Mapping soil organic carbon content using spectroscopic and environmental data: A case study in acidic soils from NW Spain

Rial, Marcela; Cortizas, Antonio Martínez; Rodríguez‐Lado, Luis

doi:10.1016/j.scitotenv.2015.08.088

Cited by 34 publications

(10 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most commonly used bands for [Na + ], [Mg 2+ ], and [Ca 2+ ] were mid-infrared region, NIR, and Vis-NIR, but the modeling performance was property specific for different bands (Soriano-Disla et al 2014). Models using the characteristic absorption band also performed well in the prediction of SOM and soil salt content (Liu et al 2013;Rial et al 2015). In this research, reflectance data in the effective spectral bands of Vis-NIR were used.…”

Section: Discussionmentioning

confidence: 99%

Modeling soil cation concentration and sodium adsorption ratio using observed diffuse reflectance spectra

Xiao

Feng

2016

Can. J. Soil. Sci.

View full text Add to dashboard Cite

12Spectral analysis is a useful tool for the rapid and accurate prediction of soil properties. Our ]) and sodium adsorption ratio (SAR). Three methods were applied, i.e., stepwise multiple 15 linear regression (SMLR), partial least squares regression (PLSR), and support vector machine 16 (SVM). Estimation models for four soil properties were developed using three different spectral 17 processing and transformation techniques, i.e., reflectance (R e ), logarithm of reciprocal R e (LR), and 18 standard normal variable of R e (SNV) were used. A total of 36 models were established. Of these, (RPD) were all smaller than the 1.4 threshold. However, the models for SAR~R using PLSR (R The prediction of the physico-chemical properties of degraded soils is very important for crop 37 growth, low quality land improvement, and environmental sustainability (Childs and Hank, 1975; 38 Metternicht and Zinck, 2003 method to quantitatively characterize soil chemical properties (Ben-Dor et al., 1995; Aldabaa et al., 48 2015), and offers both cost and statistical power advantages (Cohen et al., 2005). Reflectance 49 spectroscopy analysis of ground objects is a good means of predicting soil properties, and of different soil properties (Islam et al., 2003; Davies, 2005; Cambule et al., 2012). 52There have been a number of studies relating soil physico-chemical properties to 53 reflectance-related features (Mahesh et al., 2014; Kim et al., 2014). Lü et al. (2013) and other physical-chemical properties (Li et al. 2012; Zhang et al., 2016). 63Since reflectance data are numerous and complex, the modeling approaches are crucial to 64 achieve an accurate estimate of soil properties. Zhang and Li (2016) conducted in the region, and has achieved good results in improving soil quality. 117The soils in the region were characterized as typical grey desert soils (Luo, 1985). The samples 118were collected from the 22nd of April to the 15th of May, 2013. Plant residues present on the soil 119 surface were carefully removed before sampling. A total of 78 samples were randomly collected 120 from within a 2 to 3 km range. The sampling depth was from 0 -10 cm, and the sampling location 121 is shown in Figure 1.Each soil sample was put into a cloth bag and transported to the laboratory. All of the soil 124 samples were air-dried, ground, and passed through a 2 mm sieve. The soil moisture of all the the R e data (Buddenbaum and Steffens, 2012). LR is used to reduce the effects of noise, light, and 153 background on R e . SNV is adopted to eliminate the effects of solid particle size and optical path 154 change of surface scattering on R e . SNV is calculated by (Barnes et al., 1989):where µ is mean value of R e , and σ is standard deviation of R e . 157 Model establishment and validation 158Soil samples were randomly divided into a calibration set for model establishment, and a 171The model establishment using the SMLR method was performed using SPSS 17.0 software, The leave-one-out cross-validation was chosen as the validatio...

show abstract

Section: Discussionmentioning

confidence: 99%

Modeling soil cation concentration and sodium adsorption ratio using observed diffuse reflectance spectra

Xiao

Feng

2016

Can. J. Soil. Sci.

View full text Add to dashboard Cite

show abstract

“…The CMI index proved less variable and more robust because its increase is statistically significant for all 13 experiments, while the DI change was not significant in 4 experiments. Therefore, while FTIR spectra and derived indices can be used for fast and less expensive indications of soil properties, quality changes, and mapping [24], the more laborious chemical analyses appear slightly more robust at detecting soil organic C changes. A combination of both approaches, after careful calibration of the spectroscopic method for different soil types and geographic areas, can reduce the need for combustion-based soil analysis and will provide faster and cheaper information on SOM status and trends [25].…”

Section: Detection Of Soil Organic Carbon Changesmentioning

confidence: 99%

Chemical and Spectroscopic Parameters Are Equally Sensitive in Describing Soil Organic Matter Changes After Decades of Different Fertilization

Šimon

Madaras

2020

Agriculture

View full text Add to dashboard Cite

The composition and dynamics of soil organic matter (SOM) are decisive factors in soil quality. In this work, total organic C (Ctot), hot water extractable C (Chwl), and aliphatic and aromatic SOM components detected by Fourier transform infrared (FTIR) spectroscopy were determined to evaluate SOM quantity and quality in soil samples taken between 2004 and 2017 from 13 field experiments established in different soil and climatic conditions of the Czech Republic. In addition, the C pool index (CPI), lability index (LI), C management index (CMI), and SOM decomposition index (DI) were assessed. Treatments were selected as follows: Unfertilized control (Nil), mineral fertilized treatment (NPK), farmyard manured treatment (FYM), and organic and mineral fertilized treatment (FYM+NPK). Both organic and combined fertilization significantly increased soil Ctot, Chwl, CPI, LI, CMI, and labile aliphatic SOM components (FTIRaliph) in most of the experiments compared to unfertilized treatments (p ≤ 0.05). In contrast, the highest content of recalcitrant aromatic SOM components (FTIRarom) and increased DI were determined in majority of unfertilized soils. Our results show that: (1) fertilization regimes increased both labile and total C pools; the highest increase was nearly uniformly observed for NPK+FYM treatment; (2) SOM chemical and FTIR spectral detection had equal sensitivity to the changes; and (3) none of the parameters or indices tested can be used as a stand-alone SOM quality descriptor.

show abstract

“…The combination of spectral data and multivariate analysis techniques has gained popularity for quantifying soil properties (Rial et al, 2016). These include statistical methods such as multiple linear regression, principal component regression, partial least squares regression (PLSR), and Random Forest (RF).…”

Section: Introductionmentioning

confidence: 99%

Estimation of elemental composition of agricultural soils from West Azerbaijan, Iran, using mid-infrared spectral models

Goydaragh

Jafarzadeh

Shahbazi

et al. 2019

Rev. bras. eng. agríc. ambient.

View full text Add to dashboard Cite

Characterizing the elemental composition provides useful information about the weathering degree of soils. In Miandoab County, Northern Iran, this characterization was missing, and thus the objectives of this work were to evaluate the weathering degrees for the most typical soils in the area from their elemental compositions, and to estimate this elemental composition using Fourier transform infrared spectroscopy and Random Forest models. Five soil profiles, including Aridisols and Inceptisols, were selected as the most representative of the area. Major elemental oxides were determined in each genetic horizon by X-ray fluorescence, showing that these soils were at early developmental stages. Only Al2O3 and CaO were accurately estimated, with R2 values of 0.8, and out-of-bag mean square errors of 0.2 and 1.1, respectively. The other oxides were not predicted satisfactorily, probably due to small differences in their elemental compositions. Random Forest provided the important spectral bands related to the content of each element. For Al2O3, these bands were between 500 and 650 cm-1, which represent out-of-plane OH bending vibrations and Al-O gibbsite and alumino-silicate vibrations. For CaO, the most important bands are related to carbonate content. A combination of Fourier transform infrared spectra and Random Forest models can be used as a rapid and low-cost technique to estimate the elemental composition of arid and semi-arid soils of Northern Iran.

show abstract

Mapping soil organic carbon content using spectroscopic and environmental data: A case study in acidic soils from NW Spain

Cited by 34 publications

References 73 publications

Modeling soil cation concentration and sodium adsorption ratio using observed diffuse reflectance spectra

Modeling soil cation concentration and sodium adsorption ratio using observed diffuse reflectance spectra

Chemical and Spectroscopic Parameters Are Equally Sensitive in Describing Soil Organic Matter Changes After Decades of Different Fertilization

Estimation of elemental composition of agricultural soils from West Azerbaijan, Iran, using mid-infrared spectral models

Contact Info

Product

Resources

About