Spatial prediction of soil water retention in a Páramo landscape: Methodological insight into machine learning using random forest

Geoderma ◽  
2018 ◽  
Vol 316 ◽  
pp. 100-114 ◽  
Author(s):  
Carlos M. Guio Blanco ◽  
Victor M. Brito Gomez ◽  
Patricio Crespo ◽  
Mareike Ließ
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Soil Water ◽  
Water Retention ◽  
Spatial Prediction ◽  
Soil Water Retention ◽  
Insight Into

Global mapping of volumetric water content at 10, 33 and 1500 kPa using the WoSIS global database

2021 ◽  
Author(s):  
Maria Eliza Turek ◽  
Gerard Heuvelink ◽  
Niels Batjes ◽  
Laura Poggio
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Retention ◽  
Spatial Prediction ◽  
Volumetric Water Content ◽  
Data Availability ◽  
Pedotransfer Functions ◽  
Soil Water Retention ◽  
Direct Mapping

<p>Soil water content is a key property for modelling the water balance in hydrological, eco-hydrological and agro-hydrological models. Currently available global maps of soil water retention are mostly based on pedotransfer functions applied to maps of other basic soil properties. We developed global maps of the volumetric water content at 10, 33 and 1500 kPa by direct mapping based on soil water content data derived from the WoSIS Soil Profile Database and covariates describing vegetation, terrain morphology, climate, geology and hydrology using the SoilGrids workflow. The preparation of the input soil data consisted of the verification of available volumetric water content data and conversion of gravimetric to volumetric data using measured and estimated bulk density. In total we had 9609, 41082 and 49224 soil water content observations at 10, 33 and 1500 kPa, respectively, and prepared around 200 covariates as candidate predictors. After covariates selection, model tuning and cross-validation and final model fitting for 3D spatial prediction, results were presented for the globe with uncertainty estimation. The results were also compared to other available global maps of water retention to evaluate differences between direct mapping against other types of approaches. Directly developing global maps of soil water content, with associated uncertainty, is a novel approach for this type of properties, and contributes to improving global soil data availability and quality.</p>

An automated machine learning based ensemble approach for improving estimates of soil water retention parameters

2021 ◽  
Author(s):  
Hao Chen ◽  
Tiejun Wang ◽  
Yonggen Zhang
Keyword(s):  
Machine Learning ◽  
Soil Water ◽  
Water Retention ◽  
Soil Water Retention ◽  
Ensemble Approach ◽  
Model Average ◽  
Global Soil ◽  
Automated Machine Learning ◽  
National Cooperative ◽  
Retention Parameters

<p>Accurately mapping soil water retention parameters is vital for modeling atmosphere-land interactions but is challenged by limited measurements and simulations globally. Ensemble pedotransfer functions (PTFs) have been highly recommended for use due to the higher reliability of ensemble models and the error compensation among ensemble members. However, conventional ensemble approaches assign a fixed weight to each PTF and may not fully utilize the strengths of individual PTFs. In this work, we developed a new ensemble approach based on an automated machine learning workflow to assign varying weights to assemble 13 widely used PTFs. The AutoML-assisted ensemble approach (AutoML-Ens), as well as the simple average (MEAN), Bayesian Model Average (BMA), and the hierarchical multi-model ensemble approach (HMME), were evaluated using the global coverage National Cooperative Soil Surbey (NCSS) Soil Characterization Database. Results indicate that AutoML-Ens approach performs better than the conventional approaches in terms of the coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). Three soil hydraulic parameters, i.e., saturated water content, field capacity, and wilting points, and their corresponding uncertainties, were further derived through the AutoML-Ens approach at a 30’’×30’’ geographical spatial resolution based on a global soil composition database (SoilGrids), which can be applied in the Earth System Modeling. This study demonstrated the necessity of dynamic weights assigning in ensemble approaches and the great potential of coupling data-driven (here, the AutoML) and modeling (empirically or physically-based PTFs) approaches in mapping global soil water retention-like parameters.</p>

INFLUENCE OF SOIL MATRIX ON SOIL-WATER RETENTION CURVE AND HYDRAULIC CHARACTERISTICS

2017 ◽  
Vol 16 (4) ◽  
pp. 869-877
Author(s):  
Vasile Lucian Pavel ◽  
Florian Statescu ◽  
Dorin Cotiu.ca-Zauca ◽  
Gabriela Biali ◽  
Paula Cojocaru
Keyword(s):  
Soil Water ◽  
Water Retention ◽  
Water Retention Curve ◽  
Hydraulic Characteristics ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Soil Matrix ◽  
Retention Curve

scholarly journals Carboxylated nanocellulose superabsorbent: Biodegradation and soil water retention properties

2021 ◽  
pp. 51495
Author(s):  
Ruth M. Barajas‐Ledesma ◽  
Vanessa N. L. Wong ◽  
Karen Little ◽  
Antonio F. Patti ◽  
Gil Garnier
Keyword(s):  
Soil Water ◽  
Water Retention ◽  
Soil Water Retention ◽  
Water Retention Properties
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