Long-term(2013-2022)mapping of winter wheat in the North China Plain using Landsat data:classification with optimal zoning strategy  

作　　者：Yifei Liu Xuehong Chen Jin Chen Yunze Zang Jingyi Wang Miao Lu Liang Sun Qi Dong Bingwen Qiu Xiufang Zhu 

机构地区：[1]State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing,China [2]Beijing Engineering Research Center for Global Land Remote Sensing Products,Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing,China [3]State Key Laboratory of Crop Gene Resources and Breeding,Institute of Crop Sciences,Chinese Academy of Agricultural Sciences(CAAS),Beijing,China [4]State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China,Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing,China [5]Key Laboratory of Spatial Data Mining&Information Sharing of Ministry of Education,Academy of Digital China(Fujian),Fuzhou University,Fuzhou,China

出　　处：《Big Earth Data》2024年第3期494-521,共28页地球大数据（英文）

基　　金：supported by the National Key Research and Development Program of China[No.2022YFD2001100 and No.2017YFD0300201].

摘　　要：Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies.Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area.Although various methods,such as index-based methods,curve similarity-based methods and machine learning-based methods,have been developed for winter wheat mapping based on remote sensing,the former two often require satellite data with high temporal resolution,which are unsuitable for Landsat data with sparse time-series.Machine learning is an effective method for crop classification using Landsat data.Yet,applying machine learn-ing for winter wheat mapping in the North China Plain encounters two main issues:1)the lack of adequate and accurate samples for classifier training;and 2)the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area.To address these two issues,we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data,with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps.Then,we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion,which divided the study area into six subzones with uni-form classification features.For each subzone,a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data.Field sample validation confirmed the high accuracy of the produced maps,with an average overall accuracy of 91.1%and an average kappa coefficient of 0.810 across different years.The derived winter wheat area also has a good correlation(R2=0.949)with census area at the provincial level.The results underscore the reliability of the produced annual winter wheat maps.Additional experiments dem

关 键 词：Winter wheat mapping LANDSAT machine learning North China Plain optimal zoning 

分 类 号：S512.11[农业科学—作物学]