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Journal of Integrative Agriculture  2021, Vol. 20 Issue (7): 1958-1968    DOI: 10.1016/S2095-3119(20)63483-9
Special Issue: 农业生态环境-遥感合辑Agro-ecosystem & Environment—Romote sensing
Agro-ecosystem & Environment Advanced Online Publication | Current Issue | Archive | Adv Search |
Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model
LIU Zheng-chun1, 2, WANG Chao3, BI Ru-tian1, 2, ZHU Hong-fen1, 2, HE Peng1, 2, JING Yao-dong1, 2, YANG Wu-de3
1 College of Resource and Environment, Shanxi Agricultural University, Taigu 030801, P.R.China
2 National Experimental Teaching Demonstration Center for Agricultural Resources and Environment, Shanxi Agricultural University, Taigu 030801, P.R.China
3 College of Agriculture, Shanxi Agricultural University, Taigu 030801, P.R.China
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为有效验证Sentinel-2影像与CERES-Wheat模型同化进而提高区域作物估产的精度,本文以中国黄土高原东南部三个县(襄汾县、新绛县和闻喜县)为研究区,应用集合卡尔曼滤波算法同化Sentinel-2影像反演的LAI和CERES-Wheat模型模拟的LAI,得到冬小麦生长期逐日的LAI同化值。对比改进的层次分析法、熵值法和归一组合赋权法对不同生育期LAI赋权,并与冬小麦实测单产值进行模型构建,进而对作物进行准确估产。研究结果表明:(1)同化LAI遵循了模拟LAI在冬小麦生育期的生长变化趋势,且在Sentinel-2影像反演LAI的修正下,返青期至抽穗-灌浆期的LAI得到提高,乳熟期的LAI下降减缓,更符合冬小麦LAI的实际生长变化情况;(2)基于实测LAI数据的检验表明,同化LAI比模拟值和反演值的RMSE分别降低了0. 43 m2/m2、0.29 m2/m2,同化过程提高了时间序列LAI的估测精度;(3)归一组合赋权法计算的加权同化LAI与实测单产构建的回归模型决定系数最高R2为0.8627,RMSE最小472.92kg/ha,应用此模型对研究区冬小麦进行估产,县域估测平均单产与统计单产相对误差均小于1%,证明高时空分辨率的Sentinel-2数据融入作物模型能得到更高精度的区域估产结果。

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale.  To verify this method, we applied the ensemble Kalman filter (EnKF) to assimilate the leaf area index (LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model.  From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi.  We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat.  We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI.  With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat.  We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error (RMSE) by 0.43 and 0.29 m2 m–2, respectively, based on the measured LAI.  The assimilation improved the estimation accuracy of the LAI time series.  The highest determination coefficient (R2) was 0.8627 and the lowest RMSE was 472.92 kg ha–1 in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements.  The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with
high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.
Keywords:  data assimilation        CERES-Wheat model        Sentinel-2 images        combined weighting method        yield estimation  
Received: 08 April 2020   Accepted:
Fund: This work was supported by the National Key Research and Development Program of China (2018YFD020040103) and the National Key Research and Development Program of Shanxi Province, China (201803D221005-2).
Corresponding Authors:  Correspondence BI Ru-tian, Tel: +86-354-6288322, E-mail:   
About author:  LIU Zheng-chun, E-mail:

Cite this article: 

LIU Zheng-chun, WANG Chao, BI Ru-tian, ZHU Hong-fen, HE Peng, JING Yao-dong, YANG Wu-de. 2021. Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model. Journal of Integrative Agriculture, 20(7): 1958-1968.

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