Next Article in Journal
Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images
Previous Article in Journal
Development and Comparison of Species Distribution Models for Forest Inventories
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(6), 178; doi:10.3390/ijgi6060178

Adaptive Surface Modeling of Soil Properties in Complex Landforms

1
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
2
School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 24 April 2017 / Revised: 16 June 2017 / Accepted: 18 June 2017 / Published: 20 June 2017
View Full-Text   |   Download PDF [4804 KB, uploaded 21 June 2017]   |  

Abstract

Abstract: Spatial discontinuity often causes poor accuracy when a single model is used for the surface modeling of soil properties in complex geomorphic areas. Here we present a method for adaptive surface modeling of combined secondary variables to improve prediction accuracy during the interpolation of soil properties (ASM-SP). Using various secondary variables and multiple base interpolation models, ASM-SP was used to interpolate soil K+ in a typical complex geomorphic area (Qinghai Lake Basin, China). Five methods, including inverse distance weighting (IDW), ordinary kriging (OK), and OK combined with different secondary variables (e.g., OK-Landuse, OK-Geology, and OK-Soil), were used to validate the proposed method. The mean error (ME), mean absolute error (MAE), root mean square error (RMSE), mean relative error (MRE), and accuracy (AC) were used as evaluation indicators. Results showed that: (1) The OK interpolation result is spatially smooth and has a weak bull's-eye effect, and the IDW has a stronger ‘bull’s-eye’ effect, relatively. They both have obvious deficiencies in depicting spatial variability of soil K+. (2) The methods incorporating combinations of different secondary variables (e.g., ASM-SP, OK-Landuse, OK-Geology, and OK-Soil) were associated with lower estimation bias. Compared with IDW, OK, OK-Landuse, OK-Geology, and OK-Soil, the accuracy of ASM-SP increased by 13.63%, 10.85%, 9.98%, 8.32%, and 7.66%, respectively. Furthermore, ASM-SP was more stable, with lower MEs, MAEs, RMSEs, and MREs. (3) ASM-SP presents more details than others in the abrupt boundary, which can render the result consistent with the true secondary variables. In conclusion, ASM-SP can not only consider the nonlinear relationship between secondary variables and soil properties, but can also adaptively combine the advantages of multiple models, which contributes to making the spatial interpolation of soil K+ more reasonable. View Full-Text
Keywords: complex landform; adaptive surface modeling; spatial interpolation; geostatistics; soil properties complex landform; adaptive surface modeling; spatial interpolation; geostatistics; soil properties
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Liu, W.; Zhang, H.-R.; Yan, D.-P.; Wang, S.-L. Adaptive Surface Modeling of Soil Properties in Complex Landforms. ISPRS Int. J. Geo-Inf. 2017, 6, 178.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top