Semi-supervised learning for the spatial extrapolation of soil information

Taghizadeh–Mehrjardi, Ruhollah; Sheikhpour, Razieh; Zeraatpisheh, Mojtaba; Amirian‐Chakan, Alireza; Toomanian, Norair; Kerry, Ruth; Scholten, Thomas

doi:10.1016/j.geoderma.2022.116094

Cited by 9 publications

(6 citation statements)

References 66 publications

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“…Previous studies have found that the ratio of red to green bands is high for irrigated crops and low for saline soils with salt efflorescence (Mandal & Sharma, 2011). This result also agrees well with that of another study, wherein Taghizadeh‐Mehrjardi et al (2022) found that CAEX is among the most important covariates in digital soil mapping.…”

Section: Resultssupporting

confidence: 93%

“…It is worth noting that among all the spatially lagged environmental covariates, topographic indices (e.g., s_LSFator and s_Slope) are more critical than indices from other classes. Taghizadeh‐Mehrjardi et al (2022) reported that landscape topography is vital for water flow distribution, soil redistribution, water discharge, and water accumulation. Mougenot et al (1993) also discovered that relative elevation is one of most evident feature influencing salinity, and salt efflorescence occurs from more rapid evaporation on the margin than in the center of depressions.…”

Section: Discussionmentioning

confidence: 99%

“…SVM and SVR can transform data into higher dimensions using kernel functions. This transformation would convert the nonlinear data relationship to a linear one, making it possible to handle complex classification and regression tasks (Taghizadeh‐Mehrjardi et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

“…The SVR model aims to solve the nonlinear problem in the sample space by mapping it to a high‐dimensional feature space. Previous research has explored two significant advantages of the SVR (Taghizadeh‐Mehrjardi et al, 2022). First, it is applied to nonlinear relationships.…”

Section: Introductionmentioning

confidence: 99%

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Soil salinity estimation in Shule River Basin using support vector regression model

2023

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Soil salinization is a progressive degradation process that spreads globally and leads to a decline in soil fertility. Assessing the scale of salinization is crucial for sustainable agricultural development and saline‐land rehabilitation. In this study, we proposed a support vector regression spatial (SVR‐S) model that utilizes spatial dependence information to predict soil salinity over the irrigation area of the Shule River Basin in northwestern China. To investigate the performance of the SVR‐S model, 50 soil samples were collected in the field. Semivariograms of soil salinity (measured as total salt content and ion concentrations) were constructed to measure spatial dependence. The SVR‐S model was compared with the original SVR model and the geographically weighted regression (GWR) model regarding the salinity prediction ability. The soil salinity in the experimental area demonstrated a strong spatial dependence pattern. The SVR‐S model delivered a better performance than the SVR‐O and GWR. SVR‐S showed a correlation coefficient R of 0.87 and a root mean square error (RMSE) of 1.83%, while the performance of SVR‐O (R = 0.75, RMSE = 3.32%) and GWR (R = 0.73, RMSE = 3.47%) was comparatively poor. Topographic indices integrating spatial information contributed the most to the estimation of salinity in the study area. This study provides a new approach to integrating spatial information for accurate soil salinity mapping.

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Section: Resultssupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Soil salinity estimation in Shule River Basin using support vector regression model

2023

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“…Although ML algorithms are routinely applied throughout the world for mapping of soil classes and soil properties at various spatial scales (e.g., Grunwald, 2009), there are still some issues such as imbalanced soil data (Taghizadeh‐Mehrjardi et al., 2022), over‐ or underestimation of the contents of soil properties, country borders, or generally missing data (Hengl et al., 2017), and uneven spatial distribution of data (Meyer & Pebesma, 2022). Also, difficulties with DSM products stemming from sampling design, use and preparation of input data, and selection of calibration locations have been reported (Brus, 2019; Worsham et al., 2012).…”

Section: Pedometric Soil Modeling Approachmentioning

confidence: 99%

3–4D soil model as challenge for future soil research: Quantitative soil modeling based on the solid phase

Gerke

Vogel

Weber

et al. 2022

J. Plant Nutr. Soil Sci.

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A 3-4D soil model represents a logical step forward from one-dimensional soil columns (1D), two-dimensional soil maps (2D), and three-dimensional soil volumes (3D) toward dynamic soil models (4D), with time as the fourth dimension. The challenge is to develop modeling tools that account for the states of soil properties, including the spatial structure of solids and pores, as well as their dynamics, including soil mass and solute transfers in landscapes. Our envisioned 3-4D soil model approach aims at improving the capability to predict fundamental soil functions (e.g., plant growth, storage, matter fluxes) that provide ecosystem services in the socioeconomic context.This study provides a structured overview on current soil models, challenges, open questions, and urgent research needs for developing a 3-4D soil model. A 3-4D soil model should provide an inventory of spatially distributed and temporally variable soil properties. As basis for this, we propose a mass balance model for the solid phase, which needs to be supplemented by a model describing its structure. This should eventually provide adequate 3D parameter sets for the numerical modeling of soil functions (e.g., flow and transport). The target resolution is decameters in the horizontal plane and centimeters to decimeters in the vertical direction to represent characteristic soil properties and soil horizons. The actual state of soils and their properties can be estimated from spatial data that represent the soil forming factors, with the use of machine learning tools. Improved modeling of the dynamics of soil bulk density, biological processes, and the pore structure are required to relate the solid mass balance to matter fluxes.A 3-4D soil model can be built from several types of modeling approaches. We distinguish between (1) process models that simulate mass balances, fluxes and soil structure dynamics, (2) statistical pedometric models using machine learning and geostatistics to estimate the soil inventory within landscapes, and (3) pedotransfer functions to link observable attributes to specific model parameters required to simulate soil functions including water and matter fluxes. This should provide the prerequisites to predict the spatial distribution of soil functions and their changes in response to external forcing. This endeavor can draw upon many already established models and techniques, yet combining them into a newly created 3-4D soil model is a truly an ambitious, but promisingThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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