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Coupling remote sensing and crop growth model to estimate national wheat yield in Ethiopia

Big Earth Data 2022年第1期6卷 18-35页

作者： Awetahegn Niguse Beyene Hongwei Zeng Bingfang Wu Liang Zhu Tesfay Gebretsadkan Gebremicael Miao Zhang temesgen bezabh State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Tigray Agricultural Research Institute MekelleEthiopia Climate Change and Rural Development Mekelle UniversityMekelleEthiopia

Estimation of crop yield at a regional level is essential for making agricultural planning and addressing food security issues in *** sensing observations,particularly the leaf area index(LAI),have a strong relationship with crop *** study has proposed an approach to estimate wheat yield at field level and regional scale in Ethiopia by assimilating the retrieved MODIS time-series LAI data into the WOrld FOod STudies(WOFOST)*** improve the estimation of crop yield in the region,the Ensemble Kalman Filter(EnKF)was used to incorporate the LAI into the WOFOST *** estimation accuracy of wheat crop yield was validated using field-measured yields collected during the 2018 growing *** findings indicated that wheat yield was more precisely estimated by WOFOST(at water-limited mode)with EnKF algorithm(R^(2)=0.80 and RMSE=413 kg ha^(−1))compared to that of without assimilating remotely sensed LAI(R^(2)=0.58,RMSE=592 kg ha^(−1)).These results demonstrated that assimilating MODIS-LAI into WOFOST has high potential and practicality to give a reference for wheat yield *** findings from this study can provide information to policy,decision-makers,and other similar sectors to implement an appropriate and timely yield estimation measure.

关键词： Wheat yield WOFOST model Ensemble Kalman Filter remote sensing

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