Comparison of Two Optimization Algorithms for Estimating Regional Winter Wheat Yield by Integrating MODIS Leaf Area Index and World Food Studies Model

文献类型： 外文期刊

第一作者： Tian, Liyan

作者： Tian, Liyan;Huang, Jianxi;Su, Wei;Zhang, Chao;Liu, Junming;Li, Zhongxia;Wang, Limin

作者机构：

关键词： Remotely Sensed Data;Crop Growth Model;Optimization Method;POWELL;SCE-UA;Regional Crop Yield

期刊名称：SENSOR LETTERS （ 影响因子：0.558； 五年影响因子：0.58 ）

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收录情况： SCI

摘要： Regional crop yield estimation has significant role in ensuring national food security and sustainable agricultural development. Assimilation of remote sensing (RS) data into crop growth model with optimization algorithm is considered as an effective way to improve regional crop yield estimates. POWELL method and SCE-UA method are the two categories of widely used optimization algorithms in data assimilation field. In the present study, we compared accuracy and efficiency of these two optimization algorithms in predicting regional winter wheat yield. WOrld FOod STudies (WOFOST) model was chosen as the crop growth model and calibrated and validated by the field measured data. Time series of MODIS LAI in growing season were adjusted by field measured Leaf Area Index (LAI). Two optimization algorithms were implemented by minimizing an objective function which is established based on the difference between the time series LAI simulated by WOFOST and the adjusted MODIS LAI. Two parameters in WOFOST model (initial biomass (TDWI) and emergence date (IDEM)) were chosen as the re-initialization parameters in the optimization procedure. Our results indicate that both the POWELL algorithm (R~2 = 0.50, RMSE = 279.27 kg/ha) and the SCEUA algorithm (R~2 = 0.47, RMSE = 289.97 kg/ha) have significantly improved the regional crop yield estimates than the WOFOST simulation without assimilation (R~2 = 0.32, RMSE = 652.57 kg/ha) at county level compared to the official statistical yield data. Two optimization algorithms have achieved similar assimilation accuracy. In the aspect of execution efficiency, our results indicate that POWELL algorithm performed better than SCE-UA algorithm and high efficiency is very important to large regional crop yield estimation due to a massive data is used. In general, the results indicate that POWELL algorithm performs better than SCE-UA due to the high assimilation accuracy and the much higher running efficiency.

分类号： TP212

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