Articles | Volume 1, issue 1
https://doi.org/10.5194/soil-1-217-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/soil-1-217-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Original research article
| 
04 Mar 2015
Original research article |
| 04 Mar 2015
Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks
Leibniz Centre for Agricultural Landscape Research (ZALF) e.V., Institute of Soil Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany
S. Koszinski
Leibniz Centre for Agricultural Landscape Research (ZALF) e.V., Institute of Soil Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany
M. Wehrhan
Leibniz Centre for Agricultural Landscape Research (ZALF) e.V., Institute of Soil Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany
M. Sommer
Leibniz Centre for Agricultural Landscape Research (ZALF) e.V., Institute of Soil Landscape Research, Eberswalder Straße 84, 15374 Müncheberg, Germany
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- Leveraging legacy data with targeted field sampling for low-cost mapping of soil organic carbon stocks on extensive rangeland properties Y. Xia et al. 10.1016/j.geoderma.2024.116952
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- Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy A. Morellos et al. 10.1016/j.biosystemseng.2016.04.018
- Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring P. Baumann et al. 10.5194/soil-7-525-2021
- Short-term but not long-term perennial mugwort cropping increases soil organic carbon in Northern China Plain Z. Zhou et al. 10.3389/fpls.2022.975169
- Impact of Land Cover Changes on Soil Type Mapping in Plain Areas: Evidence from Tongzhou District of Beijing, China X. Wu et al. 10.3390/land12091696
- GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth V. Mulder et al. 10.1016/j.scitotenv.2016.07.066
- The possibility of spatial mapping of SOC content in olive groves under integrated production using easy-to-obtain ancillary data in a Mediterranean area F. Blanco Velázquez et al. 10.12688/openreseurope.14716.2
- The possibility of spatial mapping of soil organic carbon content at three depths using easy-to-obtain ancillary data in a Mediterranean area F. Blanco Velázquez et al. 10.12688/openreseurope.14716.1
- Digital mapping to extrapolate the selected soil fertility attributes in calcareous soils of a semiarid region in Iran P. Khosravani et al. 10.1007/s11368-023-03548-1
- Soil mapping, classification, and pedologic modeling: History and future directions E. Brevik et al. 10.1016/j.geoderma.2015.05.017
- Machine learning for digital soil mapping: Applications, challenges and suggested solutions A. Wadoux et al. 10.1016/j.earscirev.2020.103359
- Estimating soil organic matter using interpolation methods with a electromagnetic induction sensor and topographic parameters: a case study in a humid region A. García-Tomillo et al. 10.1007/s11119-016-9481-6
- Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling C. Schillaci et al. 10.1016/j.scitotenv.2017.05.239
- Environmental Factors Controlling Soil Organic Carbon Stocks in Two Contrasting Mediterranean Climatic Areas of Southern Spain B. Willaarts et al. 10.1002/ldr.2417
- Suitability estimation for urban development using multi-hazard assessment map G. Bathrellos et al. 10.1016/j.scitotenv.2016.10.025
- Spatial distribution dependency of soil organic carbon content to important environmental variables F. Mirchooli et al. 10.1016/j.ecolind.2020.106473
- Spatial modeling of soil organic carbon using remotely sensed indices and environmental field inventory variables A. Katebikord et al. 10.1007/s10661-022-09842-8
- S‐World: A Global Soil Map for Environmental Modelling J. Stoorvogel et al. 10.1002/ldr.2656
- Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang X. Li et al. 10.3390/rs13040769
- Changes of SOC Content in China’s Shendong Coal Mining Area during 1990–2020 Investigated Using Remote Sensing Techniques X. Yang et al. 10.3390/su14127374
- Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network Q. Qi et al. 10.1007/s40789-023-00588-3
- Carbon Storage in Cropland Soils: Insights from Iowa, United States J. Jordahl et al. 10.3390/land12081630
- Estimating soil organic carbon stock change at multiple scales using machine learning and multivariate geostatistics G. Szatmári et al. 10.1016/j.geoderma.2021.115356
- Impact of topographic aspect and vegetation (native and reforested areas) on soil organic carbon and nitrogen budgets in Mediterranean natural areas B. Lozano-García et al. 10.1016/j.scitotenv.2015.12.022
- Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales Y. Xia et al. 10.3389/fsoil.2022.890437
- Towards mapping soil carbon landscapes: Issues of sampling scale and transferability B. Miller et al. 10.1016/j.still.2015.07.004
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Short summary
There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research was to compare these two approaches for mapping SOC stocks from multiple linear regression models applied at the landscape scale via spatial association. Although the indirect approach had greater spatial variation and higher R2 values, the direct approach had a lower total estimated error.
There are many different strategies for mapping SOC, among which is to model the variables...
