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
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.
Key words:Winter wheat;Hyperspectral characteristic parameters;Photosynthetically active radiation
张 超,蔡焕杰*,李志军 . 高光谱特征参量的冬小麦吸收性光合有效辐射分量估算模型 [J]. 光谱学与光谱分析, 2015, 35(09): 2644-2649.
ZHANG Chao, CAI Huan-jie*, LI Zhi-jun . Estimation of Fraction of Absorbed Photosynthetically Active Radiation for Winter Wheat Based on Hyperspectral Characteristic Parameters. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(09): 2644-2649.
[1] Casanova D, Epema G F, Goudriaan J. Field Crops Research, 1998, 55(1/2): 83. [2] LI He-li, LUO Yi, XUE Xiao-ping, et al(李贺丽,罗 毅,薛晓萍,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 04: 201. [3] GAO Yan-hua, CHEN Liang-fu, LIU Qin-huo, et al(高彦华,陈良富,柳钦火,等). Journal of Remote Sensing(遥感学报), 2006, 10: 798. [4] WANG Pei -juan, ZHU Qi-jiang, WU Men-xin, et al(王培娟,朱启疆,吴门新,等). Remote Sensing Information(遥感信息), 2003, (3): 19. [5] YANG Fei, ZHANG Bai, SONG Kai-shan, et al(杨 飞,张 柏,宋开山,等). Acta Agronomica Sinica(作物学报), 2008, 11: 2046. [6] ZHAO Peng-ju, WANG Deng-wei, HUANG Chun-yan, et al(赵鹏举,王登伟,黄春燕,等). Cotton Science(棉花学报), 2009,05: 388. [7] Liu J G, Pattey E, Miller J R, et al. Remote Sensing of Environment, 2010, 114(6): 1167. [8] Baret F, Hagolle O, Geiger B. et al. Remote Sensing of Environment, 2007, 110(3): 275. [9] Fensholt R, Sandholt I, Rasmussen M S. Remote Sensing of Environment, 2004, 91: 490. [10] Steinberg D C, Goetz S J, Hyer E J. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44: 1818. [11] YANG Fei, ZHANG Bai, LIU Zhi-ming, et al(杨 飞,张 柏,刘志明,等). Remote Sensing for Land & Resources(国土资源遥感), 2008, 04: 9. [12] ZHANG Yong-he, CHEN Wen-hui, GUO Qiao-ying, et al(张永贺,陈文惠,郭乔影,等). Acta Ecologica Sinica(生态学报), 2013, 03: 876. [13] Savitzky A, Golay M J E. Analytical Chemistry, 1964, 36: 1627. [14] LIU Ai-jun, WANG Bao-lin, HUANG Ping-ping, et al(刘爱军,王保林,黄平平,等). Acta Agrestia Sinica(草地学报), 2012, 06: 1004. [15] YANG Fei, ZHANG Bai, SONG Kai-shan, et al(杨 飞,张 柏,宋开山,等). Scientia Agricultura Sinica(中国农业科学), 2008, 07: 1947.