The 500-meter long-term winter wheat grain protein content dataset for China from multi-source data
文献类型： 外文期刊
作者： Xu, Xiaobin 1 ; Zhou, Lili 1 ; Taylor, James 2 ; Casa, Raffaele 3 ; Fan, Chengzhi 1 ; Song, Xiaoyu 4 ; Yang, Guijun 4 ; Huang, Wenjiang 5 ; Li, Zhenhai 1 ;
作者机构： 1.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China
2.Univ Montpellier, UMRITAP, Montpellier SupAgro, Irstea, F-34000 Montpellier, France
3.Univ Tuscia, DAFNE, Via San Camillo Lellis, I-01100 Viterbo, Italy
4.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
5.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China
期刊名称：SCIENTIFIC DATA （ 影响因子：6.9； 五年影响因子：8.7 ）
ISSN：
年卷期： 2024 年 11 卷 1 期
页码：
收录情况： SCI
摘要： 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.
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