EGU2020-600, updated on 12 Jun 2020
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Geospatial modeling approaches for mapping topsoil organic carbon stock in northern part of Mongolia

Samdandorj Manaljav1 and Purevdorj Tserengunsen2
Samdandorj Manaljav and Purevdorj Tserengunsen
  • 1Department of Physical Geography and Geoinformatics, University of Szeged, Szeged 6722, Egyetem str. 2-6, Hungary (
  • 2Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar 14192, Erkhuu str., Mongolia (

Soil organic carbon (SOC) is one of the most important indicators of soil quality and agricultural productivity. This paper presents the application of Regression Kriging (RK), Geographically Weighted Regression (GWR) and Geographically Weighted Regression Kriging (GWRK) for prediction of topsoil organic carbon stock in Tarialan. A total of 25 topsoil (0-30 cm) samples were collected from Tarialan soum of Khuvsgul aimag in Mongolia. In this study, seven independent variables were used including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalized difference moisture index (NDMI), land surface temperature (LST) and terrain factors (DEM, Slope, Aspect). We used root mean square error (RMSE), mean error (ME) and determination coefficient (R2) to evaluate the performance of these methods. Validation results showed that performance of the GWRK, GWR, and RK approaches were good with not only low values of root-mean-square error (1.38 kg m-2, 1.48 kg m-2, 0.69 kg m-2), mean error (0.28 kg m-2, -0.22 kg m-2, 0.17 kg m-2) but also high values of R2 (0.76, 0.72, 0.94). The estimated SOC stock values ranged from 0.28-16.26 kg m-2, 0.72–15.24 kg m-2, 0.16–15.83 kg m-2 using GWRK, GWR, RK approaches in the study area. The highest average SOC stock value was in the wetland (6.47 kg m-2, 6.08 kg m-2, 6.44 kg m-2) and the lowest was in cropland (1.63 kg m-2, 1.48 kg m-2, 1.80 kg m-2) using these approaches. According to the validation, GWRK, GWR, and RK approaches produced satisfactory results for estimating and mapping SOC stock. However, Regression Kriging was the best model, followed by GWRK and GWR to predict topsoil organic carbon stock in Tarialan.

How to cite: Manaljav, S. and Tserengunsen, P.: Geospatial modeling approaches for mapping topsoil organic carbon stock in northern part of Mongolia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-600,, 2019

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