Measuring leaf nitrogen concentration in winter wheat using double-peak spectral reflection remote sensing data

2014 ◽  
Vol 159 ◽  
pp. 43-52 ◽  
Author(s):  
Wei Feng ◽  
Bin-Bin Guo ◽  
Zhi-Jie Wang ◽  
Li He ◽  
Xiao Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Nitrogen Concentration ◽  
Remote Sensing Data ◽  
Leaf Nitrogen ◽  
Double Peak ◽  
Leaf Nitrogen Concentration ◽  
Sensing Data ◽  
Spectral Reflection

Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data

2016 ◽  
Vol 174 ◽  
pp. 122-133 ◽  
Author(s):  
Li He ◽  
Xiao Song ◽  
Wei Feng ◽  
Bin-Bin Guo ◽  
Yuan-Shuai Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Hyperspectral Data ◽  
Leaf Nitrogen Concentration

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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