Improvement of Hyperspectral Estimation of Nitrogen Content in Winter Wheat by Leaf Surface Polarized Reflection Measurement
LIN Yi1, LIU Si-yuan1, YAN Lei1, FENG Hai-kuan2, ZHAO Shuai-yang1, ZHAO Hong-ying1*
1. Beijing Key Lab of Spatial Information Integration and 3S Application, School of Earth and Space Sciences, Peking University, Beijing 100871, China
2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
Abstract：Hyperspectral remote sensing provides an effective way for real-time prediction of plant nitrogen content (PNC) in winter wheat plants. In hyperspectral detection, energy received by the sensor is divided into unpolarized light, which comes from multiple scattering inside the plant, and partially polarized light, which is from the foliar surface, while the latter does not contain nitrogen content information. This paper aims to evaluate the influence of this part of the surface polarization reflection on the PNC estimation. The canopy bidirectional reflectance factor (BRF) in nadir direction of 48 plots in periods of jointing, flagging, flowering and grouting of winter wheat were obtained, and the polarization component was assembled in front of the spectrometer fiber. The polarized reflectance (pBRF) is obtained, and the diffused reflectance factor (dBRF), which partially removes the surface reflection, is obtained by removing the pBRF from the BRF. Using spectral regression and vegetation index (VI) methods, the results of BRF, dBRF, and the existing methods considering removing surface reflection, were compared, to prove the effectiveness and stability of the polarization method. Evaluate the correlation between PNC and BRF & dBRF spectrum; in the spectral regression method, interval partial least squares regression (iPLSR) was used for PNC estimation. The method considering first-order derivative BRF (derBRF) was also compared. For the VI method, the PNC-VI models were established by using 7 VIs. The existed modified VI (mVI) models were also compared for analysis of the advantages and stability of the polarization-dBRF method. Finally, the non-negligibility of polarization reflection, the accurate estimation of surface reflection and the main error sources of the experiment were discussed and analyzed. After the removal of the polarized reflection, the correlation between the reflectance spectrum and PNC is significant in the visible band. The correlation coefficient increased from 0.68 to 0.72 in the blue band and slightly increased in the other spectral regions. In the spectral regression method, the root means square error RMSE of the predicted-measured PNC and dBRF spectrum reduced from 0.30% to 0.23%, indicating 23%’s error reduction; the estimation result is better than derBRF. The method demonstrates the effectiveness of the polarization method. In the vegetation index method, the accuracy of the PNC estimation model of the 7 VIs after polarization removal is slightly improved, and the result is better than the mVI method, which proves the stability of the polarization method. The ND680 (NDVI), ND705 and OSAVI indices yielded better PNC estimation in flowering and grouting periods, with the modeling relative RMSE (RRMSE) within 11%; SR705 and NDNI performed the best in jointing and flagging periods, with the modeling RRMSE within 13%. This study provides a reference for improving the accuracy of remote sensing retrieval of vegetation components.
[1] YANG Fu-qin, FENG Hai-kuan, LI Zhen-hai, et al(杨福芹, 冯海宽, 李振海，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2015, 46(11): 112.
[2] WANG Chao, WANG Jian-ming, FENG Mei-chen, et al(王 超, 王建明, 冯美臣，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2018, 38(5): 1520.
[3] CHEN Yong-zhe, FU Bo-jie, FENG Xiao-ming, et al(陈永喆, 傅伯杰, 冯晓明，等). Acta Ecologica Sinica(生态学报)，2017, 37(18): 6240.
[4] Yao X, Huang Y, Shang G, et al. Remote Sensing, 2015, 7: 14939.
[5] Wang Z, Skidmore A K, Wang T, et al. International Journal of Applied Earth Observation and Geoinformation，2017, 54: 84.
[6] Tian Y C, Yao X, Yang J, et al. Field Crops Research，2011, 120: 299.
[7] YANG Bin, YAN Lei, Knyazikhin Yuri, et al(杨 彬, 晏 磊, Knyazikhin Yuri等). Journal of Remote Sensing(遥感学报)，2018, 22(6): 935.
[8] Knyazikhin Y, Schull M A, Stenberg P, et al. Proceedings of the National Academy of Sciences，2013, 110(3): E185.
[9] Li Y, Chen Y, Huang J. IEEE Transactions on Geoscience and Remote Sensing. 2019, (99): 1.
[10] Yang B, Knyazikhin Y, Möttus M, et al. Remote Sensing of Environment，2017, 198: 69.
[11] Sims D A, Gamon J A. Remote Sensing of Environment. 2002, 81: 337.
[12] LI Fen-ling, CHANG Qing-rui, SHEN Jian, et al(李粉玲, 常庆瑞, 申 健). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2016, 32(9): 157.
[13] Yang B, Knyazikhin Y, Xie D, et al. Remote Sensing，2018, 10: 1632.
