Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data

文献类型： 外文期刊

第一作者： Xu, Xiaobin

作者： Xu, Xiaobin;Teng, Cong;Chen, Liping;Li, Zhenhai;Xu, Xiaobin;Jin, Xiuliang;Xu, Xiaobin;Teng, Cong;Zhao, Yu;Du, Ying;Zhao, Chunqi;Yang, Guijun;Song, Xiaoyu;Gu, Xiaohe;Chen, Liping;Li, Zhenhai;Xu, Xiaobin;Teng, Cong;Casa, Raffaele

作者机构：

关键词： Multi-source Remote Sensing Imagery; European Center for Medium-range Weather Forecasts (ECMWF) meteorological data; grain protein content; hierarchical linear model

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2020 年 12 卷 8 期

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收录情况： SCI

摘要： Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R-2 = 1.00) and RE (R-2 = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R-2 = 0.524) demonstrated higher accuracy than the empirical linear model (R-2 = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction.

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