|
|
|
|
|
|
Soil Classification Model Based on the Characteristics of Soil Reflectance Spectrum |
LIU Huan-jun1,2, MENG Xiang-tian1, WANG Xiang1, BAO Yi-lin1, YU Zi-yang1, ZHANG Xin-le1* |
1. College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China
2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China |
|
|
Abstract The soil reflectance spectrum curve reflects the physical and chemical properties and internal structure of the soil. Hyperspectral remote sensing technology has been used to classify soil based on the soil reflectance spectrum characteristics. The first order differential principal component of soil reflectance spectrum is generally used to construct the spectral classification model, but the principal component data is lack of physical significance, contrast and limited scope of application. Compared with the first-order differential reflectivity data, the extraction of the characteristic parameters based on the de-enveloping line can improve the accuracy of soil classification and find a high-precision soil classification model. In this study, four typical soils (wind-sand soil, meadow soil, calcareous soil) were selected in Nong’an County, Jilin Province. The collected soil samples were dried, ground and treated by 2mm sieve. ASD FiledSpec®3 portable spectrometer was used to measure the visible near infrared spectrum of the treated soil samples, and the spectral data of the soil samples were obtained. The spectral data were smoothed by nine points, the noise was reduced by 10nm resampling, and the processed data were processed by the first order differential principal component and the de-enveloping line respectively. The spectral characteristic parameters were extracted by using the continuum removed line of soil samples. The first order differential principal component data and spectral characteristic parameters were input into Logistic clustering model, artificial neural network clustering model and K-means clustering model respectively. In this paper, the reflectance spectra of different soils, the difference of the envelope, the reflectivity curve of the same soil, and the advantages and disadvantages of the soil classification are determined. And the spectral characteristic parameters which can distinguish different soil types are extracted on the basis of de-enveloping line. Secondly, when the first order differential principal component is compared with the spectral characteristic parameter as input, the accuracy differences of the three spectral classification models are compared and the reasons for the difference in the accuracy of different models are analyzed. The results showed that: (1) The difference of the reflectance spectra of the four soils was small, and the spectral difference between the four soils could be greatly enhanced by the continuum removedline. The spectral characteristic parameters with clear physical meaning are constructed on the basis of the de-enveloping line. (2) The first order differential principal component and spectral characteristic parameters are introduced into the three clustering models respectively. The soil spectral classification model with spectral characteristic parameters as input is more accurate than that of the first order differential principal component model, because the spectral characteristic parameters retain the physical meaning of the original data. More accurately reflects the differences between different soil types, and due to the fact that the first order differential principal component data have a certain degree of fuzziness and are lack of contrast between different ranges, it is more advantageous to use spectral characteristic parameters as input in soil classification. (3) Among the three soil classification models, the Logistic clustering model has the highest classification accuracy of 76.67% kappa coefficient of 0.56; the average classification accuracy of the artificial neural network model is 72.50% and the Kappa coefficient is 0.48 K-mean clustering model has the lowest classification accuracy, only 65.00% . And Kappa coefficient is 0.33. The research results can provide technical support for fine mapping of soil and the development of soil classification instrument.
|
Received: 2018-08-21
Accepted: 2018-12-09
|
|
Corresponding Authors:
ZHANG Xin-le
E-mail: zhangxinle@gmail.com
|
|
[1] ZHANG Wei-li, XU Ai-guo, ZHANG Ren-lian, et al(张维理, 徐爱国, 张认连, 等). Scientia Agricultura Sinica(中国农业科学), 2014, 47(16): 3214.
[2] Stevens A, van Wesemael B, Bartholomeus H, et al. Geoderma, 2008, 144(1): 395.
[3] Fox G A, Sabbagh G J. Soil Sci. Soc. Am. J., 2002, 66: 1922.
[4] TANG Na, ZHANG Xin-le, LIU Huan-jun, et al(汤 娜, 张新乐, 刘焕军, 等). Chinese Journal of Soil Science(土壤通报),2013, 44(1): 72.
[5] Wijewardane N K, Ge Y, Morgan C L S. Geoderma, 2016, 267: 92.
[6] Roudier P, Hedley C B, Lobsey C R, et al. Geoderma, 2017, 296: 98.
[7] Condit H R. Applied Optics, 1972, 11(1): 74.
[8] SHI Zhou, WANG Qian-long, PENG Jie, et al(史 舟, 王乾龙, 彭 杰, 等). SCIENTIA SINICA Terrae(中国科学:地球科学), 2014, 44(5): 978.
[9] LI Dan,PENG Zhi-ping,HAN Liu-sheng,et al(李 丹,彭智平,韩留生,等). Tropical Geography(热带地理), 2015,35(1): 29.
[10] Vasques G M, Demattê J A M, Rossel R A V, et al. Geoderma, 2014, 223-225(1): 73.
[11] Bu Y, Chen F, Pan J. New Astronomy, 2014, 28(28): 35.
[12] Wu X, Kumar V, Quinlan J R, et al. Knowledge & Information Systems, 2008, 14(1): 1.
[13] Sun Hanmei, Thuan Nguyen, Luan Yihui, et al. Journal of Multivariate Analysis, 2018, 168: 63.
[14] Zhang X, Liu H, Zhang X, et al. Geoderma, 2018, 320: 12. |
[1] |
GUO Feng1, ZHAO Dong-e1*, YANG Xue-feng1, CHU Wen-bo2, ZHANG Bin1, ZHANG Da-shun3MENG Fan-jun3. Research on Hyperspectral Image Recognition of Iron Fragments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 997-1003. |
[2] |
HU Zheng1, ZHANG Yan1, 2*. Effect of Dimensionality Reduction and Noise Reduction on Hyperspectral Recognition During Incubation Period of Tomato Early Blight[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 744-752. |
[3] |
XU Long-xin1, 2, 3, 4, SUN Yong-hua2, 3, 4*, WU Wen-huan1, ZOU Kai2, 3, 4, HE Shi-jun2, 3, 4, ZHAO Yuan-ming2, 3, 4, YE Miao2, 3, 4, ZHANG Xiao-han2, 3, 4. Research on Classification of Construction Waste Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3927-3934. |
[4] |
YANG Si-jie1,2, FENG Wei-wei2,3,4*, CAI Zong-qi2,3, WANG Qing2,3. Study on Rapid Recognition of Marine Microplastics Based on Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2469-2473. |
[5] |
HAN Yu1, 2, LIU Huan-jun1, 2, ZHANG Xin-le1*, YU Zi-yang1, MENG Xiang-tian1, KONG Fan-chang1, SONG Shao-zhong3, HAN Jing1. Prediction Model of Rice Panicles Blast Disease Degree Based on Canopy Hyperspectral Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1220-1226. |
[6] |
LI Chen-yang1, 2, 3, CHEN Xiong-fei1, 2, 3, ZHANG Yong4, WANG Ya-wen1, 2, 3, TIAN Zhong-chao4, WANG Shi-gong4, ZHAO Zhen-yang4, LIU Ying1, 2, 3,LIU Peng-yu1, 2, 3*. Study on Identification Method Based on XGBoost Model for Aluminum Alloy Using Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 624-628. |
[7] |
HE Jin-xin1, REN Xiao-yu1, CHEN Sheng-bo2*, XIONG Yue1, XIAO Zhi-qiang1, ZHOU Hai1. Automatic Classification of Rock Spectral Features Based on Fusion Learning Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 141-144. |
[8] |
ZHANG Xiao1,2, LUO A-li1*. XGBOOST Based Stellar Spectral Classification and Quantized Feature[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3292-3296. |
[9] |
YU Zi-yang1, WANG Xiang1, MENG Xiang-tian1, ZHANG Xin-le1*, WU Dan-qian1, LIU Huan-jun1,2, ZHANG Zhong-chen3*. SPAD Prediction Model of Rice Leaves Considering the Characteristics of Water Spectral Absorption[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2528-2532. |
[10] |
LI Zhi-hao, SHEN Jun*, BIAN Rui-hua, ZHENG Jian. Accuracy Comparison of the Machine Learning Algorithm Used to Raman Real Sample Collection in the Front Line of Public Security[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(07): 2171-2175. |
[11] |
LI Ming-yang1,2, FAN Meng1*, TAO Jin-hua1, SU Lin1, WU Tong1,3, CHEN Liang-fu1, ZHANG Zi-li4. The Space-Borne Lidar Cloud and Aerosol Classification Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(02): 383-391. |
[12] |
ZHOU Meng-ran, LI Da-tong*, HU Feng, LAI Wen-hao, WANG Ya, ZHU Song. Research of the AdaBoost Arithmetic in Recognition and Classifying of Mine Water Inrush Sources Fluorescence Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(02): 485-490. |
[13] |
LIU Huan-jun1,2, WANG Xiang1, LI Hou-xuan1, MENG Xiang-tian1, JIANG Bai-wen1*, ZHANG Xin-le1, YU Zi-yang1. Effect Mechanism of Soil Minerlas on Spectral Characterisitics of Main Soil Classes in Songnen Plain[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(10): 3238-3244. |
[14] |
MENG De-shuo, ZHAO Nan-jing*, MA Ming-jun, GU Yan-hong, YU Yang, FANG Li, WANG Yuan-yuan, JIA Yao, LIU Wen-qing, LIU Jian-guo. Rapid Soil Classification with Laser Induced Breakdown Spectroscopy [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(01): 241-246. |
[15] |
LIU Zhong-bao1*, REN Juan-juan2, KONG Xiao3 . Distinguishing the Rare Spectra with the Unbalanced Classification Method Based on Mutual Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(11): 3746-3751. |
|
|
|
|