An Independent Validation of SoilGrids Accuracy for Soil Texture Components in Croatia

Radočaj, Dorijan; Jurišić, Mladen; Rapčan, Irena; Domazetović, Fran; Milošević, Rina; Plaščak, Ivan

doi:10.3390/land12051034

Cited by 5 publications

(2 citation statements)

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“…The ensemble method with bioclimatic covariates resulted in the best performance overall, achieving R 2 of 0.580 and RMSE of 10.392. This prediction accuracy is typical for the geospatial prediction of soil properties on a large scale according to the previous studies, which generally achieved prediction accuracy expressed by R 2 up to 0.5 [49][50][51]. Although RF, XGB, and SVM models also showed relative improvements with the addition of bioclimatic data, they significantly lagged the prediction accuracy of the ensemble approach.…”

Section: Prediction Accuracy Of Ensemble and Individual Machine Learn...supporting

confidence: 65%

The Effect of Bioclimatic Covariates on Ensemble Machine Learning Prediction of Total Soil Carbon in the Pannonian Biogeoregion

Radočaj,

Jurišić,

Tadić

2023

Agronomy

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This study employed an ensemble machine learning approach to evaluate the effect of bioclimatic covariates on the prediction accuracy of soil total carbon (TC) in the Pannonian biogeoregion. The analysis involved two main segments: (1) evaluation of base environmental covariates, including surface reflectance, phenology, and derived covariates, compared to the addition of bioclimatic covariates; and (2) assessment of three individual machine learning methods, including random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM), as well as their ensemble for soil TC prediction. Among the evaluated machine learning methods, the ensemble approach resulted in the highest prediction accuracy overall, outperforming the individual models. The ensemble method with bioclimatic covariates achieved an R2 of 0.580 and an RMSE of 10.392, demonstrating its effectiveness in capturing complex relationships among environmental covariates. The results of this study suggest that the ensemble model consistently outperforms individual machine learning methods (RF, XGB, and SVM), and adding bioclimatic covariates improves the predictive performance of all methods. The study highlights the importance of integrating bioclimatic covariates when modeling environmental covariates and demonstrates the benefits of ensemble machine learning for the geospatial prediction of soil TC.

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Section: Prediction Accuracy Of Ensemble and Individual Machine Learn...supporting

confidence: 65%

The Effect of Bioclimatic Covariates on Ensemble Machine Learning Prediction of Total Soil Carbon in the Pannonian Biogeoregion

Radočaj,

Jurišić,

Tadić

2023

Agronomy

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“…Globally available soil maps, such as SoilGrids [12,13], provide valuable information on soil properties at a high spatial resolution globally. However, these maps have limitations when applied to national or regional scales in terms of potential inaccuracies due to variation in soil data quality or availability across regions, limited detail in representing local soil variability, and uncertainty in extrapolating global models to smaller scales where local factors play a significant role [22]. Additionally, discrepancies may arise from differences in mapping methods, soil classification systems, and ground validation data availability, highlighting the need for creating custom digital SOC maps at specific national or regional levels.…”

Section: Introductionmentioning

confidence: 99%

Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

Radočaj,

Gašparović,

Jurišić

2024

Agriculture

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This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance in achieving United Nations’ Sustainable Development Goals (SDGs) related to hunger, climate action, and land conservation. The literature review was performed according to scientific studies indexed in the Web of Science Core Collection database since 2000. The analysis reveals a steady rise in total digital soil mapping studies since 2000, with digital SOC mapping studies accounting for over 20% of these studies in 2023, among which SDGs 2 (Zero Hunger) and 13 (Climate Action) were the most represented. Notably, countries like the United States, China, Germany, and Iran lead in digital SOC mapping research. The shift towards machine and deep learning methods in digital SOC mapping has surged post-2010, necessitating environmental covariates like topography, climate, and spectral data, which are cornerstones of machine and deep learning prediction methods. It was noted that the available climate data primarily restrict the spatial resolution of digital SOC mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m). Future directions include the integration of diverse remote sensing data sources, the development of advanced algorithms leveraging machine learning, and the utilization of high-resolution remote sensing for more precise SOC mapping.

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Locally enhanced digital soil mapping in support of a bottom-up approach is more accurate than conventional soil mapping and top-down digital soil mapping

Bohn,

Miller

2024

Geoderma

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An Independent Validation of SoilGrids Accuracy for Soil Texture Components in Croatia

Cited by 5 publications

References 50 publications

The Effect of Bioclimatic Covariates on Ensemble Machine Learning Prediction of Total Soil Carbon in the Pannonian Biogeoregion

The Effect of Bioclimatic Covariates on Ensemble Machine Learning Prediction of Total Soil Carbon in the Pannonian Biogeoregion

Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

Locally enhanced digital soil mapping in support of a bottom-up approach is more accurate than conventional soil mapping and top-down digital soil mapping

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