光谱学与光谱分析 |
|
|
|
|
|
Spectral Inversion Models for Prediction of Total Chromium Content in Subtropical Soil |
WU Ming-zhu1, LI Xiao-mei1*, SHA Jin-ming2* |
1. College of Environmental Science and Engineering, Fujian Normal University, Fuzhou 350007, China 2. College of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China |
|
|
Abstract With the high requirements and long test cycle of traditional testing method of soil heavy metal, this paper tries to establish the quantitative prediction model between soil hyperspectral and soil chromium content(tested by ICP-MS) to realize the prediction of soil chromium element quickly and accurately. The paper studied the hyperspectral response characteristics of red soil, with 135 soil samples in Fuzhou city. After monitoring the hypersectral reflection of soil samples with ASD (analytical spectral device) and total chromium contents with ICP-MS, the paper gained the spectral reflection data between 350 and 2 500 nm and soil total chromium contents. Then the paper treated the hyperspectral reflection data with 6 mathematic changes such as reciprocal logarithmic change, differentials and continuum removal in advance. The next step was to calculate the correlation coefficient of soil chromium and the above spectral information, and select the sensitive spectral bands according to the highest correlation coefficient. Finally, six kinds of models were selected to build the soil total chromium content model, and the final optimal mathematic model between soil chromium and hyperspectral information was significantly determined. Results showed that 520~530, 1 440~1 450, 2 010~2 020, and 2 230~2 240 nm were the main sensitive bands to soil total chromium, y=120.768e-7.037x was the optimal soil total chromium predicting model(in the model, the correlation coefficient R and the RMSE of total chromium were 0.568 and 0.619 μg·g-1, and the inspection correlation coefficient R and the RMSE were 0.484 μg·g-1 and 1.426 μg·g-1 respectively). The model can be used to rapidly monitor soil total chromium with hyperspectral reflection in Fuzhou area.
|
Received: 2013-08-04
Accepted: 2013-11-12
|
|
Corresponding Authors:
LI Xiao-mei,SHA Jin-ming
E-mail: lixiaomei@fjnu.edu.cn; jmsha@fjnu.edu.cn
|
|
[1] DAI Yu, YANG Chong-fa, ZHENG Yuan-ming(戴 宇, 杨重法, 郑袁明). Environmental Sciences(环境科学), 2009, 30(11): 3432. [2] LIU Feng-zhi, LIU Xiao-wei(刘凤枝, 刘潇威). Soil and Solid Waste Monitoring Analysis Technology(土壤和固体废弃物监测分析技术). Beijing: Chemical Industry Press(北京:化学工业出版社), 2006. 262. [3] Chang C W, Laird D A, Mausbach M J, et al. Soil Science Society of America Journal, 2001, 65(2): 480. [4] Idowu O J, van Es H M, Abawi G S, et al. Plant and Soil, 2008, 307(1-2): 243. [5] LIU Hua, ZHANG Li-quan(刘 华, 张利权). Acta Ecologica Sinica(生态学报), 2007, 27(8): 3427. [6] XU Ming-xing, WU Shao-hua, ZHOU Sheng-lu, et al(徐明星, 吴绍华, 周生路, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2011, 30(2): 109. [7] GONG Shao-qi, WANG Xin, SHEN Run-ping, et al(龚绍琦, 王 鑫, 沈润平, 等). Remote Sensing Technology and Application(遥感技术与应用), 2010, 25(2): 169. [8] LI Shu-min, LI Hong, SUN Dan-feng, et al(李淑敏, 李 红, 孙丹峰, 等). Infrared(红外), 2010, 31(7): 33. [9] LI Shu-min, LI Hong, SUN Dan-feng, et al(李淑敏, 李 红, 孙丹峰, 等). Chinese Journal of Soil Science(土壤通报), 2011, 42(3): 730. [10] XIE Xian-li, SUN Bo, HAO Hong-tao(解宪丽, 孙 波, 郝红涛). Acta Pedologica Sinica(土壤学报), 2007, 44(6): 982. [11] Wu Y Z, Chen J, Wu X M, et al. Applied Geochemistry, 2005, 20(6): 1051. [12] Kemper T, Sommer S. Environmental Science&Technology, 2002, 36(12): 2742. [13] Krishnan P, Alexander J D, Butler B J, et al. Soil Sci. Soc. Am. J., 1980, 44(6): 1282. |
[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] |
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. |
[5] |
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. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
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. |
[15] |
HU Meng-ying1, 2, ZHANG Peng-peng1, 2, LIU Bin1, 2, DU Xue-miao1, 2, ZHANG Ling-huo1, 2, XU Jin-li1, 2*, BAI Jin-feng1, 2. Determination of Si, Al, Fe, K in Soil by High Pressure Pelletised Sample and Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2174-2180. |
|
|
|
|