scholarly journals Layer-Specific Analysis and Spatial Prediction of Soil Organic Carbon Using Terrain Attributes and Erosion Modeling

2010 ◽  
Vol 74 (3) ◽  
pp. 922-935 ◽  
Author(s):  
Verena Dlugoß ◽  
Peter Fiener ◽  
Karl Schneider
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Prediction ◽  
Erosion Modeling ◽  
Terrain Attributes ◽  
Specific Analysis

Soil Organic Carbon Prediction at National Scale (Germany)

2020 ◽  
Author(s):  
Ali Sakhaee ◽  
Anika Gebauer ◽  
Mareike Ließ ◽  
Axel Don
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Soils ◽  
Model Performance ◽  
Spatial Prediction ◽  
Spatial Modelling ◽  
Agricultural Landscapes ◽  
Boosted Regression Trees ◽  
Environmental Drivers ◽  
National Scale

<p>Soil Organic Carbon (SOC) plays a crucial role in agricultural ecosystems. However, its abundance is spatially variable at different scales. In recent years, machine learning (ML) algorithms have become an important tool in the spatial prediction of SOC at regional to continental scales. Particularly in agricultural landscapes, the prediction of SOC is a challenging task.</p><p>In this study, our aim is to evaluate the capability of two ML algorithms (Random Forest and Boosted Regression Trees) for topsoil (0 to 30 cm) SOC prediction in soils under agricultural use at national scale for Germany. In order to build the models, 50 environmental covariates representing topography, climate factors, land use as well as soil properties were selected. The SOC data we used was from the German Agricultural Soil inventory (2947 sampling points). A nested 5-fold cross-validation was used for model tuning and evaluation. Hyperparameter tuning for both ML algorithms was done by differential evolution optimization. </p><p>This approach allows exploring an extensive set of field data in combination with state of the art pedometric tools. With a strict validation scheme, the geospatial-model performance was assessed. Current results indicate that the spatial SOC variation is to a minor extent predictable with the considered covariate data (<30% explained variance). This may partly be explained by a non-steady state of SOC content in agricultural soils with environmental drivers. We discuss the challenges of geo-spatial modelling and the value of ML algorithms in pedometrics.</p>

Mapping Soil Organic Carbon Using Local Terrain Attributes: A Comparison of Different Polynomial Models

Pedosphere ◽  
2017 ◽  
Vol 27 (4) ◽  
pp. 681-693 ◽  
Author(s):  
Xiaodong SONG ◽  
Feng LIU ◽  
Ganlin ZHANG ◽  
Decheng LI ◽  
Yuguo ZHAO ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Terrain Attributes ◽  
Polynomial Models

scholarly journals Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates

CATENA ◽  
2022 ◽  
Vol 208 ◽  
pp. 105723
Author(s):  
Mojtaba Zeraatpisheh ◽  
Younes Garosi ◽  
Hamid Reza Owliaie ◽  
Shamsollah Ayoubi ◽  
Ruhollah Taghizadeh-Mehrjardi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Prediction ◽  
Environmental Covariates

scholarly journals Terrain attributes and forage productivity predict catchment-scale soil organic carbon stocks

Geoderma ◽  
2020 ◽  
Vol 368 ◽  
pp. 114286
Author(s):  
Scott M. Devine ◽  
Anthony T. O'Geen ◽  
Han Liu ◽  
Yufang Jin ◽  
Helen E. Dahlke ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Stocks ◽  
Catchment Scale ◽  
Terrain Attributes ◽  
Soil Organic Carbon Stocks

scholarly journals Nonstationary spatial prediction of soil organic carbon: Implications for stock assessment decision making

2019 ◽  
Vol 13 (1) ◽  
pp. 165-188 ◽  
Author(s):  
Mark D. Risser ◽  
Catherine A. Calder ◽  
Veronica J. Berrocal ◽  
Candace Berrett
Keyword(s):  
Decision Making ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Stock Assessment ◽  
Spatial Prediction

Spatial prediction of soil organic carbon from data on large and variable spatial supports. I. Inventory and mapping

2012 ◽  
Vol 23 (2) ◽  
pp. 129-147 ◽  
Author(s):  
T. G. Orton ◽  
N. P. A. Saby ◽  
D. Arrouays ◽  
C. Walter ◽  
B. Lemercier ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Prediction

Spatial prediction of soil organic carbon from data on large and variable spatial supports. II. Mapping temporal change

2012 ◽  
Vol 23 (2) ◽  
pp. 148-161 ◽  
Author(s):  
T.G. Orton ◽  
N.P.A. Saby ◽  
D. Arrouays ◽  
C. Walter ◽  
B. Lemercier ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Temporal Change ◽  
Spatial Prediction
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