Estimating Soil Organic Carbon Research Articles

Necromass-derived soil organic carbon and its drivers at the global scale

Microbial residual carbon (MRC) plays an important role in maintaining soil carbon stability and is therefore a focus of soil organic carbon (SOC) estimation. However, the global pattern of MRC and its underlying determinants remains uncertain, mainly due to the lack of spatially explicit data on the ratio of gram-positive to gram-negative bacteria which are required to make the conversion from muramic acid to MRC. In the absence of such data, a constant value (65% gram-positive and 35% gram-negative) is commonly used, despite the fact that the ratio varies with environmental conditions. Here, we synthesized a comprehensive data set composed of field data on gram-positive and gram-negative bacteria from 487 sites across the globe, obtained from published literature. Our results show that the ratio is affected by a suite of environmental factors, such as soil moisture and pH. We developed a multiple linear regression model between the ratio and the influencing variables to recalculate the MRC based on amino sugar data from 654 sites. The global distribution pattern of the newly-obtained values of MRC and its driving factors were analyzed. We found striking differences in the estimation of the MRC contribution to SOC when using the newly generated coefficient compared with the currently widely used coefficient, notably in temperate grassland. MRC was greater in topsoil than in deep soil, while the converse was true for MRC/SOC. The MRC and the MRC to SOC ratio varied in different ecosystems, with the highest values of MRC and MRC/SOC occurring in temperate forest and tundra, and the lowest in desert and wetland. The key factors determining microbial residual C were microbial characteristics (microbial biomass C and N) and soil properties (SOC and total nitrogen). We provide a globally gridded map of MRC produced by the boosted regression-tree model, which further reveals that MRC in mid-high latitude areas is higher than in other regions of the northern hemisphere. These results emphasize that assessment of microbial necromass-derived SOC should consider the heterogeneity of the ratio of gram-positive to gram-negative bacteria, which would provide a huge boost to our depth of understanding of SOC composition and stability.

A Framework Combining CENTURY Modeling and Chronosequences Sampling to Estimate Soil Organic Carbon Stock in an Agricultural Region with Large Land Use Change

Agricultural land use has a remarkable influence on the stock and distribution of soil organic carbon (SOC). However, both regional soil sampling and process-based ecosystem models for SOC estimation at the regional scale have limitations when applied in areas with a large land use change. In the present study, a framework (CMCS) combining CENTURY modeling (CM) and chronosequences sampling (CS) was established, and a case study was conducted in Cangshan County, where vegetable cultivation conversion from grain production was significant in recent decades. The SOC stock (SOCS) of the non-vegetable area estimated by CM was comparable to that estimated by regional soil sampling in 2008. This result confirmed that CM was reliable in modeling SOC dynamics in a non-vegetable area without land use change. However, when applied to the overall cropland of Cangshan County, the CM, without considering the land use change, underestimated the SOCS by 0.23 Tg (6%), compared with the observed measurements (3.58 and 3.81 Tg, respectively). Using the CMCS framework of our study, the underestimation of CM was offset by the SOC sequestration estimated by CS. The SOCS estimated by the CMCS framework ranged from 3.72 to 4.02 Tg, demonstrating that this framework is reliable for the regional SOC estimation of large-area land use change. In addition, annual SOCS dynamics were obtained by this framework. The CMCS framework provides a low-cost and practicable method for the estimation of the regional SOC dynamic, which can further support the strategy of carbon peaking and carbon neutrality in China.

Satellite-Based Carbon Estimation in Scotland: AGB and SOC

The majority of state-of-the-art research employs remote sensing on AGB (Above Ground Biomass) and SOC (Soil Organic Carbon) separately, although some studies indicate a positive correlation between the two. We intend to combine the two domains in our research to improve state-of-the-art total carbon estimation. We begin by establishing a baseline model in our study area in Scotland, using state-of-the-art methodologies in the SOC and AGB domains. The effects of feature engineering techniques such as variance inflation factor and feature selection on machine learning models are then investigated. This is extended by combining predictor variables from the two domains. Finally, we leverage the possible correlation between AGB and SOC to establish a relationship between the two and propose novel models in an attempt to outperform the state-of-the-art results. We compared three machine learning techniques, boosted regression tree, random forest, and xgboost. These techniques have been demonstrated to be the most effective in both domains. This research makes three contributions: (i) Including Digital Elevation Map (DEM) as a predictor variable in the AGB model improves the model result by 13.5 % on average across the three machine learning techniques experimented, implying that DEM should be considered for AGB estimation as well, despite the fact that it has previously been used exclusively for SOC estimation. (ii) Using SOC and SOC Density improves the prediction of the AGB model by a significant 14.2% on average compared to the state-of-the-art baseline (When comparing the R2 value across all three modeling techniques in Model B and Model H, there is an increase from 0.5016 to 0.5604 for BRT, 0.4958 to 0.5925 for RF and 0.5161 to 0.5750 for XGB), which strengthens our experiment results and suggests a future research direction of combining AGB and SOC as a joint study domain. (iii) Including AGB as a predictor variable for SOC improves model performance for Random Forest, but reduced performance for Boosted Regression tree and XG Boost, indicating that the results are specific to ML models and more research is required on the feature space and modeling techniques. Additionally, we propose a method for estimating total carbon using data from Sentinel 1, Sentinel 2, Landsat 8, Digital Elevation, and the Forest Inventory.

Assessing the capability of Sentinel-2 time-series to estimate soil organic carbon and clay content at local scale in croplands

The use of remote sensing data methods is affordable for the mapping of soil properties of the plowed layer over croplands. Carried out in the framework of the ongoing STEROPES project of the European Joint H2020 Program SOIL, this work is focused on the feasibility of Sentinel-2 based approaches for the high resolution mapping of topsoil clay and organic carbon (SOC) contents at the within-farm or within-field scales, for cropland sites of contrasted climates and soil types across the Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series and several spectral indices (NDVI, NBR2, BSI, S2WI), were developed and compared for i) pure bare soil condition (maxBSI), ii) driest soil condition (minS2WI), iii) average bare soil condition (Median) and iv) dry soil conditions excluding extreme reflectance values (R90). Three spectral modeling approaches, using the Sentinel-2 bands of the output temporal mosaics as covariates, were tested and compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude and latitude as covariates (QRFxy); (iii) a hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation of the soil properties. We tested pairs of mosaic and spectral approaches on ten sites in Türkiye, Italy, Lithuania, and USA where soil samples were collected and SOC and clay content were measured in the lab. The average RPIQ of the best performances among the test sites was 2.50 both for SOC (RMSE = 0.15%) and clay (RMSE = 3.3%). Both accuracy level and uncertainty were mainly influenced by site characteristics of cloud frequency, soil types and management. Generally, the models including a spatial component (QRFxy and LMEM) were the best performing, while the best spatial mosaicking approaches mostly were Median and R90. The most frequent optimal combination of mosaicking and model type was Median or R90 and QRFxy for SOC, and R90 and LMEM for clay estimation.

Non-climate environmental factors matter to Holocene dynamics of soil organic carbon and nitrogen in an alpine permafrost wetland, Qinghai‒Tibet Plateau

Studies on the responses of soil organic carbon (SOC) and nitrogen dynamics to Holocene climate and environment in permafrost peatlands and/or wetlands might serve as analogues for future scenarios, and they can help predict the fate of the frozen SOC and nitrogen under a warming climate. To date, little is known about these issues on the Qinghai‒Tibet Plateau (QTP). Here, we investigated the accumulations of SOC and nitrogen in a permafrost wetland on the northeastern QTP, and analyzed their links with Holocene climatic and environmental changes. In order to do so, we studied grain size, soil organic matter, SOC, and nitrogen contents, bulk density, geochemical parameters, and the accelerator mass spectrometry (AMS) 14C dating of the 216-cm-deep wetland profile. SOC and nitrogen contents revealed a general uptrend over last 7300 years. SOC stocks for depths of 0–100 and 0–200 cm were 50.1 and 79.0 kgC m−2, respectively, and nitrogen stocks for the same depths were 4.3 and 6.6 kgN m−2, respectively. Overall, a cooling and drying trend for regional climate over last 7300 years was inferred from the declining chemical weathering and humidity index. Meanwhile, SOC and nitrogen accumulated rapidly in 1110–720 BP, while apparent accumulation rates of SOC and nitrogen were much lower during the other periods of the last 7300 years. Consequently, we proposed a probable conceptual framework for the concordant development of syngenetic permafrost and SOC and nitrogen accumulations in alpine permafrost wetlands. This indicates that, apart from controls of climate, non-climate environmental factors, such as dust deposition and site hydrology, matter to SOC and nitrogen accumulations in permafrost wetlands. We emphasized that environmental changes driven by climate change have important impacts on SOC and nitrogen accumulations in alpine permafrost wetlands. This study could provide data support for regional and global estimates of SOC and nitrogen pools and for global models on carbon‒climate interactions that take into account of alpine permafrost wetlands on the northeastern QTP at mid-latitudes.

Partial least square regression based machine learning models for soil organic carbon prediction using visible–near infrared spectroscopy

Monitoring and assessment of soil organic carbon (SOC) are critical for maintaining and enhancing the productivity of agricultural soils. The SOC is commonly determined through soil sampling and subsequent laboratory analysis using chemical methods. This method though very precise is time-consuming, labour-intensive and expensive. Contrarily, visible and near-infrared reflectance spectroscopy (VNIRS) may be utilised to estimate SOC in a quick, labour-saving, and cost-effective manner. In this study, 72 soil samples were collected for SOC estimation and spectra collection. This current work proposes to investigate the use of PLSR scores in place of raw spectral reflectance to increase both the computation and model efficiency by reducing the number of input variables while retaining the maximum information present in the original data. With the existing indices, ratio and normalized difference indices were calculated in all possible combinations and were regressed to SOC content to identify the best-performing indices. Ten different multivariate models were evaluated for SOC estimation using full-spectrum and partial least squares regression (PLSR) scores. The results revealed that reflectance gradually increased with increasing soil depth and decreasing SOC. The prediction models developed using existing indices were observed to be poor in predicting the SOC with the R2 values ranging from 0.009 to 0.34. The best spectral indices for estimating SOC were RI (R1888, R2015) and NDI (R1888, R2015) with R2 of 0.60, 0.61 and 0.39, 0.43 for calibration and validation datasets, respectively. The PLSR score-based multivariate models outperformed solo multivariate and optimized index-based models. Our study suggested that VNIRS with PLSR combined multivariate models can reliably be used for fast and non-invasive estimation of SOC.

Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent-boosted regression tree.

Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Disturbed and undisturbed soil samples were collected from 10-cm depth in 50 locations differed with land use and land cover. Vegetation, soil, and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Significant correlations (p≤0.01) were obtained between the indices and SOCS; thus, the remote sensing indices (ARVI 0.43, BI -0.43, GSI -0.39, GNDI 0.44, NDVI 0.44, NDWI 0.38, and SRCI 0.51) were used as covariates in multi-layer perceptron neural network (MLP) and gradient descent-boosted regression tree (GBDT) machine learning models. Mean absolute error, root mean square error, and mean absolute percentage error were 3.94 (Mg C ha -1), 6.64 (Mg C ha-1), and 9.97%, respectively. The simple ratio clay index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land cover classes were significantly different. The land cover has a significant effect on SOC in Yuksekova plain. The mean SOCS for continuously ponded fields was 45.58 Mg C ha-1, which was significantly different from the mean SOCS of arable lands. The mean SOCS in arable lands, with significant areas of natural vegetation, was 50.22 Mg C ha-1 and this amount was significantly higher from the SOCS of other land covers (p<0.01). The wetlands had the highest SOCS (61.46 Mg C ha-1), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha-1). Environmental conditions had significant effect on SOCS in the study area. The use of remote sensing indices instead of using single bands as estimators in the GBDT algorithm minimized radiometric errors, and reliable spatial SOCS information was obtained by using the estimators. Therefore, the spatial estimation of SOCS can be successfully determined with up-to-date machine learning algorithms only using remote sensing predictor variables. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming.

Amount and Vertical Distribution of Soil Organic Carbon and Total Nitrogen in a Dry Tropical Forest Ecosystem, Tanzania

There is a growing interest in understanding soil organic carbon (SOC) and total nitrogen (TN) of various ecosystems worldwide, because they are important indicators of soil quality and soil fertility, especially on the availability of essential nutrients for plant growth; and climate change mitigation. We tested the hypothesis that the amount and vertical distribution of SOC and TN in 0-30cm and 30-100cm depths differ significantly in miombo woodland ecosystems. Soil samples were collected from 15m-radius circular plots (n=33). SOC was determined by Mid-Infrared (MIR) spectroscopy (ICRAF approach) and the Walkley and Black method (NAFORMA approach). The mean amount of SOC and TN at 30-100cm depth were significantly higher (<i>p</i>=0.003 and <i>p</i>=0.0001, respectively) than that within the 0-30cm depth. The amount of SOC at 20-40cm (39tCha^-1) was found to be significantly (<i>p</i>=0.0007) higher than at 0-20cm (32tCha^-1) followed by decreasing pattern to 100cm. On the other hand, TN decreased substantially from 0-20cm to 100cm depth. SOC was significantly (<i>p</i><0.05) and positively correlated with TN. The NAFORMA approach estimated significantly (<I>P</I><<i>0.05</i>) higher SOC than ICRAF approach. Clearing of forests for sesame cultivation invariably resulted in increased nitrogen in the top soil due to addition of ammonium fertilizers, but loss of SOC is due to removal of biomass (including slash burning) and a reduction in the quantity and quality of organic inputs added to the soil. Accurate estimation of SOC at national and regional scales should use the modern methods complimented by the standard methods in different ecosystems.

Examining the influence of bare soil UAV imagery combined with auxiliary datasets to estimate and map soil organic carbon distribution in an erosion-prone agricultural field

Soil organic content (SOC), an indicator of soil fertility, can be estimated quickly and accurately with remote sensing (RS) datasets; however, the issue of vegetation cover on the field still remains a major concern. In order to minimize the effects of vegetation cover, studies relating reflectance spectra to SOC may require bare soil. However, acquiring satellite images devoid of vegetation is still an enormous challenge for RS techniques. This is because the area that may have been accurately predicted at a targeted date is sometimes limited since many pixels are covered by vegetation. The study goal was to assess the impact of using UAV-borne imagery coupled with auxiliary datasets, which include spectral indices (SPIs) and terrain attributes (TAs) (at 20 cm and 30 m resolution), singly or merged, to estimate and map SOC in an erosion-prone agricultural field. Both field samples and UAV imagery were acquired while the fields were bare. Using a grid sampling design, 133 soil surface samples were collected. The models used include partial least square regression (PLSR), extreme gradient boosting (EGB), multivariate adaptive regression splines (MARS), and regularised random forest (RFF). The models were evaluated using the root mean squared error (RMSE), the coefficient of determination (R2), ratio of performance to interquartile distance (RPIQ), and the mean absolute error (MAE). For prediction, the three merged datasets (R2val = 0.86, RMSEval = 0.13, MAEval = 0.11, RPIQval = 4.19) outperformed the best separate dataset (R2val = 0.82, RMSEval = 0.15, MAEval = 0.10, RPIQval = 2.08). Though all datasets detected both low and high estimates of soil SOC, the three merged datasets with EGB showed a less extreme prediction error. This study demonstrated that SOC can be estimated with high accuracy using completely bare soil UAV imagery with other auxiliary data, and it is thus highly recommended.

Valid inferences about soil carbon in heterogeneous landscapes

Using soil organic carbon (SOC) to generate carbon offsets requires reliably quantifying SOC sequestration. However, accuracy of SOC measurement is limited by inherent spatial heterogeneity, variability of laboratory assays, unmet statistical assumptions, and the relatively small magnitude of SOC changes over time, among other things. Most SOC measurement protocols currently used to generate offsets for C markets do not adequately address these issues, threatening to undermine climate change mitigation efforts. Using analyses and simulations from 1,117 soil samples collected from California crop and rangelands, we quantified measurement errors and sources of uncertainty to optimize SOC measurement. We demonstrate that (1) spatial heterogeneity is a primary driver of uncertainty; (2) dry combustion assays contribute little to uncertainty, although inorganic C can increase error; (3) common statistical methods—Student’s t-test and its relatives—can be unreliable for SOC (e.g. at low to medium sample sizes or when the distribution of SOC is skewed), which can lead to incorrect interpretations of SOC sequestration; and (4) common sample sizes (10–30 cores) are insufficiently powered to detect the modest SOC changes expected from management in heterogeneous agricultural landscapes. To reduce error and improve the reliability of future SOC offsets, protocols should: (1) require power analyses that include spatial heterogeneity to determine minimum sample sizes, rather than allowing arbitrarily small sample sizes; (2) minimize the use of compositing; (3) require dry combustion analysis, by the same lab for all assays; and (4) use nonparametric statistical tests and confidence intervals to control Type I error rates. While these changes might increase costs, they will make SOC estimates more accurate and more reliable, adding credibility to soil management as a climate change mitigation strategy.

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems, and accurate estimation of soil organic carbon (SOC) stocks in forest ecosystems is rather challenging. This study compared the prediction performance of three empirical model approaches namely, regression kriging (RK), multiple stepwise regression (MSR), random forest (RF), and boosted regression trees (BRT) to predict SOC stocks in Northeast China for 1990 and 2015. Furthermore, the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified. A total of 82 (in 1990) and 157 (in 2015) topsoil (0–20 ​cm) samples with 12 environmental factors (soil property, climate, topography and biology) were selected for model construction. Randomly selected 80% of the soil sample data were used to train the models and the other 20% data for model verification using mean absolute error, root mean square error, coefficient of determination and Lin's consistency correlation coefficient indices. We found BRT model as the best prediction model and it could explain 67% and 60% spatial variation of SOC stocks, in 1990, and 2015, respectively. Predicted maps of all models in both periods showed similar spatial distribution characteristics, with the lower SOC in northeast and higher SOC in southwest. Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods. SOC stocks were mainly stored under Cambosols, Gleyosols and Isohumosols, accounting for 95.6% (1990) and 95.9% (2015). Overall, SOC stocks increased by 471 ​Tg ​C during the past 25 years. Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories. The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks. Overall, our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Reducing Moisture Effects on Soil Organic Carbon Content Estimation in Vis-NIR Spectra With a Deep Learning Algorithm

When estimating soil organic carbon (SOC) using visible and near-infrared (Vis-NIR) spectra measured in situ, the interference of soil moisture content (SMC) needs to be eliminated. The existing SMC removal methods are mainly based on spectral transformation, but they change the original form of the soil spectrum. In this paper, a new deep learning-based SMC influence removal network (MIRNet) is proposed to establish the relationship between the spectra of moist soil and that of dry soil. This method constructs a spectral extraction module (SEM) with two one-dimensional (1-D) ghost modules to extract soil spectral characteristics and a context extraction module (CEM) with a two-layer dilated convolutional neural network (DiCNN) to extract the context information of the spectra. Then these extracted features are combined to reconstruct the SMC influence with a two-layer deconvolution using residual learning (Res). Finally, a new loss function that combining spectral distance and spectral shape measurement (D-S loss) is proposed. The input of MIRNet is the moist soil spectra, and the output is the dry soil spectra. Black soil collected from Harbin and yellow-brown soil collected from Nanjing are selected as the research objects. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> reaches 0.703, 0.747, 0.907, 0.892, 0.866, 0.907, and 0.926, respectively, when using spectra processed by external parameter orthogonalization (EPO), orthogonal signal correction (OSC), support vector regression (SVR), convolutional neural network (CNN), deep neural network (DNN), denoising convolutional neural network (DnCNN), and MIRNet. Therefore, the proposed MIRNet achieves competitive results compared with these state-of-the-art methods.

Using Mid-Infrared Spectroscopy to Optimize Throughput and Costs of Soil Organic Carbon and Nitrogen Estimates: An Assessment in Grassland Soils

Low-cost techniques, such as mid-infrared (MIR) spectroscopy, are increasingly necessary to detect soil organic carbon (SOC) and nitrogen (N) changes in rangelands following improved grazing management. Specifically, Adaptive Multi-Paddock (AMP) grazing is being implemented to restore grassland ecosystems and sequester SOC often for commercialization in C markets. To determine how the accuracy of SOC and N predictions using MIR spectroscopy is affected by the number of calibration samples and by different predictive models, we analyzed 1000 samples from grassland soils. We tested the effect of calibration sample size from 100 to 1000 samples, as well as the predictive ability of the partial least squares (PLS), random forest (RF) and support vector machine (SVM) algorithms on SOC and N predictions. The samples were obtained from five different farm pairs corresponding to AMP and Conventional Grazing (CG), covering a 0–50 cm soil depth profile along a latitudinal gradient in the Southeast USA. Overall, the sample size had only a moderate influence on these predictions. The predictive accuracy of all three models was less affected by variation in sample size when &gt;400 samples were used. The predictive ability of non-linear models SVM and RF was similar to classical PLS. Additionally, all three models performed better for the deeper soil samples, i.e., from below the A horizon to the –50 cm depth. For topsoil samples, the particulate organic matter (POM) content also influenced the model accuracy. The selection of representative calibration samples efficiently reduces analysis costs without affecting the quality of results. Our study is an effort to improve the efficiency of SOC and N monitoring techniques.

Soil Organic Carbon Pools in Rice Growing Inceptisols of Northeast India

ABSTRACT Estimation of soil organic carbon (SOC) stock and its pools is important for understanding C budgeting and ecosystems services of any land use. The present study was conducted to quantify SOC stock and its pools of different oxidizability (very labile, labile, less labile and non-labile C), microbial biomass C and mineralizable C at different depths (0–0.15, 0.15–0.30 and 0.30–0.50 m) of selected 26 rice growing Inceptisols located in the subtropical Manipur valley, India. Results of this study showed that organic C stock in 0–0.50 m depth of the rice growing Inceptisols varied from 55.7 to 133.5 Mg ha−1 and there was a decrease in SOC stock along depth. The rice growing Inceptisols under subtropical climate of northeast India are characterized by cooler climatic conditions and soil acidity, which are favorable for SOC accumulation. The study also showed that more than two-third (72.0%) of SOC is present in active pools with rapid turnover. Therefore, much of the SOC in these soils is very susceptible to changes in management practices such as increased tillage intensity, inappropriate cropping without legume and inadequate fertilization and in weather conditions such as increased temperature.

Mapping Soil Organic Carbon Content in Patagonian Forests Based on Climate, Topography and Vegetation Metrics from Satellite Imagery

Soil organic carbon (SOC) content supports several ecosystem services. Quantifying SOC requires: (i) accurate C estimates of forest components, and (ii) soil estimates. However, SOC is difficult to measure, so predictive models are needed. Our objective was to model SOC stocks within 30 cm depth in Patagonian forests based on climatic, topographic and vegetation productivity measures from satellite images, including Dynamic Habitat Indices and Land Surface Temperature derived from Landsat-8. We used data from 1320 stands of different forest types in Patagonia, and random forest regression to map SOC. The model captured SOC variability well (R² = 0.60, RMSE = 22.1%), considering the huge latitudinal extension (36.4° to 55.1° SL) and the great diversity of forest types. Mean SOC was 134.4 ton C ha−1 ± 25.2, totaling 404.2 million ton C across Patagonia. Overall, SOC values were highest in valleys of the Andes mountains and in southern Tierra del Fuego, ranging from 53.5 to 277.8 ton C ha−1 for the whole Patagonia region. Soil organic carbon is a metric relevant to many applications, connecting major issues such as forest management, conservation, and livestock production, and having spatially explicit estimates of SOC enables managers to fulfil the international agreements that Argentina has joined.

Mapping the distribution, trends, and drivers of soil organic carbon in China from 1982 to 2019

The evaluation of soil organic carbon (SOC) dynamics and its driving factors is important for developing sustainable soil C sequestration practices. A key challenge is the lack of a time series of spatially explicit SOC estimates; most previous SOC estimates are static. In this study, we applied a digital soil mapping framework to model the relationship between SOC and environmental factors based on a combined dataset of 4695 soil samples collected in China from the early 1980 s and during the 2000–2014 period. We predicted the annual SOC distribution at a depth of 0–100 cm at a 1 km resolution for China between 1982 and 2019. We mapped trends of SOC change and identified change hotspots. To obtain the pixel-level association between SOC change and environmental factors, we used a linear function to quantify the contribution of individual predictors to SOC change. The results show that the accuracy of SOC prediction is acceptable, with a mean R2 value of 0.58 for validation. SOC storage in China was estimated to range from 89.03 to 97.41 Pg C and showed a slight increase (slope = 0.024 Pg C/yr) from 1982 to 2019. Specifically, two distinct phases of SOC were identified as follows: (1) from 1982 to 2004, a considerable decline occurred, which was mainly driven by increased temperature; (2) from 2004 to 2019, SOC increased slowly, resulting from the contribution of greening to sustaining C sequestration in soils under warming conditions. Approximately 17 % of all areas have been characterized by a significant decrease in SOC since 1982, predominantly in central and western China. Environmental controls exhibited large spatial variation, where approximately 69 % of all areas were predominantly controlled by climate factors. For grasslands, the SOC losses in northern China were largely driven by temperature and precipitation, while the SOC losses were primarily attributed to decreased organic inputs driven by grassland degradation on the Tibetan Plateau. Policy-driven land use change has led to an increase in SOC in some cropland areas (e.g., northeast and east China). The implementation of ecological restoration has shown a positive influence on SOC sequestration. This study highlights the diverse spatial patterns and heterogeneity of SOC changes and their controls. Given this spatial differentiation, spatially explicit assessments of soil C dynamics are important for the formulation of regional soil-based mitigation strategies and guidance for effective restoration interventions based on geographic location.

Initial soil organic carbon stocks govern changes in soil carbon: Reality or artifact?

Changes in soil organic carbon (SOC) storage have the potential to affect global climate; hence identifying environments with a high capacity to gain or lose SOC is of broad interest. Many cross-site studies have found that SOC-poor soils tend to gain or retain carbon more readily than SOC-rich soils. While this pattern may partly reflect reality, here we argue that it can also be created by a pair of statistical artifacts. First, soils that appear SOC-poor purely due to random variation will tend to yield more moderate SOC estimates upon resampling and hence will appear to accrue or retain more SOC than SOC-rich soils. This phenomenon is an example of regression to the mean. Second, normalized metrics of SOC change-such as relative rates and response ratios-will by definition show larger changes in SOC at lower initial SOC levels, even when the absolute change in SOC does not depend on initial SOC. These two artifacts create an exaggerated impression that initial SOC stocks are a major control on SOC dynamics. To address this problem, we recommend applying statistical corrections to eliminate the effect of regression to the mean, and avoiding normalized metrics when testing relationships between SOC change and initial SOC. Careful consideration of these issues in future cross-site studies will support clearer scientific inference that can better inform environmental management.

Assessing a VisNIR penetrometer system for in-situ estimation of soil organic carbon under variable soil moisture conditions

Soils are one of the main terrestrial carbon sinks and contain more carbon than the combined total in the atmosphere and terrestrial vegetation. A major challenge is quantifying how much carbon is stored in the soil and how much it has been sequestered due to changed management practices. In this study, a visible and near-infrared (VisNIR) penetrometer system was developed to measure in-situ soil VisNIR reflectance spectra to a depth of 90 cm into the soil profile. A spectral library of VisNIR soil spectra collected in air-dry and ground states were used to calibrate Cubist regression models for estimating soil organic carbon (SOC) contents. The External Parameter Orthogonalisation (EPO) transformation was applied to the in-situ spectra to remove soil moisture and other in-situ effects from the spectra. This penetrometer system was used to estimate SOC contents at three sites on three different days under variable soil moisture states. The penetrometer EPO performed better compared to the EPO constructed from moist cores. After applying EPO transformation matrix, soil wetness did not have any influence on soil SOC content estimations. The SOC contents were estimated using the in-situ VisNIR system were validated against lab-measured SOC content with mean R 2 , RMSE, and bias of 0.88, 0.32%, and 0.15%, respectively. It is concluded that the VisNIR penetrometer can potentially be used to measure SOC content rapidly and cost-effectively in-situ with a fine depth resolution. • A penetrometer system was used to acquire in-situ soil VisNIR spectra. • A spectral library from agricultural regions of NSW was used for calibration. • External Parameter Orthogonalisation (EPO) reduced confounding soil moisture effect. • External validation was performed to measure soil organic carbon contents.

Carbon Sequestration Potential of Roadside Trees in Southern Punjab, Pakistan

Trees along roadside play an important role in climate change mitigation through carbon sequestration. Vehicular pollution is not only responsible for environmental degradation but also cause various health issues to inhabitants resides in the vicinity of such roads. Present study was conducted to observe the role of trees grown along roadside and their underneath soil are efficient in carbon storage at four different sites of Multan city, Punjab, Pakistan. Four sites covering whole Multan city were chosen namely Khaniwal road (S1); Nangshah road (S2); Shujaabad road (S3). and Boson road (S4). The most abundant of the tree species viz-a-viz, Dalbergia sissoo, Eucalyptus camadulensis and Vachellia nilotica were sampled having 10 trees across 04 sites and measured for their diameter and height, whereas, underneath each tree soil samples were extracted at two depths viz., (0-20) (20-40) were recorded during the field visit. The study calculated biomass using allometric equations while soil organic carbon and organic carbon was assessed using Walkely Black method. Although species have different height and diameter, hence, their sequestration rate was also different. Data revealed that in all four sites the carbon sequestration rate remained higher in E. camaldulensis for biomass (Above ground, Belowground and total biomass), carbon and soil organic carbon estimation. as compared to D.sissoo and V. nilotica at all four sites. This research concluded that E. camaldulensis species may be be planted along the roadside due to its higher carbon sequestration rate in maintaining health roadside ecosystem.

Interrelationships among mountain relief, surface organic layer, soil organic carbon, and its mineral association under subarctic forest tundra

Efforts to estimate the impact of climate change-induced forest expansion on soil carbon stocks in cold regions are hindered by the lack of soil organic carbon (SOC) concentration data. The presented study addressed the information gap by establishing SOC concentration and its variability in two catchments inside the vast, remote, and rugged Putorana Plateau. Additionally, it explored interrelationships among the terrain relief, vegetation cover, surface organic layer, SOC and its mineral association on the northernmost boundary of the forest-tundra biome traversing the northwestern part of the Central Siberian Tableland. Soil samples were taken from the active layer on the slope base, middle, and below the upper forest boundary. Subsequently, they were analyzed for SOC concentration by dry combustion. Multiple linear regression identified associations between slope angle and surface organic layer thickness and between SOC concentration and surface organic layer thickness, clay content, and dithionite-extracted Al. Clay content and surface organic layer thickness explained 68% of the overall SOC concentration variability. When used with data produced by remote sensing-based multipurpose large-scale mapping of selected biophysical factors, the acquired regression equations could aid the estimation of SOC across the rugged terrain of the Siberian Traps.