中国农业气象2026，Vol.47Issue(2)：180-190,11.DOI:10.3969/j.issn.1000-6362.2026.02.002
基于多源卫星时序数据的当季冬小麦识别技术
Identification Technique for In-season Winter Wheat Based on Multi-source Satellite Time-series Data
摘要
Abstract
Crop intercropping and fragmented planting patterns are major factors limiting the accuracy of crop identification using remote sensing.Multi-source satellite time-series data can effectively distinguish target crops from other vegetation by capturing their unique growth characteristics during specific phenological stages.In this study,the spatial distribution of winter wheat was extracted by integrating the EVI derived from Sentinel-2 across various growth stages,along with NDBI,SAVI,FY-3D EVI time series data,and VV,VH and VH/VV time series data from Sentinel-1.Principal component analysis and the random forest algorithm were employed for feature selection and classification.The results showed that the EVI trends of winter wheat during the emergence to green-up stages differed significantly from those of other vegetation.Similarly,VV and VH/VV backscatter features showed clear distinctions after the green-up stage.The overall classification accuracies using time-series data from sowing to wintering,heading and maturity stages were 95.58%,98.41%,and 98.65%,respectively.Data from the sowing-heading period achieved higher accuracy for field roads and boundaries compared to pre-wintering data.The addition of the FY-3D dataset improved the overall identification accuracy by 1.71pp to 4.10pp across different growth stages,while the inclusion of Sentinel-1 data increased accuracy by 0.21pp to 1.66pp.关键词
冬小麦/生长期/植被指数/SAR数据/随机森林Key words
Winter wheat/Growth stage/Vegetation index/SAR data/Random forest引用本文复制引用
陈心桐,王圆圆,张宏群,谢铁军,王状,张凯迪,霍彦峰,荀尚培..基于多源卫星时序数据的当季冬小麦识别技术[J].中国农业气象,2026,47(2):180-190,11.基金项目
国家重点研发计划专项课题(2023YFB3905802) （2023YFB3905802）
中国气象局创新发展专项项目(CXFZ2025J118) （CXFZ2025J118）
安徽省气象局创新发展专项项目(CXB202301 （）
CXM202306 （）
YJG202203) （）
中国气象局青年创新团队"高标准农田智意气象保障技术"项目(CMA2024QN03) （CMA2024QN03）
北京市科学技术协会青年人才托举工程项目(BYESS2023205) （BYESS2023205）
安徽省自然科学基金"江淮气象"联合基金项目(2208085UQ04) （2208085UQ04）
中国气象局气象能力提升联合研究专项项目(22NLTSY006 （）
22NLTSQ011) （）
