| 光谱学与光谱分析 |
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| Estimation of Fraction of Absorbed Photosynthetically Active Radiation for Winter Wheat Based on Hyperspectral Characteristic Parameters |
| ZHANG Chao, CAI Huan-jie*, LI Zhi-jun |
| Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University，Yangling 712100, China |
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Abstract Estimating fraction of absorbed photosynthetically active radiation (FPAR) precisely has great importance for detecting vegetation water content, energy and carbon cycle balance. Based on this, ASD FieldSpec 3 and SunScan canopy analyzer were applied to measure the canopy spectral reflectance and photosynthetically active radiation over whole growth stage of winter wheat. Canopy reflectance spectral data was used to build up 24 hyperspectral characteristic parameters and the correlation between FPAR and different spectral characteristic parameters were analyzed to establish the estimation model of FPAR for winter wheat. The results indicated that there were extremely significant correlations (p＜0.01) between FPAR and hyperspectral characteristic parameters except the slope of blue edge (Db). The correlation coefficient between FPAR and the ratio of red edge area to blue edge area (VI4) was the highest, reaching at 0.836. Seven spectral parameters with higher correlation coefficient were selected to establish optimal linear and nonlinear estimation models of FPAR, and the best estimating models of FPAR were obtained by accuracy analysis. For the linear model, the inversion model between green edge and FPAR was the best, with R2, RMSE and RRMSE of predicted model reaching 0.679, 0.111 and 20.82% respectively. For the nonlinear model, the inversion model between VI2 (normalized ratio of green peak to red valley of reflectivity) and FPAR was the best, with R2, RMSE and RRMSE of predicted model reaching 0.724, 0.088 and 21.84% for. In order to further improve the precision of the model, the multiple linear regression and BP neural network methods were used to establish models with multiple high spectral parameters BP neural network model (R2=0.906, RMSE=0.08, RRMSE=16.57%) could significantly improve the inversion precision compared with the single variable model. The results show that using hyperspectral characteristic parameters to estimate FPAR of winter wheat is feasible. It provides a new method and theoretical basis for monitoring the dynamic change of FPAR in real time, effectively and accurately during the growth stage of winter wheat.
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Received: 2014-07-30
Accepted: 2014-11-25
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Corresponding Authors:
CAI Huan-jie
E-mail: caihj@nwsuaf.edu.cn
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