scholarly journals Application of Spectrally Derived Soil Type as Ancillary Data to Improve the Estimation of Soil Organic Carbon by Using the Chinese Soil Vis-NIR Spectral Library

2018 ◽  
Vol 10 (11) ◽  
pp. 1747 ◽  
Author(s):  
Yi Liu ◽  
Zhou Shi ◽  
Ganlin Zhang ◽  
Yiyun Chen ◽  
Shuo Li ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Type ◽  
Near Infrared ◽  
Expert Knowledge ◽  
Nir Spectroscopy ◽  
Soil Classification ◽  
Soil Types ◽  
Spectral Library ◽  
Ancillary Data

Ancillary data, such as soil type, may improve the visible and near-infrared (vis-NIR) estimation of soil organic carbon (SOC); however, they require data collection or expert knowledge. The application of a national soil spectral library to local SOC estimations usually requires soil type information, because the relationships between vis-NIR spectra and SOC from different populations may vary. Using 515 samples of five soil types (genetic soil classification of China, GSCC) from the Chinese soil spectral library (CSSL), we compared three strategies in the vis-NIR estimation of SOC. Different regression models were calibrated using the entire dataset (Strategy I, without using soil type as ancillary data) and the subsets stratified by soil type from CSSL as ancillary data (strategies II and III). In Strategy II, the subsets were stratified by soil type from the CSSL for validation. In Strategy III, the subsets were stratified by spectrally derived soil type for validation. The results showed that 86.72% of the samples were successfully discriminated for the soil types by using the vis-NIR spectra. The coefficients of determination in the prediction ( R p 2 ) of SOC estimation by strategies I, II, and III were 0.74, 0.83, and 0.82, respectively. The stratified calibration strategies (strategies II and III) improved the vis-NIR estimation of SOC. The misclassification of the soil type in the application of Strategy III slightly affected the SOC estimations. Nevertheless, this strategy is inexpensive and beneficial when expert knowledge on soil classification is lacking. We concluded that vis-NIR spectroscopy could be applied to distinguish some soil types in terms of GSCC, which further provided essential and easily accessible ancillary data for the application of stratified calibration strategies in the vis-NIR estimation of SOC.

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

Geoderma ◽  
2012 ◽  
Vol 183-184 ◽  
pp. 41-48 ◽  
Author(s):  
A.H. Cambule ◽  
D.G. Rossiter ◽  
J.J. Stoorvogel ◽  
E.M.A. Smaling
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
National Park ◽  
Near Infrared ◽  
Spectral Library ◽  
Infrared Spectral

scholarly journals Aggregate stability affects carbon sequestration potential of different tropical soils

2018 ◽  
pp. 71-88 ◽  
Author(s):  
Leo Jude Villasica ◽  
Suzette Lina ◽  
Victor Asio
Keyword(s):  
Carbon Sequestration ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Type ◽  
Aggregate Stability ◽  
Tropical Soils ◽  
Soil Types ◽  
Carbon Sequestration Potential ◽  
Sequestration Potential ◽  
Binding Agents

Aggregate stability and carbon (C) sequestration in soils are closely related phenomena. However, high aggregate stability does not always ensure high carbon sequestration to some soil types since other binding agents could dominate other than carbon. Thus, this study aimed to determine the relationship between aggregate stability and carbon sequestration of different tropical soils which basically differ in geology, genesis, and possibly in their dominant aggregating agents. The study selected four representative soil types (Haplic Acrisol, Calcaric Cambisol, Silic Andosol and Haplic Ferralsol) found in Leyte and Samar that were characterized by previous workers. Soil Organic Carbon (SOC) and Aggregate Stability (AS) in dry and wet conditions were quantified using standard procedures. Some pertinent secondary data were also recorded as reference for each soil type. Results revealed that only Silic Andosol showed positive significant correlation (0.93) between aggregate stability and soil organic carbon (SOC). The other soil types showed weak and negative correlation between aggregate stability and SOC; however, their stability revealed a strong positive relationship with inorganic binding agents. Therefore, each soil type reflects a different relationship between aggregate stability in wet condition and SOC and that the variations could be attributed to the differences in the morpho-physical and geochemical nature of the soils. Moreover, SOC is found to greatly influence the aggregate stability in Silic Andosol, thus the soil carbon sequestration potential of this soil type is generally related to its aggregate stability. However, in other soil types like Haplic Acrisol, Calcaric Cambisol, and Haplic Ferralsol, other binding agents like Calcium (Ca) and iron oxides dominate and control the formation and stability of aggregates rather than SOC.

An Integrated Model Approach for Quantifying Carbon Emissions From Residue-Based Biofuel Production

2013 ◽  
Author(s):  
Jared M. Abodeely ◽  
David J. Muth ◽  
Joshua Koch ◽  
Kenneth M. Bryden
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Co2 Emissions ◽  
Land Management ◽  
Soil Type ◽  
Co2 Emission ◽  
Integrated Model ◽  
Soil Types ◽  
County Level ◽  
Residue Removal

This paper presents an agricultural residue removal decision framework that couples the environmental process models WEPS, RUSLE2, SCI, and DAYCENT. One of the goals of this integrated model is to quantify the impacts of land management strategies on soil organic carbon and CO2 emissions. Soil, climate, and land management practices are considered in determining sustainable residue removal rates using wind- and water-induced soil erosion and qualitative soil organic carbon constraints and to quantify the long-term impacts of sustainable residue removal on soil organic carbon and greenhouse gas emissions. Using this integrated model sustainable residue removal for four crop rotations, three tillage regimes, and four soil types representing nearly 70% of the arable acres in Boone County, Iowa are examined. Each scenario was performed for a twenty-year period. Soil organic carbon and CO2 emission results are aggregated by soil type using crop rotation and tillage statistics. The soil type results are aggregated using a normalized percentage area to provide a county level estimate of soil organic carbon changes and CO2 emissions. Results show that for the largest sustainable residue removal rate that soil organic carbon increased 3.53–6.63 Mg/ha over the 20 year simulation and that CO2 emissions ranged from 3.50–4.23 Mg/ha across the four soil types resulting in an average increase of soil organic carbon of 4.85 Mg/ha and CO2 emission of 3.77 Mg/ha at the county level.

Estimating Soil Organic Carbon Content with Visible–Near-Infrared (Vis-NIR) Spectroscopy

2014 ◽  
Vol 68 (7) ◽  
pp. 712-722 ◽  
Author(s):  
Yin Gao ◽  
Lijuan Cui ◽  
Bing Lei ◽  
Yanfang Zhai ◽  
Tiezhu Shi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Organic Carbon Content ◽  
Soil Organic Carbon Content

scholarly journals Online Measurement of Soil Organic Carbon as Correlated with Wheat Normalised Difference Vegetation Index in a Vertisol Field

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Yücel Tekin ◽  
Yahya Ulusoy ◽  
Zeynal Tümsavaş ◽  
Abdul M. Mouazen
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Vegetation Index ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Wheat Crop ◽  
Kappa Statistics ◽  
Calibration Model ◽  
Online Measurement ◽  
Normalised Difference Vegetation Index

This study explores the potential of visible and near infrared (vis-NIR) spectroscopy for online measurement of soil organic carbon (SOC). It also attempts to explore correlations and similarities between the spatial distribution of SOC and normalized differential vegetation index (NDVI) of a wheat crop. The online measurement was carried out in a clay vertisol field covering 10 ha of area in Karacabey, Bursa, Turkey. Kappa statistics were carried out between different SOC and NDVI data to investigate potential similarities. Calibration model of SOC in full cross-validationresulted in a good accuracy (R2=0.75, root mean squares error of prediction (RMSEP) = 0.17%, and ratio of prediction deviation (RPD) = 1.81). The validation of the calibration model using laboratory spectra provided comparatively better prediction accuracy (R2=0.70, RMSEP = 0.15%, and RPD = 1.78), as compared to the online measured spectra (R2=0.60, RMSEP = 0.20%, and RPD = 1.41). Although visual similarity was clear, low similarity indicated by a low Kappa value of 0.259 was observed between the online vis-NIR predicted full-point (based on all points measured in the field, e.g., 6486 points) map of SOC and NDVI map.

scholarly journals Properties of humic acids depending on the land use in different parts of Slovakia

2021 ◽  
Author(s):  
Magdalena Banach-Szott ◽  
Bozena Debska ◽  
Erika Tobiasova
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Size Distribution ◽  
Humic Acids ◽  
Forest Ecosystem ◽  
Soil Type ◽  
Soil Types ◽  
Organic Matter Quality ◽  
Degree Of Maturity ◽  
Effects Of Land Use

AbstractMany studies report organic carbon stabilization by clay minerals, but the effects of land use and soil type on the properties of humic acids (HAs) are missing. The aim of the paper is to determine the effects of land use and soil types on the characteristics of HAs, which have a considerable influence on organic matter quality. It was hypothesised that the effect of the land use on HAs properties depends on the particular size distribution. The research was performed in three ecosystems: agricultural, forest, and meadow, located in Slovakia. From each of them, the samples of 4 soil types were taken: Chernozem, Luvisol, Planosol, and Cambisol. The soil samples were assayed for the content of total organic carbon (TOC) and the particle size distribution. HAs were extracted with the Schnitzer method and analysed for the elemental composition, spectrometric parameters in the UV-VIS range, and hydrophilic and hydrophobic properties, and the infrared spectra were produced. The research results have shown that the properties of HAs can be modified by the land use and the scope and that the direction of changes depends on the soil type. The HAs of Chernozem and Luvisol in the agri-ecosystem were identified with a higher “degree of maturity”, as reflected by atomic ratios (H/C, O/C, O/H), absorbance coefficients, and the FT-IR spectra, as compared with the HAs of the meadow and forest ecosystem. However, as for the HAs of Cambisol, a higher “degree of maturity” was demonstrated for the meadow ecosystem, as compared with the HAs of the agri- and forest ecosystem. The present research has clearly identified that the content of clay is the factor determining the HAs properties. Soils with a higher content of the clay fraction contain HAs with a higher “degree of maturity”.

scholarly journals Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging

2014 ◽  
Vol 7 (3) ◽  
pp. 1197-1210 ◽  
Author(s):  
M. Nussbaum ◽  
A. Papritz ◽  
A. Baltensweiler ◽  
L. Walthert
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Likelihood Method ◽  
Standard Errors ◽  
Soil Profiles ◽  
Near Infrared Reflectance ◽  
Soc Stock ◽  
Soc Stocks ◽  
External Drift

Abstract. Accurate estimates of soil organic carbon (SOC) stocks are required to quantify carbon sources and sinks caused by land use change at national scale. This study presents a novel robust kriging method to precisely estimate regional and national mean SOC stocks, along with truthful standard errors. We used this new approach to estimate mean forest SOC stock for Switzerland and for its five main ecoregions. Using data of 1033 forest soil profiles, we modelled stocks of two compartments (0–30, 0–100 cm depth) of mineral soils. Log-normal regression models that accounted for correlation between SOC stocks and environmental covariates and residual (spatial) auto-correlation were fitted by a newly developed robust restricted maximum likelihood method, which is insensitive to outliers in the data. Precipitation, near-infrared reflectance, topographic and aggregated information of a soil and a geotechnical map were retained in the models. Both models showed weak but significant residual autocorrelation. The predictive power of the fitted models, evaluated by comparing predictions with independent data of 175 soil profiles, was moderate (robust R2 = 0.34 for SOC stock in 0–30 cm and R2 = 0.40 in 0–100 cm). Prediction standard errors (SE), validated by comparing point prediction intervals with data, proved to be conservative. Using the fitted models, we mapped forest SOC stock by robust external-drift point kriging at high resolution across Switzerland. Predicted mean stocks in 0–30 and 0–100 cm depth were equal to 7.99 kg m−2 (SE 0.15 kg m−2) and 12.58 kg m−2 (SE 0.24 kg m−2), respectively. Hence, topsoils store about 64% of SOC stocks down to 100 cm depth. Previous studies underestimated SOC stocks of topsoil slightly and those of subsoils strongly. The comparison further revealed that our estimates have substantially smaller SE than previous estimates.

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