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Article

Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data

1
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
Ecosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
3
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
4
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
*
Author to whom correspondence should be addressed.
Academic Editors: Onisimo Mutanga, Nicolas Baghdadi, Clement Atzberger and Prasad S. Thenkabail
Remote Sens. 2016, 8(12), 972; https://doi.org/10.3390/rs8120972
Received: 9 August 2016 / Revised: 16 November 2016 / Accepted: 18 November 2016 / Published: 24 November 2016
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
Knowledge of spatial and temporal variations in crop growth is important for crop management and stable crop production for the food security of a country. A combination of crop growth models and remote sensing data is a useful method for monitoring crop growth status and estimating crop yield. The objective of this study was to use spectral-based biomass values generated from spectral indices to calibrate the AquaCrop model using the particle swarm optimization (PSO) algorithm to improve biomass and yield estimations. Spectral reflectance and concurrent biomass and yield were measured at the Xiaotangshan experimental site in Beijing, China, during four winter wheat-growing seasons. The results showed that all of the measured spectral indices were correlated with biomass to varying degrees. The normalized difference matter index (NDMI) was the best spectral index for estimating biomass, with the coefficient of determination (R2), root mean square error (RMSE), and relative RMSE (RRMSE) values of 0.77, 1.80 ton/ha, and 25.75%, respectively. The data assimilation method (R2 = 0.83, RMSE = 1.65 ton/ha, and RRMSE = 23.60%) achieved the most accurate biomass estimations compared with the spectral index method. The estimated yield was in good agreement with the measured yield (R2 = 0.82, RMSE = 0.55 ton/ha, and RRMSE = 8.77%). This study offers a new method for agricultural resource management through consistent assessments of winter wheat biomass and yield based on the AquaCrop model and remote sensing data. View Full-Text
Keywords: biomass; yield; AquaCrop model; spectral index; particle swarm optimization; winter wheat biomass; yield; AquaCrop model; spectral index; particle swarm optimization; winter wheat
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MDPI and ACS Style

Jin, X.; Kumar, L.; Li, Z.; Xu, X.; Yang, G.; Wang, J. Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data. Remote Sens. 2016, 8, 972. https://doi.org/10.3390/rs8120972

AMA Style

Jin X, Kumar L, Li Z, Xu X, Yang G, Wang J. Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data. Remote Sensing. 2016; 8(12):972. https://doi.org/10.3390/rs8120972

Chicago/Turabian Style

Jin, Xiuliang, Lalit Kumar, Zhenhai Li, Xingang Xu, Guijun Yang, and Jihua Wang. 2016. "Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data" Remote Sensing 8, no. 12: 972. https://doi.org/10.3390/rs8120972

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