基于PCA-MLR和PCA-BPN的莱州湾南岸滨海平原土壤有机质高光谱预测研究

doi:10.3964/j.issn.1000-0593(2018)08-2556-07

Abstract
References
Related Citation (15)

Download: PDF (3831 KB)
Export: BibTeX | EndNote (RIS)

Abstract Soil organic matter (SOM) content is an important indicator of soil quality, which could be predicted using hyper spectral data rapidly. A total of 111 soil samples and hyperspectral data (325~1 075 nm) were collected from the coastal plain on the southern of Laizhou Bay. In previous study, most prediction model is based on the characteristic band and the linear empirical model, ignoring the information redundancy and collinearity between bands, the prediction accuracy is not high and it is difficult to be extended to other regions. In order to maximize the elimination of band information noise and improve the model prediction accuracy,the organic matter content of soil samples was measured as dependent variable. Through principal component analysis (PCA), the measured spectral is reduced to 6 principal components, and water and vegetation spectral characteristic indices are extracted as the independent variables. At last, we analyzed the prediction effect of different model on soil organic matter with MLR and BPN models. The results show: (1) the six principal components extracted from PCA on spectral information could be used to characterize the spectral characteristics of chlorophyll, salt, humic acid, materialized slag and micro- Landform. (2) The prediction accuracy of BPN model based on 6 principal components as independent variable is better than that of MLR model with R² of 0.704 and 0.643 respectively. After adding water and vegetation spectral characteristic index as an independent variable to prediction models, the prediction accuracy of MLR and BPN increased by 6.1% and 5.2%, and R² reached 0.712 and 0.764, respectively; (3) BPN model based spectral principal components and spectral characteristic indices as independent variables could predict soil organic matter with the highest accuracy, which has a potential application in soil organic matter prediction and mapping.

Key words：PCA-MLR; PCA-BPN; Soil organic matter(SOM); Hyperspectral data; Coastal plain

Received: 2017-11-17 Accepted: 2018-03-20

ZTFLH:

TP79

Corresponding Authors: LÜ Jian-shu, WU Quan-yuan E-mail: lvjianshu@126.com; wqy6420582@163.com

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)08-2556-07 OR https://www.gpxygpfx.com/EN/Y2018/V38/I08/2556

[1] Jin X, Song K, Du J, et al. Agricultural and Forest Meteorology, 2017, 244-245: 57.
[2] Viscarra Rossel R A, Behrens T, Ben-Dor E, et al. Earth-Science Reviews, 2016, 155: 198.
[3] Yeasmin S, Singh B, Johnston C T, et al. Geoderma, 2016, 304: 83.
[4] Ben-Dor E, Levin N, Saaroni H. International Journal of Remote Sensing, 2001, 22(11): 2193.
[5] Bowers S, Hanks R. Soil Science, 1965, 100(2): 130.
[6] Kirshnan P, Alexander J D, Butler B J, et al. Soil Science Society of America Journal, 1980, 44(6): 1282.
[7] Brown D J, Shepherd K D, Walsh M G, et al. Geoderma, 2006, 132(3-4): 273.
[8] WANG Jing, LIU Xiang-nan, HUANG Fang, et al(王静, 刘湘南, 黄方, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2009, 25(12): 161.
[9] Åsmund Rinnan, Berg F V D. Trac Trends in Analytical Chemistry, 2009, 28(10): 1201.
[10] YU Lei, HONG Yong-sheng, ZHOU Yong, et al(于雷, 洪永胜, 周勇, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(13): 95.
[11] Shi T, Chen Y, Liu H, et al. Applied Spectroscopy, 2014, 68(8): 831.
[12] Peng X, Shi T, Song A, et al. Remote Sensing, 2014, 6(4): 2699.
[13] Jin X, Du J, Liu H, et al. Agricultural & Forest Meteorology, 2016, 218-219: 250.
[14] YUAN Zheng, LI Xi-can, YU Tao, et al(袁征, 李希灿, 于涛, 等). Science of Surveying and Mapping(测绘科学), 2014, 39(5): 117.
[15] YE Qin, JIANG Xue-qin, LI Xi-can, et al(叶勤, 姜雪芹, 李西灿, 等). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报), 2017, 48(3): 164.
[16] Ben-Dor E, Inbar Y, Chen Y. Remote Sensing of Environment, 1997, 61(1): 1.
[17] Xie X L, Li A B. Geoderma, 2008, 325: 90.
[18] Haboudane D, Miller J R, Pattey E, et al. Remote Sensing of Environment, 2004, 90(3): 337.
[19] Shi T, Liu H, Chen Y, et al. Journal of Hazardous Materials, 2016, 308: 243.
[20] Liu F, Rossiter D G, Song X D, et al. Geoderma, 2015, 263: 254.
[21] Shi Z, Ji W, Viscarra Rossel R A, et al. European Journal of Soil Science, 2015, 66(4): 679.
[22] LIU Wen-quan, YU Hong-jun, XU Xing-yong(刘文全, 于洪军, 徐兴永). Acta Pedologica Sinica(土壤学报), 2014，51(6): 1213.
[23] Gregory W, Reeves，Wang C, et al. Soil Science Society of America Journal, 2016, 80(4): 1078.
[24] DING Jian-li, YANG Ai-xia(丁建丽, 杨爱霞). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(18): 162.
[25] WANG Fei, WU Quan-yuan, LÜ Jian-shu, et al(王菲, 吴泉源, 吕建树, 等). Environmental Science(环境科学), 2016, 37(8): 3144.
[26] Viscarra Rossel R A, Behrens T. Geoderma, 2010, 158(1-2): 46.
[27] Ben-Dor E, Chabrillat S, Demattê J A M, et al. Remote Sensing of Environment, 2009, 113(1): S38.
[28] Ladoni M, Bahrami H A, Alavipanah S K, et al. Precision Agriculture, 2010, 11(1): 82.