首页|Improved simulation of winter wheat yield in North China Plain by using PRYM-Wheat integrated dry matter distribution coefficient
Improved simulation of winter wheat yield in North China Plain by using PRYM-Wheat integrated dry matter distribution coefficient
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国家科技期刊平台
NETL
NSTL
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The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.Presently,most remote sensing process models use the"biomass×harvest index(HI)"method to simulate regional-scale winter wheat yield.However,spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale.Time-series dry matter partition coefficients(Fr)can dynamically reflect the dry matter partition of winter wheat.In this study,Fr equations were fitted for each organ of winter wheat using site-scale data.These equations were then coupled into a process-based and remote sensing-driven crop yield model for wheat(PRYM-Wheat)to improve the regional simulation of winter wheat yield over the North China Plain(NCP).The improved PRYM-Wheat model integrated with the fitted Fr equations(PRYM-Wheat-Fr)was validated using data obtained from provincial yearbooks.A 3-year(2000-2002)averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination(R2=0.55)and lower root mean square error(RMSE=0.94 t ha-1)than PRYM-Wheat with a stable HI(abbreviated as PRYM-Wheat-HI),which had R2 and RMSE values of 0.30 and 1.62 t ha-1,respectively.The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years(2013-2015).In conclusion,the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model,making it a useful tool for the simulation of regional winter wheat yield.
dry matter partitionremote sensing modelwinter wheat yieldNorth China Plain
Xuan Li、Shaowen Wang、Yifan Chen、Danwen Zhang、Shanshan Yang、Jingwen Wang、Jiahua Zhang、Yun Bai、Sha Zhang
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Research Center for Remote Sensing and Digital Earth,College of Computer Science and Technology,Qingdao University,Qingdao 266071,China
Center for Geospatial Information,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China
School of Geographical Sciences,Hebei Normal University,Shijiazhuang 050024,China
国家科技期刊平台
NSTL
万方数据
