Spatio-temporal variability in physical properties of different textured soils under similar management and semi-arid climatic conditions

Aksakal, Ekrem Lütfi; Barik, Kenan; Angin, İlker; Sari, Serdar; Islam, Rafiq

doi:10.1016/j.catena.2018.09.017

Cited by 15 publications

(5 citation statements)

References 60 publications

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“… ρ b is routinely measured during soil characterization and is used to estimate total porosity. Within a given textural class, variation in bulk density can be directly attributed to the degree of compactness (Hakansson & Lipiec, ) or aggregation (Aksakal et al, ). Therefore, ρ b has been an essential variable in both physically based and empirical models of hydraulic conductivity (Assouline & Or, , and references therein.).…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations

Araya

Ghezzehei

2019

Water Resources Research

131

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Saturated hydraulic conductivity (Ks) is a fundamental soil property that regulates the fate of water in soils. Its measurement, however, is cumbersome and instead pedotransfer functions (PTFs) are routinely used to estimate it. Despite much progress over the years, the performance of current generic PTFs estimating Ks remains poor. Using machine learning, high‐performance computing, and a large database of over 18,000 soils, we developed new PTFs to predict Ks. We compared the performances of four machine learning algorithms and different predictor sets. We evaluated the relative importance of soil properties in explaining Ks. PTF models based on boosted regression tree algorithm produced the best models with root‐mean‐squared log‐transformed error in ranges of 0.4 to 0.3 (log10(cm/day)). The 10th percentile particle diameter (d10) was found to be the most important predictor followed by clay content, bulk density (ρb), and organic carbon content (C). The sensitivity of Ks to soil structure was investigated using ρb and C as proxies for soil structure. An inverse relationship was observed between ρb and Ks, with the highest sensitivity at around 1.8 g/cm3 for most textural classes. Soil C showed a complex relationship with Ks with an overall positive relation for fine‐textured and midtextured soils but an inverse relation for coarse‐textured soils. This study sought to maximize the extraction of information from a large database to develop generic machine learning‐based PTFs for estimating Ks. Models developed here have been made publicly available and can be readily used to predict Ks.

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Section: Introductionmentioning

confidence: 99%

Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations

Araya

Ghezzehei

2019

Water Resources Research

131

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“…The soil bulk density was not found as one of the most important predictors in the results from the entire dataset and from the tension infiltrometer data. The bulk density can reflect different aspects of soil structure caused by compaction (Hakansson & Lipiec, 2000) or aggregation (Aksakal et al., 2019) in the soil. It is also possible that the land use provides enough information for predictions of the best equation and makes the bulk density less critical predictor.…”

Section: Discussionmentioning

confidence: 99%

The site‐specific selection of the infiltration model based on the global dataset and random forest algorithm

Kim

Karahan

Шарма

et al. 2021

Vadose Zone Journal

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The selection of the infiltration equation is important in various water research and management fields. Different infiltration equations were found to perform better than others in previous studies. Our objective was to use the Soil Water Infiltration Global (SWIG) database for (a) evaluating and comparing infiltration equations and (b) using the random forest algorithm to investigate how soil properties, land use, and infiltration measurement methods influence the infiltration equation selection. The performance of six equations (Horton, Mezencev, Collis-George, Green-Ampt, Philip, and Swartzendruber) was characterized by the Akaike information criterion obtained after fitting them to 4,830 cumulative infiltration datasets from the SWIG database.Then, the random forest algorithm was applied to predict the best infiltration equation using soil texture class, organic matter content, bulk density, saturated hydraulic conductivity, land use, and infiltration measurement method as inputs. The Horton, Mezencev, and Collis-George models were the best in 36, 24, and 12% of cases, respectively. Swartzendruber, Philip, and Green-Ampt were the best in 11, 10, and 7% cases, respectively. The different error of predicting the best infiltration equation was observed in different parts of input variable space. The infiltration method was by far the most important predictor for a model being the best across the whole database, followed by the organic C content and land use type. Organic C content and land use type were the most important predictors for the tension infiltrometer datasets. The SWIG database presents the opportunity to forecast which infiltration equation will work best in site-specific conditions.

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“…Studies have shown that an increase in soil bulk density from 1.1 to 1.4 g/cm 3 yielded a 42% reduction in oxygen diffusion rate through waterlogged salt marsh soil, while the induced changes in soil bulk density from 1.1 to 1.7 g/cm 3 resulted in a 75% reduction in the rate of oxygen diffusion [ 35 ]. Within a given type of soil texture, variations in bulk density are directly related to the degree of compactness [ 36 ], aggregation [ 37 ], and organic matter content [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

“…In wetlands, bulk density is a fundamental parameter influencing hydraulic conductivity [ 37 , 39 ], and this parameter is used for calculating the total storage of a given nutrient per unit area [ 2 , 40 ]. Nutrient stock (carbon or nitrogen) in a wetland environment could be estimated by soil bulk density, and soils with low bulk density have a greater capacity for holding nitrogen and carbon than soils with high bulk density [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Hikouei

Kim

Mishra

2021

Sensors

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Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

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Spatio-temporal variability in physical properties of different textured soils under similar management and semi-arid climatic conditions

Cited by 15 publications

References 60 publications

Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations

Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations

The site‐specific selection of the infiltration model based on the global dataset and random forest algorithm

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

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