|
|
|
|
|
|
Analyzing Errors due to Measurement Positions and Sampling Locations for In Situ Measurements of Soil Organic Matter Using Vis-NIR Spectroscopy |
ZHANG Hao-dan1,SUN Xiao-lin1, 2*,WANG Xiao-qing1,WANG Hui-li3 |
1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
3. Guangxi Zhuang Autonomous Region Forestry Research lnstitute, Nanning 530002, China |
|
|
Abstract Due to the heterogeneity of soil, there are spectral differences between different measurement positions of a soil sample and among different samples of a soil genetic layer. As a result, an estimated value of a soil property using in situ spectra of soil inevitably has errors. However, these errors have not been revealed so far. In this paper, 80 soil profiles and 38 soil surface samples were collected twice with cutting ring from a small area of woodland in typical hilly areas in southern China. Then, the soil organic matter content was measured by in-situ spectrometry and traditional laboratory method, so as to analyze the estimation error of soil organic matter content caused by different spectral test points and sampling locations. The results show that, the spectral difference of each sample at a total of 18 test points ranges from 0.12° to 8.13°, with an average value of 1.55°. The spectral difference between two repeated sampling locations of each sample is 0.18°~3.65°, with an average value of 0.88°. The estimated error of soil organic matter due to the different positions of test points was 0.92~14.66 g·kg-1, accounting for 3.8%~428% of the measured organic matter content. The estimation error of soil organic matter caused by different sampling locations is 0.005 7~11.46 g·kg-1, accounting for 0.017%~92% of the measured organic matter. Moreover, the error caused by the former is larger than that caused by the partial least squares regression model used in this paper, while the error caused by the latter is slightly smaller than that caused by this model. In addition, it is found that these two errors increase with the increase of measured organic matter content. Hence, this paper argues that the errors caused by different test points and sampling locations should be paid attention to in future studies, especially in soils with higher organic matter content, and calls for research on more effective methods to reduce these errors.
|
Received: 2019-11-13
Accepted: 2020-04-07
|
|
Corresponding Authors:
SUN Xiao-lin
E-mail: sun_xiaolin@yahoo.com
|
|
[1] SHI Zhou(史 舟). Principle and Method of of Soil Surface Hyperspectral Remote Sensing(土壤地面高光谱遥感原理与方法). Beijing: Science Press(北京:科学出版社), 2014.
[2] XIONG Jing-ling, ZHU Xi-cun, GAO Hua-guang, et al(熊静玲, 朱西存, 高华光, 等). Acta Pedologica Sinica(土壤学报), 2018, 55(6): 1.
[3] Viscarra Rossel R A, Walvoort D J J, Mcbratney A B, et al. Geoderma, 2006, 131(1-2): 59.
[4] Viscarra Rossel R A, Cattle S R, Ortega A, et al. Geoderma, 2009, 150(3-4): 253.
[5] Li S, Shi Z, Chen S, et al. Environmental Science & Technology, 2015, 49(8): 4980.
[6] 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.
[7] HOU Yan-ping, LÜ Cheng-wen, XIANG Hong-liang, et al(侯燕平, 吕成文, 项宏亮, 等). Chinese Journal of Soil Science, 2015, 46(2): 287.
[8] Stenberg B. Geoderma, 2010, 158(1-2): 15.
[9] Morgan C L S, Waiser T H, Brown D J, et al. Geoderma, 2009, 151(3-4): 249.
[10] Lobsey C R, Viscarra Rossel R A. European Journal of Soil Science, 2016, 67(4): 504.
[11] ZHANG Gan-lin, ZHU A-xing, SHI Zhou, et al(张甘霖, 朱阿兴, 史 舟, 等). Progress in Geography(地理科学进展), 2018, 37(1): 57.
[12] WANG Xiao-qing, SUN Xiao-lin, WANG Hui-li(王晓晴, 孙孝林, 王会利). Acta Pedologica Sinica(土壤学报), 2019, 56(4):doi: 10.117661trxb201808310246.
[13] Shepherd K D, Walsh M G. Soil Science Society of America Journal, 2002, 66(3): 988.
[14] Chang C W, Laird D A, Mausbach M J, et al. Soil Science Society of America Journal, 2001, 65(2): 480.
[15] JI Geng-shan, XU Bin-bin(季耿善, 徐彬彬). Acta Pedologica Sinica(土壤学报), 1987, 24(1): 67.
[16] SHA Jin-ming, CHEN Peng-cheng, CHEN Song-lin(沙晋明,陈鹏程,陈松林). Research of Soil and Water Conservation(水土保持研究), 2003, 10(2):21.
|
[1] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[2] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[3] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[4] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
[5] |
LIU Rui-min, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Extraction of 3D Fluorescence Feature Information Based on Multivariate Statistical Analysis Coupled With Wavelet Packet Energy for Monitoring Quality Change of Cucumber During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2967-2973. |
[6] |
TANG Ruo-han1, 2, LI Xiu-hua1, 2*, LÜ Xue-gang1, 2, ZHANG Mu-qing2, 3, YAO Wei2, 3. Transmittance Vis-NIR Spectroscopy for Detecting Fibre Content of
Living Sugarcane[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2419-2425. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
LIN Jing-tao, XIN Chen-xing, LI Yan*. Spectral Characteristics of “Trapiche-Like Sapphire” From ChangLe, Shandong Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1199-1204. |
[11] |
MAO Xiao-tian1, CHEN Chang2, YIN Zuo-wei1*, WANG Zi-min1. Spectra Characterization of Cr-Grossular (Tsavorite) With “Frogspawn” Color Zoning From Canada[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 520-525. |
[12] |
BAO Pei-jin1, CHEN Quan-li1, 2*, WU Yan-han1, LI Xuan1, ZHAO An-di1. Spectroscopy Characteristics of Emerald From Swat Valley, Pakistan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 213-219. |
[13] |
FENG Hai-kuan1, 2, TAO Hui-lin1, ZHAO Yu1, YANG Fu-qin3, FAN Yi-guang1, YANG Gui-jun1*. Estimation of Chlorophyll Content in Winter Wheat Based on UAV Hyperspectral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3575-3580. |
[14] |
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. |
[15] |
ZHANG Fu1, 2, 3, WANG Xin-yue2, CUI Xia-hua2, CAO Wei-hua2, ZHANG Xiao-dong1*, ZHANG Ya-kun2. Classification of Qianxi Tomatoes by Visible/Near Infrared Spectroscopy Combined With GMO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3291-3297. |
|
|
|
|