|
|
|
|
|
|
| Prediction Soil Heavy Metal Zinc Based on Spectral Reflectance in Karst Area |
| WANG Jin-feng1, 2, 5, WANG Shi-jie2, 3, BAI Xiao-yong2, 3*, LIU Fang1, LU Qian1, 2, TIAN Shi-qi2, 4, WANG Ming-ming2 |
1. College of Resource and Environment, Guizhou University, Guiyang 550001, China
2. State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550001, China
3. Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, China
4. School of Geographyical and Environmental Sciences, Guizhou Normal University, Guiyang 550001, China
5. School of Tourism and Histrical Culture, Liupanshui Normal University, Liupanshui 553004, China |
|
|
|
|
Abstract In order to solve the problem of inefficiency in measuring heavy metal zinc contentand soil samples collection difficultly with traditional way in karst area, it is greatly essential to get zinc content in soil by effective measures. The institutional area is a typical Karst region, soil zinc content as well as reflectance spectral of soil data were collected by inductively coupled plasma mass and Spectrophotometer. The reflectance spectra of measurement were handed by these steps. Firstly, 7 kinds of mathematical transformations were used including continuum removed, first differential, second differential, reciprocal transformation, absorbance transformation, first differential of absorbance, and second differential of absorbance. Secondly, spectral characteristic variables were determined based on the characteristic absorption band of spectral absorption of heavy metals. And then, further spectral characteristic variables were selected by correlation analysis. Finally, stepwise regression was used to determine the effective modeling spectral bands. Mapping relationships between Spectral bands and heavy metal zinc content were revealed by linear and nonlinear estimation algorithm, and the results aim to measure the heavy metal zinc in soil. It shows that the characteristic bands of zinc are associated with iron oxide, organic matter and clay mineral absorption band. It’s focused on 580，810，1 410，1 910，2 160，2 260，2 270，2 350，2 430 nm, and these results reveal that the absorption characteristics of heavy metal zinc possible were captured in karst area. The models were funded by Random Forests, Support Vector Machines, Partial Least Squares Regression to precision evaluation by coefficient of determination and the root mean square error of prediction. The best estimation model was obtained from spectrum transformation and model performance. The algorithm of Random forests for second differential transformation has the highest accuracy and is chosen as the best model. The content of heavy metal zinc was estimated by spectral reflectance. It is a rapid, efficient method for indirect evaluation of zinc. It provides a technical support for the dynamic monitoring of heavy metal content in karst areas.
|
|
Received: 2018-10-24
Accepted: 2019-02-22
|
|
|
|
Corresponding Authors:
BAI Xiao-yong
E-mail: baixiaoyong@126.com
|
|
[1] XU Liang-jiang, HUANG Chang-chun, LI Yun-mei, et al(徐良将, 黄昌春, 李云梅, 等). Remote Sensing Technology and Application(遥感技术与应用)，2013, 28(4): 681.
[2] Riedel F, Denk M, Müller L, et al. Geoderma, 2018, 315: 188.
[3] JIANG Zhen-lan, YANG Yu-sheng, SHA Jin-ming(江振蓝, 杨玉盛, 沙晋明). Acta Ecologica Sinica(生态学报), 2017, 37(23)：8118.
[4] Khosravi V, Ardejani F D, Yousefi S, et al. Geofisica Internacional, 2018, 31(8): 30.
[5] Liu M, Liu X, Li J, et al. International Journal of Applied Earth Observation & Geoinformation, 2012, 19(19): 14.
[6] Xia J, Chanussot J, Du P, et al. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(5): 2532.
[7] Raczko E, Zagajewski B. European Journal of Remote Sensing, 2017, 50(1): 146.
[8] Li S, Shao Q, Lu Z, et al. Spectrochim. Acta A: Mol. Biomol. Spectrosc., 2018, 190: 284.
[9] ZHANG Wei, GAO Xiao-hong, YANG Yang, et al(张 威, 高小红, 杨 扬, 等). Soil(土壤), 2014，(6): 1052.
[10] CHENG Xian-feng, SONG Ting-ting, CHEN Yu, et al（程先锋, 宋婷婷, 陈 玉, 等). Acta Petrologica Et Mineralogica(岩石矿物学杂志), 2017, 36(1): 67.
[11] Sun W, Zhang X. International Journal of Applied Earth Observation & Geoinformation, 2017, 58(1): 126.
[12] YU Lei, HONG Yong-sheng, ZHOU Yong, et al（于 雷, 洪永胜, 周 勇, 等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2016, 32(13): 95. |
| [1] |
JIANG Dan-yang1, WANG Zhi-feng1*, GAO Cheng1, 2, LI Chang-jun1. Spectral Reflectance Reconstruction With Color Constancy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1044-1048. |
| [2] |
LUO De-fang1, PENG Jie1*, FENG Chun-hui1, LIU Wei-yang1, JI Wen-jun2, WANG Nan3. Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3069-3076. |
| [3] |
LI Fu-hao, LI Chang-jun*. Spectral Reflectance Reconstruction Based on Camera Raw RGB Using Weighted Third-Order Polynomial and Wiener Estimation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3281-3285. |
| [4] |
ZHANG Shi-ya1, LÜ Xiao-min2, 3*, ZHOU Guang-sheng2, 3, 4*, REN Hong-rui1. Spectral Characteristics During Leaf Flourishing Development of Quercus Mongolica and Its Influencing Factors[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2924-2929. |
| [5] |
MU Bing-yu1, ZHANG Shu-juan1, LI Ze-zhen2, WANG Kai1, LI Zi-hui1, XUE Jian-xin1*. Early Detection of Cauliflower Gray Mold Based on Near-Infrared Spectrum Feature Extraction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2543-2548. |
| [6] |
LIU Tan1, 2, XU Tong-yu1, 2*, YU Feng-hua1, 2, YUAN Qing-yun1, 2, GUO Zhong-hui1, XU Bo1. Chlorophyll Content Estimation of Northeast Japonica Rice Based on Improved Feature Band Selection and Hybrid Integrated Modeling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2556-2564. |
| [7] |
ZHENG Yu-dong1, XU Yun-cheng1, YAN Hai-jun1*, ZHENG Yong-jun2. Analysis of Influencing Factors in Wheat/Maize Canopy RVI and NDVI Acquisition Using Ground-Based Remote Sensing System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2578-2585. |
| [8] |
WU Ting1, 2, 3, LIANG Long1, 3, ZHU Hua3, DENG Yong-jun1, 3, FANG Gui-gan1, 3*. Near-Infrared Analysis and Models Optimization of Main Components in Pulpwood of Hainan Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1404-1409. |
| [9] |
ZHAO Shou-bo1,2, LI Xiu-hong1,2. Research on Reflection Spectrum Reconstruction Algorithm Based on Compressed Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1092-1096. |
| [10] |
CHANG Tao, XIE Xin, GUAN Mei, ZHANG Qiu-ping, ZHANG Zhen-qian*, GUAN Chun-yun. Analysis of the Relationship Between Hyperspectral Reflectance and Yield of Rape Under Different Micro Fertilizer Conditions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 552-557. |
| [11] |
FENG Shuai1, CAO Ying-li1,2*, XU Tong-yu1,2, YU Feng-hua1,2, CHEN Chun-ling1,2, ZHAO Dong-xue1, JIN Yan1. Inversion Based on High Spectrum and NSGA2-ELM Algorithm for the Nitrogen Content of Japonica Rice Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(08): 2584-2591. |
| [12] |
ZHOU Mo1, 2, ZOU Bin1, 2*, TU Yu-long1, 2, XIA Ji-pin1, 2. Hyperspectral Modeling of Pb Content in Mining Area Based on Spectral Feature Band Extracted from Near Standard Soil Samples[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2182-2187. |
| [13] |
GAO Peng1, YANG Ke-ming1*, RONG Kun-peng1, CHENG Feng1, LI Yan1, WANG Si-jia2. Feature Band Extraction and Degree Monitoring of Corn Pollution under Copper Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 529-534. |
| [14] |
YUAN Zi-ran, WEI Li-fei*, ZHANG Yang-xi, YU Ming, YAN Xin-ru. Hyperspectral Inversion and Analysis of Heavy Metal Arsenic Content in Farmland Soil Based on Optimizing CARS Combined with PSO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 567-573. |
| [15] |
LI Yan, YANG Ke-ming*, RONG Kun-peng, ZHANG Chao, GAO Peng, CHENG Feng. Spectral Characteristics and Identification Research of Corn under Copper Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2823-2828. |
|
|
|
|
