Towards prediction of soil erodibility, SOM and CaCO3 using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran

Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, H A; Abbasi, Mozhgan; Demattê, José Alexandre Melo; Arthur, Emmanuel; Panagos, Panos

doi:10.1016/j.geoderma.2017.11.014

Cited by 80 publications

(52 citation statements)

References 48 publications

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“…4). The effects of soil particle on erodibility mainly depend on: (1) fine particles are more easily migrated by runoff, and (2) high soil permeability in coarse texture soils reduces water erosion (Ostovari et al, 2018). Soil organic matters, which act as matrixes to absorb soil water, improve anti-erosion ability through decreasing detachment of soil particles by runoff and raindrops and increasing soil shear strength (Zhu et al, 2010).…”

Section: Limitation In Soil Erodibility K Factor Of the Epic Model Inmentioning

confidence: 99%

Assessing soil degradation under land-use change: insight from soil erosion and soil aggregate stability in a small karst catchment in southwest China

Liu

2020

PeerJ

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Background Soil erodibility (K factor) and soil aggregate stability are often used to assess soil degradation in an erodible environment. However, their applicability under land-use change is uncertain, especially agricultural abandonment. Methods Different land-use types, including cropland, abandoned cropland, and native vegetation land, were converted into the successive stages following agricultural abandonment by space-for-time substitution approach in a small karst catchment, Southwest China. The indexes of soil aggregate stability and K factor of the Erosion Productivity Impact Calculator (EPIC) model in soil profiles were calculated to identify which method is suitable to indicate soil degradation under land-use change. Results The indexes of soil aggregate stability in the soils at 0∼30 cm depth under native vegetation land were significantly larger than those under cropland and slightly larger than those under abandoned cropland. The K factor was not significantly different among the three land use examples because the EPIC model does not consider soil permeability. In the soil organic carbon (SOC)-rich soils (>2%), the K factor was significantly correlated with silt and clay content ranging within a narrow scope of near 0.010 t hm2 h/hm2/MJ/mm. While in the SOC-poor soils, the K factor was significantly increased with decreasing SOC content and was significantly correlated with soil aggregate stability. Conclusions Soil aggregate stability is more suitable to indicate soil degradation under land-use change. Sufficient SOC in erodible soils would restrain soil degradation, while SOC loss can significantly increase soil erosion risk.

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Section: Limitation In Soil Erodibility K Factor Of the Epic Model Inmentioning

confidence: 99%

Assessing soil degradation under land-use change: insight from soil erosion and soil aggregate stability in a small karst catchment in southwest China

Liu

2020

PeerJ

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“…The soils in the JRC are coarser textured and have lower SOC content than those in the CC (Figure 2). Coarse soil texture means large and more numerous soil pores, which benefits downward transport of fine particle in the process of surface water infiltration [42,43]. Soil particle organic matters easily combine with fine particles to form organic-inorganic complexes or soil aggregates, which reduces the moving ability of clay and silt [12,13].…”

Section: Discussionmentioning

confidence: 99%

Effects of Soil Properties on K Factor in the Granite and Limestone Regions of China

Liu

Zhang

et al. 2020

IJERPH

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Soil erosion has become a serious ecological problem in many catchments. Soil erodibility K factor can be estimated based on a series of soil properties, however, the identification of dominant soil properties that affect K factor prediction at different soil types has been little concerned. In this study, 3 soil profiles from the Jiulongjiang River Catchment (JRC) of granite region in Fujian province and 18 soil profiles from the Chenqi Catchment (CC) of karst region in Guizhou province were selected. Soil properties, including soil particle size distribution, soil organic carbon (SOC) and soil organic nitrogen (SON) content, and soil pH, were determined, and the K factors were estimated in the erosion productivity impact calculator (EPIC) model. The soils in the granite region were characteristic for coarse texture, low SOC and SON, and strong acidity compared with limestone soils. Although the K factors in both regions ranged from 0.009 to 0.018, they were overestimated in limestone soils due to frequent soil aggregation, which enhanced soil permeability, hence reduced soil erodibility. The results of principal component analysis (PCA) and structural equation model (SEM) showed that (1) K factor estimation in the soils of the granite region mainly depended on soil texture, of which silt was the most important factor; (2) while K factor in limestone soils was mainly controlled by soil organic matter (SOM) content, other soil properties, including soil pH, clay and silt contents, could indirectly affect prediction of K factor by affecting SOM accumulation.

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“…Either integrated or individual employment of innovative techniques such as satellite remote sensing, geostatistics, geomorphology, field spectroscopy, machine learning, and combined in situ and laboratory soil reflectance measurements are considered to be promising approaches to estimate various soil properties [10]. Geostatistics, including interpolation and spatial linear regression methods, have been used by several researchers in order to monitor soil organic matter (SOM), CaCO 3 , and soil erodibility (K-factor) [11][12][13]. Laboratory analysis is sometimes proven inadequate when trying to investigate the soil erosion regime, especially in wider areas.…”

Section: Introductionmentioning

confidence: 99%

“…Satellite remote sensing is virtually the only data source that permits a repeated monitoring of land degradation dynamics [18]. In this context, both [10] and [13] applied integrated use of geostatistics, geoinformatics, and field spectroscopy to study the correlation between soil erosion and various soil parameters such as SOM, CaCO 3 , and K-factor. Initially, the most common field scale model for the estimation of soil erodibility risk was the Universal Soil Loss Equation (USLE) [19], which was later revised to the Revised Universal Soil Loss Equation (RUSLE) [20].…”

Section: Introductionmentioning

confidence: 99%

Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

et al. 2019

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Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.

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Towards prediction of soil erodibility, SOM and CaCO3 using laboratory Vis-NIR spectra: A case study in a semi-arid region of Iran

Cited by 80 publications

References 48 publications

Assessing soil degradation under land-use change: insight from soil erosion and soil aggregate stability in a small karst catchment in southwest China

Assessing soil degradation under land-use change: insight from soil erosion and soil aggregate stability in a small karst catchment in southwest China

Effects of Soil Properties on K Factor in the Granite and Limestone Regions of China

Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion

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