Estimate Soil Organic Carbon Content Research Articles

Developing a digital mapping of soil organic carbon on a national scale using Sentinel-2 and hybrid models at varying spatial resolutions

Mapping the spatial distribution of soil organic carbon (SOC) is crucial for monitoring soil health, understanding ecosystem functions, and contributing to global carbon cycling. However, few studies have directly compared the influence of hybrid models and individual models with varying spatial resolutions on SOC prediction at a national scale. In this study, by combining remote sensing data, we utilized the LUCAS 2018 soil dataset to evaluate the potential capacities of hybrid models for predicting SOC content at different spatial resolutions in Germany. The hybrid models PLSRK and RFK consisted of partial least square regression (PLSR) with residual original kriging (OK) models, and random forest (RF) models with residual OK models, respectively. Individual PLSR and RF models were used as reference models. All these models were applied to estimate SOC content at 10 m, 50 m, 100 m, and 200 m spatial resolutions. Sentinel-2 bands, band indices, and topography variables were as predictors. The results revealed that hybrid models had a more accurate prediction of SOC content with higher explanations and lower prediction errors compared with individual models. The RFK model at the spatial resolution of 100 m was the fittest model with R2 = 0.416, RMSE = 0.545, and RPIQ = 1.647, which enhanced 3.74% of explanation compared with the performance of RF model. The results also showed that hybrid models at a relatively coarse resolution (100 m) had better accuracy instead of those at high spatial resolution (10 m, 50 m). Sentinel-2 remote sensing data showed significant predictive capabilities for estimating SOC content. The predicted spatial distribution of SOC content revealed that the high SOC concentrated in the northwest grassland, central and southwestern mountains, and the Alps in Germany. Our study provided a benchmark SOC map in Germany for monitoring the changes resulting from land use and climate impacts, and we illustrated the accuracy of hybrid models and the effects of spatial resolutions on SOC predictions at a national scale.

Spatial Estimation of Soil Organic Carbon Content Utilizing PlanetScope, Sentinel-2, and Sentinel-1 Data

The accurate prediction of soil organic carbon (SOC) is important for agriculture and land management. Methods using remote sensing data are helpful for estimating SOC in bare soils. To overcome the challenge of predicting SOC under vegetation cover, this study extracted spectral, radar, and topographic variables from multi-temporal optical satellite images (high-resolution PlanetScope and medium-resolution Sentinel-2), synthetic aperture radar satellite images (Sentinel-1), and digital elevation model, respectively, to estimate SOC content in arable soils in the Wuling Mountain region of Southwest China. These variables were modeled at four different spatial resolutions (3 m, 20 m, 30 m, and 80 m) using the eXtreme Gradient Boosting algorithm. The results showed that modeling resolution, the combination of multi-source remote sensing data, and temporal phases all influenced SOC prediction performance. The models generally yielded better results at a medium (20 m) modeling resolution than at fine (3 m) and coarse (80 m) resolutions. The combination of PlanetScope, Sentinel-2, and topography factors gave satisfactory predictions for dry land (R2 = 0.673, MAE = 0.107%, RMSE = 0.135%). The addition of Sentinel-1 indicators gave the best predictions for paddy field (R2 = 0.699, MAE = 0.114%, RMSE = 0.148%). The values of R2 of the optimal models for paddy field and dry land improved by 36.0% and 33.4%, respectively, compared to that for the entire study area. The optical images in winter played a dominant role in the prediction of SOC for both paddy field and dry land. This study offers valuable insights into effectively modeling soil properties under vegetation cover at various scales using multi-source and multi-temporal remote sensing data.

Environmental variables improve the accuracy of remote sensing estimation of soil organic carbon content

Accurately and quickly estimating the soil organic carbon (SOC) content is crucial in the monitoring of global carbon. Environmental variables play a significant role in improving the accuracy of the SOC content estimation model. This study focuses on modeling methodologies and environmental variables, which significantly influence the SOC content estimation model. The modeling methods used in this research comprise multiple linear regression (MLR), partial least squares regression (PLSR), random forest, and support vector machines (SVM). The analyzed environmental variables include terrain, climate, soil, and vegetation cover factors. The original spectral reflectance (OSR) of Landsat 5 TM images and the spectral reflectivity after the derivative processing were combined with the above environmental variables to estimate SOC content. The results showed that: (1) The SOC content can be efficiently estimated using the OSR of Landsat 5 TM, however, the derived processing method cannot significantly improve the estimation accuracy. (2) Environmental variables can effectively improve the accuracy of SOC content estimation, with climate and soil factors producing the most significant improvements. (3) Machine learning modeling methods provide better estimation accuracy than MLR and PLSR, especially the SVM model which has the highest accuracy. According to our observations, the best estimation model in the study area was the “OSR + SVM” model (R2 = 0.9590, RMSE = 13.9887, MAE = 10.8075), which considered four environmental factors. This study highlights the significance of environmental variables in monitoring SOC content, offering insights for more precise future SOC assessments. It also provides crucial data support for soil health monitoring and sustainable agricultural development in the study area.

Estimating soil organic carbon content of multiple soil horizons in the middle and upper reaches of the Heihe River Basin

Estimating the spatial distribution of soil organic carbon (SOC) content is essential for evaluating ecosystem functions and establishing global climate change models. Most of the previous SOC mapping studies have focused on estimating SOC content at fixed depths. However, soils at the same depth are not always belonging to the same soil horizon. Therefore, the characteristics of soil horizons should be considered. In this study, the SOC content of multiple soil horizons in the middle and upper reaches of the Heihe River Basin was estimated using multivariate factors and ensemble learning (EL). The results show that EL obtains the best SOC content estimation performance, where the R2 values are 0.58, 0.57 and 0.69 for the O-horizon, A-horizon, and B-horizon, respectively. Environmental factors show a high degree of contribution to estimate SOC content, and mean annual precipitation (MAP) presents the highest contribution amongst all the factors. For the middle and upper reaches of the Heihe River Basin, the SOC content is high in the southwest and low in the northeast. Amongst the multiple soil horizons, the O-horizon usually showed higher SOC content than the A-horizon and the B-horizon. But the SOC content of the A-horizon is higher than that of the O-horizon for farmland. From 2012 to 2013, the SOC content of forestland and farmland increased in the O-horizon and A-horizon. The application of EL improves the mapping performance of SOC content in different soil horizons, and the involvement of multivariable factors reveals the distribution of SOC content in the Heihe River Basin. This study creates maps of SOC content in different soil horizons and analyses the effect of land covers on SOC content, which provides insights for ecological conservation and regional sustainable development.

Satellite-based estimation of soil organic carbon in Portuguese grasslands

Introduction:Soil organic carbon (SOC) sequestration is one of the main ecosystem services provided by well-managed grasslands. In the Mediterranean region, sown biodiverse pastures (SBP) rich in legumes are a nature-based, innovative, and economically competitive livestock production system. As a co-benefit of increased yield, they also contribute to carbon sequestration through SOC accumulation. However, SOC monitoring in SBP require time-consuming and costly field work.Methods:In this study, we propose an expedited and cost-effective indirect method to estimate SOC content. In this study, we developed models for estimating SOC concentration by combining remote sensing (RS) and machine learning (ML) approaches. We used field-measured data collected from nine different farms during four production years (between 2017 and 2021). We utilized RS data from both Sentinel-1 and Sentinel-2, including reflectance bands and vegetation indices. We also used other covariates such as climatic, soil, and terrain variables, for a total of 49 inputs. To reduce multicollinearity problems between the different variables, we performed feature selection using the sequential feature selection approach. We then estimated SOC content using both the complete dataset and the selected features. Multiple ML methods were tested and compared, including multiple linear regression (MLR), random forests (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN). We used a random cross-validation approach (with 10 folds). To find the hyperparameters that led to the best performance, we used a Bayesian optimization approach.Results:Results showed that the XGB method led to higher estimation accuracy than the other methods, and the estimation performance was not significantly influenced by the feature selection approach. For XGB, the average root mean square error (RMSE), measured on the test set among all folds, was 2.78 g kg−1(r2equal to 0.68) without feature selection, and 2.77 g kg−1(r2equal to 0.68) with feature selection (average SOC content is 13 g kg−1). The models were applied to obtain SOC content maps for all farms.Discussion:This work demonstrated that combining RS and ML can help obtain quick estimations of SOC content to assist with SBP management.

A new approach to estimate soil organic carbon content targets in European croplands topsoils

Adopting land management practices that increase the stock of soil organic carbon (SOC) in croplands is widely promoted as a win-win strategy to enhance soil health and mitigate climate change. In this context, the definition of reference SOC content and stock values is needed to provide reliable targets to farmers, policymakers, and stakeholders. In this study, we used the LUCAS dataset to compare different methods for evaluating reference SOC content and stock values in European croplands topsoils (0–20 cm depth). Methods gave generally similar estimates although being built on very different assumptions. In the absence of an objective criterion to establish which approach is the most suitable to determine SOC reference values, we propose an ensemble modelling approach that consists in extracting the estimates using different relevant methods and retaining the median value among them. Interestingly, this approach led us to select values from the three different approaches with similar frequencies. Using estimated bulk density values, we obtained a first rough estimate of 3.5 Gt C of SOC storage potential in the cropland topsoils that we interpret as a long-term aspirational target that would be reachable only under extreme changes in agricultural practices. The use of additional methods in the ensemble modelling approach and more valid statistical spatial estimates may further refine our approach designed for the estimation of SOC reference values for croplands.

Use of remote sensing data to predict soil organic carbon in some agricultural soils of Iran

Quantifying and monitoring of SOC (soil organic carbon) content in an agriculture soil is essential for understanding the ecological environment, influencing the climate change and implementing the best agriculture management. Present study tried to derive some machine learning-based models for predicting SOC content by employing remote sensing data in some agriculture soils enriched by calcareous materials, in central part of Iran. Soil samples were taken from 336 points at arable and permanent agriculture soils. Soil features such as SOC, sand, silt, clay, bulk density and calcium carbonate equivalent contents were measured at each point. After that, machine learning models including artificial neural networks (ANN), support vector regression (SVR) and genetic expression programming (GEP) were employed to estimate SOC content. The input variables were spectral indices extracted from Landsat-8, Sentinel-2, Sentinel-3 and MODIS images (Scenario I), integrating of the spectral indices extracted from Landsat-8—MODIS and Sentinel-2—Sentinel-3 images (Scenario II) and the spectral indices extracted from the fusing Sentinel-2 and Sentinel-3 images (Scenario IIII). The results of present study showed that the SVR model was the best performance (MAE = 0.0056%, RMSE = 0.620% and R2 = 0.627 at test data sets) for all sampled data by using the extracted remote sensing information at scenario II. Moreover, SVR model yielded the better result in the arable and permanent agriculture soils by using the extracted remote sensing information at scenario II (MAE = 0.0042%, RMSE = 0.672% and R2 = 0.574) and scenario III (MAE = 0.0032%, RMSE = 0.342% and R2 = 0.381), respectively. It was concluded that the combining of remote sensing data extracted from different types of satellites in a various method could improve the accuracy of SOC prediction.

Soil water retention capacity surpasses climate humidity in determining soil organic carbon content but not plant production in the steppe zone of Northern China

Soil hydraulic properties determine water transport, distribution and storage in soils, thus affect ecosystem functioning. Soil water retention parameters, such as saturation water content (SAT), water drained upper limit (DUL) and lower limit (DLL), characterize soil water-holding capacity, thus are important for plant growth and carbon and nutrient cycling in grassland ecosystem. However, we are still unclear to what a content these soil water retention parameters may regulate plant production and soil organic matter content in natural ecosystems in semi-arid grassland environment. We investigated plant species richness (SR) and aboveground biomass (AGB), soil organic carbon (SOC) and nitrogen (SON) contents, and soil water retention and other physical parameters in the steppe ecosystems along a climate transect, which spans the desert steppe, typical steppe and meadow steppe zones in Northern China. We analyzed the relative importance of climate factors, soil water retention parameters and other soil physical parameters in regulating plant AGB and SOC and SON contents using partial correlation analysis, and quantified the contribution of each separate and combined climate and soil physical parameters in predicting plant AGB and SOC and SON contents using geographic detectors method. We found that (i) plant SR and AGB, soil SAT and DUL, and SOC and SON are all significantly inter-correlated, and increased with the increase of climate humidity. (ii) Climate, but not soil physical parameters, was the most preponderant factor in predicting plant SR and AGB. (iii) Soil physical parameters were more important than climate parameters in regulating soil organic matter content; soil DUL was the best predictor to SOC content. Our results suggest that soil water retention parameters especially soil DUL, may play a greater role than climate factors in modulating SOC content, though climate is more predominant in regulating plant SR and production. Our results imply that we need to be cautious in estimating SOC content based on climate factors and vegetation biomass or remotely-sensed vegetation indices, and that soil physical properties need also to be incorporated along with vegetation parameters for improving SOC estimation at a regional scale.

Changes of SOC Content in China’s Shendong Coal Mining Area during 1990–2020 Investigated Using Remote Sensing Techniques

Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R2 > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg−1 in 1990 to 9.23 gC·kg−1 in 2010 and then declined to 5.31 gC·Kg−1 in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.

Wavelet geographically weighted regression for spectroscopic modelling of soil properties

Soil properties, such as organic carbon, pH and clay content, are critical indicators of ecosystem function. Visible–near infrared (vis–NIR) reflectance spectroscopy has been widely used to cost-efficiently estimate such soil properties. Multivariate modelling, such as partial least squares regression (PLSR), and machine learning are the most common methods for modelling soil properties with spectra. Often, such models do not account for the multiresolution information presented in the vis–NIR signal, or the spatial variation in the data. To address these potential shortcomings, we used wavelets to decompose the vis–NIR spectra of 226 soils from agricultural and forested regions in south-western Western Australia and developed a wavelet geographically weighted regression (WGWR) for estimating soil organic carbon content, clay content and pH. To evaluate the WGWR models, we compared them to linear models derived with multiresolution data from a wavelet decomposition (WLR) and PLSR without multiresolution information. Overall, validation of the WGWR models produced more accurate estimates of the soil properties than WLR and PLSR. Around 3.5–49.1% of the improvement in the estimates was due to the multiresolution analysis and 1.0–5.2% due to the integration of spatial information in the modelling. The WGWR improves the modelling of soil properties with spectra.

Soil organic carbon (SOC) enrichment in aeolian sediments and SOC loss by dust emission in the desert steppe, China

Dust emission is an important mechanism for carbon exchange between terrestrial and atmospheric carbon pools. However, undetermined soil organic carbon (SOC) enrichment in aeolian sediment limits the accurate estimation of SOC loss induced by wind erosion. Herein, we examined wind erosion and SOC loss measurements in the desert steppe of Inner Mongolia, China. By testing the particle size distributions (PSDs) and SOC contents across different particle size groups of the soil samples and aeolian sediments, we found that the finer soil particles generally had higher SOC contents. According to the measured results, we recognized that the mechanism of SOC enrichment in aeolian sediment is the inconstant distribution of SOC across the different soil particle size groups and the differences between the PSDs of soils and aeolian sediments. Based on the mechanism, we proposed a method to calculate the SOC content in aeolian sediment, and the calculated results are highly consistent with the measured results. Compared with the previous method, our calculation method provided a more precise result. Integrating our method for estimating SOC content in dust (diameter less than 50 μm) and a dust emission model, we simulated the SOC loss induced by wind erosion in this region by a wind erosion model, and the results show SOC loss induced by dust emissions ranging from 0 to 39 g/m2/y during the period of 2001 to 2017. We believe the study method of dust SOC content calculation we proposed could be interested by the scholars in the field of carbon cycling, and the simulated results of SOC loss could provide robust data for the estimation of carbon budget in the desert steppe.

Soil organic carbon stock of different land uses of Mizoram, Northeast India

The study was conducted to assess soil organic carbon (SOC) concentration and stock under eight major land uses: shifting cultivation, wet rice cultivation, homegardens, forest (natural), grassland, bamboo plantation, oil palm plantation and teak plantation of Mizoram, Northeast India. Soil samples at different depths (0–15, 15–30 and 30–45 cm) were collected from each of the land uses under study to estimate SOC content in the laboratory. Forest recorded the highest mean SOC concentration with 2.74% at 0–45cm depth and lowest in the bamboo plantation (1.09%). Mean SOC stock for 0–45 cm soil depth ranged from 27.68 to 52.74 Mg C ha−1 in grassland and forest respectively. Both SOC concentration and SOC stock decreased with increasing soil depth. Soil bulk density of fine soil (<2 mm) was significantly negatively correlated with SOC concentration and positive with SOC stock. SOC stock loss estimated following its conversion from forest was maximum with shifting cultivation (−5.74 Mg C ha−1 yr−1) followed by oil palm plantation (−2.29 Mg C ha−1 yr−1), bamboo plantation (−1.56 Mg C ha−1 yr−1) and the least in homegardens (−0.14 Mg C ha−1 yr−1). The study results indicate the importance of SOC stocks in different land uses which may help devise appropriate management practices to increase the soil carbon sequestration potential in the wake of mitigating climate change.

Estimation of soil organic carbon in arable soil in Belgium and Luxembourg with the LUCAS topsoil database

SummaryQuantification of the soil organic carbon (SOC) content over large areas is mandatory to obtain accurate soil characterization and classification, which can improve site‐specific management at local or regional scales. In this context, soil spectroscopy is a well‐consolidated and widespread method to estimate soil variables, and in particular SOC content, at a low cost for routine analysis. The increasing number of large soil spectral libraries collected worldwide reflects the importance of spectroscopy in soil science. These large libraries contain soil samples derived from a large number of pedological regions and thus from different parent materials and soil types. In the light of the huge variation in the spectral responses to SOC content and composition, a rigorous process is necessary to subdivide large spectral libraries to avoid calibration with global models that fail to predict local variation in SOC content. Here, we propose to classify the European LUCAS topsoil database with a cluster analysis based on a large number of soil properties. The soil samples collected from arable land in the LUCAS database were chosen to apply a standardized multivariate calibration approach, valid for large areas, to calibrate local models without the need for further field and laboratory work. Cluster analysis detected seven soil classes and the samples belonging to each class were used to calibrate specific partial least squares regression (PLSR) models to estimate SOC content in three spectral libraries collected in Belgium and Luxembourg. Soil organic carbon was predicted with good accuracy, both within each library (root mean square error (RMSE), 1.2–5.1 g kg−1; ratio of performance to prediction (RPD), 1.41–2.24) and for the samples of the three libraries together (RMSE, 3.7 g kg−1; RPD, 2.54). The proposed approach could enable SOC to be estimated for arable soils in Europe with only the spectra of soil samples and without the need for laboratory analyses.HighlightsWe investigated the potential of the LUCAS database to estimate SOC in spectral libraries. We proposed a routine approach to estimate SOC with less laboratory work. The classification of the LUCAS topsoil dataset improved estimation accuracy of SOC SOC content can be predicted from soil spectra with models calibrated on the LUCAS database.

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland–Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model’s accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5–4.9 g·kg−1; ratio of performance to deviation (RPD) = 1.4–1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within- and between-field SOC variability.

Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

Abstract. Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological–thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon–climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological–thermal processes associated with annual freeze–thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets – including soil liquid water content, temperature and electrical resistivity tomography (ERT) data – to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological–thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface–subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice–liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological–thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological–thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface–subsurface, deterministic–stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological–thermal dynamics.

Estimating organic carbon content of soil in Papua New Guinea using infrared spectroscopy

Quantification of soil organic carbon (SOC) content is important for sustainable agricultural management and accurate carbon accounting. Infrared (IR) absorbance can be used to estimate SOC content, but the relationship differs between regions due to matrix effects. We developed an IR-based model specific for SOC in Papua New Guinean soils. A total of 437 samples from 0.0–0.3m depth were analysed for SOC using Dumas combustion. IR absorption spectra were collected from the same samples, and a predictive regression model was developed using the 6000–1030cm–1 spectral range. Using a validation set, predicted SOC values resulting from the IR-based model compared well with values from Dumas combustion (R2=0.905; ratio of performance-to-deviation=5.64). Constraining wavelengths to positively correlated regions of the spectra was also explored and showed improved model performance (R2=0.932). Overall, IR analysis provides a robust method for estimating SOC content for a range of Papua New Guinean soils.

Estimating soil organic carbon content with visible-near-infrared (vis-NIR) spectroscopy.

The selection of a calibration method is one of the main factors influencing measurement accuracy with visible-near-infrared (Vis-NIR, 350-2500 nm) spectroscopy. This study, based on both air-dried unground (DU) and air-dried ground (DG) soil samples, used nine spectral preprocessing methods and their combinations, with the aim to compare the commonly used partial least squares regression (PLSR) method with the new machine learning method of support vector machine regression (SVMR) to find a robust method for soil organic carbon (SOC) content estimation, and to further explore an effective Vis-NIR spectral preprocessing strategy. In total, 100 heterogeneous soil samples collected from Southeast China were used as the dataset for the model calibration and independent validation. The determination coefficient (R(2)), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile range were used for the model evaluation. The results of this study show that both the PLSR and SVMR models were significantly improved by the absorbance transformation (LOG), standard normal variate with wavelet detrending (SW), first derivative (FD), and mean centering (MC) spectral preprocessing methods and their combinations. SVMR obtained optimal models for both the DU and DG soil, with R(2), RMSE, and RPD values of 0.72, 2.48 g/kg, and 1.83 for DU soil and 0.86, 1.84 g/kg, and 2.60 for DG soil, respectively. Among all the PLSR and SVMR models, SVMR showed a more stable performance than PLSR, and it also outperformed PLSR, with a smaller mean RMSE of 0.69 g/kg for DU soil and 0.50 g/kg for DG soil. This study concludes that PLSR is an effective linear algorithm, but it might not be sufficient when dealing with a nonlinear relationship, and SVMR turned out to be a more suitable nonlinear regression method for SOC estimation. Effective SOC estimation was obtained based on the DG soil samples, but the accurate estimation of SOC with DU soil samples needs to be further explored. In addition, LOG, SW, FD, and MC are valuable spectral preprocessing methods for Vis-NIR optimization, and choosing two of them (except for FD + SW and LOG + FD) in a simple combination is a good way to get acceptable results.

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R2V = 0.73, RMSEV = 2.78 g∙kg−1 and RPDV = 1.89) and outperformed the SVMR model (R2V = 0.72, RMSEV = 2.83 g∙kg−1 and RPDV = 1.86) and the SPA-PLSR model (R2V = 0.62, RMSEV = 3.23 g∙kg−1 and RPDV = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types.

Predictions of soil organic carbon using laboratory-based hyperspectral data in the northern Tianshan mountains, China

This paper presents a study dealing with soil organic carbon (SOC) estimation of soil through the combination of soil spectroscopy and multivariate stepwise linear regression. Soil samples were collected in the three sub-regions, dominated by brown calcic soil, in the northern Tianshan Mountains, China. Spectral measurements for all soil samples were performed in a controlled laboratory environment by a portable ASD FieldSpec FR spectrometer (350-2,500 nm). Twelve types of transformations were applied to the soil reflectance to remove the noise and to linearize the correlation between reflectance and SOC content. Based on the spectral reflectance and its derivatives, hyperspectral models can be built using correlation analysis and multivariable statistical methods. The results show that the main response range of soil organic carbon is between 400 and 750 nm. Correlation analysis indicated that SOC has stronger correlation with the second derivative than with the original reflectance and other transformations data. The two models developed with laboratory spectra gave good predictions of SOC, with root mean square error (RMSE) <5.0. The use of the full visible near-infrared spectral range gave better SOC predictions than using visible separately. The multivariate stepwise linear regression of second derivate model (model A) is optimal for estimating SOC content, with a determination coefficient of 0.894 and RMSE of 0.322. The results of this research study indicated that, for the grassland regions, combining soil spectroscopy and mathematical statistical methods does favor accurate prediction of SOC.

Accumulation of Excess Ground Ice in an Age Sequence of Drained Thermokarst Lake Basins, Arctic Alaska

ABSTRACTThe excess ice content of near‐surface permafrost near Barrow, Alaska, was estimated using cores collected from 57 drained thermokarst‐lake basins and additional cores from a nearby landform unaffected by thaw‐lake processes. The excess ice content, estimated using soil cryostructures, increased with surface age: from 20 per cent in young basins &lt; 50 years in age to 40 per cent in ancient basins that drained 2000–5500 years ago. The frequency of ice wedges encountered during coring increased from 0 per cent in young basins to 50 per cent in ancient basins. These results indicate that the volume of ground ice increases rapidly immediately following lake drainage, as permafrost aggrades into unfrozen basin sediments. Ice enrichment continues over time by incorporating meteoric water as ice veins and lenses, and expanding networks of ice wedges. To test the efficacy of visually estimating excess ice content, the ice content was measured on a subsample of cores; measured volumetric values were strongly correlated (r2 = 0.72; p &lt; 0.001) to the estimated excess ice content. The results of this study have important implications for estimating soil organic carbon content of soils with abundant excess ice, and for evaluating the susceptibility of these soils to thermokarst. Copyright © 2012 John Wiley &amp; Sons, Ltd.