Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2019, Vol. 11 Issue (3): 385-399    DOI: 10.1007/s40333-019-0054-1     CSTR: 32276.14.s40333-019-0054-1
    
Multi-scale spatial relationships between soil total nitrogen and influencing factors in a basin landscape based on multivariate empirical mode decomposition
Hongfen ZHU1,2,3, Yi CAO1, Yaodong JING1,2, Geng LIU4, Rutian BI1,2,*(), Wude YANG3
1College of Resource and Environment, Shanxi Agricultural University, Taigu 030801, China
2National Experimental Teaching Demonstration Center for Agricultural Resources and Environment, Shanxi Agricultural University, Taigu030801, China
3 College of Agriculture, Shanxi Agricultural University, Taigu 030801, China
4 Research Center for Scientific Development in Fenhe River Valley, Taiyuan Normal University, Jinzhong 030619, China
Download: HTML     PDF(1781KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The relationships between soil total nitrogen (STN) and influencing factors are scale-dependent. The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors (elevation, slope and topographic wetness index), intrinsic soil factors (soil bulk density, sand content, silt content, and clay content) and combined environmental factors (including the first two principal components (PC1 and PC2) of the Vis-NIR soil spectra) along three sampling transects located at the upstream, midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.Weseparated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions (IMFs) and one residue by multivariate empirical mode decomposition (MEMD). Meanwhile, we obtained the predicted equations of STN based on MEMD bystepwise multiple linear regression (SMLR). The results indicated that the dominant scales of explained variance in STN were at scale995 m for transect 1, at scales 956 and 8852 m for transect 2, and at scales 972,5716 and 12,317 mfor transect 3. Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2. The goodness of fit root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of determination (R2) indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly. Therefore, the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.

Key words： intrinsic mode function      multivariate empirical mode decomposition      multi-scale spatial relationship      sampling transect      soil total nitrogen      Chinese Loess Plateau     
Received: 05 March 2018      Published: 10 June 2019
Corresponding Authors:
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Hongfen ZHU
Yi CAO
Yaodong JING
Geng LIU
Rutian BI
Wude YANG
Cite this article:

Hongfen ZHU, Yi CAO, Yaodong JING, Geng LIU, Rutian BI, Wude YANG. Multi-scale spatial relationships between soil total nitrogen and influencing factors in a basin landscape based on multivariate empirical mode decomposition. Journal of Arid Land, 2019, 11(3): 385-399.

URL:

http://jal.xjegi.com/10.1007/s40333-019-0054-1     OR     http://jal.xjegi.com/Y2019/V11/I3/385

[1] Basso B, Dumont B, Cammarano D, et al.2016. Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone.Science of the Total Environment, 545-546: 227-235.
[2] Boussadia O, Steppe K, Zgallai H, et al.2010. Effects of nitrogen deficiency on leaf photosynthesis, carbohydrate status and biomass production in twoolive cultivars 'Meski' and 'Koroneiki'. Scientia Horticulturae, 123(3): 336-342.
[3] Bremner J M, Tabatabai M A.1972. Use of an ammonia electrode for determination of ammonium in Kjeldahl analysis of soils. Communications in Soil Science and Plant Analysis, 3(2): 159-165.
[4] Carpenter S R, Caraco N F, Correll D L, et al.1998. Nnonpoint pollution of surface waters with phosphorus and nitrogen. Ecological Applications, 8(3): 559-568.
[5] Fleureau J, Kachenoura A, Albera L, et al.2011. Multivariate empirical mode decomposition and application to multichannel filtering. Signal Processing, 91(12): 2783-2792.
[6] Franzluebbers A J, Stuedemann J A.2009. Soil-profile organic carbon and total nitrogen during 12 years of pasture management in the Southern Piedmont USA. Agriculture, Ecosystems & Environment, 129(1-3): 28-36.
[7] Gee G W, Bauder J W.1986. Particle-size Analysis. In: KluteA. Methods of Soil Analysis. Part 1. Physical and Mineralogical Methods (2nd ed.). Madison: Soil Science Society of America, 383-411.
[8] Hossain M F, Chen W, Zhang Y.2015. Bulk density of mineral and organic soils in the Canada's arctic and sub-arctic. Information Processing in Agriculture, 2(3-4): 183-190.
[9] Hu K L, Li H, Wang S Y, et al.2014. Spatial scaling effects on variability of soil organic matter and total nitrogen in suburban Beijing. Geoderma, 226-227: 54-63.
[10] Hu W, Si B C.2013a. Soil water prediction based on its scale-specific control using multivariate empirical mode decomposition. Geoderma, 193-194: 180-188.
[11] Hu W, Biswas A, Si B.2013b. Application of multivariate empirical mode decomposition for revealing scale-and season-specific time stability of soil water storage. Catena, 113(1): 377-385.
[12] Hu W, Chau H W, Si B C.2015. Vis-Near IR reflectance spectroscopy for soil organic carbon content measurement in the Canadian Prairies.CLEAN-Soil Air Water, 43(8): 1215-1223.
[13] Jacksonblake L, Helliwell R C, Britton A J, et al.2012. Controls on soil solution nitrogen along an altitudinal gradient in the Scottish uplands. Science of the Total Environment, 431(5): 100-108.
[14] Li D, Gao G, Lü Y, et al.2016. Multi-scale variability of soil carbon and nitrogen in the middle reaches of the Heihe River basin, northwestern China. Catena, 137: 328-339.
[15] Li Y, Yang J, Hu X.2000. Origin of ground fissures in the Shanxi Graben System, Northern China. Engineering Geology, 55(4): 267-275.
[16] Lin H S, Wheeler D, Bell J, et al.2005. Assessment of soil spatial variability at multiple scales. Ecological Modelling, 182(3-4): 271-290.
[17] Liu X M, Zhao K L, Xu J M, et al.2008. Spatial variability of soil organic matter and nutrients in paddy fields at various scales in southeast China. Environmental Geology, 53(5): 1139-1147.
[18] Momtaz H R, Jafarzadeh A A, Torabi H, et al.2009. An assessment of the variation in soil properties within and between landform in the Amol region, Iran. Geoderma, 149(1-2): 10-18.
[19] Nachtergaele F, van Velthuizen H, Verelst L, et al.2009. Harmonized world soil database. Food and Agriculture Organization of the United Nations. Rome, Italy and IIASA; Laxenburg, Austria.
[20] Rehman N, Mandic D P.2009. Qualitative analysis of rotational modes within three-dimensional empirical mode decomposition. In: IEEE International Conference on Acoustics. Washington: IEEE Computer Society,3449-3452.
[21] Rehman N, Mandic D P.2010. Multivariate empirical mode decomposition. Proceedings of the Royal Society A, 466(2117): 1291-1302.
[22] Rode M, Thiel E, Franko U, et al.2009. Impact of selected agricultural management options on the reduction of nitrogen loads in three representative meso scale catchments in central Germany. Science of the Total Environment, 407(11): 3459-3472.
[23] Rossel R A V, Walvoort D J J, McBratney A B, et al.2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 131(1-2): 59-75.
[24] She D, Zheng J, Shao M A, et al.2015. Multivariate empirical mode decomposition derived multi-scale spatial relationships between saturated hydraulic conductivity and basic soil properties.CLEAN-Soil Air Water, 43(6): 910-918.
[25] Velthof G L, Lesschen J P, Webb J, et al.2014. The impact of the Nitrates Directive on nitrogen emissions from agriculture in the EU-27 during 2000-2008. Science of the Total Environment, 468-469: 1225-1233.
[26] Wang D D, Shi X Z, Wang H J, et al.2010. Scale effect of climate and soil texture on soil organic carbon in the uplands of Northeast China. Pedosphere, 20(4): 525-535.
[27] Zeleke T B, Si B C.2005. Scaling relationships between saturated hydraulic conductivity and soil physical properties. Soil Science Society of America Journal, 69(6): 1691-1702.
[28] Zhou Y, Biswas A, Ma Z, et al.2016. Revealing the scale-specific controls of soil organic matter at large scale in Northeast and North China Plain. Geoderma, 271: 71-79.
[29] Zhu H, Hu W, Bi R, et al.2016. Scale- and location-specific relationships between soil available micronutrients and environmental factors in the Fen River basin on the Chinese Loess Plateau. Catena, 147: 764-772.
[1] WANG Yaobin, SHANGGUAN Zhouping. Formation mechanisms and remediation techniques for low-efficiency artificial shelter forests on the Chinese Loess Plateau[J]. Journal of Arid Land, 2022, 14(8): 837-848.
[2] WU Huining, CUI Qiaoyu. High-frequency climatic fluctuations over the past 30 ka in northwestern margin of the East Asian monsoon region, China[J]. Journal of Arid Land, 2022, 14(12): 1331-1343.
[3] LING Xinying, MA Jinzhu, CHEN Peiyuan, LIU Changjie, Juske HORITA. Isotope implications of groundwater recharge, residence time and hydrogeochemical evolution of the Longdong Loess Basin, Northwest China[J]. Journal of Arid Land, 2022, 14(1): 34-55.
[4] CHEN Shumin, JIN Zhao, ZHANG Jing, YANG Siqi. Soil quality assessment in different dammed-valley farmlands in the hilly-gully mountain areas of the northern Loess Plateau, China[J]. Journal of Arid Land, 2021, 13(8): 777-789.
[5] Xu BI, Bo LI, Bo NAN, Yao FAN, Qi FU, Xinshi ZHANG. Characteristics of soil organic carbon and total nitrogen under various grassland types along a transect in a mountain-basin system in Xinjiang, China[J]. Journal of Arid Land, 2018, 10(4): 612-627.