capability and its sensitivity to the crop structural information.Methods:First,we identified the 5 phenological phases of the oilseed rape on the test area with 5 time series full-polarization Radarsat-2 data,which covers the whole growth period of the oilseed rape.6 typical Stokes parameters are extracted and applied in the identification of oilseed rape phenological phases,the extracted Stokes parameters includ averaged intensity(g0),normalized average inten⁃sity(g0m),averaged degree of polarization(ρm),perimeter degree of zero orientation route(Pdor),inclination de⁃gree of zero aperture route(Idap),and arc asymmetry degree of zero aperture route(Aadap).Then,The pheno⁃logical phases of oilseed rape is identified by the decision tree(DT)algorithm based on the comparative analysis of the dynamic response of the 6 special Stokes parameters to rape growth stages.Results and Conclu⁃sions:Among the extracted Stokes parameters applied in this study,exceptρm and Aadap,other parameters show great sensitivity to the change of the oilseed rape phenological phases.The DT algorithm also per⁃form well in the classification of the oilseed rape phenological phases.The classification results agree well with the field measured samples,and the overall classification accuracy is 87.4%,while the highest classifi⁃cation accuracy of each phenological phase is 94.3%.
作者
张永鑫
张王菲
徐昆鹏
李建刚
ZHANG Yongxin;ZHANG Wangfei;XU Kunpeng;LI Jiangang(School of Forestry,Southwest Forestry University,Kunming 650224,China;Research Institute of Forest Resource Information Techniques,China Academy of Forestry,Beijing 100091,China;Kunming Real Estate Ownership Investigation Center,Kunming Institute of Surveying and Mapping of Land Planning and Prospecting,Kunming 650216,China)
出处
《武汉大学学报（信息科学版）》
EI
CAS
CSCD
北大核心
2023年第8期1322-1330,共9页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金(31860240,42161059,32160365)。
关键词
油菜
物候期识别
Stokes参数
决策树算法
oilseed rape
phenological identification
Stokes parameters
decision tree algorithm
作者简介
第一作者:张永鑫,硕士生,主要从事合成孔径雷达森林高度反演研究。zyx@swfu.edu.cn;通讯作者:张王菲,博士,教授。mewhff@163.com。
