Regional‐scale characterization of the geomorphic control of the spatial distribution of soil organic carbon in cropland

Stevens, François; Bogaert, Pierre-Maurice; Oost, Kristof Van; Doetterl, Sebastian; Wesemael, Bas van

doi:10.1111/ejss.12153

Cited by 13 publications

(12 citation statements)

References 33 publications

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“…This finding is logical since in the mountains there are relatively many shallow soils and the topography is highly variable. These findings confirm the relation between SOC and topography‐dominated landscapes, which was previously demonstrated by, e.g., Stevens et al []. In addition, a high evapotranspiration and low sand content are important for explaining the subsoil carbon (Figure ).…”

Section: Resultssupporting

confidence: 90%

Understanding large‐extent controls of soil organic carbon storage in relation to soil depth and soil‐landscape systems

Mulder¹,

Lacoste

Martín³

et al. 2015

Global Biogeochemical Cycles

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In this work we aimed at developing a conceptual framework in which we improve our understanding of the controlling factors for soil organic carbon (SOC) over vast areas at different depths. We postulated that variability in SOC levels may be better explained by modeling SOC within soil-landscape systems (SLSs). The study was performed in mainland France, and explanatory SOC models were developed for the sampled topsoil (0-30 cm) and subsoil (>30 cm), using both directed and undirected data-mining techniques. With this study we demonstrated that there is a shift in controlling factors both in space and depth which were mainly related to (1) typical SLS characteristics and (2) human-induced changes to SLSs. The controlling factors in relation to depth alter from predominantly biotic to more abiotic with increasing depth. Especially, water availability, soil texture, and physical protection control deeper stored SOC. In SLSs with similar SOC levels, different combinations of physical protection, the input of organic matter, and climatic conditions largely determined the SOC level. The SLS approach provided the means to partition the data into data sets that were having homogenous conditions with respect to this combination of controlling factors. This information may provide important information on the carbon storage and sequestration potential of a soil.

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Section: Resultssupporting

confidence: 90%

Understanding large‐extent controls of soil organic carbon storage in relation to soil depth and soil‐landscape systems

Mulder¹,

Lacoste

Martín³

et al. 2015

Global Biogeochemical Cycles

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show abstract

“…Additionally, the DEM data with a resolution of 10m was obtained from aerial imageries and used to extract landform, hydrological, and spatial indices. The topography was classified using the approaches described in FAO guidelines for soil description [62]. Additional landform characteristics of the study area was described using different landscape metrics including slope, relief, curvature, TWI, TPI, and others.…”

Section: Data Sources Software and Extraction Of Environmental Covamentioning

confidence: 99%

Modeling Soil Organic Carbon Using Remotely-Sensed Predictors: A Case Study from Fuzhou City, China

Sodango

Sha

et al. 2020

Preprint

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Background: Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since, variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit for optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely-sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the RF and Classification and Regression Tree (CART).Results: The results indicated that the RF model has good prediction performance with corresponding R2 and RMSE values of 0.96, and 0.91 mg/g, respectively. The distribution of SOC content showed variability across landforms (CV=78.67%), land-use (CV=93%), and lithology (CV=64.67%). Forestland had the highest SOC (13.60 mg/g) followed by agriculture (10.43 mg/g), urban (9.74 mg/g), and water body (4.55 mg/g) land-uses. Furthermore, bauxite and laterite lithology had the highest SOC content (14.69 mg/g) followed by fluvial (14.52 mg/g) and shale (13.57 mg/g), whereas the lowest was predicted in sandstone (5.53mg/g). The mean SOC concentration was 11.70 mg/g, where the majority of area was classified as humous and organo-humus, distributing in the mountainous regions. The biophysical land surface indices, brightness removed vegetation indices, topographic indices (, and soil spectral bands, respectively were the most influential predictors of SOC. Conclusion: The spatial variability of SOC may be influenced by landform, land-use, and lithology of the study area. Remotely-sensed predictors including land moisture, land surface temperature and built-up indices added valuable information for prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm sub-tropical urban regions.

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“…Field boundaries may also coincide with landscape features (e.g., hedges, streams, mounds) that have an influence on SOC by preventing lateral transport of sediments and SOC between adjacent fields (see for example Viaud et al, 2010;Ghariani et al, 2014). The presence of a field effect may explain the difficulty of Stevens et al (2014) to efficiently predict topsoil SOC (in an area of homogeneous texture) using only topographic information (terrain attributes). Doetterl et al (2013) also showed that, within our study area, the topography became a good predictor of SOC only for the subsoil at depths below 20 cm, where the influence of field-related processes was expected to be weaker.…”

Section: Rigorous Assessment Of Texture and Fields Effectmentioning

confidence: 99%

Detecting and quantifying field-related spatial variation of soil organic carbon using mixed-effect models and airborne imagery

2015

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Regional‐scale characterization of the geomorphic control of the spatial distribution of soil organic carbon in cropland

Cited by 13 publications

References 33 publications

Understanding large‐extent controls of soil organic carbon storage in relation to soil depth and soil‐landscape systems

Understanding large‐extent controls of soil organic carbon storage in relation to soil depth and soil‐landscape systems

Modeling Soil Organic Carbon Using Remotely-Sensed Predictors: A Case Study from Fuzhou City, China

Detecting and quantifying field-related spatial variation of soil organic carbon using mixed-effect models and airborne imagery

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