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Calibration and validation of SiBcrop Model for simulating LAI and surface heat fluxes of winter wheat in the North China Plain
CHEN Ying, LIU Feng-shan, TAO Fu-lu, GE Quan-sheng, JIANG Min, WANG Meng, ZHAO Feng-hua
2020, 19 (9): 2206-2215.   DOI: 10.1016/S2095-3119(20)63178-1
Abstract127)      PDF in ScienceDirect      
The accurate representation of surface characteristic is an important process to simulate surface energy and water flux in land-atmosphere boundary layer.  Coupling crop growth model in land surface model is an important method to accurately express the surface characteristics and biophysical processes in farmland.  However, the previous work mainly focused on crops in single cropping system, less work was done in multiple cropping systems.  This article described how to modify the sub-model in the SiBcrop to realize the accuracy simulation of leaf area index (LAI), latent heat flux (LHF) and sensible heat flux (SHF) of winter wheat growing in double cropping system in the North China Plain (NCP).  The seeding date of winter wheat was firstly reset according to the actual growing environment in the NCP.  The phenophases, LAI and heat fluxes in 2004–2006 at Yucheng Station, Shandong Province, China were used to calibrate the model.  The validations of LHF and SHF were based on the measurements at Yucheng Station in 2007–2010 and at Guantao Station, Hebei Province, China in 2009–2010.  The results showed the significant accuracy of the calibrated model in simulating these variables, with which the R2, root mean square error (RMSE) and index of agreement (IOA) between simulated and observed variables were obviously improved than the original code.  The sensitivities of the above variables to seeding date were also displayed to further explain the simulation error of the SiBcrop Model.  Overall, the research results indicated the modified SiBcrop Model can be applied to simulate the growth and flux process of winter wheat growing in double cropping system in the NCP. 
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Corn Yield Forecasting in Northeast China Using Remotely Sensed Spectral Indices and Crop Phenology Metrics
WANG Meng, TAO Fu-lu , SHI Wen-jiao
2014, 13 (7): 1538-1545.   DOI: 10.1016/S2095-3119(14)60817-0
Abstract1786)      PDF in ScienceDirect      
Early crop yield forecasting is important for food safety as well as large-scale food related planning. The phenology-adjusted spectral indices derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to develop liner regression models with the county-level corn yield data in Northeast China. We also compared the different spectral indices in predicting yield. The results showed that, using Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Land Surface Water Index (LSWI), the best time to predict corn yields was 55-60 days after green-up date. LSWI showed the strongest correlation (R2=0.568), followed by EVI (R2=0.497) and NDWI (R2=0.495). The peak correlation between Wide Dynamic Range Vegetation Index (WDRVI) and yield was detected 85 days after green-up date (R2=0.506). The correlation was generally low for Normalized Difference Vegetation Index (NDVI) (R2=0.385) and no obvious peak correlation existed for NDVI. The coefficients of determination of the different spectral indices varied from year to year, which were greater in 2001 and 2004 than in other years. Leave-one-year-out approach was used to test the performance of the model. Normalized root mean square error (NRMSE) ranged from 7.3 to 16.9% for different spectral indices. Overall, our results showed that crop phenology-tuned spectral indices were feasible and helpful for regional corn yield forecasting.
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