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Article

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

1
College of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225009, China
2
Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada
3
Key Laboratory of Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest Agriculture and Forestry University, Yangling 712100, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1684; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141684
Received: 19 May 2019 / Revised: 11 July 2019 / Accepted: 13 July 2019 / Published: 16 July 2019
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. View Full-Text
Keywords: winter wheat; crop model; remote sensing; leaf area index; soil moisture; evapotranspiration winter wheat; crop model; remote sensing; leaf area index; soil moisture; evapotranspiration
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MDPI and ACS Style

Zhang, C.; Liu, J.; Dong, T.; Pattey, E.; Shang, J.; Tang, M.; Cai, H.; Saddique, Q. Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand. Remote Sens. 2019, 11, 1684. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141684

AMA Style

Zhang C, Liu J, Dong T, Pattey E, Shang J, Tang M, Cai H, Saddique Q. Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand. Remote Sensing. 2019; 11(14):1684. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141684

Chicago/Turabian Style

Zhang, Chao, Jiangui Liu, Taifeng Dong, Elizabeth Pattey, Jiali Shang, Min Tang, Huanjie Cai, and Qaisar Saddique. 2019. "Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand" Remote Sensing 11, no. 14: 1684. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141684

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