Adaptive surface modeling of soil total potassium content based on integrated learning support in complex geomorphological regions

doi:10.13474/j.cnki.11-2246.2019.0045

测绘通报 ›› 2019, Vol. 0 ›› Issue (2): 63-70.doi: 10.13474/j.cnki.11-2246.2019.0045

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Adaptive surface modeling of soil total potassium content based on integrated learning support in complex geomorphological regions

LIU Yongkun¹, LIU Wei¹, WANG Changyang², CHEN Jiaming³, MA Jin¹

1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China;
2. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Received:2018-04-28 Revised:2018-10-30 Online:2019-02-25 Published:2019-03-05

Abstract

Abstract:

The spatial distribution of soil attributes is influenced by the elements of complex geoscience environment, and the spatial differentiation characteristics are very obvious. It is difficult to simulate soil attributes with a single global interpolation model. For the characteristics of spatial discontinuity, limited precision of global interpolation model and poor adaptability, a soil attribute adaptive surface modeling method(ASM-SP) based on integrated learning support and integrated geoscience environment variables was proposed. Based on 110 samples collected in 2013, regression Kriging(RK), Bayesian Kriging (BK), ordinary Kriging(OK) and inverse distance weighting method(IDW), ASM-SP were used to interpolate the soil total potassium content in the complex geomorphological area of Qinghai Lake, respectively. In this paper, the simulation accuracy of different interpolation methods was evaluated by Leave One Out Validation. ASM-SP not only considers the nonlinear relationship between geo-environmental variables and soil attributes, but also combines the adaptive advantages of multiple models. It is a new method to simulate soil total potassium content in complex geomorphological regions with high precision.

Key words: spatial interpolation, adaptive surface modeling, environmental variables, linear scanning algorithm, soil total potassium content, leave one out validati

CLC Number:

P208

LIU Yongkun, LIU Wei, WANG Changyang, CHEN Jiaming, MA Jin. Adaptive surface modeling of soil total potassium content based on integrated learning support in complex geomorphological regions[J]. 测绘通报, 2019, 0(2): 63-70.

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Adaptive surface modeling of soil total potassium content based on integrated learning support in complex geomorphological regions

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