|
|
|
|
|
|
| Predicting Soil Available Nitrogen with Field Spectra Corrected by Y-Gradient General Least Square Weighting |
| QI Hai-jun1, 2, Karnieli Arnon2, LI Shao-wen1* |
1. School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
2. The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boger Campus 84990, Israel |
|
|
|
|
Abstract Soil available nitrogen is supposed to be an important nutrient constituent for the growth and development of crops. In-situ field visible-near infrared (VIS-NIR, 350~2 500 nm) spectroscopic analysis is a rapid and non-destructive method that has the potential to predict nitrogen. Further, it is cost-effective method compared with traditional laboratory analysis and can be used to provide a database for the development of real-time soil nutrient sensors. However, prediction accuracy was greatly reduced due to unexpected environmental factors under field condition. In the current research, field work contained 76 samples from two sites located in the center and north parts of Israel. Y-gradient general least squares weighting (Y-GLSW) algorithm was investigated to filtering correct the field VIS-NIR spectra for improving the prediction ability of nitrogen. Firstly, Savitzky-Golay (SG) smoothing algorithm, first derivative transformation and standard normal variate were sequentially conducted to preprocess and transform the raw field spectra (RS). Then, a filtering model was established based on the Y-GLSW algorithm to correct the preprocessed and transformed spectra (PPT). After that, partial least square - regression (PLS-R) algorithm was applied to build regression models with RS, PPT, and Y-GLSW corrected spectra, respectively. As a result, the regression model based on RS was proved to be unfeasible. The ratio of performance to deviation (RPD) and the ratio between interpretable sum squared deviation and real sum squared deviation (SSR/SST) of the test set of the PPT-based regression model were found to be 1.41 and 0.57, respectively. The results of Y-GLSW-based regression model were RPD = 2.07 and SSR/SST=0.69 that significantly increased by 46.81% and 21.05% compared with PPT-based regression model. The results indicated that Y-GLSW was suitable to remove some unexpected variations (like the effect of environmental factors) of field spectra and improved the prediction accuracy and explanation ability of PLS-R model for predicting nitrogen.
|
|
Received: 2017-02-28
Accepted: 2017-07-05
|
|
|
|
Corresponding Authors:
LI Shao-wen
E-mail: shwli@ahau.edu.cn
|
|
[1] Paz-Kagan T, Shachak M, Zaady E, et al. Geoderma, 2014, s230-231(7): 171.
[2] WU Qian, YANG Yu-hong, XU Zhao-li, et al(吴 茜，杨宇虹，徐照丽，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(8): 2102.
[3] Shao Y, He Y. Soil Research, 2011, 49(2): 166.
[4] Ji W, Shi Z, Huang J, et al. PLoS ONE, 2014, 9(8): e105708.
[5] Kodaira M, Shibusawa S. Geoderma, 2013, 199(4): 64.
[6] Liu X, Guo Y, Wang Q L, et al. Proc. SPIE, 2013, 8910: 891020(doi: 10.1117/12.2035010).
[7] Ji W, Viscarra Rossel R A, Shi Z. European Journal of Soil Science, 2015, 66(3): 555.
[8] Wold S, Antti H, Lindgren F, et al. Chemometrics and Intelligent Laboratory Systems，1998, 44(1): 175.
[9] Roger J M, Chauchard F, Bellon-Maurel V. Chemometrics and Intelligent Laboratory Systems, 2003, 66(2): 191.
[10] Martens H, Hy M, Wise B M, et al. Journal of Chemometrics, 2003, 17(3): 153.
[11] Liu Y, Jiang Q, Shi T, et al. Acta Agriculturae Scandinavica Section B-Soil and Plant Science, 2014, 64(3): 267.
[12] Jiang Q, Chen Y, Guo L, et al. Remote Sensing, 2016, 8(9): 755.
[13] Paz-Kagan T, Zaady E, Salbach C, et al. Remote Sensing, 2015, 7(11): 15748.
[14] Zorzetti B M, Shaver J M, Harynuk J J. Analytica Chimica Acta, 2011, 694(1): 31.
[15] Zhong P, Xu Y, Zhao Y. Neural Computing and Applications, 2012, 21(2): 399. |
| [1] |
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. |
| [2] |
CHAI Shu1, PENG Hai-meng1, WU Wen-dong1, 2*. Acoustic-Based Spectral Correction Method for Laser-Induced Breakdown Spectroscopy in High Temperature Environment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1401-1407. |
| [3] |
LIU Wei, YU Qiang*, NIU Teng, YANG Lin-zhe, LIU Hong-jun, YAN Fei. Study on the Relationship Between Element As in Soil of Agricultural Land and Leaf Spectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2866-2871. |
| [4] |
BAI Xue-bing1, LI Xin-xing1, ZHANG Xiao-shuan2, LUO Hai-ling3, FU Ze-tian2*. Quantitative Detection of Mutton Hardness Based on Twice Iterative Monte Carlo Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2057-2063. |
| [5] |
WANG Cheng1, YU Hang1, YAO Wei-rong1, CHENG Yu-liang1, GUO Ya-hui1, QIAN He1, TAN Zhi-qiang2, XIE Yun-fei1*. A Study on the Screening of Anti-Inflammatory Drug Diclofenac Sodium in Dietary Supplements by Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 592-598. |
| [6] |
YIN Tao1, 2, LIU Zi-wei1, 2, CAI Chen1, QI Zhi-mei1,2*. Determination of Surface Plasmon Resonance Wavelength by Combination of Radiation-Based Spectral Correction With Self-Adaptive Fitting[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 32-38. |
| [7] |
BIAN Kai, ZHOU Meng-ran*, HU Feng, LAI Wen-hao, YAN Peng-cheng, SONG Hong-ping, DAI Rong-ying, HU Tian-yu. RF-CARS Combined with LIF Spectroscopy for Prediction and Assessment of Mine Water Inflow[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2170-2175. |
| [8] |
ZHU Ji-you1, HE Wei-jun2, 3, WANG Hong-qiang3, YAO Jiang-ming3, QIN Guo-ming2, XU Cheng-yang1*, HUANG Tao1. The Response of Spectral Characteristics and Leaf Functional Traits of Euonymus Japonicus to Leaf Dustfall[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1620-1625. |
| [9] |
ZHU Ji-you1, YU Qiang1*, LIU Xiao-xi2, YU Yang3, YAO Jiang-ming4, SU Kai1, NIU Teng1, ZHU Hua5, ZHU Qiu-yu5. Effect of Leaf Dust Retention on Spectral Characteristics of Euonymus japonicus and Its Dust Retention Prediction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 517-522. |
| [10] |
BAI Xue-bing, YU Jian-shu, FU Ze-tian, ZHANG Ling-xian, LI Xin-xing*. Segmentation and Detection of Cucumber Powdery Mildew Based on Visible Spectrum and Image Processing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(11): 3592-3598. |
| [11] |
Yasenjiang Kahaer1, 2, Rukeya Sawut1, 2, Nijat Kasim1, 2, Nigara Tashpolat1, 2*, ZHANG Fei1, 2*, Abdugheni Abliz2, 3, SHI Qing-dong1, 2, 3. Estimation of Heavy Metal Contents in Soil Around Open Pit Coal Mine Area Based on Optimized Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2486-2494. |
| [12] |
ZHAO Xu-ting1, 2, 3, ZHANG Shu-juan1, LI Bin2, 3, 4*, LI Yin-kun5. Study on Moisture Content of Soybean Canopy Leaves under Drought Stress Using Terahertz Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2350-2354. |
| [13] |
ZHANG Hong-guang, LU Jian-gang* . An Improved ELM Algorithm for Near Infrared Spectral Quantitative Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(09): 2784-2788. |
| [14] |
LI Bin, ZHANG Tao, JIA Er-hui . STR Fluorescence Spectral Analysis Based on Matrix Analysis [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(02): 429-432. |
| [15] |
LU Yu, LI Xiang-ru*, YANG Tan, WANG Yong-jun . A New Method of Sparse Feature Extraction for Stellar Spectra [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(08): 2279-2283. |
|
|
|
|
