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 t...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 demonstrate that our proposed optimal zoning method outper-forms other zoning methods,including Köppen climate zoning,wheat planting zoning and non-zoning methods,in enhancing wheat mapping accuracy.It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.展开更多
基金supported by the National Key Research and Development Program of China[No.2022YFD2001100 and No.2017YFD0300201].
文摘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 demonstrate that our proposed optimal zoning method outper-forms other zoning methods,including Köppen climate zoning,wheat planting zoning and non-zoning methods,in enhancing wheat mapping accuracy.It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.
