Scientia Agricultura Sinica ›› 2020, Vol. 53 ›› Issue (9): 1795-1805.doi: 10.3864/j.issn.0578-1752.2020.09.008

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

LAI Estimation Based on Multi-Spectral Remote Sensing of UAV and Its Application in Saline Soil Improvement

FengZhi SHI1,2,RuiYan WANG1,2(),YuHuan LI1,2,Hong YAN3,XiaoXin ZHANG1   

  1. 1 College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, Shandong;
    2 National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Tai’an 271018, Shandong;
    3 Agricultural and Rural Bureau of Shanghe County, Shanghe 276200, Shandong
  • Received:2019-08-23 Accepted:2020-03-07 Online:2020-05-01 Published:2020-05-13
  • Contact: RuiYan WANG E-mail:wry@sdau.edu.cn

RichHTML

16

PDF

710

Abstract:

【Objective】Vegetation yield can comprehensively and intuitively reflect the improvement effect of saline soil. Leaf area index (LAI) of winter wheat during its vigorous growth period is a commonly used measure of vegetation yield. In this study, unmanned aerial vehicle (UAV) multispectral remote sensing was used to obtain the LAI distribution data of winter wheat during its vigorous growth period to objectively and accurately evaluate the improvement effect of saline soil, so as to provide scientific guidance for people to screen saline soil improvement technology and products. 【Method】Taking the experimental area of coastal saline soil improvement in Bohai granary in Wudi county as the research area, based on UAV multispectral remote sensing data, linear regression analysis, partial least squares, random forest, and support vector machine were used to construct LAI inversion model of winter wheat. The factor analysis method was used to evaluate the improvement effect of the sampled land in saline soil area, and the LAI evaluation model of saline soil improvement effect was established to evaluate the improvement effect of saline soil in the entire experimental area. 【Result】The results showed that, for the winter wheat LAI remote sensing estimation, it was not that the higher the resolution, the better, but the smoothed 5×5 mean spectral data corresponded best to the LAI of a ridge of wheat. Among the LAI remote sensing estimation models, the models were built by using SVM with the highest accuracy. The prediction result of the improvement effect LAI evaluation model showed that the prediction accuracy of the improvement effect of saline soil by LAI was higher, and the best improvement land numbers were 26, 27, 28, 29, 30, and 31, and the optimal improvement method was cited. The best improvement method was comprehensive improvement measures of diversion irrigation and adding organic fertilizer. 【Conclusion】UAV remote sensing could accurately invert the LAI of winter wheat at the jointing stage in saline soil area. The evaluation of the improvement effect of saline soil based on the results of LAI inversion could locate the optimal improvement effect from many experimental plots. Compared with the traditional method, this method had the advantages of low cost and high accuracy. The research results had a broad prospect and could provide important technical support for the improvement of saline soil.

Key words: multi-spectral of UAV, improvement effect of saline soil, jointing stage of winter wheat, leaf area index, remote sensing estimation

Fig. 1

The experimental region and sample sites"

Table 1

Correlation between characteristic band and LAI after different filtering windows"

不同窗口
Different window		 波段名称Band name
1×1 2×2 3×3 4×4 5×5 6×6 7×7
绿光GRE -0.53** -0.54** -0.54** -0.58** -0.57** -0.58** -0.58**
红光RED -0.58** -0.60** -0.59** -0.60** -0.61** -0.59** -0.61**
红边REG -0.18 -0.19 -0.25* -0.32** -0.34** -0.43** -0.38**
近红NIR 0.55** 0.55** 0.53** 0.47** 0.49** 0.38** 0.45**

Table 2

Vegetation indexes and formulas"

植被指数 Vegetation index 计算公式 Calculation formula
归一化植被指数
NDVI[25]		 NDVI=(ρnir-ρred)/(ρnir+ρred)
比值植被指数
RVI[26]		 RVI=ρnir/ρred
绿度归一化植被指数
GNDVI[27]		 GNDVI=(ρnir-ρgre)/(ρnir+ρgre)
绿色比值植被指数
GRVI[28]		 GRVI=ρnir/ρgre

Table 3

Correlation between spectral index and leaf area index after six filtering treatments"

不同窗口
Different window		 植被指数 Vegetation index
1×1 2×2 3×3 4×4 5×5 6×6 7×7
NDVI 0.62** 0.66** 0.64** 0.66** 0.67** 0.62** 0.67**
RVI 0.78** 0.80** 0.81** 0.81** 0.82** 0.79** 0.79**
GNDVI 0.59** 0.63** 0.62** 0.67** 0.66** 0.63** 0.67**
GRVI 0.75** 0.79** 0.77** 0.79** 0.79** 0.77** 0.79**
(G+R)/NIR 0.55** 0.58** 0.57** 0.60** 0.60** 0.56** 0.61**

Table 4

Modeling and verification of vegetation index and LAI"

建模集Modeling set (n=44) 验证集Validation set (n=19)
多元线性回归
Multiple linear regression		 偏最小二乘
PLS		 支持向量机
SVM		 随机森林
RF		 多元线性回归
Multiple linear regression		 偏最小二乘
PLS		 支持向量机
SVM		 随机森林
RF
R2 0.84 0.75 0.85 0.94 0.69 0.66 0.62 0.56
RMSE 0.51 0.63 0.48 0.32 0.66 0.67 0.74 0.79
RPD 2.30 1.70 2.40 3.50 1.40 1.50 1.30 1.20

Fig. 2

Comparison of measured and predicted values of winter wheat LAI"

Fig. 3

Leaf area index grade distribution map"

Table 5

Total variance analysis"

成分Ingredient 初始特征值Initial eigenvalue 提取载荷平方和Extract load sum of squares 旋转载荷平方和Sum of rotation load squares
总计
Total		 方差百分比
Variance percentage		 累积
Accumulation (%)		 总计
Total		 方差百分比
Variance percentage (%)		 累积Accumulation (%) 总计
Total		 方差百分比
Variance percentage		 累积
Accumulation
(%)
1 2.330 58.248 58.248 2.330 58.248 58.248 2.114 52.852 52.852
2 0.959 23.987 82.235 0.959 23.987 82.235 1.175 29.383 82.235
3 0.458 11.445 93.680
4 0.253 6.320 100.000

Table 6

Component score matrix"

成分1
Ingredient 1		 成分2
Ingredient 2
有机质Organic matter 0.411 -0.023
盐分Salinity -0.275 -0.260
含氮量Nitrogen content 0.502 -0.263
水分Moisture -0.171 0.899

Table 7

Ranking of scores by parcel"

地块编号
Parcel number		 得分
Score		 LAI 排名
Ranking		 地块编号
Parcel number		 得分
Score		 LAI 排名
Ranking		 地块编号
Parcel number		 得分
Score		 LAI 排名
Ranking
6 0.67 4.76 1 9 0.42 2.35 7 12 0.33 1.55 12
1 0.59 3.84 2 15 0.42 2.40 6 13 0.32 1.88 11
8 0.49 2.81 4 10 0.39 2.51 8 16 0.22 1.70 13
5 0.48 3.24 3 18 0.34 2.19 9 17 0.02 1.39 14
11 0.46 2.66 5 14 0.33 1.76 10

Fig. 4

Experimental field improvement level classification map"

Fig. 5

Four kinds of window filter processing false color synthesis UAV image (black spot is sampling point)"

[1] 季洪亮, 路艳 . 滨海盐碱地生态修复效果评价. 西北林学院学报, 2017(2):301-307.
JI H L, LU Y . Evaluation of the eco-restoration effect on coastal saline-alkali soil. Journal of Northwest Forestry University, 2017(2):301-307. (in Chinese)
[2] 许经勇, 黄焕文 . 有关我国粮食安全的几个问题研究. 财经研究, 2004,30(5):122-129.
XU J Y, HUANG H W . A Study on the issue of food security in China. Journal of Finance and Economics, 2004,30(5):122-129. (in Chinese)
[3] 刘东兴, 宫伟光 . 大庆盐碱地立地质量评价. 中国水土保持科学, 2009,3(3):98-103.
LIU D X, GONG W G . Evaluation of site quality for saline-alkali soil in Daqing. Science of Soil and Water Conservation, 2009,3(3):98-103. (in Chinese)
[4] 刘杰 . 不同盐碱土改良剂在棉花上的应用效果初报. 新疆农垦科技, 2015(7):73-74.
LIU J . Preliminary report on the application effect of different saline-alkaline soil improvers on cotton. Xinjiang Farm Research of Science and Technology, 2015(7):73-74. (in Chinese)
[5] 何小安 . 基于无人机遥感影像的冬小麦播种效果与空间变异评价[D]. 西安: 西安科技大学, 2018.
HE X A . Evaluation of sowing effect and spatial variation in winter wheat based on unmanned aerial vehicle remote sensing images[D]. Xi’an: Xi’an University of Science and Technology, 2018. (in Chinese)
[6] 杨贵军, 李长春, 于海洋, 徐波, 冯海宽, 高林, 朱冬梅 . 农用无人机多传感器遥感辅助小麦育种信息获取. 农业工程学报, 2015,31(21):184-190.
YANG G J, LI C C, YU H Y, XU B, FENG H K, GAO L, ZHU D M . UAV based multi-load remote sensing technologies for wheat breeding information acquirement. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(21):184-190. (in Chinese)
[7] 高林, 杨贵军, 王宝山, 于海洋, 徐波, 冯海宽 . 基于无人机遥感影像的大豆叶面积指数反演研究. 中国生态农业学报, 2015,23(7):868-876.
pmid: 19662996
GAO L, YANG G J, WANG B S, YU H Y, XU B, FENG H K . Soybean leaf area index retrieval with UAV remote sensing imagery. Chinese Journal of Eco-Agriculture, 2015,23(7):868-876. (in Chinese)
pmid: 19662996
[8] DUAN B, LIU Y, GONG Y, PENG Y, WANG X, ZHU R, FANG S H . Remote estimation of rice LAI based on Fourier spectrum texture from UAV image. Plant Methods, 2019,15(1):1-12.
doi: 10.1186/s13007-019-0507-8 pmid: 31695729
[9] 张春兰, 杨贵军, 李贺丽, 汤伏全, 刘畅, 张丽妍 . 基于随机森林算法的冬小麦叶面积指数遥感反演研究. 中国农业科学, 2018,51(5):855-867.
ZHANG C L, YANG G J, LI H L, TANG F Q, LIU C, ZHANG L Y . Remote sensing inversion of leaf area index of winter wheat based on random forest algorithm. Scientia Agricultura Sinica, 2018,51(5):855-867. (in Chinese)
[10] 褚洪亮, 肖青, 柏军华, 程娟 . 基于无人机遥感的叶面积指数反演. 遥感技术与应用, 2017,32(1):140-148.
CHU H L, XIAO Q, BAI J H, CHENG J . The retrieval of leaf area index based on remote sensing by unmanned aerial vehicle. Remote Sensing Technology and Application, 2017,32(1):140-148. (in Chinese)
[11] 李长春, 牛庆林, 杨贵军, 冯海宽, 刘建刚, 王艳杰 . 基于无人机数码影像的大豆育种材料叶面积指数估测. 农业机械学报, 2017,48(8):147-158.
LI C C, NIU Q L, YANG G J, FENG H K, LIU J G, WANG Y J . Estimation of leaf area index of soybean breeding materials based on UAV digital images. Transactions of the Chinese Society for Agricultural Machinery, 2017,48(8):147-158. (in Chinese)
[12] 顾婷, 高晓雯, 孙成明, 王燕君, 刘涛, 武威, 陈雯 . 基于无人机图像的水稻拔节期叶面积指数估算. 农业网络信息, 2015(12):63-65.
GU T, GAO X W, SUN C M, WANG Y J, LIU T, WU W, CHEN W . Estimation on the rice leaf area index in jointing stage based on unmanned aerial vehicle (UAV) image. Agriculture Network Information, 2015(12):63-65. (in Chinese)
[13] 雷亚平, 韩迎春, 杨北方, 王康丽, 王国平, 冯璐, 王占彪, 李亚兵 . 利用无人机数字图像监测不同棉花品种叶面积指数. 中国棉花, 2018,45(12):9-15.
LEI Y P, HAN Y C, YANG B F, WANG K L, WANG G P, FENG L, WANG Z B, LI Y B . Monitoring of leaf area index of different cotton varieties using UAV-acquired digital images. China Cotton, 2018,45(12):9-15. (in Chinese)
[14] 李子扬, 钱永刚, 申庆丰, 王宁, 刘耀开, 马灵玲, 孔祥生 . 基于高光谱数据的叶面积指数遥感反演. 红外与激光工程, 2014,43(3):944-949.
YANG Z Y, QIAN Y G, SHEN Q F, WANG N, LIU Y K, MA L L, KONG X S . Leaf area index retrieval from remotely sensed hyperspectral data. Infrared and Laser Engineering, 2014,43(3):944-949. (in Chinese)
[15] 田明璐, 班松涛, 常庆瑞, 由明明, 罗丹, 王力, 王烁 . 基于低空无人机成像光谱仪影像估算棉花叶面积指数. 农业工程学报, 2016,32(21):102-108.
TIAN M L, BAN S T, CHANG Q R, YOU M M, LUO D, WANG L, WANG S . Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(21):102-108. (in Chinese)
[16] LIU K, ZHOU Q B, WU W B, XIA T, TANG H J . Estimating the crop leaf area index using hyperspectral remote sensing. Journal of Integrative Agriculture, 2016,15(2):475-491.
doi: 10.2134/jeq2005.0204 pmid: 16738391
[17] ROOSJEN P P J, BREDE B, SUOMALAINEN J M, BARTHOLOMEUS H M, KOOISTRA L, CLEVERS J G P W . Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data-potential of unmanned aerial vehicle imagery. International Journal of Applied Earth Observation and Geoinformation, 2018,66:14-26.
doi: 10.1016/j.jag.2017.10.012
[18] 高林, 杨贵军, 李红军, 李振海, 冯海宽, 王磊, 董锦绘, 贺鹏 . 基于无人机数码影像的冬小麦叶面积指数探测研究. 中国生态农业学报, 2016,24(9):1254-1264.
GAO L, YANG G J, LI H J, LI Z H, FENG H K, WANG L, DONG J H, HE P . Winter wheat LAI estimation using unmanned aerial vehicle RGB-imaging. Chinese Journal of Eco-Agriculture, 2016,24(9):1254-1264. (in Chinese)
[19] 罗建美, 靳根会, 罗仲朋, 王红营, 齐永青, 刘兴冉, 沈彦俊 . 河北平原中低产区小麦与玉米生产现状及增产潜力分析. 中国生态农业学报, 2016,24(8):1123-1134.
LUO J M, JIN G H, LUO Z P, WANG H Y, QI Y Q, LIU X R, SHEN Y J . Production state and yield potential of wheat and maize in low-medium yield farmlands in Hebei plain. Chinese Journal of Eco-Agriculture, 2016,24(8):1123-1134. (in Chinese)
[20] SAKAMOTO T, SHIBAYAMA M, KIMURA A, TAKADA E . Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth. ISPRS Journal of Photogrammetry and Remote Sensing, 2011,66(6):872-882.
doi: 10.1016/j.isprsjprs.2011.08.005
[21] 刘昌华, 王哲, 陈志超, 周兰, 岳学智, 苗宇新 . 基于无人机遥感影像的冬小麦氮素监测. 农业机械学报, 2018(6):207-214.
LIU C H, WANG Z, CHEN Z C, ZHOU L, YUE X Z, MIAO Y X . Nitrogen monitoring of winter wheat based on unmanned aerial vehicle remote sensing image. Transactions of the Chinese Society of Agricultural Machinery, 2018(6):207-214.
[22] CAO C, DANG H, KEJIANG L I, JUNYONG M A, ZHENG C . Winter wheat salt tolerance appraisal indices with varied mineral content of saline water. Agricultural Science & Technology, 2014,15(7):1134-1139.
[23] 姚宇, 朱昌达 . “渤海粮仓”典型区土壤养分时空变异. 中国人口·资源与环境, 2018,28(S1):165-168.
YAO Y, ZHU C D . Spatio-temporal variability of soil characteristics of the ‘Bohai Granary’ typical area. China Population, Resources and Environment, 2018,28(S1):165-168. (in Chinese)
[24] 申艳, 张晓平, 梁爱珍, 时秀焕, 范如芹, 杨学明 . 近红外光谱分析法测定东北黑土有机碳和全氮含量. 应用生态学报, 2010,21(1):109-114.
pmid: 20387431
SHEN Y, ZHANG X P, LAING A Z, SHI X H, FAN R Q, YANG X M . Near infrared spectroscopy in determining organic carbon and total nitrogen in black soil of Northeast China. Chinese Journal of Applied Ecology, 2010,21(1):109-114. (in Chinese)
pmid: 20387431
[25] HUETE A, DIDAN K, MIURA T, RODRIGUEZ E P, GAO X, FERREIRA L G . Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 2002,83(1/2):195-213.
[26] GITELSON A A, KAUFAMAN Y J, MERZLYAK M N . Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 1996,58(3):289-298.
[27] SHIBAYAMA M T, AKIYAMA T . Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment, 1989,27(2):119-127.
[28] MOTOHKA T, NASAHARS K N, OGUMA H, TSUCHIDA S . Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing, 2010,2(10):2369-2387.
[29] 夏天, 吴文斌, 周清波, 周勇 . 冬小麦叶面积指数高光谱遥感反演方法对比. 农业工程学报, 2013(3):139-147.
XIA T, WU W B, ZHOU Q B, ZHOU Y . Comparison of two inversion methods for winter wheat leaf area index based on hyperspectral remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2013(3):139-147. (in Chinese)
[30] 郑踊谦, 董恒, 张城芳, 黄鹏 . 植被指数与作物叶面积指数的相关关系研究. 农机化研究, 2019,41(10):7-12.
ZHENG Y Q, DONG H, ZHANG C F, HUANG P . Study on the relationship between vegetation indices and leaf area index of crop. Journal of Agricultural Mechanization Research, 2019,41(10):7-12. (in Chinese)
[31] 刘思汝, 石伟琦, 马海洋, 王国安, 陈清, 徐明岗 . 果树水肥一体化高效利用技术研究进展. 果树学报, 2019,36(3):366-384.
LIU S R, SHI W Q, MA H Y, WANG G A, CHEN Q, XU M G . Advances in research on efficient utilization of fertigation in fruit trees. Journal of Fruit Science, 2019,36(3):366-384. (in Chinese)
[1] YIN Bo, YU AiZhong, WANG PengFei, YANG XueHui, WANG YuLong, SHANG YongPan, ZHANG DongLing, LIU YaLong, LI Yue, WANG Feng. Effects of Green Manure Returning Combined with Nitrogen Fertilizer Reduction on Hydrothermal Characteristics of Wheat Field and Grain Yield in Oasis Irrigation Area [J]. Scientia Agricultura Sinica, 2025, 58(7): 1366-1380.
[2] CHEN Ge, GU Yu, WEN Jiong, FU YueFeng, HE Xi, LI Wei, ZHOU JunYu, LIU QiongFeng, WU HaiYong. Effects of Fallow Weeds Returning to the Field on Photosynthetic Matter Production and Yield of Rice [J]. Scientia Agricultura Sinica, 2025, 58(4): 647-659.
[3] SONG XuHui, ZHAO XueYing, ZHAO Bin, REN BaiZhao, ZHANG JiWang, LIU Peng, REN Hao. Effects of Row Ratio Allocation on Light Distribution and Photosynthetic Production Capacity of Maize-Soybean Strip Intercropping [J]. Scientia Agricultura Sinica, 2025, 58(23): 4858-4871.
[4] LI Fei, XIONG Cai, GU JiaJia, CAO Xin, WANG ShanShan, HU Wei, ZHOU ZhiGuo, CHEN BingLin. Effects of Late Sowing on Yield, Quality, Photosynthetic Source Succession and Loadability Characteristics of Rape [J]. Scientia Agricultura Sinica, 2024, 57(23): 4673-4685.
[5] SHI HaoLei, CAO HongXia, ZHANG WeiJie, ZHU Shan, HE ZiJian, ZHANG Ze. Leaf Area Index Inversion of Cotton Based on Drone Multi-Spectral and Multiple Growth Stages [J]. Scientia Agricultura Sinica, 2024, 57(1): 80-95.
[6] WEN Ming, LI MingHua, JIANG JiaLe, MA XueHua, LI RongWang, ZHAO WenQing, CUI Jing, LIU Yang, MA FuYu. Effects of Nitrogen, Phosphorus and Potassium on Drip-Irrigated Cotton Growth and Yield in Northern Xinjiang [J]. Scientia Agricultura Sinica, 2021, 54(16): 3473-3487.
[7] HOU JiaMin,LUO Ning,WANG Su,MENG QingFeng,WANG Pu. Effects of Increasing Planting Density on Grain Yield, Leaf Area Index and Photosynthetic Rate of Maize in China [J]. Scientia Agricultura Sinica, 2021, 54(12): 2538-2546.
[8] NIE ZhiGang,LI Guang,WANG Jun,MA WeiWei,LUO CuiPing,DONG LiXia,LU YuLan. Parameter Optimization for the Simulation of Leaf Area Index of Dryland Wheat with the APSIM Model [J]. Scientia Agricultura Sinica, 2019, 52(12): 2056-2068.
[9] WANG HuBing, CAO HongXia, HAO ShuXue, PAN XiaoYan. Responses of Plant Nutrient and Photosynthesis in Greenhouse Tomato to Water-Fertilizer Coupling and Their Relationship with Yield [J]. Scientia Agricultura Sinica, 2019, 52(10): 1761-1771.
[10] CHEN PengFei, LI Gang, SHI YaJiao, XU ZhiTao, YANG FenTuan, CAO QingJun. Validation of an Unmanned Aerial Vehicle Hyperspectral Sensor and Its Application in Maize Leaf Area Index Estimation [J]. Scientia Agricultura Sinica, 2018, 51(8): 1464-1474.
[11] ZHANG ChunLan, YANG GuiJun, LI HeLi, TANG FuQuan, LIU Chang, ZHANG LiYan. Remote Sensing Inversion of Leaf Area Index of Winter Wheat Based on Random Forest Algorithm [J]. Scientia Agricultura Sinica, 2018, 51(5): 855-867.
[12] MeiYan SHU, XiaoHe GU, Lin SUN, JinShan ZHU, GuiJun YANG, YanCang WANG, LiYan ZHANG. High Spectral Inversion of Winter Wheat LAI Based on New Vegetation Index [J]. Scientia Agricultura Sinica, 2018, 51(18): 3486-3496.
[13] HAN Xue-1, 2 , WANG Li-1, 2 , WANG Yan-Ping-1. Canopy Interception of Summer Corn and Its Influencing Factors Under Natural Rainfall [J]. Scientia Agricultura Sinica, 2014, 47(8): 1541-1549.
[14] GU Yun-Qian, LIU Xue, ZHANG Wei, QI Chun-Jie, TANG Kai-Lei, ZHAO Yang, ZHANG Yan, LI Gang, WANG Bin, ZHAO Chun-Jiang, LUO Wei-Hong. The Simulation Model of the Effects of Low Level of Radiation at Milk Filling Stage on Wheat Growth and Yield [J]. Scientia Agricultura Sinica, 2013, 46(5): 898-908.
[15] LIANG Liang, ZHANG Lian-Peng, LIN Hui, LI Chun-Mei, YANG Min-Hua. Estimating Canopy Leaf Water Content in Wheat Based on Derivative Spectra [J]. Scientia Agricultura Sinica, 2013, 46(1): 18-29.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd