Welcome to Acta Agronomica Sinica，

Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (2): 485-496.doi: 10.3724/SP.J.1006.2023.21005

;

• TILLAGE & CULTIVATION · PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Difference between bidirectional reflectance factor and directional-hemispherical reflectance factor spectra and its effect on the estimation of leaf chlorophyll content in wheat

WANG Xue(), WANG Wen-Hui, LI Dong, YAO Xia, ZHU Yan, CAO Wei-Xing, CHENG Tao()   

  1. National Engineering and Technology Center for Information Agriculture / Jiangsu Provincial Key Laboratory of Information Agriculture / Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs / Engineering Research Center for Intelligent Agriculture, Ministry of Education / Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing 210095, Jiangsu, China
  • Received:2022-01-24 Accepted:2022-07-21 Online:2022-08-22 Published:2022-08-22
  • Contact: CHENG Tao E-mail:wangxue@njau.edu.cn;tcheng@njau.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41871259)

RichHTML407

15

PDF

1934

Abstract:

Bidirectional reflectance factor (BRF) and directional-hemispherical reflectance factor (DHRF) spectra are two common types of reflectance measurements. However, most studies ignored the differences between BRF and DHRF spectra and their effects on the estimation of leaf chlorophyll content (LCC) while monitoring the biochemical parameters of crops. In this study, we collected leaf-level data from field trials of winter wheat with different varieties, densities, and nitrogen rates. We calculated the vegetation indices (VIs) and wavelet coefficients (WCs) based on BRF and DHRF spectra and then established their relationships with LCC. Finally, we evaluated the ability of VIs and WCs in reducing the differences between BRF and DHRF spectra, and their effects on LCC estimation. Results were as follows: (1) changes in BRF and DHRF were consistent with the variation of LCC, but there were significant differences between the two types of spectra, and the reflectance in BRF was higher than that in DHRF. (2) To some extent, the use of either VIs or WCs could eliminate the influence of the differences between BRF and DHRF spectra. For example, the Normalized Differential Red Edge Vegetation Index (NDRE) and the Red Edge Chlorophyll Index (CIred-edge) could reduce this effect (R2=0.930), but the performance of wavelet coefficients were better than those of NDRE and CIred-edge (R2=0.995). (3) The performance of VIs and WCs based on DHRF data was better than that of BRF data for LCC estimation. NDRE was the best among all VIs evaluated (DHRF: R2=0.957; BRF: R2=0.938; All: R2=0.892). Furthermore, the WC at the fourth scale of 765 nm [WF(4, 675)] was better than NDRE (DHRF: R2=0.985; BRF: R2=0.971; All: R2=0.973), and it had a stronger ability to eliminate the influence of the spectral differences on LCC estimation than NDRE (WF (4, 675): R2=0.973; NDRE: R2 = 0.892). In summary, there was a difference between BRF and DHRF data, and this difference could not be ignored directly. To some extent, the use of suitable VIs and WCs can eliminate the difference and improve the accuracy of LCC estimation. This study determined the differences between BRF and DHRF spectra of the same leaf samples of winter wheat, which provided a theoretical basis for establishing a unified model across BRF and DHRF spectra and improving the accurate estimation of LCC at the canopy level.

Key words: wheat, BRF, DHRF, leaf chlorophyll content, vegetation index, wavelet analysis

Fig. 1

Map of plot distribution V, D, and N represent variety, density, and nitrogen treatments, respectively."

Fig. 2

Experimental setup for the acquisition of leaf spectral by (A) the integrating sphere and (B) the leaf clip a: a leaf holder; b: a spectrometer; c, d: represent the light sources for the integrating sphere and leaf clip; e: the standard white plate, respectively. FOV is the field of view for the bara fiber optics of the spectrometer. Adapted from Li et al.[3]."

Table 1

Four vegetation indices commonly used"

植被指数 Vegetation index 公式 Formulation 参考文献 Reference
归一化植被指数 NDVI (R890−R695)/(R890+R695) Chen et al. [14]
归一化红边植被指数 NDRE (R790−R720)/(R790+R720) Barnes et al. [15]
红边叶绿素指数 CIred-edge R850/R730−1 Wang et al. [16]
叶面积指数不敏感叶绿素指数 LICI R735/R720−(R573−R680)/(R573+R680) Li et al. [6]

Fig. 3

Difference of reflectance in BRF and DHRF spectra A: the relationships of mean reflectance derived from BRF and DHRF spectra; B: the changes of different reflectance between BRF and DHRF spectra with various leaf chlorophyll content."

Table 2

t-test between BRF and DHRF spectra derived from different LCC and nitrogen applications"

成对差分
Paired difference		 均值
Mean		 标准差
Standard deviation		 均值的标准误
Std error of mean		 t值
t-value		 置信度
Degree of confidence		 显著性
Significance
LCC=22 μg cm-2 0.050 0.012 0.001 81.660 450 0.000
LCC=32 μg cm-2 0.049 0.014 0.001 74.340 450 0.000
LCC=42 μg cm-2 0.038 0.011 0.001 72.770 450 0.000
LCC=48 μg cm-2 0.031 0.014 0.001 46.530 450 0.000
N0 0.050 0.018 0.001 59.290 450 0.000
N1 0.047 0.014 0.001 71.520 450 0.000
N2 0.044 0.014 0.001 66.050 450 0.000

Table 3

Paired t-test between BRF and DHRF wavelet coefficient derived from different nitrogen applications"

成对差分
Paired difference		 均值
Mean		 标准差
Standard deviation		 相关系数
Correlation coefficient		 t值
t-value		 置信度
DF		 显著性
Significance
N0 0.001 0.018 0.996** 0.000 450 1.00
N1 0.002 0.016 0.998** 0.251 450 0.802
N2 0.003 0.021 0.999** 0.307 450 0.759

Fig. 4

Differences of different vegetation indices from the reflectance derived from BRF and DHRF Different lowercase indicate that there are significant difference at P < 0.05."

Fig. 5

Relationships of different vegetation indices from the reflectance obtained by BRF and DHRF **: P < 0.01."

Fig. 6

Relationships of different wavelet coefficient derived from BRF and DHRF spectra A: the difference of wavelet coefficient; B: the correction of wavelet coefficient. **: P < 0.01."

Fig. 7

Relationships between LCC and vegetation indices derived from BRF and DHRF spectra"

Fig. 8

Relationships between LCC and wavelet coefficient computed by BRF and DHRF spectra A: all wavelet coefficient at the fourth scale; B: the wavelet coefficient with 720 band at the fourth scale; C: the wavelet coefficient with 765 band at the fourth scale."

Table 4

Validation of LCC model based on NDRE and WF (4, 765) derived from BRF and DHRF spectra with different nitrogen applications"

光谱
Spectra		 氮处理
Nitrogen treatment		 归一化红边植被指数 NDRE 第4尺度765波段的小波系数 WF (4, 765)
决定系数 R2 均方根误差 RMSE (μg cm-2) 决定系数 R2 均方根误差 RMSE (μg cm-2)
BRF N0 0.845** 2.630 0.951** 1.904
N1 0.896** 2.410 0.940** 1.829
N2 0.902** 2.401 0.963** 1.714
All 0.901** 2.041 0.971** 1.513
DHRF N0 0.869** 2.224 0.948** 1.458
N1 0.904** 2.004 0.952** 1.326
N2 0.924** 1.943 0.974** 1.223
All 0.926** 1.842 0.985** 1.099
[1] Lyu Z F, Liu X J, Cao W X, Zhu Y. Climate change impacts on regional winter wheat production in main wheat production regions of China. Agric For Meteorol, 2013, 171: 234-248.
[2] Gitelson A A, Gritz Y, Merzlyak M N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol, 2003, 160: 271-282.
doi: 10.1078/0176-1617-00887
[3] Li D, Tian L, Wan Z F, Jia M, Yao X, Tian Y C, Zhu Y, Cao W X, Cheng T. Assessment of unified models for estimating leaf chlorophyll content across directional-hemispherical reflectance and bidirectional reflectance spectra. Remot Sens Environ, 2019, 231: 111240.
doi: 10.1016/j.rse.2019.111240
[4] Dong L, Cheng T, Zhou K, Zheng H B, Yao X, Tain Y C, Zhu Y, Cao W X. WREP: a wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops. ISPRS J Photogramm, 2017, 129: 103-117.
doi: 10.1016/j.isprsjprs.2017.04.024
[5] Gitelson A A. Remote estimation of canopy chlorophyll content in crops. Geophy Res Lett, 2005, 32: 8403.
[6] Li D, Chen J M, Zhang X, Yan Y, Zhu J, Zheng H B, Zhou K, Yao X, Tian Y C, Zhu Y, Cheng T, Cao W X. Improved estimation of leaf chlorophyll content of row crops from canopy reflectance spectra through minimizing canopy structural effects and optimizing off-noon observation time. Remot Sens Environ, 2020, 248: 111985.
doi: 10.1016/j.rse.2020.111985
[7] Jay S, Maupas F, Bendoula R, Gorretta N. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Res, 2017, 210: 33-46.
doi: 10.1016/j.fcr.2017.05.005
[8] Gitelson A A, Merzlyak M N. Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens, 1997, 18: 2691-2697.
doi: 10.1080/014311697217558
[9] 童倩倩, 李莉婕, 赵泽英, 岳延滨, 刘海. 基于离散小波算法定量反演贵州百香果叶片叶绿素含量的研究. 西南农业学报, 2020, 33: 2927-2932.
Tong Q Q, Li L J, Zhao Z Y, Yue Y B, Liu H. Quantitative inversion of chlorophyll content in passiflora edulis leaves based on discrete wavelete algorithm in Guizhou province. Southest China J Agric Sci, 2020, 33: 2927-2932 (in Chinese with English abstract).
[10] Yoder B J, Pettigrew-Crosby R E. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400-2500 nm) at leaf and canopy scales. Remot Sens Environ, 1995, 53: 199-211.
doi: 10.1016/0034-4257(95)00135-N
[11] 王浩云, 曹雪莲, 孙云晓, 闫明壮, 王江波, 徐焕良. 基于光学特性参数反演的绿萝叶绿素含量估测研究. 农业机械学报, 2021, 52(3): 202-209.
Wang H Y, Cao X L, Sun Y X, Yan M Z, Wang J B, Xu H L. Estimation of chlorophyll content of epipremnum aureum based on optical characteristic parameter inversion. Trans CSAM, 2021, 52(3): 202-209. (in Chinese with English abstract)
[12] Ma Z G, Chen X, Wang Q, Li P H, Jiapaer G L. Retrieval of leaf biochemical properties by inversed PROSPECT model and hyperspectral indices: an application to Populus euphratica polymorphic leaves. J Arid Land, 2012, 4: 52-62.
doi: 10.3724/SP.J.1227.2012.00052
[13] Li P H, Wang Q. Retrieval of leaf biochemical parameters using PROSPECT inversion: a new approach for alleviating ill-posed problems. IEEE Trans Geosci Remot, 2011, 49: 2499-2506.
doi: 10.1109/TGRS.2011.2109390
[14] Chen J. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remot Sens Environ, 2004, 91: 332-344.
doi: 10.1016/j.rse.2004.03.014
[15] Barnes E M, Clarke T R, Richards S E, Colaizzi P D, Thompson T. Coincident Detection of Crop Water Stress, Nitrogen Status, and Canopy Density Using Ground Based Multispectral Data. Proceedings of the 5th International Conference on Precision Agriculture and other resource management July 16-19, 2000, Bloomington, MN USA 2000.
[16] Wang W H, Wu Y P, Zhang Q F, Zheng H B, Yao X, Zhu Y, Cao W X, Cheng T. AAVI: a novel approach to estimating leaf nitrogen concentration in rice from unmanned aerial vehicle multispectral imagery at early and middle growth stages. IEEE J-STARS, 2021, 14: 6716-6728.
[17] Cheng T, Rivard B, Sánchez-Azofeifa A G, Féret J B, Jacquemoud S, Ustin S L. Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis. ISPRS J Photogr, 2014, 87: 28-38.
doi: 10.1016/j.isprsjprs.2013.10.009
[18] Cheng T, Rivard B, Sánchez-Azofeifa A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens Environ, 2010, 115: 659-670.
doi: 10.1016/j.rse.2010.11.001
[19] Huang J F, Blackburn G A. Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data. Int J Remote Sens, 2011, 32: 9375-9396.
doi: 10.1080/01431161.2011.558130
[20] 李辰, 王延仓, 李旭青, 杨秀峰, 顾晓鹤. 基于小波技术的冬小麦植株组分含水率估测模型. 农业机械学报, 2021, 52(3): 193-201.
Li C, Wang Y C, Li X Q, Yang X F, Gu X H. Quantitative inversion of water content of plant components in winter wheat based on wavelet technology. Trans CSAM, 2021, 52(3): 193-201. (in Chinese with English abstract)
[21] 张骁, 闫岩, 王文辉, 郑恒彪, 姚霞, 朱艳, 程涛. 基于小波分析的水稻籽粒直链淀粉含量高光谱预测. 作物学报, 2021, 47: 1563-1580.
doi: 10.3724/SP.J.1006.2021.02063
Zhang X, Yan Y, Wang W H, Zheng H B, Yao X, Zhu Y, Cheng T. Application of continuous wavelet analysis to laboratory reflectance spectra for the prediction of grain amylose content in rice. Acta Agron Sin, 2021, 47: 1563-1580. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2021.02063
[22] Yan G, Bo D, Zhu R S, Wu X T, Ma Y, Peng Y. Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis. Plant Methods, 2018, 14: 70.
doi: 10.1186/s13007-018-0338-z pmid: 30151031
[23] Croft H, Arabian J, Chen J M, Shang J L, Liu J G. Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery. Prec Agric, 2020, 21: 856-880.
doi: 10.1007/s11119-019-09698-y
[24] Zhang Y Q, Chen J M, Miller J R, Noland T L. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery. Remote Sens Environ, 2008, 112: 3234-3247.
doi: 10.1016/j.rse.2008.04.005
[25] Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ, 2002, 81: 337-354.
doi: 10.1016/S0034-4257(02)00010-X
[26] Main R, Cho M A, Mathieu R, Kennedy M M O, Ramoelo A, Koch S. An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS J Photogr, 2011, 66: 751-761.
doi: 10.1016/j.isprsjprs.2011.08.001
[27] Féret J B, François C, Gitelson A, Asner G P, Barry K M, Panigada C, Richardson A D, Jacquemoud S. Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sens Environ, 2011, 115: 2742-2750.
doi: 10.1016/j.rse.2011.06.016
[28] Maccioni A, Agati G, Mazzinghi P. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J Photochem Photobiol Biol, 2001, 61: 52-61.
doi: 10.1016/S1011-1344(01)00145-2
[29] Datt B. Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. Int J Remote Sens, 1999, 20: 2741-2759.
doi: 10.1080/014311699211778
[30] Cheng T, Ria O D, Ustin S L. Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote Sens Environ, 2014, 143: 39-53.
doi: 10.1016/j.rse.2013.11.018
[31] Cheng T, Rivard B, Sánchez-Azofeifa A G, Féret J B, Ustin S L. Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. J Plant Physiol, 2012, 169: 1134-1142.
doi: 10.1016/j.jplph.2012.04.006
[32] Cheng T, Rivard B, Sánchez-Azofeifa G A, Feng J, Calvo-polanco M. Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sens Environ, 2010, 114: 899-910.
doi: 10.1016/j.rse.2009.12.005
[33] Wang Y Y, Zhang K, Tang C L, Cao Q, Tian Y C, Zhu Y, Cao W X, Liu X J. Estimation of rice growth parameters based on linear mixed-effect model using multispectral images from fixed-wing unmanned aerial vehicles. Remote Sens-based, 2019, 11: 1371.
[34] Li S Y, Liu X J, Tian Y C, Zhu Y, Cao Q. Comparison RGB digital camera with active canopy sensor based on UAV for rice nitrogen status monitoring. In: 2018 7th International Conference on Agro-geoinformatics, 2018. pp 1-6.
[35] Grohs D S, Bredemeier C, Mundstock C M, Poletto N. Model for yield potential estimation in wheat and barley using the GreenSeeker sensor. Eng Agr-jaboticabal, 2009, 29: 101-112.
[1] Zhai Sheng-Nan, Cao Xin-You, Li Hao-Sheng, Li Ji-Hu, Li Fa-Ji, Liu Jin-Dong, Xia Xian-Chun, Lyu Ying-Ying, Ma Rui-Feng, Wang Ying, Geng Hong-Wei, Liu Jian-Jun. Analysis of the genetic effects of allelic variation at the Pod-A1, Pod-D1, and Pod-2D loci on peroxidase activity in wheat grains [J]. Acta Agronomica Sinica, 2026, 52(6): 1593-1603.
[2] Xi Qian-Hui, Xu Zi-Yuan, Liu Meng-Meng, Wang Hong-Yi, Lang Kai-Lin, Jing Zhen-Hai, Chen Feng, Zhao Lei. Genome-wide association study and candidate gene prediction of grain copper content in wheat [J]. Acta Agronomica Sinica, 2026, 52(6): 1604-1617.
[3] Mao Jia-Qi, Huang Peng-Yu, Zhao Jia-Jia, Zheng Xing-Wei, Wu Bang-Bang, Hao Yu-Qiong, Qu Fei, Liu Cheng, Ma Peng-Tao, Zheng Jun. Evaluation of powdery mildew resistance in wheat cultivars and molecular detection of resistance genes in Shanxi province, China [J]. Acta Agronomica Sinica, 2026, 52(6): 1669-1681.
[4] Hu Chuan, Zhao Kai-Nan, Huang Xiu-Li, Wu Jin-Zhi, Ren Kai-Ming, Wang He-Zheng, Fu Guo-Zhan, Huang Ming, Li You-Jun. Effects of tillage methods and nitrogen rates on yield and quality of dryland wheat under one-off irrigation [J]. Acta Agronomica Sinica, 2026, 52(6): 1830-1846.
[5] Chen Xue-Yan, He Hua-Chuan, Li Zheng-Jia, Dong Xin-Pan, Li Ou-Qi, Liu Xiao-Yun, Li Dan-Ping, Chen Zhi-Wei, Liu Guo-Xia, Lyu Sheng-Yuan, Wu Yin-Ying, Zhao Zhen-Dong, Cao Xin-You, Wan He-Ping. Dynamic changes in root organic acid secretion and its transcriptional regulatory mechanisms in ‘Jimai 60’ seedlings under combined salinity-alkalinity stress in hydroponics [J]. Acta Agronomica Sinica, 2026, 52(6): 1859-1875.
[6] Gao Pei-Yang, Li Jin-Xuan, Dong Yu-Kui, Shi Yu, Zhang Zhen, Zhang Yong-Li. Response of wheat tillering and spike formation to nitrogen rate under supplementary irrigation based on soil moisture content [J]. Acta Agronomica Sinica, 2026, 52(6): 1847-1858.
[7] Zhang Xian-Feng, Guo Li-Jian, Li Kang-Chun, Kong Bin-Xue, Liu Yu-Fang, Che Zhuo, Yang De-Long. Identification of the ABHD6 gene family and development of functional markers for grain weight in wheat [J]. Acta Agronomica Sinica, 2026, 52(6): 1711-1727.
[8] Wang Zhuang-Zhuang, Wu Zi-Jun, Zhang Yong-Xin, Zhang Xin-Yuan, Yuan Li-Xue, Chen Ru-Xue, Liu Shi-Ju, Duan Jian-Zhao, Feng Wei, Wang Tong-Chao, Wang Yong-Hua. Optimized water-nitrogen synergy enhances winter wheat yield and nitrogen use efficiency in clay-loam fluvo-aquic soils of southeastern Henan, China [J]. Acta Agronomica Sinica, 2026, 52(5): 1501-1521.
[9] He Wan-Long, Geng Hong-Wei, Zhang Fei-Fei, Mikereayi·Ababaikere , Luo Zi-Yang, Li Peng-Cheng, Zhou Zhao-Yu, Cheng Yu-Kun. Development of a deep learning-based image recognition system for major wheat diseases [J]. Acta Agronomica Sinica, 2026, 52(5): 1401-1417.
[10] Zhang Zhen, Feng Lian-Jie, Shi Yu, Yu Zhen-Wen, Zhang Yong-Li. Yield formation of wheat with different ear types under water-saving supplementary irrigation conditions [J]. Acta Agronomica Sinica, 2026, 52(5): 1522-1535.
[11] Hou Si-Yu, Wang Guo-Cui, Wei Jin-Gui, Xie Wei-Xin, Yin Wen, Fan Zhi-Long, Chai Qiang, Hu Fa-Long. Effects of green manure combined with chemical nitrogen fertilizer on dry matter accumulation and yield formation of wheat in arid irrigation areas of northwestern China [J]. Acta Agronomica Sinica, 2026, 52(4): 1208-1219.
[12] Shang Yun-Qiu, Zhao Zhu, Chen Huan, Ding Yong-Gang, Qiao Yu-Qiang, Li Wei, Zhang Xiang-Qian, Cao Cheng-Fu, Du Shi-Zhou. Effects of long-term tillage practices on grain-filling and yield formation in rain-fed wheat [J]. Acta Agronomica Sinica, 2026, 52(4): 1236-1250.
[13] Qiao Yu-Xin, Li Cheng-Yue, Kang Xiao-Yu, Zhang Xin-Qi, Jia Shao-Hui, Liu Qian, Cao Ya-Li, Shi Xin-Rui, Hao Xing-Yu, Li Ping. Study on the effects of long-term no-tillage straw mulching on wheat yield improvement in dryland areas based on the APSIM model [J]. Acta Agronomica Sinica, 2026, 52(4): 1181-1192.
[14] Li Can, Zhang Xi-Wei, Zhu Bo-Tao, Zhang Pei-Pei. Functional characterization of wheat GSK kinase TaSK41 and screening for interacting proteins [J]. Acta Agronomica Sinica, 2026, 52(3): 677-687.
[15] Hou Jie, Fu Duo-Duo, Wu Hai-Feng, Hao Yu-Qiong, Zheng Xing-Wei, Wu Bang-Bang, Zhou Kai, Li Xiao-Hua, Zheng Jun, Zhao Jia-Jia. Chromosome diversity and its effects in wheat landraces from Shanxi province, China [J]. Acta Agronomica Sinica, 2026, 52(3): 746-763.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd