Improving winter wheat biomass and evapotranspiration simulation by assimilating leaf area index from spectral information into a crop growth model
Improving winter wheat biomass and evapotranspiration simulation by assimilating leaf area index from spectral information into a crop growth model
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通过将光谱信息中的叶面积指数同化到作物生长模型中来改善冬小麦生物量和蒸散模拟
DOI：
10.1016/j.agwat.2021.107057
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发表时间：
2021-09
影响因子：
6.7
通讯作者：
Huanjie Cai
中科院分区：
文献类型：
--
作者：
Chao Zhang;Jiangui Liu;Jiali Shang;Taifeng Dong;Min Tang;Shaoyuan Feng;Huanjie Cai
数据同化是一种将遥感数据与动力学模型相结合以提高模型性能的方法，在陆面过程模拟中得到了广泛应用。利用不同水分条件下的数据同化技术，可以了解作物对不同供水率的响应，这对干旱半干旱地区的农业水分管理具有重要意义。为此，我们开发了一种通用的数据同化方法，通过将混合复杂进化（SCE）和Enklemen卡尔曼滤波（EnKF）算法集成到简单算法的产量和蒸散量（SAFYE）模型，以提供更好的模拟冬小麦生物量和产量，并模拟不同供水情况下的蒸散量（ET）。在2013-2015年的生长周期中进行了九种灌溉情景的实验。利用田间光谱数据检索叶面积指数（LAI），然后将其作为单个状态变量，使用全局优化算法确定SAFYE模型中的其他参数。基于EnKF算法，将时间序列LAI同化到SAFYE模型中，以改善整体模型模拟。结果表明，在同化估算的叶面积指数的情况下，模拟的作物生长动态在大多数情况下与实测值吻合较好。模拟的生物量在日步长的准确性有所提高，2013-2014年和2014-2015年生长季的最大RMSE分别从199.4 g m− 2和466.6 g m− 2降至123.8 g m− 2和393.4 g m− 2。两个生长季节的估计和实地测量的粮食产量之间也达到了很好的一致性（R2= 0.901，RMSE= 31.9 g m-2，RRMSE=6.55%）。对0-20 cm土层土壤含水量的模拟结果（RMSE：3.3-5.0 mm）优于对0-100 cm土层土壤含水量的模拟结果（RMSE：11.7-29.6 mm）。前期水分亏缺情景下的模拟精度较低，两个生长季的正平均相对误差为14%（3.4-24.3%）。本研究表明，遥感数据的耦合，以提高模拟冬小麦生长的SAFYE模型的性能的巨大潜力。
Data assimilation, a state-of-the-art method that merges remote sensing data with a dynamic model to improve model performance, has been widely used in land surface process modeling. Application of data assimilation under various water conditions can provide insight of crop response to different water supply rates, which is useful for agricultural water management in arid and semi-arid regions. For this purpose, we developed a generic data assimilation methodology by integrating both the Shuffled Complex Evolution (SCE) and the Ensemble Kalman Filter (EnKF) algorithms into the Simple Algorithm For Yield and Evapotranspiration (SAFYE) model to provide improved simulation of winter wheat biomass and yield, and simulation of evapotranspiration (ET) under different water-supply scenarios. An experiment with nine irrigation scenarios was conducted during the 2013—2015 growing cycles. Field spectral data were employed to retrieve the leaf area index (LAI), which was then used as a single state variable to determine other parameters in the SAFYE model using a global optimization algorithm. Time-series LAI was eventually assimilated in the SAFYE model based on the EnKF algorithm to improve overall model simulation. The results showed that the simulated crop growth dynamics followed the measurements well in most cases when the estimated LAI was assimilated. The accuracy of simulated biomass at the daily step was improved, with the maximum RMSE decreased from 199.4 to 123.8 g m−2and from 466.6 to 393.4 g m−2for the 2013–2014 and 2014–2015 growing seasons respectively. A good agreement was also achieved between the estimated and field measured grain yield (R2= 0.901, RMSE= 31.9 g m−2, RRMSE=6.55%) for both growing seasons. The simulation of soil water content in the top 0—20 cm soil layer was better (RMSE: 3.3—5.0 mm) than that of 0—100 cm layer (RMSE: 11.7—29.6 mm). Accuracy of the simulated ET under early-stage water deficit scenarios was lower than that under other scenarios, with a positive mean relative error of 14% (3.4—24.3%) during two growing seasons. This study demonstrates the great potential of coupling remote sensing data to improve the performance of SAFYE model in modeling winter wheat growth.
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不同灌溉情景下冬小麦产量估算模型简单算法的评价
影响因子：
2.1
作者：
Chao Zhang;Jiangui Liu;Taifeng Dong;Jiali Shang;Min Tang;Lili Zhao;Huanjie Cai
通讯作者：
Huanjie Cai
影响因子：
3.7
作者：
Silvestro PC;Pignatti S;Yang H;Yang G;Pascucci S;Castaldi F;Casa R
通讯作者：
Casa R
DOI：
--
发表时间：
2007
期刊：
--
影响因子：
--
作者：
B. Duchemin;P. Maisongrande;G. Boulet;I. Benhadj
通讯作者：
B. Duchemin;P. Maisongrande;G. Boulet;I. Benhadj
DOI：
10.1016/j.agwat.2005.07.020
发表时间：
2006-04
期刊：
--
影响因子：
--
作者：
Yh Yang
通讯作者：
Yh Yang
DOI：
10.1007/978-3-540-71056-1_2
发表时间：
2009
期刊：
--
影响因子：
--
作者：
Ionel M. Navon
通讯作者：
Ionel M. Navon