Spatial Distribution Of Soil Organic Carbon Stocks Research Articles

Factors controlling peat soil thickness and carbon storage in temperate peatlands based on UAV high-resolution remote sensing

Peatlands store a large amount of carbon. However, peatlands are complex ecosystems, and acquiring reliable estimates of how much carbon is stored underneath the Earth’s surface is inherently challenging, even at small scales. Here, we aim to establish links between the above- and below-ground factors that control soil carbon status, identify the key environmental variables associated with carbon storage, as well as to explore the potential for using Unmanned Aerial Vehicle (UAV) remote sensing for spatial mapping of peatlands. We combine UAVs equipped with Red-Green-Blue (RGB), multispectral, thermal infrared, and light detection and ranging (LiDAR) sensors with ground-penetrating radar (GPR) technology and traditional field surveys to provide a comprehensive, 3-dimensional mapping of a peatland hillslope-floodplain landscape in the Belgian Hautes Fagnes. Our results indicate that both peat thickness and soil organic carbon (SOC) stock (top 1 m) are spatially heterogeneous and that the contributions from the surface topography to peat thickness and SOC stock varied from micro- to macro-scales. Peat thickness was more strongly controlled by macro-topography (R2 = 0.46) than SOC stock, which was more influenced by micro-topography (R2 = 0.21). Current vegetation had little predictive power for explaining their spatial variability. Additionally, the UAV data provided accurate estimates of both peat thickness and SOC stock, with RMSE and R2 values of 0.16 m and 0.85 for the peat thickness, and 59.25 t/ha and 0.85 for the SOC stock. However, similar performance can already be achieved by using only topographical data from the LiDAR sensor (for peat thickness) and a combination of peat thickness and topography (for SOC stock) as predictor variables. Our study bridges the gap between surface observations and the hidden carbon reservoir below. This not only allows us to improve our ability to assess the spatial distribution of SOC stocks, but also contributes to our understanding of the environmental factors associated with SOC storage in these highly heterogeneous landscapes, providing insights for environmental science and climate projections.

Predicting soil organic carbon stocks under future land use and climate change conditions in Northeast China

There are large uncertainties in predicting soil organic carbon (SOC) in response of future changing climate and human activities. This study estimates SOC stocks under future changing land use and climate conditions in Northeast China using a state-of-the-art digital soil mapping technique. A total of 487 soil samples and 12 environmental variables from 37 landscape units (derived from soil, topography, climate, and human activity data) combined with boosted regression trees (BRT) and random forest (RF) models are first used to map the topsoil (0–20 cm) SOC stocks in Northeast China in 2015. The primary environmental variables influencing the variability of SOC stocks are mean annual temperature, elevation, mean annual precipitation, and land use. We then applied the space-for-time substitution method in conjunction with the BRT model to predict the spatial distribution of SOC stocks under future (the 2050s and 2090s) climate and land use scenarios. SOC stocks under the scenarios of shared socioeconomic pathways (SSP245) and SSP585 (average and upper estimate of the increase in atmospheric greenhouse gases for that time) decreased by 1.5% and 4.5% in the 2050s, respectively, compared with 2015 (5293 Tg C). For the 2090s, the SOC stocks under the SSP245 scenario increased by 1.9%, and those under the SSP585 scenario decreased by 0.4%. The SOC stocks in both future periods are mainly stored in farmlands and forests, accounting for 90% and 92% of the total SOC stocks, respectively. Our high-resolution estimated SOC maps provide a scientific basis for optimizing ecological management in Northeast China.

High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra

Soil organic carbon (SOC) in Arctic coastal polygonal tundra is vulnerable to climate change, especially in soils with occurrence of large amounts of ground ice. Pan-arctic studies of mapping SOC exist, yet they fail to describe the high spatial variability of SOC storage in permafrost landscapes. An important factor is the landscape history which determines landform development and consequently the spatial variability of SOC. Our aim was to map SOC stocks, and which environmental variables that determine SOC, in two adjacent coastal areas along Canadian Beaufort Sea coast with different glacial history. We used the machine learning technique random forest and environmental variables to map the spatial distribution of SOC stocks down to 1 m depth at a spatial resolution of 2 m for depth increments of 0–5, 5–15, 15–30, 30–60 and 60–100 cm.The results show that the two study areas had large differences in SOC stocks in the depth 60–100 cm due to high amounts of ground ice in one of the study areas. There are also differences in variable importance of the explanatory variables between the two areas. The area low in ground ice content had with 66.6 kg C/m−2 more stored SOC than the area rich in ground ice content with 40.0 kg C/m−2. However, this SOC stock could be potentially more vulnerable to climate change if ground ice melts and the ground subsides. The average N stock of the area low in ground ice is 3.77 kg m−2 and of the area rich in ground ice is 3.83 kg m−2.These findings support that there is a strong correlation between ground ice and SOC, with less SOC in ice-rich layers on a small scale. In addition to small scale studies of SOC mapping, detailed maps of ground ice content and distribution are needed for a validation of large-scale quantifications of SOC stocks and transferability of models.

Soil Organic Carbon Stock Prediction: Fate under 2050 Climate Scenarios, the Case of Eastern Ethiopia

Soil Organic carbon (SOC) is vital to the soil’s ecosystem functioning as well as improving soil fertility. Slight variation in C in the soil has significant potential to be either a source of CO2 in the atmosphere or a sink to be stored in the form of soil organic matter. However, modeling SOC spatiotemporal changes was challenging due to lack of data to represent the high spatial heterogeneity in soil properties. Less expensive techniques, digital soil mapping (DSM) combined with space-for-time substitution (SFTS), were applied to predict the present and projected SOC stock for temperature and rainfall projections under different climate scenarios represented by the four Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5, RCP6, and RCP8.5). The relationship between environmental covariates (n = 16) and measured SOC stock (148 samples) was developed using a random forest model. Then, the temporal changes in SOC stock over the baseline were developed for the top 30 cm soil depth of the selected districts (Chiro Zuria, Kuni, Gemechis and Mieso) of West Hararghe Zone at 30 m resolution. The model validation using the random sample of 20% of the data showed that the model explained 44% of the variance (R2) with a root mean square error (RMSE) of 8.96, a mean error (ME) of 0.16, and a Lin’s concordance correlation coefficient (CCC) of 0.88. Temperature was the most important predictor factor influencing the spatial distribution of SOC stock. An overall net gain of SOC stock over the present C stock was expected in the study area by 2050. The gain in areas with the lower baseline SOC stock counterbalanced the loss in areas with the higher baseline stock. The changes in the SOC stock depended on land use land cover (LULC), soil type, and agro-ecological zones. By 2050, cropland is supposed to lose its SOC stock under all RCPs; therefore, appropriate decisions are crucial to compensate for the loss of C.

Using environmental covariates to predict soil organic carbon stocks in Vertisols of Sudan

Soil organic carbon (SOC) stocks account for nearly 75% of the active carbon pool in terrestrial ecosystems. SOC is also one of the most important soil quality components and has a vital role in agricultural productivity. The main objective of this study was to test the ability of regression kriging (RK) to predict the spatial distribution of SOC stocks in the Vertisols of Gezira State, Sudan using environmental covariates (ECOVs) and legacy soil data. The variables involved 2098 legacy soil datasets (LSDs). Seven ECOVs were used: precipitation (PR), temperature (T), normalized difference vegetation index (NDVI), land use/cover (LULC), digital elevation model (DEM), terrain slope (TS), and valley depth (VD), all downloaded from the repository of the United States Geology Survey (USGS) except PR and T. The RK model was validated using root mean squared error (RMSE), normalized root mean squared error (nRMSE), mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2). The NDVI and topography were the major ECOVs affecting spatial SOC distribution and its stocks in the area. The results showed that low elevation areas had higher SOC content compared to higher regions and the SOC stocks were 4.14 Kg C ha−1, 0.86 Kg C ha−1, 0.11 Kg C ha−1, 0.02 Kg C ha−1, and 0.004 Kg C ha−1 at gradient levels of <5°, 5–10°, 10–15°, 15–20°, and < 20°, respectively. Additionally, the spatial prediction revealed that agricultural land use (cropland) had the greatest SOC stocks (4.49 Kg C ha−1), whereas grasslands and forest lands were the lowest (0.01 Kg C ha−1). The RK model performed fairly well (RMSE = 1.83 g kg−1; nRMSE = 36%; MAE = 1.7%; MSE = 3.63%; R2 = 0.98) and gave satisfactory results to predict SOC stocks in the top 30 cm of the Vertisols. The findings of this study highlight that Vertisols could play a vital role in climate change mitigation via high SOC stocks and ability for C sequestration. It was recommended that the RK model should be extended to include much denser LSDs and test it with additional ECOVs combined with most specific soil properties that would have significant influence on SOC stocks in Vertisols, particularly soil pH, clay content, and clay mineralogy to achieve higher accuracy.

Storage, patterns, and environmental controls of soil organic carbon stocks in the permafrost regions of the Northern Hemisphere

Large stocks of soil organic carbon (SOC) accumulated in the Northern Hemisphere permafrost regions may be vulnerable to climatic warming, but global estimates of SOC distribution and magnitude in permafrost regions still have large uncertainties. Based on multiple high-resolution environmental variables and a compiled soil sample dataset (>3000 soil profiles), we used machine-learning methods to estimate the size and spatial distribution of SOC for the top 3 m soils in the Northern Hemisphere permafrost regions. We also identified key environmental predictors of SOC. The results showed that the SOC storage for the top 3 m soil was 1079 ± 174 Pg C across the Northern Hemisphere permafrost regions (20.8 × 106 km2), including 1057 ± 167 Pg C in the northern permafrost regions and 22 ± 7 Pg C in the Third Pole permafrost regions. The mean annual air temperature and NDVI are the main controlling factors for the spatial distribution of SOC stocks in the northern and the Third Pole permafrost regions. Our estimations were more accurate than the existing global SOC stock maps. The results improve our understanding of the regional and global permafrost carbon cycle and their feedback to the climate system.

Selecting environmental factors to predict spatial distribution of soil organic carbon stocks, northwestern Iran.

Knowledge of environmental factors controlling soil organic carbon (SOC) stocks can help predict spatial distribution SOC stocks. So, this study was carried out to select the best environmental factors to model and estimate the spatial distribution of SOC stocks in northwestern Iran. Soil sampling was performed at 210 points by multiple conditioned Latin Hypercube method (cLHm) and SOC stocks were measured. Also, environmental factors, including terrain attributes, moisture index, and normalized difference vegetation index (NDVI), were calculated. SOC stocks were modeled using random forest (RF) and partial least squares regression (PLSR) models. Modeling SOC stocks by RF model showed that the efficient factors for estimating the SOC stocks were slope height (slph), terrain surface texture (texture), standardized height (standh), elevation, relative slope position (rsp), and normalized height (normalh). Also, the PLSR model selected standardized height (standh), relative slope position (rsp), slope, and channel network base level (chnl base) to model SOC stocks. In both RF and PLSR methods, the standh and rsp factors were suitable parameters for estimating the SOC stocks. Predicting the spatial distribution of SOC stocks using environmental factors showed that the R2 values for RF and PLSR models were 0.81 and 0.40, respectively. The result of this study showed that in areas with complex land features, terrain attributes can be good predictors for estimating SOC stocks. These predictors allow more accurate estimates of SOC stocks and contribute considerably to the effective application of land management strategies in arid and semiarid area.

Investigating the spatio-temporal variability of soil organic carbon stocks in different ecosystems of China

Soil organic carbon (SOC) significantly influences soil fertility, soil water holding capacity, and plant productivity. In this study, we applied two boosted regression tree (BRT) models to map SOC stocks across China in the 1980s and the 2010s. The models incorporated nine environmental variables (climate, topography, and biology) and 8897 (in the 1980s) and 4534 (in the 2010s) topsoil (0–20 cm) samples. During the two study periods, 20% of the soil samples were randomly selected for model testing, and the remaining samples were used as a training set to construct the models. The verification results showed that incorporating climate environment variables significantly improved the model prediction in both study periods. Mean annual temperature, mean annual precipitation, elevation, and the normalized difference vegetation index were the dominant environmental factors affecting the spatial distribution of SOC stocks. The full-variable model predicted similar spatial distributions of SOC stocks for the 1980s and the 2010s. SOC stocks in China showed an increasing trend over the past 30 years, from 3.9 kg m−2 in the 1980s to 4.6 kg m−2 in the 2010s. In both periods, topsoil SOC stocks were mainly stored in agroecosystems, forests, and grasslands in the 1980s, with values of 9.5, 12.0, and 11.4 Pg C, respectively. Our study provides reliable information on Chain's carbon distribution, which can be used by land managers and the national government to formulate relevant land use and carbon sequestration policies.

Soil organic carbon stocks in an investigated watershed transect linked to ecological restoration practices on the Loess Plateau

AbstractLager scale ecological restoration practices are increasing soil organic carbon (SOC) stocks. Meta‐analysis is by far the most widely applied technique for describing SOC at large spatial scales, but there is a lack of field‐based investigations on a series of individual watersheds. In this study, a watershed transect of six watersheds distributed in a hilly and gully area of the Loess Plateau was investigated in order to achieve sustainable practices. The results indicate that ecological restoration practices significantly increased SOC stocks to varying degrees (p &lt; 0.05). Grasslands were associated with the largest SOC stocks (39.42 ± 22.21 t hm−2), which were larger than the SOC stocks of artificial immature forest (&lt;20 years) (26.58 ± 14.32 t hm−2) in the 50 cm soil profile. However, in the long term, afforestation seems to increase SOC stocks to the level of secondary forest, 81.15 ± 4.69 t hm−2, in a suitable environment. Appropriate land use management is also an effective approach to improving soil C sequestration capacity. SOC stocks were measured as 34.11 ± 7.66 t hm−2 in abandoned terraced farmland, which was similar to the grassland SOC stocks. Soil and plant factors contributed approximately 60% to the variation in the spatial distribution of SOC stocks at 0–50 cm; their contributions increased and diminished with increasing soil depth, respectively. This study indicates naturally restored grassland and engineering practices are suitable on the Loess Plateau by investigating a watershed transect and enhances our understanding of the comprehensive contributions of environmental factors to the spatial distribution of SOC stocks on a large spatial scale.

Impacts of urbanization on soil organic carbon stocks in the northeast coastal agricultural areas of China

Dynamic changes in soil organic carbon pools have significant impacts on regional and global carbon balance. Due to rapid development in urbanized areas, the land use changes dramatically, impacting soil organic carbon (SOC) stocks in topsoil. This study aimed to document the impacts of urbanization on SOC stocks in a rapidly urbanized area from northeastern China. A total of 12 auxiliary variables were as SOC predictors including elevation, slope aspect, slope gradient, topographic wetness index, Landsat TM band3, Landsat TM band4, Landsat TM5, and normalized difference vegetation index. Urban-specific variables including population (POP), gross domestic product (GDP), distance to the socio-economic center, and distance to the roads are also considered. A set of 523 (in 1990) and 847 (in 2015) top soil samples with SOC measurement were collected. Two random forest (RF) models, one with all auxiliary variables except urban-specific variable (MA) and the other with all auxiliary variables (MB) were used to map the spatial distribution of SOC stocks in the two periods. Ten-fold cross-validation was conducted to evaluate the performance of RF models. We find that the full auxiliary variables model had a better performance for the both periods. POP and GDP were key auxiliary variables affecting spatial variability of SOC stocks in 2015. Over a 25-year period, SOC stocks decreased from 2.77 ± 1.09 kg m−2 to 2.16 ± 0.93 kg m−2, resulting in 3.78 Tg SOC loss in this region. Rapid urbanization led to drastic land- use change, which was the main reason for the decrease of SOC stocks. Additionally, urban-specific variables should be used as the main auxiliary variables when predicting SOC stocks in the areas that experience rapid urbanization. We believe that accurate prediction and mapping of SOC stocks will help manage land use and facilitate soil quality assessment so as to increase soil carbon sequestration in the region.

Spatial prediction of soil organic carbon stocks in Ghana using legacy data

Soil organic carbon (SOC) is a major driver of the multiple functions of soils in the delivery of ecosystem services. While the spatial prediction and mapping of SOC stocks are of key importance for land managers to understand the synergies between SOC management and soil health, a spatially-explicit map of the distribution of SOC stocks in Ghana is non-existent. Therefore, we quantified the spatial distribution of SOC stocks and associated uncertainties to a target depth of 0–30 cm based on regression-kriging modelling to fill this knowledge gap. The mean error (ME) of the predictions is negligible. The mean absolute error (MAE) shows that the model has prediction errors of about 0.48%. The coefficient of determination (R2) shows that the model explains 34% of the variation in model predictions of SOC stocks. The RMSE is 0.63% of the prediction errors. The predicted SOC stocks show significant variation in their spatial distribution throughout the country. Generally, a trend of decreasing SOC stocks from the southwest to the northeast is clearly recognized. SOC stocks are highest in the Semi-Deciduous agro-ecological zone (43.5 Mg C ha−1) and lowest in the Guinea Savannah agro-ecological zone (0.05 Mg C ha−1). About 5.4 Tg of SOC stocks is stored in the top 0–30 cm of the soils in Ghana. This preliminary work at a spatial resolution of 30 arc-seconds (~1 km) has been accomplished within the framework and guidelines of the Global Soil Partnership (GSP). To our knowledge, this is the first time ever in Ghana that a soil property map has been produced along with its uncertainties. Thus, this study represents a significant first step towards revolutionizing future soil property mapping in Ghana. Even though there remains the need to improve on the quality and spatial resolution, the SOC stocks map presented herein is a satisfactory first step to guide future research on soil organic carbon management at both national and global scales.

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Forest soil organic carbon (SOC) accounts for a large portion of global soil carbon stocks. Accurately mapping forest SOC stocks is a necessity for quantifying forest carbon cycling and forest soil sustainable management. In this study, we used a boosted regression trees (BRT) model to predict the spatial distribution of SOC stocks during two time periods (1990 and 2015) and calculated their spatiotemporal changes during 25 years in Liaoning Province, China. A total of 367 (1990) and 539 (2015) sampling sites and 9 environmental variables (climate, topography, remote sensing) were used in the BRT model. The ten-fold cross-validation technique was used to evaluate the prediction performance and uncertainty of the BRT model in two periods. It was found that the BRT model could account for 65% and 59% of SOC stocks, respectively for the two periods. MAP and NDVI were the main environmental variables controlling the spatial variability of SOC stocks. Over the 25-year period, the average SOC stocks increased from 5.66 to 6.61 kg m−2. In the whole study area, the SOC stocks were the highest in the northeast, followed by the southwest, and the lowest in the middle of the spatial distribution pattern in the two periods. Our accurate mapping of SOC stocks, their spatial distribution characteristics, influencing factors, and main controlling factors in forest areas will assist soil management and help assess environmental changes in the region.

Spatial distribution of soil organic carbon stocks in Masson pine (Pinus massoniana) forests in subtropical China

Masson pine (Pinus massoniana) is a typical reforestation species in subtropical China, which plays a key role in soil and water conservation. Site-specific forest management requires an accurate estimation of the soil organic carbon (SOC) stocks, and information about the spatial distribution of SOC stocks is essential for improving the soil quality and ecosystem productivity. We examined the spatial distribution of SOC stocks using 91 soil samples from Masson pine forests in subtropical China. Ordinary kriging (OK) and inverse distance weighting (IDW) methods were used to compare the spatial patterns of the SOC stocks. A moderate spatial dependence of the SOC stocks suggested that extrinsic and intrinsic factors affected the SOC stocks. Similar spatial distributions but different cross-validation accuracies indicated that OK outperformed IDW. The soil pools at a depth of 0–60 cm were 774.06 Gg and 761.61 Gg as determined by the OK and IDW methods, respectively, which were higher than that measured by the conventional method (CM, 734.22 Gg). This highlights the need to apply different methods when studying the regional SOC pools. On the basis of the comparison of OK, IDW, and CM methods, OK is recommended for determining nonhomogeneous sampling point distributions. The present results enhance our understanding of method selection when studying the spatial distribution of SOC stocks.

Agricultural soil organic carbon stocks in the north-eastern Iberian Peninsula: Drivers and spatial variability

Estimating soil organic carbon (SOC) stocks under agriculture, assessing the importance of their drivers and understanding the spatial distribution of SOC stocks are crucial to predicting possible future SOC stocks scenarios under climate change conditions and to designing appropriate mitigation and adaptation strategies. This study characterized and modelled SOC stocks at two soil depth intervals, topsoil (0–30 cm) and subsoil (30–100 cm), based on both legacy and recent data from 7245 agricultural soil profiles and using environmental drivers (climate, agricultural practices and soil properties) for agricultural soils in Catalonia (NE Spain). Generalized Least Square (GLS) and Geographical Weighted Regression (GWR) were used as modelling approaches to: (i) assess the main SOC stock drivers and their effects on SOC stocks; (ii) analyse spatial variability of SOC stocks and their relationships with the main drivers; and (iii) predict and map SOC stocks at the regional scale. While topsoil variation of SOC stocks depended mainly on climate, soil texture and agricultural variables, subsoil SOC stocks changes depended mainly on soil attributes such us soil texture, clay content, soil type or depth to bedrock. The GWR model revealed that the relationship between SOC stocks and drivers varied spatially. Finally, the study was only able to predict and map topsoil SOC stocks at the regional scale, because controlling factors of SOC stocks at the subsoil level were largely unavailable for digital mapping. According to the resulting map, the mean SOC stock value for Catalan agriculture at the topsoil level was 4.88 ± 0.89 kg/m2 and the total magnitude of the carbon pool in agricultural soils of Catalonia up to 30 cm reached 47.9 Tg. The present study findings are useful for defining carbon sequestration strategies at the regional scale related with agricultural land use changes and agricultural management practices in a context of climate change.

National soil organic carbon estimates can improve global estimates

Global inventories of spatial and vertical distribution of soil organic carbon (SOC) stocks are being used in national and global initiatives targeted to mitigate climate change and land degradation impacts. Yet, national level high-resolution estimates of SOC stocks can be useful for improving the accuracy of global SOC inventories. We estimated spatially resolved SOC stocks of surface 0–30 cm and subsurface 30–100 cm layers at a spatial resolution of 30 m in tropical Island, Sri Lanka using a legacy harmonized soil database of 122 soil profiles. The national estimates were compared with two global estimates derived from WISE30sec and SoilGrids250m. The tropical Island (land area = 64,610 km2) occupying 0.03% of global land area showed a considerable heterogeneity in SOC stocks ranging from 2.0–342.5 Mg ha−1 and 2.7–391.7 Mg ha−1 in the surface and subsurface soil layers, respectively. We found, elevation, precipitation and slope angle as main environmental controllers of the spatial distribution of SOC stocks under tropical climate. Incorporating the pedogenic information (derived from soil series level legacy map, soil orders and suborders) with environmental controllers resulted in better regression models of predicting surface (R2 = 0.61) and subsurface (R2 = 0.81) SOC stocks. Geographically weighted regression kriging derived maps of SOC stocks revealed that 0–100 cm soil layer of the tropical Island stored 500 Tg C contributing for 0.04% of the global SOC stocks. The validation results of our estimates showed low Mean Estimation Error (MEE: surface −1.6 and subsurface −1.6 Mg ha−1) and Root Mean Square Error (RMSE: surface 29.5 and subsurface 24.9 Mg ha−1) indicating a low bias and satisfactory predictions. The relative improvement of the prediction accuracy of the SOC stocks of our geospatial estimates in the 0–30 cm layer in comparison to SoilGrids250m and WISE30sec data derived SOC stocks were 51.7% and 35.2%, respectively. The SOC stocks predictions of the 30–100 cm soil layer showed even better relative improvement compared to SoilGrids250m (78.4%) and WISE30sec (57.4%) SOC estimates. Compared to estimates of total SOC stocks resulted in this study, WISE30sec data derived SOC stock maps showed 30% over estimation of the C stock in surface 0–30 cm (332 Tg C) and 41% overestimation in 30–100 cm layer (343 Tg C). The over estimation of total SOC stocks by the SoilGrids250 SOC stocks map for the surface 0–30 cm layer was 122% (567 Tg C) and for the 30–100 cm layer it was 209% (750 Tg C). We conclude that the fusion of legacy soil information of SOC stocks with appropriate environmental covariates and pedogenic information derived from legacy area-class soil maps at national level can produce more accurate inventories of spatial and vertical distribution of SOC stocks. These national inventories have a great potential of upgrading global inventories of SOC stocks.

Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R 2) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.

Spatial distribution of soil organic carbon stock in Moso bamboo forests in subtropical China

Moso bamboo (Phyllostachys heterocycla (Carr.) Mitford cv. Pubescens) is an important timber substitute in China. Site specific stand management requires an accurate estimate of soil organic carbon (SOC) stock for maintaining stand productivity and understanding global carbon cycling. This study compared ordinary kriging (OK) and inverse distance weighting (IDW) approaches to study the spatial distribution of SOC stock within 0–60 cm using 111 soil samples in Moso bamboo forests in subtropical China. Similar spatial patterns but different spatial distribution ranges of SOC stock from OK and IDW highlighted the necessity to apply different approaches to obtain accurate and consistent results of SOC stock distribution. Different spatial patterns of SOC stock suggested the use of different fertilization treatments in Moso bamboo forests across the study area. SOC pool within 0–60 cm was 6.46 and 6.22 Tg for OK and IDW; results which were lower than that of conventional approach (CA, 7.41 Tg). CA is not recommended unless coordinates of the sampling locations are missing and the spatial patterns of SOC stock are not required. OK is recommended for the uneven distribution of sampling locations. Our results can improve methodology selection for investigating spatial distribution of SOC stock in Moso bamboo forests.

Modelling the topsoil carbon stock of agricultural lands with the Stochastic Gradient Treeboost in a semi-arid Mediterranean region

Efficient modelling methods to assess soil organic carbon (SOC) stocks have a pivotal importance as inputs for global carbon cycle studies and decision-making processes. However, laboratory analyses of SOC field samples are costly and time consuming. Global-scale estimates of SOC were recently made according to categorical variables, including land use and soil texture. Remote sensing (RS) data can contribute to the better modelling of the spatial distribution of SOC stock at a regional scale. In the present study, we used Stochastic Gradient Treeboost (SGT) to estimate the topsoil (0–30cm) SOC stock of a Mediterranean semiarid area (Sicily, Italy, 25,286km2). In particular, our study examined agricultural lands, which represent approximately 64% of the entire region. An extensive soil dataset (2202 samples, 1 profile/7.31km2 on average) was acquired from the soil database of Sicily. The georeferenced field observations were intersected with remotely sensed environmental data and other spatial data, including climatic data from WORLDCLIM, land cover from CORINE, soil texture, topography and derived indices. Finally, the SGT was compared to published global estimates (GSOC) and data from the International Soil Reference and Information Centre (ISRIC) Soil Grids by comparing the pseudo-regressions of the SGT, GSOC and ISRIC with soil observations. The mean SOC stock across the entire region that was estimated by GSOC and ISRIC was 3.9% lower and 46.2% higher compared to the SGT. The SGT efficiently predicted SOC stocks that were <70tha−1 (corresponding to the 90th percentile of the observed values). On average, the coefficient of variation of the SGT model was 3.6% when computed on the whole dataset and remained lower than 23% when computed on a distribution basis. The SGT mean absolute error was 14.84tha−1, 18.4% and 36.3% lower than GSOC and ISRIC, respectively. The mean annual rainfall, soil texture, land use, mean annual temperature and Landsat 7 ETM+ panchromatic Band 8 were the most important predictors of SOC stock. Finally, SOC stocks were estimated for each land cover class. SGT predicted SOC stock better than GSOC and ISRIC for most data. This resulted in a percentage of data in the prediction confidence interval±50% compared to the observed values of 71.4%, 65.8%, and 50.7% for SGT, GSOC, and SGT, respectively. This consisted of a higher R2 and a slope (β) that was closer to 1 for the pseudo-regression constructed with SGT compared to GSOC and ISRIC. In conclusion, the results of the present study showed that the integration of RS with climatic and soil texture spatial data could strongly improve SOC prediction in a semi-arid Mediterranean region. In addition, the panchromatic band of Landsat 7 ETM+ was more predictive compared to the conventionally used NDVI. This information is crucial to guiding decision-making processes, especially at a regional scale and/or in semi-arid Mediterranean areas. The model performance of the SGT could be further improved by adopting predictors with greater spatial resolutions. The results of the present experiment yield valuable information, especially for assessing climate change or land use change scenarios for SOC stocks and their spatial distribution.

Tree species identity mediates mechanisms of top soil carbon sequestration in a Norway spruce and European beech mixed forest

Combined effects of litterfall and root turnover significantly increase topsoil carbon stocks in Norway spruce and European beech mixed forests, indicating local complementarity effects mediated by tree species mixtures. The establishment of mixed stands by intermingling individuals of European beech and Norway spruce is an ongoing trend in adaptive forest management strategies. However, our understanding of the potential of these strategies to promote C sequestration remains limited. This study aims to assess the effect of species composition on SOC stock in a mixed forest of Norway spruce and European beech. We studied C stocks in the uppermost soil layers in two stands dominated either by Norway spruce or European beech and in a mixture of both species. We evaluated the effect of litterfall and root turnover on soil organic carbon (SOC) stocks and its spatial distribution by combining structural equation models and geostatistical techniques. C stocks in the forest floor were highest in Norway spruce, whereas in the mineral soil, the highest values were in the mixed stand. The proportion of Norway spruce litterfall was positively related to C stock in the forest floor across stands. Root turnover was positively related to C stock in the mineral soil of the mixed stand. Our results confirm a contrasting role of root turnover and litterfall between soil layers in the studied stands, suggesting that tree species composition can mediate the spatial distribution of SOC stocks in mixed forests.

Global distribution of soil organic carbon – Part 1: Masses and frequency distributions of SOC stocks for the tropics, permafrost regions, wetlands, and the world

Abstract. The global soil organic carbon (SOC) mass is relevant for the carbon cycle budget and thus atmospheric carbon concentrations. We review current estimates of SOC stocks and mass (stock × area) in wetlands, permafrost and tropical regions and the world in the upper 1 m of soil. The Harmonized World Soil Database (HWSD) v.1.2 provides one of the most recent and coherent global data sets of SOC, giving a total mass of 2476 Pg when using the original values for bulk density. Adjusting the HWSD's bulk density (BD) of soil high in organic carbon results in a mass of 1230 Pg, and additionally setting the BD of Histosols to 0.1 g cm−3 (typical of peat soils), results in a mass of 1062 Pg. The uncertainty in BD of Histosols alone introduces a range of −56 to +180 Pg C into the estimate of global SOC mass in the top 1 m, larger than estimates of global soil respiration. We report the spatial distribution of SOC stocks per 0.5 arcminutes; the areal masses of SOC; and the quantiles of SOC stocks by continents, wetland types, and permafrost types. Depending on the definition of "wetland", wetland soils contain between 82 and 158 Pg SOC. With more detailed estimates for permafrost from the Northern Circumpolar Soil Carbon Database (496 Pg SOC) and tropical peatland carbon incorporated, global soils contain 1325 Pg SOC in the upper 1 m, including 421 Pg in tropical soils, whereof 40 Pg occurs in tropical wetlands. Global SOC amounts to just under 3000 Pg when estimates for deeper soil layers are included. Variability in estimates is due to variation in definitions of soil units, differences in soil property databases, scarcity of information about soil carbon at depths &gt; 1 m in peatlands, and variation in definitions of "peatland".