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

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>

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 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. 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

Spatial prediction of soil organic carbon using machine learning techniques in western Iran

2020 ◽  
Vol 21 ◽  
pp. e00260 ◽  
Author(s):  
Hamid Mahmoudzadeh ◽  
Hamid Reza Matinfar ◽  
Ruhollah Taghizadeh-Mehrjardi ◽  
Ruth Kerry
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Prediction ◽  
Machine Learning Techniques ◽  
Western Iran ◽  
Learning Techniques

Spatial prediction of soil organic carbon stocks in Ghana using legacy data

Geoderma ◽  
2020 ◽  
Vol 360 ◽  
pp. 114008 ◽  
Author(s):  
Stephen Owusu ◽  
Yusuf Yigini ◽  
Guillermo F. Olmedo ◽  
Christian T. Omuto
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Prediction ◽  
Carbon Stocks ◽  
Soil Organic Carbon Stocks ◽  
Legacy Data

Spatial prediction of soil organic carbon at different depths using digital soil mapping

GlobalSoilMap ◽  
2014 ◽  
pp. 181-184
Author(s):  
F Collard ◽  
N Saby ◽  
A de Forges ◽  
S Lehmann ◽  
J Paroissien ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Prediction ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Different Depths
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