Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia

Miloš, Boško; Bensa, Alban

doi:10.18393/ejss.319208

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…The both A and B horizon are characterized by a wide range of SOC content. These results correspond to literature findings for Terra rossa soil of Dalmatia, Croatia (Miloš and Bensa, 2017).…”

Section: Description Of Soil Propertiessupporting

confidence: 92%

Lead and zinc concentration in genetic horizons of Terra rossa soil at a local scale. How these concentrations differ?

Miloš¹,

Bensa²

2020

JCEA

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The goals of this study were to (i) determine the concentration of the Pb and Zn in Terra rossa soil, (ii) reveal its origin and (iii) establish how the concentration of these elements between genetic horizons differ. In a total of 128 soil samples collected from the genetic A and B horizon of 64 Terra Rossa soil profiles were analyzed for aqua regia soluble concentration of lead (Pb A and Pb B) and zinc (Zn A and Zn B) as well as soil properties: pH in KCl, CaCO 3 , soil organic carbon (SOC) and particle size distribution. The median value of Pb A , Pb B , Zn A , and Zn B were 41.6, 33.8, 106.1 and 103.8 mg/ kg, respectively. The principal component analyses have shown that the Pb and Zn contribute most to the variability of A horizon and have the same and natural origin-they are accumulated by geochemical weathering and soil-forming processes. Using non-parametric techniques (the two-sided Kolmogorov-Smirnov test (K-S) and a shift function (SF) we estimated that (i) the Pb A and Pb B distribution differ in spread and median and (ii) the Pb B distribution need to be progressively shifted, at the whole range starting from 2.8 mg/kg for the first decile; 7.2 mg/kg for the fifth decile (median) to 12.2 mg/kg for the ninth decile to match Pb A distribution. In the case of Zn, there is no evidence that the distribution of Zn A is in any way different than the distribution of Zn B. This implies that the Zn A and Zn B have a remarkably similar shape of their distribution functions and a noticeable slight shift with a value of 2.3 mg/kg at fifth decile. The analysis presented here illustrated why detailed statistical descriptions can help to better understand how and by how much lead and zinc distributions in Terra rossa genetic horizon differ.

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Section: Description Of Soil Propertiessupporting

confidence: 92%

Lead and zinc concentration in genetic horizons of Terra rossa soil at a local scale. How these concentrations differ?

Miloš¹,

Bensa²

2020

JCEA

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“…(He, et al, 2009) and deposits in the VIS (visible) bands (660, 570 and 410nm) (Viscarra/ Rossel et al, 2006). Typically, soil spectrum characterized with reflectance increasing with increasing wavelengths, the wavelengths contributing most to the prediction of SOC content were between 1050 to 2380 nm (Milos and Bensa 2017). Sun and Niu, (2018) discovered that the spectral curves of the soil with different SOC content grades exhibited a uniform pattern, which increased rapidly in the visible band (400-760 nm), but then ascended gradually in the short NIR and NIR long wave bands (780-1300 nm).…”

Section: Soil Laboratory Analysis Resultsmentioning

confidence: 99%

A Studying the Possibility of Estimating Soil Organic Carbon of Soils Under PINUS BRUTIA and QUERCUS AEGILOPS L. Trees in Sarke-Duhok By Using ASD FieldSpec 3 Spectroradiometer

Gravi

Ibrahim

2020

SJUOZ

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Visible and Near-infrared (VNIRS) spectroscopy is a very fast non-destructive and environmentally friendly analytical technique. It has been suggested as an alternative to conventional methods for assessing and monitoring soil quality. Accurate VNIRS prediction of soil organic carbon (SOC) has been reported by many researchers in different world regions. Sixteen surface soil samples (0-30cm depth) from Sarke - Duhok were collected, eight samples under Zawita Pine (Pinus brutia) and the other eight-under oak (Quercus aegilops L.) trees. Soil colour was measured in the field and soil samples were air-dried and sieved by using a 2.0 mm sieve and analyzed in the laboratory to estimate the PH, EC, CaCo3, texture, and SOC according to Walkley and Black methods. Laboratory reflectance spectra were acquired for each sample from 350 to 2500 nm by using the Analytical Spectral Device (ASD) FieldSpec 3spectroradiometer for each of the different spectrum bands (410, 570, 660, 849, 1543 and 2187nm). Results showed that the reflectivity values of soil under both Oak and Pine trees in the IR region (849, 1543, 2187nm) were almost more than that at the visible portion (410,570, 660nm). Also, the determination coefficient (R2) results indicated that the bands 570, 1543 and 2187nm showed significant relationships between soil reflectivity and SOC% under Oak trees, R2 was 58.2 %, 89.9%, and 93.9% respectively. While under the Pine tree, the only band that showed a significant relationship was band 1543nm, its R2 was 58.3%. From the current results, as the main objectives, it is obvious that the ASD FieldSpec 3 spectroradiometer is quite an efficient and un-destructive tool that can be used for SOC estimation under Oak & pine trees especially at the IR spectrum (band 1543nm). The relationship between the variables was moderately strong; R2 was 89.88 and 58.3 alternatively. The standard error was low (0.0399, 0.0185), which indicates that the predicted values are close to the real values. Besides that, under Oak trees results indicated that there was a high R2 between the variables at bands 570 & 2187 nm, the R2 values were 58.21 and 93.92, alternatively.

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“…B11 is a single multispectral band that belongs to the short-wave infrared range. Miloš et al [34], Viscarra Rossel and Hicks [35], and Wang et al [36] have reported that the SOM-responsive bands were in the short-wave infrared wavelength range and were dominated by C-H vibrations, kaolinite, soil adsorbed water, and Al-OH clay minerals. In contrast, Liu et al [33] found that the B3 and B6 bands in the visible wavelength range were closely correlated with SOM content of black soils.…”

Section: Using the Band Combination Indices For Characterizing The So...mentioning

confidence: 99%

Mapping Soil Organic Matter Content during the Bare Soil Period by Using Satellite Data and an Improved Deep Learning Network

Zhai

2022

Sustainability

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Soil function degradation has impaired global work in the implementation of sustainable development goals (SDGs), and soil organic matter (SOM) is a basic and the most important indicator. The deep learning neural network (i.e., DL network) has become a popular tool for mapping SOM content at a regional scale. However, outlier sample data caused by environmental factors (e.g., moisture and vegetation) and uncertain noise (e.g., random noise and instability effects) have interfered with the determination of the function mapping relationship between target soil properties and spectral features, leading to DL networks with low generalization capability. Therefore, we introduced a spatial association module into a deep neural network to remove outlier sample data in order to construct an optimal sample set for calibrating an improved deep learning (IDL) network for SOM mapping. A total of 707 soil samples and a Sentinel-2B multispectral image were acquired during the bare soil period in Weibei, China. The variable importance in the projection approach was used to select the SOM-responsive spectral features for model inputs. Measured SOM contents were taken as the dependent variable, and the IDL network was constructed and applied to map SOM at the regional scale. The results showed that Band 11 was the most important band for SOM prediction. The band difference transformation method was able to integrate multiple-band information and enhance the absorption signal of SOM. The optimal SOM-responsive spectral features included B11, B1–B11, B2−B11, B3−B11, B4−B11, and B1−B12. The IDL network exhibited better performance (R2 = 0.92; RPIQ = 4.57) regarding SOM estimation compared with the DL model performance (R2 = 0.84; RPIQ = 2.84), being improved by 9.52% (for R2) and 60.92% (for RPIQ). After introducing the spatial association module, the DL network generalization capability was enhanced. SOM distribution showed a high-value (>20 g kg−1) area in the south, and a low-value (<6 g kg−1) area in the north of the study area (the area affected by seawater intrusion). These results provide a strategy based on an IDL network and satellite data for effectively and accurately mapping SOM at the regional scale.

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Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia

Cited by 10 publications

References 35 publications

Lead and zinc concentration in genetic horizons of Terra rossa soil at a local scale. How these concentrations differ?

Lead and zinc concentration in genetic horizons of Terra rossa soil at a local scale. How these concentrations differ?

A Studying the Possibility of Estimating Soil Organic Carbon of Soils Under PINUS BRUTIA and QUERCUS AEGILOPS L. Trees in Sarke-Duhok By Using ASD FieldSpec 3 Spectroradiometer

Mapping Soil Organic Matter Content during the Bare Soil Period by Using Satellite Data and an Improved Deep Learning Network

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