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The 10-m crop type maps in Northeast China during 2017-2019.
You, Nanshan; Dong, Jinwei; Huang, Jianxi; Du, Guoming; Zhang, Geli; He, Yingli; Yang, Tong; Di, Yuanyuan; Xiao, Xiangming.
Afiliación
  • You N; Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
  • Dong J; University of Chinese Academy of Sciences, Beijing, 100049, China.
  • Huang J; Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. dongjw@igsnrr.ac.cn.
  • Du G; College of Land Science and Technology, China Agricultural University, Beijing, 100083, China.
  • Zhang G; School of Public Administration and Law, Northeast Agricultural University, Harbin, 150030, China.
  • He Y; College of Land Science and Technology, China Agricultural University, Beijing, 100083, China.
  • Yang T; Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
  • Di Y; College of Land Science and Technology, China Agricultural University, Beijing, 100083, China.
  • Xiao X; College of Land Science and Technology, China Agricultural University, Beijing, 100083, China.
Sci Data ; 8(1): 41, 2021 02 02.
Article en En | MEDLINE | ID: mdl-33531510
ABSTRACT
Northeast China is the leading grain production region in China where one-fifth of the national grain is produced; however, consistent and reliable crop maps are still unavailable, impeding crop management decisions for regional and national food security. Here, we produced annual 10-m crop maps of the major crops (maize, soybean, and rice) in Northeast China from 2017 to 2019, by using (1) a hierarchical mapping strategy (cropland mapping followed by crop classification), (2) agro-climate zone-specific random forest classifiers, (3) interpolated and smoothed 10-day Sentinel-2 time series data, and (4) optimized features from spectral, temporal, and texture characteristics of the land surface. The resultant maps have high overall accuracies (OA) spanning from 0.81 to 0.86 based on abundant ground truth data. The satellite estimates agreed well with the statistical data for most of the municipalities (R2 ≥ 0.83, p < 0.01). This is the first effort on regional annual crop mapping in China at the 10-m resolution, which permits assessing the performance of the soybean rejuvenation plan and crop rotation practice in China.

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Data Año: 2021 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Sci Data Año: 2021 Tipo del documento: Article