National-scale spectroscopic assessment of soil organic carbon in forests of the Czech Republic

Gholizadeh, Asa; Rossel, Raphael A. Viscarra; Saberioon, Mohammadmehdi; Borůvka, Luboš; Kratina, Josef; Pavlů, Lenka

doi:10.1016/j.geoderma.2020.114832

Cited by 25 publications

(33 citation statements)

References 69 publications

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“…It was, therefore, concluded that MIR spectroscopy may have similar applicability in the paleopedological and paleoecological reconstructions as NIR spectroscopy. As shown in Figure 7, MIR spectra discriminate clearly between topsoil samples in relation to dominant forest vegetation, which confirms the applicability of this technique for studies of forest soil transformation, as previously confirmed for NIR spectroscopy [48][49][50][51].…”

Section: Discussionsupporting

confidence: 82%

Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils

Dudek

Kabała

Łabaz

et al. 2021

Land

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Spectroscopic methods combined with statistics have recently gathered substantial interest in pedological studies. Near-infrared (NIR) spectroscopy has been utilized, for example, for reconstructions of the history and transformations of Chernozems, although no similar research was conducted based on mid-infrared (MIR). In this paper, the relevance of MIR spectroscopy was tested in studies on the origin/affinity of organic matter from chernozemic soils. Samples collected from three vegetation classes (grasslands, forests and arable lands) were investigated using MIR spectroscopy in order to create a statistical model, which was applied on buried profiles of unknown origin. The results showed a clear disjunction of vegetation classes. Samples of buried soil were placed in the space between all classes, indicating the relation to variable vegetation. Therefore, arable lands should not be omitted in paleoecological reconstructions, because we cannot exclude the cultivation of fertile soils before their burial. It was concluded that MIR methods may have similar applicability to NIR spectroscopy. Additionally, MIR spectra may also be discriminated according to the recognized soil type, which allows for direct reconstructions of the transformation trends in buried profiles.

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Section: Discussionsupporting

confidence: 82%

Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils

Dudek

Kabała

Łabaz

et al. 2021

Land

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“…Another potential source of auxiliary information for SOC prediction can be found in soil spectroscopy (Gholizadeh et al 2021). Using some covariates in a more detailed resolution can possibly improve the prediction.…”

Section: Resultsmentioning

confidence: 99%

Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape

Borůvka¹,

Vašát²,

Šrámek³

et al. 2022

Soil & Water Res.

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Forest soils have a high potential to store carbon and thus mitigate climate change. The information on spatial distribution of soil organic carbon (SOC) stocks is thus very important. This study aims to analyse the importance of environmental predictors for forest SOC stock prediction at the regional and national scale in the Czech Republic. A big database of forest soil data for more than 7 000 sites was compiled from several surveys. SOC stocks were calculated from SOC content and bulk density for the topsoil mineral layer 0–30 cm. Spatial prediction models were developed separately for individual natural forest areas and for four subsets with different altitude range, using random forest method. The importance of environmental predictors in the models strongly differs between regions and altitudes. At lower altitudes, forest edaphic series and soil classes are strong predictors, while at higher altitudes the predictors related to topography become more important. The importance of soil classes depends on the pedodiversity level and on the difference in SOC stock between the classes. The contribution of forest types as predictors is limited when one (mostly coniferous) type dominates. Better prediction results can be obtained in smaller, but consistent regions, like some natural forest areas.

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“…The concentrations of SOC, N and consequently C:N level in forest ecosystem are different depending not only on the geographic location but also on soil horizon (Lal, 2008;Brahma et al, 2018) due to different stages of decomposition, erosion and leaching (Gibson et al, 2002;Black et al, 2014). For instance, the decreasing trend of SOC concentration with depth in different forested sites of China and the Czech Republic were reported (Jia et al, 2017;Gholizadeh et al, 2021) and linked to the reduction of below-ground plant biomass with soil depth that as a result has a considerable effect on C:N vertical alteration. Moreover, C:N vertical changes can also happen in response to N leaching (Cools et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Developing large-scale (e.g., national, continental and global) spectral-based model though may be an approach to enhance the success of the technique (Viscarra Rossel et al, 2016). Gholizadeh et al (2021) used VNIR-SWIR spectroscopy to predict and characterize SOC in different horizons of forest soils over the Czech Republic; however, so far, the capability of VNIR-SWIR spectroscopy for large-scale prediction and spatial analysis of C:N in forest soil layers has not yet fully been exploited.…”

Section: Introductionmentioning

confidence: 99%

Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy

Gholizadeh¹,

Saberioon²,

Pouladi³

et al. 2022

Preprint

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Forest soils have large contents of carbon (C) and nitrogen (N), which have significant spatial variability laterally across landscapes and vertically with depth due to decomposition, erosion and leaching. Therefore, the ratio of C to N contents (C:N), a crucial indicator of soil quality and health, is also different depending on soil horizon. These attributes can cost-effectively and rapidly be estimated using visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy. Nevertheless, the effect of different soil layers, particularly over large scales of highly heterogeneous forest soils, on the performance of the technique has rarely been attempted. This study evaluated the potential of VNIR–SWIR spectroscopy in quantification and variability analysis of C:N in soils from different organic and mineral layers of forested sites of the Czech Republic. At each site, we collected samples from the litter (L), fragmented (F) and Humus (H) organic layers, and from the A1 (depth of 2–10 cm) and A2 (depth of 10–40 cm) mineral layers providing a total of 2505 samples. Support vector machine regression (SVMR) was used to train the prediction models of the selected attributes at each individual soil layer and the merged layer (profile). We further produced the spatial distribution maps of C:N as the target attribute at each soil layer. Results showed that the prediction accuracy based on the profile spectral data was adequate for all attributes. Moreover, F was the most accurately predicted layer, regardless of the soil attribute. C:N models and maps in the organic layers performed well although in mineral layers, models were poor and maps were reliable only in areas with low and moderate C:N. On the other hand, the study indicated that VNIR–SWIR spectroscopy could efficiently predict and map organic layers of the Czech Republic’s forested sites but in mineral layers, it didn’t differentiate classes of C:N higher than 50.

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National-scale spectroscopic assessment of soil organic carbon in forests of the Czech Republic

Cited by 25 publications

References 69 publications

Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils

Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils

Predictors for digital mapping of forest soil organic carbon stocks in different types of landscape

Quantification and depth distribution analysis of carbon to nitrogen ratio in forest soils using reflectance spectroscopy

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