|
|
|
|
|
|
Spectral Angles of Plant Leaves as Indicators of Uranium Pollution in Soil |
WANG Wei-hong1, 2*, LUO Xue-gang1, 3, WU Feng-qiang1, 2, LIN Ling1, 2, LI Jun-jie1, 2 |
1. Environment and Resource College, Southwest University of Science and Technology, Mianyang 621010,China
2. Mianyang S&T City Division,the National Remote Sensing Center of China, Mianyang 621010,China
3. Engineering Research Center of Biomass Materials of Ministry of Education,Mianyang 621010,China
|
|
|
Abstract In this paper, five plants (ramie, Indian mustard, Rumex, Brassica napus and maize) were pot cultured with 0 (control group), 25, 75, 125, 175, 275, 375 and 485 μg·g-1 uranium in the soil. The qualitative and quantitative indicating effects of leaf spectral angle on soil uranium pollution in different growth stages were studied, and the relationship between quantitative indicating effect and leaf uranium content was analyzed It provides an effective way to quickly and safely carry out the background investigation and dynamic monitoring of soil uranium content through field measurement of plant leaf spectrum. The results and main conclusions are as follows: (1) Based on the leaf reflectance spectra of experimental plants in different growth stages, the spectral angles of soil polluted by uranium in five bands (350~716 nm for leaf pigment, 717~975 nm for red edge and near infrared platform, 976~1 265, 1 266~1 770 and 1 771~2 500 nm for water) were calculated. In most cases, the spectral angles of the five experimental plants were greater than the thresholds of the control group. The spectral angles of leaves havecomprehensive responses of 350~2 500 nm to theuranium in soil, which can qualitatively indicate whether the soil is polluted by uranium or not. (2) Eight linear regression equationspassing the significance test with spectral angles as independent variables were obtained, covering all five experimental plants. The coefficient of determination R2 of 7 linear regression equations were >0.64, and R2 of 3 linear regression equations (ramie-seedling stage, Indian mustard-flowering stage and rape-bud bolting stage) were>0.81. Combined with other inversion effect evaluation indexes, it can be considered that leaf spectral angles can quantitatively indicate the degree of soil uranium pollution, but the function of the quantitative indicator varies with plant species and growth period. (3) There was a positive correlation between leaf spectral angles and uranium contents in soil. (4) The leaf spectral angles of ramie and Indian mustard at the seedling stage can be used to retrieve soil uranium content, which is an outstanding characteristic for indicating soil pollution status as early as possible through plant spectrum.
|
Received: 2021-03-27
Accepted: 2021-06-02
|
|
Corresponding Authors:
WANG Wei-hong
E-mail: wangweihong@swust.edu.cn
|
|
[1] LIU Yan-ping, LUO Qing, CHENG He-fa(刘彦平, 罗 晴, 程和发). Journal of Agro-Environment Science(农业环境科学学报), 2020, 39(12): 2699.
[2] YU Qing, WU Quan-yuan, YAO Lei, et al(于 庆, 吴泉源, 姚 磊, 等). Journal of Henan Agricultural Sciences(河南农业科学), 2018, 47(8): 54.
[3] Seongjoo Kang, Keum-young Lee, Eui-ik Jeon, et al. Spatial Information Research, 2018, 26(2):213.
[4] LIU Xiao-qing, LIU Yun-long(刘晓清, 柳云龙). Environmental Science & Technology(环境科学与技术), 2019, 42(5): 230.
[5] HE Jun-liang, CUI Jun-li, ZHANG Shu-yuan, et al(贺军亮, 崔军丽, 张淑媛, 等). Remote Sensing Technology and Application(遥感技术与应用), 2019, 34(5): 998.
[6] SHI Chao, HUANG Chao, LI Shu,et al(史 超, 黄 超, 李 书, 等). Bulletin of Geological Science and Technology(地质科技通报), 2020, 39(3): 202.
[7] YANG Ling-yu,GAO Xiao-hong,ZHANG Wei, et al(杨灵玉, 高小红, 张 威, 等). Chinese Journal of Applied Ecology(应用生态学报), 2016, 27(27): 1775.
[8] ZHU Ye-qing, QU Yong-hua, LIU Su-hong, et al(朱叶青, 屈永华, 刘素红, 等). Journal of Remote Sensing(遥感学报), 2014, 18(2): 335.
[9] YANG Ke-ming, SUN Tong-tong, ZHANG Wei, et al(杨可明, 孙彤彤, 张 伟, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2018, 37(1): 80.
[10] Galal T M, Shedeed Z A, Hassan L M. International Journal of Phytoremediation, 2019, 21(14): 1397.
|
[1] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
[2] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[3] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[4] |
MENG Shan1, 2, LI Xin-guo1, 2*. Estimation of Surface Soil Organic Carbon Content in Lakeside Oasis Based on Hyperspectral Wavelet Energy Feature Vector[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3853-3861. |
[5] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[6] |
CUI Xiang-yu1, 3, CHENG Lu1, 2, 3*, YANG Yue-ru1, WU Yan-feng1, XIA Xin1, 3, LI Yong-gui2. Color Mechanism Analysis During Blended Spinning of Viscose Fibers Based on Spectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3916-3923. |
[7] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[8] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
[9] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[10] |
AN Bai-song1, 2, WANG Xue-mei1, 2*, HUANG Xiao-yu1, 2, KAWUQIATI Bai-shan1, 2. Hyperspectral Estimation of Soil Lead Content Based on Random Frog Band Selection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3302-3309. |
[11] |
DENG Yun1, 2, NIU Zhao-wen1, 2, FENG Qi-yao1, 2, WANG Yu1, 2*. A Novel Hyperspectral Prediction Model of Organic Matter in Red Soil Based on Improved Temporal Convolutional Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2942-2951. |
[12] |
CAI Hai-hui1, ZHOU Ling2, SHI Zhou3, JI Wen-jun4, LUO De-fang1, PENG Jie1, FENG Chun-hui5*. Hyperspectral Inversion of Soil Organic Matter in Jujube Orchard
in Southern Xinjiang Using CARS-BPNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2568-2573. |
[13] |
XIA Chen-zhen1, 2, 3, JIANG Yan-yan4, ZHANG Xing-yu1, 2, 3, SHA Ye5, CUI Shuai1, 2, 3, MI Guo-hua5, GAO Qiang1, 2, 3, ZHANG Yue1, 2, 3*. Estimation of Soil Organic Matter in Maize Field of Black Soil Area Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2617-2626. |
[14] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
[15] |
ZHANG Xia1, WANG Wei-hao1, 2*, SUN Wei-chao1, DING Song-tao1, 2, WANG Yi-bo1, 2. Soil Zn Content Inversion by Hyperspectral Remote Sensing Data and Considering Soil Types[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2019-2026. |
|
|
|
|