Coupling hyperspectral remote sensing data with a crop model to study winter wheat water demand

Citation

Zhang, C., Liu, J., Dong, T., Pattey, E., Shang, J., Tang, M., Cai, H., Saddique, Q. (2019). Coupling hyperspectral remote sensing data with a crop model to study winter wheat water demand. Remote Sensing, [online] 11(14), http://dx.doi.org/10.3390/rs11141684

Plain language summary

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 a global optimization algorithms. 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. 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.

Abstract

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.

Publication date

2019-01-01

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