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The 500-meter long-term winter wheat grain protein content dataset for China from multi-source data.
Xu, Xiaobin; Zhou, Lili; Taylor, James; Casa, Raffaele; Fan, Chengzhi; Song, Xiaoyu; Yang, Guijun; Huang, Wenjiang; Li, Zhenhai.
Afiliación
  • Xu X; College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, PR China.
  • Zhou L; College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, PR China.
  • Taylor J; UMRITAP, Montpellier SupAgro, Irstea, Univ. Montpellier, Montpellier, 34000, France.
  • Casa R; DAFNE, Università della Tuscia, Via San Camillo de Lellis, 01100, Viterbo, Italy.
  • Fan C; College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, PR China.
  • Song X; Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
  • Yang G; Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
  • Huang W; State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
  • Li Z; College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, PR China. lizh323@126.com.
Sci Data ; 11(1): 1025, 2024 Sep 19.
Article en En | MEDLINE | ID: mdl-39300179
ABSTRACT
In China, the exigency for precise wheat grain protein content (GPC) data rises with growing food consumption demands and global market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diverse geographical terrain in China exacerbates the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS. The results demonstrate that the GPC estimation model based on hierarchical linear model significantly outperformed other conventional models. The validation dataset exhibited an R2 of 0.45 and an RMSE of 0.96%. In cross-validation, the RMSE values ranged from 0.90% in Gansu to 1.32% in Anhui. For leave-one-year-out cross-validation, the RMSE values ranged from 0.77% to 1.11%. CNWheatGPC-500 offers valuable insights for enhancing wheat production, quality control, and agricultural decision-making.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Triticum País/Región como asunto: Asia Idioma: En Revista: Sci Data Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Triticum País/Región como asunto: Asia Idioma: En Revista: Sci Data Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido