|
|
|
|
|
|
Quantitative Inversion of Organic Matter Content Based on Interconnection Traditional Spectral Transform and Continuous Wavelet Transform |
WANG Yan-cang1, 3, JIN Yong-tao1, 3, WANG Xiao-ning1, 3, LIAO Qin-hong5, GU Xiao-he2, 4*, ZHAO Zi-hui1, 3, YANG Xiu-feng1,3 |
1. Institute of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering,Langfang 065000, China
2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province,Langfang 065000, China
4. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
5. College of Life Science and Forestry, Chongqing University of Arts and Sciences, Chongqing 402160, China |
|
|
Abstract In this study, the soil organic matter content of 96 alluvial soil collected from Beijing area was taken as the object of study; Compared with the traditional spectral transform technology, this paper studied on the analysis of the traditional spectral transform and continuous wavelet technology coupling in the feasibility of estimating soil organic matter content. Firstly, the traditional spectral transform technique and the continuous wavelet transform were used to deal with the soil spectral data. Then The correlation between the spectral data and the soil organic matter content was analyzed, and the sensitive bands were extracted. Finally the estimation model of soil organic matter content was constructed by partial least square method. The results showed that the coupling of traditional spectral transform and continuous wavelet technology can greatly improve the spectral sensitivity of organic matter content, and the correlation coefficient (R2) was up to 0.714, which indicate that the coupling of the traditional spectral transform and continuous wavelet technology can dig the useful signal of the spectral information; Compared with the traditional spectral transform technology, the accuracy of the model based on the interconnection of traditional technique and continuous wavelet transform was higher and better stability; Among of the model based on the interconnection of traditional technique and continuous wavelet transform, the model construct by the differential transform was the Optimal model; Its coefficient of decision and root mean square error were 0.774 and 0.223 respectively, which indicated that the interconnection of traditional technique and continuous wavelet transform spectral technique can effectively suppress noise, improving the spectral stability.
|
Received: 2017-08-06
Accepted: 2017-12-26
|
|
Corresponding Authors:
GU Xiao-he
E-mail: guxh@nercita.org.cn
|
|
[1] ZHAO Chun-jiang(赵春江). Agriculture Network Information(农业网络信息),2010, (4): 5.
[2] XU Xin, ZHANG Hao, XI Lei, et al(许 鑫,张 浩,席 磊,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2011,27(S2):94.
[3] CHEN Gui-fen, MA Li, CHEN Hang(陈桂芬, 马 丽, 陈 航). Journal of Jilin Agricultural University(吉林农业大学学报),2013, 35(3): 253.
[4] MENG Ji-hua, WU Bing-fang, DU Xin, et al(蒙继华, 吴炳方, 杜 鑫,等). Remote Sensing for Land & Resources(国土资源遥感),2011, 90(3): 1.
[5] YANG Bang-jie, PEI Zhi-yuan, ZHOU Qing-bo, et al(杨邦杰, 裴志远, 周清波,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报). 2002, 18(3): 191.
[6] YANG Jian-feng, MA Jun-cheng, WANG Ling-chao(杨建锋, 马军成, 王令超). Geospatial Information(地理空间信息),2015, 13(2): 47.
[7] Masserschmidt I, Cuelbas C J, Poppi R J, et al. Journal of Chemometrics,1999, 13:265.
[8] WU Jie, LI Yu-huan, LI Zeng-bing, et al(武 婕, 李玉环, 李增兵,等). Acta Ecologica Sinica(生态学报),2014, 34(6): 1596.
[9] Moslem Ladoni, Hosein Ali Bahrami, Sayed Kazem Alavipanah, et al. Precision Agric.,2010,11: 82.
[10] Krishnan P, Alexander J D, Butler B J, et al. Soil Science Society of America Journal,1980,44:1282.
[11] Alicia Palacios-Orueta, Jorge E Pinzon, Susan L Ustin, et al. Remote Sensing of Environment,1998, 68:138.
[12] Liu H J, Zhang Y Z, Zhang B, et al. Environmental Monitoring and Assessment,2009, 154: 147.
[13] JI Wen-jun, SHI Zhou, ZHOU Qing, et al(纪文君, 史 舟, 周 清,等). Journal of Infrared and Millimeter Waves(红外与毫米波学报),2012, 31(3): 277.
[14] HE Ting, WANG Jing, et al(何 挺, 王 静,等). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版),2006, 31(11): 975.
[15] Viscarra Rossela R A, Chappella A, de Caritatb P,et al. European Journal of Soil Science, 2011, 62:442.
[16] LIAO Qin-hong, GU Xiao-he, LI Cun-jun, et al(廖钦洪, 顾晓鹤, 李存军,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2012, 28(23): 132.
[17] XU Yong-ming, LIN Qi-zhong, et al(徐永明, 蔺启忠,等). Acta Pedologica Sinica(土壤学报),2006, 43(5): 709.
[18] ZHAO Ze-hai, ZU Yuan-gang, CONG Pei-tong(赵则海, 祖元刚, 丛沛桐). Acta Ecologica Sinica(生态学报),2002, 22(10): 1660. |
[1] |
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. |
[2] |
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. |
[3] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
[4] |
LIU Xia-yan1, CAO Hao-xuan1, MIAO Chuang-he1, LI Li-jun2, ZHOU Hu1, LÜ Yi-zhong1*. Three-Dimensional Fluorescence Spectra of Dissolved Organic Matter in Fluvo-Aquic Soil Profile Under Long-Term Composting Treatment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 674-684. |
[5] |
HU Guo-tian1, 2, 3, SHANG Hui-wei1, 2, 3, TAN Rui-hong1, XU Xiang-hu1, PAN Wei-dong1. Research on Model Transfer Method of Organic Matter Content
Estimation of Different Soils Using VNIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3148-3154. |
[6] |
WANG Fan1, 2, CHEN Long-yue2, 3, DUAN Dan-dan1, 2, 4*, CAO Qiong1, 4, ZHAO Yu1, LAN Wan-rong5. Estimation of Total Nitrogen Content in Fresh Tea Leaves Based on
Wavelet Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3235-3242. |
[7] |
ZHONG Xiang-jun1,2, YANG Li1,2*, ZHANG Dong-xing1,2, CUI Tao1,2, HE Xian-tao1,2, DU Zhao-hui1,2. Prediction of Organic Matter Content in Sandy Fluvo-Aquic Soil by
Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2924-2930. |
[8] |
ZHONG Xiang-jun1, 2, YANG Li1, 2*, ZHANG Dong-xing1, 2, CUI Tao1, 2, HE Xian-tao1, 2, DU Zhao-hui1, 2. Effect of Different Particle Sizes on the Prediction of Soil Organic Matter Content by Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2542-2550. |
[9] |
Yumiti Maiming1, WANG Xue-mei1, 2*. Hyperspectral Estimation of Soil Organic Matter Content Based on Continuous Wavelet Transformation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1278-1284. |
[10] |
ZHAO Rui1, SONG Hai-yan1*, ZHAO Yao2, SU Qin1, LI Wei1, SUN Yi-shu1, CHEN Ying-min1. Research on Anti-Moisture Interference Soil Organic Matter ModelBased on Characteristic Wavelength Integration Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 984-989. |
[11] |
LUO De-fang1, LIU Wei-yang1*, PENG Jie1, FENG Chun-hui1, JI Wen-jun2, BAI Zi-jin1. Field in Situ Spectral Inversion of Cotton Organic Matter Based on Soil Water Removal Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 222-228. |
[12] |
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. |
[13] |
ZHANG Hao-dan1,SUN Xiao-lin1, 2*,WANG Xiao-qing1,WANG Hui-li3. Analyzing Errors due to Measurement Positions and Sampling Locations for In Situ Measurements of Soil Organic Matter Using Vis-NIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3499-3507. |
[14] |
JIAO Cai-xia1, ZHENG Guang-hui1*, XIE Xian-li2, CUI Xue-feng3, SHANG Gang1. Prediction of Soil Organic Matter Using Visible-Short Near-Infrared Imaging Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(10): 3277-3281. |
[15] |
SHU Mei-yan1, 2, 3, 4, GU Xiao-he1, 2, 3*, SUN Lin4, ZHU Jin-shan4, YANG Gui-jun1, 2, 3, WANG Yan-cang5, SUN Qian1, ZHOU Long-fei1. Structural Characteristics Change and Spectral Response Analysis of Maize Canopy under Lodging Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3553-3559. |
|
|
|
|