铜胁迫下玉米污染特征波段提取与程度监测

doi:10.3964/j.issn.1000-0593(2020)02-0529-06

Abstract
References
Related Citation (15)

Download: PDF (3028 KB)
Export: BibTeX | EndNote (RIS)

Abstract The situation of heavy metal pollution in farmland isn’t optimistic. The heavy metals in soil can affect normal growth and development of crops after being absorbed by the roots, reduce quality of agricultural products, and then enter human body through food chain, endangering human health. Hyperspectral Remote Sensing provides possibility for a real-time, dynamic and efficient monitoring of heavy metal pollution in crops. The potted corn experiment with different Cu²⁺ stress gradients was set up, the spectral data of old, middle and new leaves in seedling, jointing and spike stages were collected, and the chlorophyll content and leaves Cu²⁺ content were determined in different growth periods. Based on the spectral data, chlorophyll content and leaves Cu²⁺ content, OIF-PLS method was constructed to extract feature bands containing Cu²⁺ pollution information by combining correlation analysis, optimal index factor (OIF) and partial least square (PLS). Firstly, the characteristic bands were preliminarily screened according to correlation coefficient between chlorophyll content in leaves at seedling stage, jointing stage and spike stage and Cu²⁺ content in leaves at spike stage and corresponding leaf spectra. Then, three bands were selected to calculate optimum index factor, and the three bands were taken as independent variables to carry out partial least squares regression analysis on Cu²⁺ content in corn leaves to calculate root mean square error. Finally, the best feature band was selected according to principle of maximum optimum index factor and minimum root mean square error. The vegetation index OIFPLSI was constructed based on the characteristic bands selected by OIF-PLS method to monitor heavy metal copper pollution, and compared with red edge normalized difference vegetation index (NDVI₇₀₅), modified red edge simple ratio vegetation index (mSR₇₀₅), red-edge vegetation stress index(RVSI) and photochemical reflectance index (PRI) monitoring results to verify the effectiveness and superiority of OIFPLSI. In addition, the applicability and stability of OIFPLSI were verified by using the data obtained from different years under same experimental method. The experimental results show that the feature bands (542, 701, 712 nm) extracted from OIF-PLS can better reflect Cu²⁺ pollution information than the feature bands (602, 711, 712 nm) based on OIF. OIFPLSI was significantly positively correlated with leaf Cu²⁺ content, and the correlation was better than NDVI₇₀₅, mSR₇₀₅, RVSI and PRI. OIFPLSI was significantly negatively correlated with leaf chlorophyll content and positively correlated with Cu²⁺ content in soil. The correlation between OIFPLSI and Cu²⁺ content in soil at different growth stages is successively higher in jointing stage, ear stage and seedling stage. Based on the data of different years, the results show that OIFPLSI is positively correlated with leaf Cu²⁺ content, and OIFPLSI has strong stability. OIFPLSI based on the characteristic bands extracted by OIF-PLS method can better diagnose and analyze copper pollution level of corn leaves, which can provide a certain technical reference for crop heavy metal pollution monitoring.

Key words：Heavy metal pollution; Spectral analysis; Feature band; Vegetation index; Crop

Received: 2019-01-08 Accepted: 2019-05-25

ZTFLH:

TP75

Corresponding Authors: YANG Ke-ming E-mail: ykm69@163.com

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)02-0529-06 OR https://www.gpxygpfx.com/EN/Y2020/V40/I02/529

[1] ZHAO Ji-xin, YIN Peng-cheng, YUE Rong, et al(赵纪新, 尹鹏程, 岳荣, 等). Journal of Anhui Agricultural Sciences(安徽农业科学), 2018, 46(4): 19.
[2] Teng Y G, Wu J, Lu S, et al. Environment International, 2014, 69: 177.
[3] 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.
[4] Feng W, Yao X, Tian Y, et al. Australian Journal of Agricultural Research, 2008, 59(8): 748.
[5] GUO Hui, YANG Ke-ming, ZHANG Wen-wen, et al(郭辉, 杨可明, 张文文, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(1): 212.
[6] Dunagan S C, Gilmore M S, Varekamp J C. Enviromnetal Pollution, 2007, 148(10): 301.
[7] Hede A N H, Kashiwaya K, Koike K. Remote Sensing of Environment, 2015, 171: 83.
[8] YANG Ke-ming, WAGN Xiao-feng, ZHANG Wei, et al(杨可明, 王晓峰, 张伟, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2018, 49(2): 191.
[9] CHENG Zhi-qing, ZHANG Jin-song, MENG Ping, et al(程志庆, 张劲松, 孟平, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(12): 179.
[10] CHENG Zhi-qing, ZHANG Jin-song, MENG Ping, et al(程志庆, 张劲松, 孟平, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2015, 46(8): 264.
[11] YU Lei, HONHG Yong-sheng, GENG Lei, et al(于雷, 洪永胜, 耿雷, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(14): 103.
[12] Anatoly Gitelson, Mark N. Merzlyak. Journal of Plant Physiology, 1994, 143(3): 286.
[13] Sims D A, Gamon J A. Remote Sensing of Environment, 2002, 81: 337.
[14] Gamon J A, Penuelas J, Field C B. Remote Sense Environ, 1992, 41: 35.
[15] Merton R, Huntington J. Summaries of the Eight JPL Airborne Earth Science Workshop, 1999, 99(17): 299.
[16] XIONG Chun-hong, CAO Fang, TU Bei-ping, et al(熊春红, 曹芳, 涂北平, 等). Journal of Food Science and Biotechnology(食品与生物技术学报), 2012, 31(1): 55.
[17] LIU Su-hong, LIU Xin-hui, HOU Juan, et al(刘素红, 刘新会, 侯娟, 等). Scientia Sinica·Technologica(中国科学·科学技术)，2007, 37(5): 693.
[18] 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, 32(2): 171.