Simulation of regional winter wheat growth by using remote sensing data and crop model

2012 ◽  
Author(s):  
Jianmao Guo ◽  
Qi Wang ◽  
Tengfei Zheng ◽  
Junyi Shi ◽  
Jinhui Zhu
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Wheat Growth ◽  
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

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

Estimation of daily CO2 fluxes and of the components of the carbon budget for winter wheat by the assimilation of Sentinel 2-like remote sensing data into a crop model

Geoderma ◽  
2020 ◽  
Vol 376 ◽  
pp. 114428 ◽  
Author(s):  
Gaétan Pique ◽  
Rémy Fieuzal ◽  
Ahmad Al Bitar ◽  
Amanda Veloso ◽  
Tiphaine Tallec ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Carbon Budget ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Co2 Fluxes ◽  
Sensing Data ◽  
Sentinel 2

Simulation of regional rice growth by combination remote sensing data and crop model

2014 ◽  
Author(s):  
Jianmao Guo ◽  
Yanghua Gao ◽  
Junwei Liu ◽  
Dunyue Fei ◽  
Qian Wang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Sensing Data ◽  
Rice Growth
Sign in / Sign up

Export Citation Format

Share Document