Abstract:Identifying and monitoring the heavy metal pollution information of crops isthe research focus by hyperspectral remote sensing technology today. The potted corn experiments were set up with different Cu2+ and Pb2+ stress gradients in this research, measuring the spectral data, the content of heavy metals icon and chlorophyll of corn leaves. On the basis of the collected data, the spectra were divided into six spectral characteristic intervals: purple vallley, blue edge, green peak, red valley, red edge and red shoulder, and spectral characteristic intervals were transformed and analyzed by spatial spectrum, which was constructed by first order differential and 2D multiple signal classification (2D-MUSIC) algorithm. The analyzed and processed results show, the spatial spectra of the array signals of the blue edge, green peak and red edge are double peaks under Cu2+ stress. However, the spatial spectra of the array signals of the blue edge, green peak and red edge were single peak under Pb2+ stress. Thus, the heavy metals elements categories of Cu2+ and Pb2+ in polluted corn could be quickly and visually distinguished. Azimuth spectrum peaks of array signal spatial spectra of red valley and red shoulder were gradually decreased under Cu2+ stress, and the correlation coefficients of azimuth spectrum peak values of red valley and red shoulder and the Cu2+ contents in corn leaves reached -0.954 5 and -0.964 8. It was indicated that the effect was ideal when monitoring the level of Cu2+ pollution; azimuth spectrum peaks of array signal spatial spectrum of purple valley were gradually decreased under Pb2+ stress, and the correlation coefficient of azimuth spectrum peak value of purple vallley and the Pb2+ contents in corn leaves reached 0.999 8, it was indicated that the effect was ideal when the level of Pb2+ pollution was monitored. At the same time, the application results of the spatial spectrum were analyzed and compared with the results obtained by some conventional methods such as green-peak height (GH), red edge position (REP), maximum-value of red-edge (MR) and first-derivative area of red-edge (FAR) for monitoring the crop heavy metal pollution information, the spatial spectrum theory was verified to have better effectiveness and superiority in monitoring heavy metal pollution information of the corn leaves.
Key words:Heavy metal pollution; Corn leaf spectrum; 2D multiple signal classification; Spatial spectrum; Spectral characteristic intervals
杨可明,张 伟,王晓峰,孙彤彤,程 龙. 基于空间谱的玉米叶片铜铅污染区分及程度监测[J]. 光谱学与光谱分析, 2018, 38(07): 2200-2208.
YANG Ke-ming, ZHANG Wei, WANG Xiao-feng, SUN Tong-tong, CHENG Long. Differentiation and Level Monitoring of Corn Leaf Stressed by Cu and Pb Derived from Spatial Spectrum. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2200-2208.
[1] JIANG Fei-fei, SUN Dan-feng, LI Hong, et al(姜菲菲, 孙丹峰, 李 红, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(8): 330.
[2] DONG Wen-hong, YANG Hai, LINGHU Wen-sheng(董文洪, 杨 海, 令狐文生). Chemical Reagents(化学试剂), 2016, 38(12): 1170.
[3] WEI Ting-ting, ZHANG Long-chong, DUO Hai-rui, et al(魏婷婷, 张龙冲, 朵海瑞, 等). Journal of Henan Agricultural University(河南农业大学学报), 2010, 44(4): 471.
[4] LIU Xiao-hong, YU Xi-jun(刘晓红, 虞锡君). Ecological Economy(生态经济), 2010, (10): 164.
[5] ZHAO Ming(赵 铭). Resources Economization & Environmental Protection(资源节约与环保), 2016, (4): 181.
[6] SU Chun-tian, TANG Jian-sheng, PAN Xiao-dong, et al(苏春田, 唐健生, 潘晓东, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2011, 27(8): 323.
[7] FU Shu-qing, WEI Zhen-quan(付淑清, 韦振权). Journal of Anhui Agricultural Sciences(安徽农业科学), 2013, 41(6): 2623.
[8] Shafri H Z M, Taherzadeh E, Mansor S, et al. Research Journal of Applied Sciences, Engineering and Technology, 2012, 4(11): 1557.
[9] TANG Peng, LIU Guang, XU Jun-feng(唐 鹏, 刘 光, 徐俊锋). Journal of Hangzhou Normal University·Natural Science Edition(杭州师范大学学报·自然科学版), 2014, 13(6): 634.
[10] PAN Pan, YANG Jun-cheng, DENG Shi-huai, et al(潘 攀, 杨俊诚, 邓仕槐, 等). Journal of Agro-Environment Science(农业环境科学学报), 2011, 30(12): 2389.
[11] JIANG Qing-hu, TONG Fang, YU Ming-zhu, et al(姜庆虎, 童 芳, 余明珠). Plant Science Journal(植物科学学报), 2015, 33(5): 633.
[12] Montzka C, Canty M, Kreins P, et al. Environment Modeling and Software, 2008, 23(8): 1070.
[13] ZHANG Long, PAN Jia-rong, ZHU Cheng(张 龙, 潘家荣, 朱 诚). Journal of Zhejiang University·Agriculture and Life Sciences(浙江大学学报·农业与生命科学版), 2013, 39(1): 50.
[14] Smith K L, Steven M D, Colls J J. Remote Sensing of Environment, 2004, 92(2): 207.
[15] Zhu Yeqing, Qu Yonghua, Liu Suhong, et al. Journal of Remote Sensing, 2014, 18(2): 335.
[16] MA Bao-dong, XU Ao, FANG Dan-mei, et al(马保东, 徐 奥, 方丹梅, 等). Geography and Geo-information Science(地理与地理信息科学), 2016, 32(2): 17.
[17] GUAN Li, LIU Xiang-nan(关 丽,刘湘南). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2009, 25(6): 168.
[18] TENG Jing, HE Zheng-wei, NI Zhong-yun, et al(滕 靖, 何政伟, 倪忠云, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(11): 3637.
[19] Zhang X, Gao X, Chen W. Journal of Electromagnetic Waves and Applications, 2009, 23(5): 593.
[20] Hua Y B, Sarkar T P K, Donald D Weiner. IEEE Trans. Antennas and Propagation, 1991, 39(2): 143.
[21] Wax M, Shan T J, Kailath T. IEEE Trans. Aerospace and Electronic Systems, 1984, 32(4): 817.
[22] WANG Hui, ZENG Lu-sheng, SUN Yong-hong, et al(王 慧, 曾路生, 孙永红, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(2): 171.