Regional yields simulation for winter wheat in North China based on assimilating remote sensing data

2006 ◽  
Author(s):  
Yuping Ma ◽  
Li Zhang ◽  
Shili Wang
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
North China ◽  
Remote Sensing Data ◽  
Sensing Data

Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

2008 ◽  
Vol 10 (4) ◽  
pp. 426-437 ◽  
Author(s):  
Ma Yuping ◽  
Wang Shili ◽  
Zhang Li ◽  
Hou Yingyu ◽  
Zhuang Liwei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
North China ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Wheat Growth ◽  
Sensing Data

Drought monitoring in North China using HJ-1 satellite remote sensing data

2009 ◽  
Author(s):  
Bingfeng Yang ◽  
Qiao Wang ◽  
Changzuo Wang ◽  
Huawei Wan ◽  
Yipeng Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
North China ◽  
Remote Sensing Data ◽  
Drought Monitoring ◽  
Sensing Data

scholarly journals Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand

2019 ◽  
Vol 11 (14) ◽  
pp. 1684 ◽  
Author(s):  
Chao Zhang ◽  
Jiangui Liu ◽  
Taifeng Dong ◽  
Elizabeth Pattey ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Winter Wheat ◽  
Water Demand ◽  
Stress Condition ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Actual Evapotranspiration ◽  
Sensing Data ◽  
Water Stress Condition

Accurate information of crop growth conditions and water status can improve irrigation management. The objective of this study was to evaluate the performance of SAFYE (simple algorithm for yield and evapotranspiration estimation) crop model for simulating winter wheat growth and estimating water demand by assimilating leaf are index (LAI) derived from canopy reflectance measurements. A refined water stress function was used to account for high crop water stress. An experiment with nine irrigation scenarios corresponding to different levels of water supply was conducted over two consecutive winter wheat growing seasons (2013–2014 and 2014–2015). The calibration of four model parameters was based on the global optimization algorithms SCE-UA. Results showed that the estimated and retrieved LAI were in good agreement in most cases, with a minimum and maximum RMSE of 0.173 and 0.736, respectively. Good performance for accumulated biomass estimation was achieved under a moderate water stress condition while an underestimation occurred under a severe water stress condition. Grain yields were also well estimated for both years (R2 = 0.83; RMSE = 0.48 t∙ha−1; MRE = 8.4%). The dynamics of simulated soil moisture in the top 20 cm layer was consistent with field observations for all scenarios; whereas, a general underestimation was observed for total water storage in the 1 m layer, leading to an overestimation of the actual evapotranspiration. This research provides a scheme for estimating crop growth properties, grain yield and actual evapotranspiration by coupling crop model with remote sensing data.

Estimation of winter wheat yield by using remote sensing data and crop model

2012 ◽  
Author(s):  
Jianmao Guo ◽  
Tengfei Zheng ◽  
Qi Wang ◽  
Jia Yang ◽  
Junyi Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Sensing Data ◽  
Winter Wheat Yield
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