| 光谱学与光谱分析 |
|
|
|
|
|
| Quantification of Agricultural In-Situ Surface Soil Moisture Content Using Near Infrared Diffuse Reflectance Spectroscopy: A Comparison of Modeling Methods |
| WU Yong-feng1, DONG Yi-wei1, HU Xin2, Lü Guo-hua1, REN De-chao2, SONG Ji-qing1* |
1. Key Laboratory of Agricultural Environment，Ministry of Agriculture，Institute of Environment and Sustainable Development in Agriculture，Chinese Academy of Agricultural Sciences，Beijing 100081，China
2. Wheat Research Laboratory，Shangqiu Academy of Agriculture and Forestry Sciences，Shangqiu 476000，China |
|
|
|
|
Abstract At field scale, surface soil had special characteristics of volumetric moisture content (VMC) with a relatively little difference and spatial heterogeneity induced by physical and chemical properties, roughness, straw residues, etc. It has been a great challenge for near infrared diffuse reflectance spectroscopy (NIR-DRS) measurement of surface soil moisture in situ. In this study, exonential decay models based on seven water-related wavelengths (1 200, 1 400, 1 450, 1 820, 1 940, 2 000 and 2 250 nm), linear models of normalized difference soil moisture index (NSMI) and relative absorption depth (RAD) based on wavelength combinations, linear or quadratic model of width of the inflection (σ), center amplitude of the function (Rd) and area under the Gaussian curve (A) from soil moisture Gaussian model (SMGM), and partial least square (PLS) regression models based on bands were used to quantify VMC. The results indicated that (1) of all the single wavelengths, 2 000 nm showed the best validation result, indicated by the lowest RMSEp (2.463) and the highest RPD value (1.060). (2) Comparing with RAD, the validation of NSMI was satisfactory with higher R2 (0.312), lower RMSEp (2.133) and higher RPD value (1.224). (3) In the validation results of SMGM parameters and PLS fitting, Rd was found to produce the best fitting quality identified by the highest R2 (0.253), the lowest RMSEp (2.222), and the highest RPD value (1.175). (4) Comprehensively, a linear model based on NSMI showed the highest validation accuracy of all the methods. What is more, its calculation process is simple and easy to operate, and therefore become the preferred method to quantify surface soil moisture content in situ.
|
|
Received: 2014-07-16
Accepted: 2014-11-22
|
|
|
|
Corresponding Authors:
SONG Ji-qing
E-mail: songjiqing@caas.cn
|
|
[1] Mouazen A M, Maleki M R, De Baerdemaeker J, et al. Soil & Tillage Research, 2007, 93(1): 13.
[2] Stenberg B, Viscarra Rossel R A, Mouazen A M, et al. Advances in Agronomy, 2010, 107, 163.
[3] Minasny B, McBratney A B, Bellon-Maurel V, et al. Geoderma, 2011, 167-168: 118.
[4] Lobell D, Asner G. Moisture Effects on Soil Reflectance. Soil Science Society of America, 2002, 66(3): 722.
[5] Liu W D, Baret F, Gu X F, et al. Remote Sensing of Environment, 2002, 81(2): 238.
[6] Liu W D, Baret F, Gu X F, et al. International Journal of Remote Sensing, 2003, 24(10): 2069.
[7] Zhu Y D, Weindorf D C, Chakraborty S, et al. Journal of Hydrology, 2010, 391(1-2): 133.
[8] Haubrock S N, Chabrillat S, Lemmnitz C, et al. International Journal of Remote Sensing, 2008, 29(1): 3.
[9] Yin Z, Lei T W, Yan Q H, et al. Computers and Electronics in Agriculture, 2013, 99: 101.
[10] Whiting M L, Li L, Ustin S L. Remote Sensing of Environment, 2004, 89(4): 535.
[11] Mouazen A M, De Baerdemaeker J, Ramon H. Soil & Tillage Research, 2005, 80(1-2): 171.
[12] Hummel J W, Sudduth K A, Hollinger S E. Computers and Electronics in Agriculture, 2001, 32(2): 149.
[13] LIU Yan-de, XIONG Song-sheng, CHEN Dong-bin(刘燕德, 熊松盛, 陈洞滨). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(10): 2679.
[14] Nocita M, Stevens A, Noon C, et al. Geoderma, 2013, 199: 37. |
| [1] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
| [2] |
HUANG Bao-kun1*, ZHAO Qian-nan2, LIU Ye-fan2, ZHU Lin1, ZHANG Hong2, ZHANG Yun-hong3*, LIU Yan4*. In Situ Detection of Fuel Engine Exhaust Components by Raman
Integrating Sphere[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3310-3313. |
| [3] |
LIU Wen-bo, LIU Jin, HAN Tong-shuai*, GE Qing, LIU Rong. Simulation of the Effect of Dermal Thickness on Non-Invasive Blood Glucose Measurement by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2699-2704. |
| [4] |
LIU Guo-peng1, YOU Jing-lin1*, WANG Jian1, GONG Xiao-ye1, ZHAO Yu-fan1, ZHANG Qing-li2, WAN Song-ming2. Application of Aerodynamic Levitator Laser Heating Technique: Microstructures of MgTi2O5 Crystal and Melt by in-situ Superhigh Temperature Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2507-2513. |
| [5] |
WANG Dong1, 2, FENG Hai-zhi3, LI Long3, HAN Ping1, 2*. Compare of the Quantitative Models of SSC in Tomato by Two Types of NIR Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1351-1357. |
| [6] |
XU Ming-kun, LIN Jia-xiang, ZHANG Xiao-lin, LI Zhen-yin, WANG Ya-ming, LIU Chun-tai, SHEN Chang-yu, SHAO Chun-guang*. In Situ Detection of Structural Evolution of Isotropic Polypropylene Crystals During Uniaxial Stretching by Two-Dimensional X-Ray Diffraction Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1426-1433. |
| [7] |
GAO Lu-yue1, SHEN Ling2, ZHANG Juan1*, ZHANG Hui1. Non-Destructive Analysis for Dyeing Process of Memorials From the Late Qing Dynasty[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1063-1067. |
| [8] |
JIN Wang-jun1, 2, LI Yan1, 2, ZHAO Yue3, MEI Sheng-hua1*. In Situ Raman Study and Kinetic Analysis of Hydrothermal Liquefaction of Glycine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3448-3453. |
| [9] |
ZHANG Yao1, GUO Hong1, YANG Jian-du2, LI Bo2, TIAN Jin-feng3, YE Rong-bo4*. The Application of Nondestructive In-Situ Analysis on the Murals of the Xianrenya Grottoes in Tianshui[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3526-3532. |
| [10] |
HAN Bing1, SUN Dan-dan2*, WAN Wei-hao1, WANG Hui3, DONG Cai-chang2, ZHAO Lei3, WANG Hai-zhou3*. Element Segregation of Cast-Rolled 7B05 Aluminum Alloy Based on
Microbeam X-Ray Fluorescence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1413-1419. |
| [11] |
SHENG Liang1, YUAN Liang-jing2*, LI Dong-ling2, ZHANG Xiao-fen3, ZHANG Qiao-chu2, YU Lei2, JIA Yun-hai1*. Determination on Complex Inclusions of High-Speed Railway Wheel Using Spark Source Original Position Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1122-1128. |
| [12] |
DU Bao-lu, LI Meng, GUO Jin-jia*, ZHANG Zhi-hao, YE Wang-quan, ZHENG Rong-er. The Experimental Research on In-Situ Detection for Dissolved CO2 in
Seawater Based on Tunable Diode Laser Absorption Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1264-1269. |
| [13] |
AN Zhen-hua1, ZHAO Dong-yan2, YE Yan1, YANG Rui1*, WANG Yu-bo2, SHAO Jin2, ZHANG Peng2, CHEN Yan-ning2, 3, ZHOU Min2, WANG Wen-he2, WANG Zheng2, HUANG Hai-chao2, WANG Li-cheng3, ZHONG Ming-chen3, ZHEN Yan2, WAN Yong2. A Novel Aging Evaluation Method of Nylon[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 43-48. |
| [14] |
SHEN Xue-jing1, 2, GUO Fei-fei2, XU Peng2, CUI Fei-peng2, LI Xiao-peng2, LIU Jia1, 2. Original Position Statistic Distribution Analysis （OPA） and Characterization of Components in Titanium Alloy Welding Sample by Laser Induced Breakdown Spectroscopy （LIBS）[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3869-3875. |
| [15] |
PENG Ya1，2, LI Dong-ling2，3*, WAN Wei-hao1，2, ZHOU Qing-qing3，4, CAI Wen-yi1，2, LI Fu-lin1, LIU Qing-bin2，3, WANG Hai-zhou2，3. Analysis of Composition Distribution of New Cast-Forging FGH4096 Alloy Turbine Disk Based on Microbeam X-Ray Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3498-3505. |
|
|
|
|
