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• Smartphones show excellent ability to predict original soil properties. • Image segmentation significantly improves soil property prediction accuracy. • Significant differences in dominant regions between segmentation methods. • The K-means clustering segmentation method outperforms the OUST method. Prediction of soil properties based on images of original soil acquired from smartphones is considered a potential alternative to conventional soil property determination due to its convenience and low cost. However, images of the original soil may contain distracting information such as fissures, shadows, and rocks, which may affect the image feature extraction and property prediction results. Four unsupervised image segmentation methods, namely the maximum interclass variance (OUST) method, the local adaptive threshold segmentation (LATS) method, the K-means clustering segmentation (KMCS) method, and the natural break point segmentation (NBPS) method, were used to segment 62 images of original soil taken by smartphones in outdoor sunny conditions. The number of segmentation levels (N) for KMCS and NBPS varied from 2 to 10. The dominant region, determined as the region with the highest percentage of image area after segmentation, was selected for subsequent property prediction. The results of the segmentation methods revealed significant differences in the dominant regions, leading to significant variations in color and texture features (p < 0.05). The random forest approach was used to construct predictive models for soil Munsell colour, soil organic matter (SOM) content, and soil texture content. The validation results demonstrated that the image segmentation methods improved the prediction accuracy of all three soil properties compared to that of the unsegmented images. Among the segmentation methods, KMCS (N = 3 and 4) exhibited the highest segmentation performance, surpassing the widely used OUST method. This research highlights the significance of image segmentation methods in predicting soil properties based on images of original soil samples and recommended it as an essential process for future soil image processing. The findings of this study have the potential to advance the development of smartphone-based prediction of properties of original soils. [ABSTRACT FROM AUTHOR]

• Surface horizon delineation affect topsoil organic carbon density (TSOCD) results. • TSOCD rises with an increase in the thickness of surface horizon delineation. • Diagnostic surface horizon (DSH) proven to be effective in assessing the TSOCD. • Conventional surface horizon (CSH) methods may incorrectly assess the TSOCD. • Some CSH-based thicknesses are recommended under certain conditions. Accurate assessment of the topsoil organic carbon density (TSOCD) is of great importance for the management and conservation of the topsoil organic carbon pools. The conventional surface horizon (CSH) delineation method, which involves specifying a uniform topsoil thickness for all sampling points across a study area, carries the potential risk of inaccurate TSOCD estimation. Chinese soil scientists developed the definition and determination criteria for diagnostic surface horizon (DSH) in Chinese Soil Taxonomy (CST), which were based on the distinctive characteristics of topsoil in China. This study employed a DSH-based topsoil classification method to evaluate the TSOCD of China, based on the findings of the recent national soil survey. Also, TSOCD was calculated for all sample points based on the CSH method, where the thickness of CSH was defined as 5, 10, 15, 20, 30, and 60 cm from the surface. The results showed that DSH-based TSOCD in China ranged from 0.03 to 74.63 kg m−2 with a mean value of 4.34 kg m−2. The standardized topsoil delineation based on the DSH method shows great potential for improving the accuracy of TSOCD assessment and different DSH types exhibited very significant differences in TSOCD (p < 0.01). There was a significant difference between the TSOCD results based on the six CSH delineation thicknesses (p < 0.01). Excessive or insufficient uniform CSH thickness settings may result in over- or underestimation of TSOCD values in comparison to DSH. This study provides evidence that specific uniform thickness delineations are suitable for particular soil types, climatic conditions, and land use patterns, and that the mean TSOCD results derived from these delineations do not significantly differ (p < 0.01) from those based on DSH. The findings of this study have the potential to support the development of more accurate surface horizon delineation methods for managing and making decisions related to topsoil organic carbon pools. [ABSTRACT FROM AUTHOR]

• Pedodiversity was higher in southern China than northern China. • Analysis unit sizes had little effect on spatial pattern analysis of pedodiversity. • Overall, parent material and topography were major driving factors of pedodiversity. Knowledge of pedodiversity is essential for the protection and management of soil resources. The analysis of regional pedodiversity is typically based on analysis units of different sizes. However, the effect of analysis unit size on regional pedodiversity studies remains unclear. In this paper, the effects of analysis unit size on the studies of spatial distribution characteristics of pedodiversity and its major driving factor in China were studied based on two levels of geomorphological regions. A combined method encompassing the moving window, soil richness, and Shannon index was used to analyze the spatial distribution pattern of pedodiversity in China. A geographical detector was used to determine a major driving factor of pedodiversity in each geomorphological region. The spatial distribution pattern of pedodiversity based on two levels of geomorphological regions both showed that pedodiversity was highest in southern China, followed by northern China, and lowest in the Qinghai-Tibet Plateau, which indicated that analysis unit sizes had little effect on the study on spatial distribution pattern of pedodiversity. The major driving factor analysis of pedodiversity based on first-order geomorphological regions showed that the major driving factor in China, except for the Qinghai-Tibet Plateau, was parent material. However, the results based on second-order geomorphological regions showed that the major driving factors of pedodiversity, except for the Qinghai-Tibet Plateau, were parent material and topography in hilly regions, topography in mountainous regions, and parent material in regions occupied by alluvial landforms, aeolian landforms, or karst landforms. The driving factor analysis of pedodiversity based on the two levels of geomorphological regions suggested that the effect of the analysis unit sizes on the major driving factor study was significant. The results of this study are an important supplement for pedodiversity studies. [ABSTRACT FROM AUTHOR]