Abstract
Soil organic carbon constitutes an important indicator of soil fertility. The purpose of this study was to predict soil organic carbon content in the mountainous terrain of eastern Lesotho, southern Africa, which is an area of high endemic biodiversity as well as an area extensively used for small-scale agriculture. An integrated field and laboratory approach was undertaken, through measurements of reflectance spectra of soil using an Analytical Spectral Device (ASD) FieldSpec® 4 optical sensor. Soil spectra were collected on the land surface under field conditions and then on soil in the laboratory, in order to assess the accuracy of field spectroscopy-based models. The predictive performance of two different statistical models (random forest and partial least square regression) was compared. Results show that random forest regression can most accurately predict the soil organic carbon contents on an independent dataset using the field spectroscopy data. In contrast, the partial least square regression model overfits the calibration dataset. Important wavelengths to predict soil organic contents were localised around the visible range (400–700 nm). This study shows that soil organic carbon can be most accurately estimated using derivative field spectroscopy measurements and random forest regression.
Highlights
Soil organic carbon (SOC) is an important property related to soil biological, physical, and chemical characteristics and constitutes a major component of the global carbon cycle [1]
Plagioclase within the basalts has been affected by zeolitization and chloritization, and olivine in particular has been replaced by iron oxides, serpentine, and clays [18]. ese weathering products make the resulting soil susceptible to erosion by surface sheet flow, subsurface clay expansion, slaking and soil piping, and landslide/debris flow activity caused by subsurface waterlogging and failure (e.g., [19])
SOC values ranged from 1.93 g 100 g−1 to 10.6 g 100 g−1 with a mean value of 5.04 g 100 g−1 and a standard deviation of 2.11. e Kolmogorov–Smirnov test shows that all datasets were not normally distributed (p < 0.05 values of 0.0126, 0.0020, and 0.2100 for the whole dataset, calibration dataset, and validation dataset, respectively)
Summary
Soil organic carbon (SOC) is an important property related to soil biological, physical, and chemical characteristics and constitutes a major component of the global carbon cycle [1]. SOC depletion as a result of accelerated soil erosion can lead to reduced crop yields, lowered moisture retention capacity, and reduced nutrient status [7,8,9,10]. Soil physical properties are highly variable spatially, where changes in slope steepness, depth of weathering products, slope processes, and microclimate give rise to variations in vegetation types and soil properties and high rates of soil erosion [13]. Ese weathering products make the resulting soil susceptible to erosion by surface sheet flow, subsurface clay expansion, slaking and soil piping, and landslide/debris flow activity caused by subsurface waterlogging and failure (e.g., [19]). Many studies have been concerned with calculating soil volume loss by erosion through gullies (locally known as dongas)
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