Predicted Soil Organic Carbon Stocks Research Articles

Remote sensing of depth-induced variations in soil organic carbon stocks distribution within different vegetated landscapes

The preservation and augmentation of soil organic carbon (SOC) stocks is critical to designing climate change mitigation strategies and alleviating global warming. However, due to the susceptibility of SOC stocks to environmental and topo-climatic variability and changes, it is essential to obtain a comprehensive understanding of the state of current SOC stocks both spatially and vertically. Consequently, to effectively assess SOC storage and sequestration capacity, precise evaluations at multiple soil depths are required. Hence, this study implemented an advanced Deep Neural Network (DNN) model incorporating Sentinel-1 Synthetic Aperture Radar (SAR) data, topo-climatic features, and soil physical properties to predict SOC stocks at multiple depths (0–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm) across diverse land-use categories in the KwaZulu-Natal province, South Africa. There was a general decline in the accuracy of the DNN model’s prediction with increasing soil depth, with the root mean square error (RMSE) ranging from 8.34 t/h to 11.97 t/h for the four depths. These findings imply that the link between environmental covariates and SOC stocks weakens with soil depth. Additionally, distinct factors driving SOC stocks were discovered in both topsoil and deep-soil, with vegetation having the strongest effect in topsoil, and topo-climate factors and soil physical properties becoming more important as depth increases. This underscores the importance of incorporating depth-related soil properties in SOC modelling. Grasslands had the largest SOC stocks, while commercial forests have the highest SOC sequestration rates per unit area. This study offers valuable insights to policymakers and provides a basis for devising regional management strategies that can be used to effectively mitigate climate change.

Investigating the effects of climate change and anthropogenic activities on SOC storage and cumulative CO2 emissions in forest soils across altitudinal gradients using the century model

This study investigated the impact of climate change, grazing, manure application, and liming on soil organic carbon (SOC) stock and cumulative carbon dioxide (CO2) emissions in forest soils across different altitudes. Despite similar soil texture, acidity, and salinity across elevations, SOC stock significantly increased with altitude due to cooler temperatures and higher precipitation. The highest SOC stock (97.46 t ha−1) was observed at 2000–2500 m, compared to the lowest (44.23 t ha−1) at 500–1000 m. The Century C Model accurately predicted SOC stock, with correlation and determination coefficients exceeding 0.98. A climate change scenario projecting decreased precipitation (2.15 mm per decade) and increased temperature (0.4 °C) revealed potential SOC stock losses ranging from 28.36 to 36.35 %, particularly at higher altitudes. Grazing further decreased SOC stock, with a more pronounced effect at higher elevations. However, manure application (40 t ha−1 every four years) and liming (7–10 t ha−1 every three years) had positive effects on SOC stock, again amplified at higher altitudes and with an increase in lime application rate. In scenarios combining climate change with manure application and climate change with liming, manure application and liming mitigated some negative impacts of climate change, but could not fully offset them, resulting in 1.49–5.42 % and 0.39–4.07 % decreases respectively. Simulations of cumulative CO2 emissions mirrored the distribution of SOC stock, with higher emissions observed at higher altitudes and with management practices that increased SOC stock. This study emphasizes the critical role of conserving high-altitude forest soils and implementing optimal forest management strategies to combat climate change by minimizing SOC losses.

Monitoring soil organic carbon under coastal restoration using time series Sentinel‐1

AbstractSalt marsh soils are key reservoirs of organic carbon. Although the potential for using Sentinel‐1 data to estimate organic carbon in marsh soils has been recognized, there is still a need to find suitable proxies and effective algorithms that enhance predictions. In this study, we assessed the effectiveness of Sentinel‐1 data for predicting soil organic carbon (SOC) stocks in salt marshes located on the coast of eastern‐central China. We defined the relations between SOC stocks and remotely sensed data using a knowledge‐based approach (i.e., structural equation modeling (SEM)), and the results were compared with those obtained using a common data‐driven approach (i.e., random forest (RF)). Predictive models were developed using (1) refined images associated with phenological stages (phenological variables), (2) refined images that were classified as statistically important using Monte Carlo feature selection (MCFS‐based variables), and (3) images from the combination of these refined datasets. The predictions were validated using a leave‐one‐out cross‐validation approach. The results showed that SEM was more accurate than RF when predicting SOC stocks using the same type of predictor variables. Models using phenological variables alone yielded the least accurate predictions. Adding phenological variables to the structural equation model with MCFS‐based variables increased the prediction accuracy by 36% in terms of the R2. The results of the case study suggest that images related to phenological stages are good predictors for mapping SOC stocks in marshes. This study highlights the superiority of SEM over RF for developing effective remote sensing‐based models to quantify and map SOC stocks in salt marshes.

Delineation of high‐resolution soil carbon management zones using digital soil mapping: A step towards mitigating climate change in the Northeastern Himalayas, India

AbstractDelineation of carbon management zones (CMZs) by capturing geospatial distribution of soil organic carbon (SOC) stock down the profile is an effective strategy for precision agriculture and climate change mitigation. Satellite (Landsat OLI 8), terrain (SRTM 30 m DEM) and bioclimatic (WorldClim dataset) factors were used as covariables in this digital soil mapping approach. Depth harmonization using the quadratic spline method (equal‐area) was carried out prior to quantile regression forest (QRF) algorithm‐based modelling to estimate SOC stock at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm). Soil depth and SOC stock for the whole soil profile were used for the delineation of CMZs using fuzzy k‐means clustering. The predicted SOC stock, varied from 14.68 to 42.35 Mg ha−1 in the top layer (0–5 cm depth), while 17.91 to 36.88, 14.15 to 34.70, 12.55 to 35.59, 10.30 to 28.52 and 7.26 to 20.16 Mg ha−1 in the depths of 5–15, 15–30, 30–60, 60–100 and 100–200 cm, respectively. The QRF algorithm performed well in predicting SOC stock with high R2, which ranged from .67 to .83 for all the soil depths. To delineate three CMZs, modified partitioning entropy and the fuzzy performance index were used. In CMZ2, there was a significant increase in SOC stock, followed by CMZ1 and CMZ3. This zone (CMZ2) was located in the central region of the study area and was mostly covered by dense forest and perennial plantations (rubber). The CMZs provided the necessary foundation for the development of site‐specific carbon management techniques that can enhance ecosystem service and meet climate change mitigation goals.

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.

Predicting changes in soil organic carbon after a low dosage and one-time addition of biochar blended with manure and nitrogen fertilizer

Modeling plays an important role in predicting the long-term effects of biochar on soil organic carbon dynamics. The objective of our study was to apply the Century model to assess changes in temporal soil organic carbon in soil amended with manure and nitrogen fertilizer (MN), with manure and biochar (MB) or with manure, nitrogen fertilizer and biochar (MNB). We determined that, after 115 years, soil organic carbon stocks could not reach a steady state (equilibrium) or pre-cultivation levels, regardless of amendment type. Our results showed that a biennial input of manure and nitrogen fertilizer (MN) led to a 84% increase in soil organic carbon compared to a 79% (MNB) and 70% (MB) increase when amendments contained biochar. However, the quantity of organic matter input from crop residues and amendments was sufficient to increase the active fraction, with a turnover time of months to years, by 86%. In fact, carbon associated with the slow fraction, with a turnover time of 20 to 50 years, was the key driver for soil organic carbon accumulation in all amendment types. Although the passive fraction is the most stable form of carbon in the soil, with a turnover time of 400 to 100 years, once manure and biochar were added to the soil, this fraction increased up to 32%. Our results provided further insight into the ability of Century to accurately predict changes in soil organic carbon stocks when a combination of manure, nitrogen fertilizer or biochar were added to soil. Century predicted soil organic carbon stocks within -1% to +9% of measured values. However, further fine-tuning of the model is required since biochar undergoes chemical transformations (e.g., ageing) and changes soil physical parameters (e.g., bulk density) that can not be currently accounted for in the Century model. Addressing these limitations of Century will also help to increase the relationship between measured and predicted values.

Predicting soil organic carbon stocks in different layers of forest soils in the Czech Republic

Carbon dioxide, the most produced anthropogenic greenhouse gas, could be moderated by sequestering carbon in forest soils. Forest soils store more carbon than there is in the atmosphere. Thus, the smallest variation in soil carbon levels could trigger a significant change in atmospheric carbon. This study focused on predicting the spatial distribution of carbon stocks within surface organic and mineral topsoil and subsoil layers of the forty-one natural forest areas of the Czech Republic. Cubist and Random Forests machine learning algorithms were employed with a grid search hyper tuning to improve prediction accuracy. We used the five-fold cross-validation to verify the model accuracy using Root Mean Square Error (RMSE), coefficient of determination (R2), and Mean Absolute Error (MAE). Random Forests yielded lower RMSE of 1.10 kg/m2, 3.85 kg/m2, and 4.77 kg/m2 in the surface organic horizon (F + H layer), mineral topsoil (0–30 cm layers) and subsoil horizons (30–80 cm layers), respectively, compared to the RMSE values of Cubist, which were 1.14 kg/m2, 3.90 kg/m2, and 4.91 kg/m2 in the surface organic, mineral topsoil and subsoil horizons, respectively. R2 values of both models were low for all three horizons considered. Random Forests were the preferred algorithm for SOC stock prediction in all layers of the forest soils. Cubist predicted the spatial distribution of SOC stocks with more covariates than Random Forests. Altitude was the most important covariate for the spatial distribution of SOC stocks for both Random Forests and Cubist in all soil horizons considered. High SOC stocks for all soil horizons are spatially concentrated in soil horizons along the country borders in the mountaineous natural forest areas.

Impacts of land use conversions on soil organic carbon in a warming-induced agricultural frontier in Northern Ontario, Canada under historical and future climate

Land conversion from forest to agriculture in higher latitude regions such as the Great Clay Belt of northern Ontario, Canada is expected to intensify due to warming. It is critical to minimize soil organic carbon (SOC) loss, but there is limited understanding of best land management practices for these Northern agricultural frontiers. We conducted a measurement campaign to assess changes in SOC after land conversion from native forest to either permanent pasture or to arable cropping by measuring SOC content (0–60 cm) at six farms with different land uses over the past 68 years (1950–2018). The Denitrification-Decomposition (DNDC) model was validated for historical SOC trends and then used to assess the impacts of future climate change on crop yields, SOC stocks, and SOC sequestration rates from 1954 to 2070 under three Shared Socio-economic Pathways (SSPs) scenarios from the Coupled Model Intercomparison Project Phase 6. Three land uses (1) continuous barley-oats (BO), (2) continuous Legume hay-BO, (3) continuous Pasture; and two land use conversions (4) pasture from 1954 to 2014 and then converted to BO from 2015 to 2070 (Pasture » BO) and (5) BO from 1954 to 2014 and then converted to pasture from 2015 to 2070 (BO » Pasture) were considered. Averaged across all six farms, the measured SOC stocks 68 years after conversion from forest were 22.7 and 25.3% higher under pasture than under forest and arable cropping, respectively. The DNDC demonstrated good performance in simulating yields of annual crops, legume hay and pasture as well as SOC stocks across all sites. Under future climate scenarios, higher SOC stocks were predicted for all land use conversions relative to the historical period, except for Pasture » BO. Under different land use, predicted SOC stocks were highest for continuous pasture, followed by BO » Pasture, Pasture » BO, Legume hay-BO and lowest for continuous BO. The DNDC predicted that the SOC sequestration rate would decrease for all land uses in the period 2015–2070 relative to 1954–2014, except for BO » Pasture. The predicted change declined from 0.35 to 0.22–0.28 Mg C ha−1 yr−1 for continuous pasture, but when change in management occurred from BO to pasture the rate of SOC gain increased. This underscores the potential role of pasture and crop diversification in avoiding SOC losses in agricultural frontiers induced by climate-warming.

Deep SOC stock dynamics under contrasting management systems: Is the EPIC model ready for carbon farming implementation?

Process-based models have been recognized as cost-effective tools to assess carbon farming mechanisms through quantifying the C fluxes in the agroecosystems. A result-based approach is suggested however the wide variability of agricultural environments makes further model implementation necessary to limit the uncertainty of the results, especially on deep soil organic carbon (SOC) stock estimation and stratification and in agroecosystems characterized by a shallow water table. In this study, a comprehensive soil and crop dataset collected over a seven-year period from different pedoclimatic conditions across the Veneto Region (NE Italy) was used for EPIC model calibration and validation of SOC stock dynamics. Experimental data included yields from several crops (corn, winter and spring wheat, rapeseed, and soybean), continuous monitoring of soil water content, and SOC stocks (1872 total samples within the 0–50 cm soil profile) under conventional, cover crop and conservation agriculture systems. Modelling was performed by testing two N-C sub-models (CENTURY and PHOENIX), which differentiated in terms of mineralization/immobilization rates.Results showed that the procedure was able to obtain parameters valuable for most of the management system and pedoclimatic condition, reproducing well the tested variables. The EPIC model acceptably captured soil water dynamics (Nash-Sutcliffe coefficient – NSE – was up to 0.26), especially in the topsoil. Furthermore, simulation of weed—crop competition in conservation agriculture strongly contributed to properly explain the variability in crop production among the contrasting agricultural systems (R2 ranged from 0.51 to 0.71). Likewise, EPIC skillfully simulated SOC stocks within the 0–50-cm profile regardless of the sub-model used (NSE was up to 0.64). Moreover, the model acceptably captured the profile SOC stratification among the different management practices. This study highlights the EPIC robustness for predicting SOC stocks and assessing with high accuracy carbon farming results.

The combined impacts of land use change and climate change on soil organic carbon stocks in the Ethiopian highlands

Land Use Change (LUC), especially deforestation in tropical regions, significantly contributes to global anthropogenic greenhouse gas (GHG) emissions. Here, we address potential combined impacts of LUC and Climate Change (CC) on Soil Organic Carbon (SOC) stocks in the Ethiopian highlands. The soil model Q was employed to predict SOC stocks for various combinations of LUC and CC scenarios until the year 2100. Four reference scenarios (cropland, bushland, natural forest, and Eucalyptus plantations under contemporary climatic conditions) were evaluated against reported measurements of SOC stocks. We studied impacts of six common LUC scenarios, including deforestation and planting Eucalyptus, on SOC stocks under contemporary and future climates. To assess the impact of CC, effects of elevated temperature (mean annual temperature + 2.6 °C) together with three litterfall scenarios (no change in litterfall, a 5% reduction and 22% increase, designated CC0, CCd, and CCi, respectively) were considered to test potential vegetation responses to increases in temperature and atmospheric CO2 concentrations. Most of the tested combinations of LUC and CC led to losses of SOC stocks. Losses were most severe, both relatively and absolutely, in the deforestation scenarios: up to 30% was lost if natural forest was converted to cropland and temperature increased (under the CC0 scenario). Gains in SOC stocks of 4–19% were modelled when sparse vegetation was converted to more dense vegetation like Eucalyptus plantation with substantially increased litterfall (the CCi scenario). Elevated temperature accelerated decomposition rates, leading to circa 8% losses of SOC stocks.We conclude that effects of LUC and CC on SOC stocks are additive and changes in litterfall caused by LUC determine which has the largest impact. Hence, deforestation is the biggest threat to SOC stocks in the Ethiopian highlands, and stocks in sparse vegetation systems like cropland and bushland are more sensitive to CC0 than LUC. We recommend conservation of natural forests and longer rotation periods for Eucalyptus plantations to preserve SOC stocks.Finally, we suggest that use of the Q model is a viable option for national reporting changes in SOC stocks at Tier 3 within the LULUCF sector to the United Nations Framework Convention on Climate Change (UNFCCC) as it is widely applicable and robust, although it only requires input data on a few generally available variables.

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.

Cropland carbon stocks driven by soil characteristics, rainfall and elevation

Soil organic carbon (SOC) can influence atmospheric CO2 concentration and then the extent to which the climate emergency is mitigated globally. It follows the elucidation of the driving factors of cropland SOC stocks, which is fundamental to reducing soil carbon loss and promoting soil carbon sequestration. Here, we examined the influence of 16 environmental variables on SOC stocks and sequestration based on three machine learning soil mapping methods, i.e. multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBOOST), with 2875 observed soil samples from cropland topsoil across Hunan Province, China in 2010. We employed a structural equation model (SEM) to extricate the driving mechanisms of environmental variables on SOC stocks at the regional scale. Our results show that XGBOOST had the most reliable performance in predicting SOC stocks, explaining 66 % of the total SOC stock variation. Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas. The partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing. In addition, the grid-based SEM results clearly presented the direct and indirect routes of environmental variables' impacts on cropland SOC stocks. Soil properties regulated by elevation, were the most influential natural factor on SOC stocks. Precipitation and elevation drove SOC stocks through direct and indirect effects respectively. Our SEM combined with machine learning approach can provide an effective explanation of the driving mechanism for SOC accumulation. We expect our proposed modelling approach can be applied to other regions and offer new insights, as a reference for mitigating cropland soil carbon loss under climate emergency conditions.

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.

Empirical relationships between environmental factors and soil organic carbon produce comparable prediction accuracy to machine learning

AbstractAccurate representation of environmental controllers of soil organic carbon (SOC) stocks in Earth System Model (ESM) land models could reduce uncertainties in future carbon–climate feedback projections. Using empirical relationships between environmental factors and SOC stocks to evaluate land models can help modelers understand prediction biases beyond what can be achieved with the observed SOC stocks alone. In this study, we used 31 observed environmental factors, field SOC observations (n = 6,213) from the continental United States, and two machine learning approaches (random forest [RF] and generalized additive modeling [GAM]) to (a) select important environmental predictors of SOC stocks, (b) derive empirical relationships between environmental factors and SOC stocks, and (c) use the derived relationships to predict SOC stocks and compare the prediction accuracy of simpler model developed with the machine learning predictions. Out of the 31 environmental factors we investigated, 12 were identified as important predictors of SOC stocks by the RF approach. In contrast, the GAM approach identified six (of those 12) environmental factors as important controllers of SOC stocks: potential evapotranspiration, normalized difference vegetation index, soil drainage condition, precipitation, elevation, and net primary productivity. The GAM approach showed minimal SOC predictive importance of the remaining six environmental factors identified by the RF approach. Our derived empirical relations produced comparable prediction accuracy to the GAM and RF approach using only a subset of environmental factors. The empirical relationships we derived using the GAM approach can serve as important benchmarks to evaluate environmental control representations of SOC stocks in ESMs, which could reduce uncertainty in predicting future carbon–climate feedbacks.

Soil organic carbon estimation along an altitudinal gradient of chir pine forests in the Garhwal Himalaya, India: A field inventory to remote sensing approach

AbstractChir pine (Pinus roxburghii, Sarg.) forests are dominant in the Indian Himalayan region and act as a huge carbon (C) sink. However, measuring the C sink in soil is complex and time‐intensive, and therefore the present study attempts to estimate the soil organic carbon (SOC) through a remote sensing (RS) approach. We estimated SOC stock of chir pine forests along an altitudinal gradient at three soil depths (0–30, 30–60 and 60–100 cm) in the Garhwal Himalaya, Uttarakhand. Fourteen forest stands at four altitudes, viz., &lt;1000 m above sea level (m asl), 1001–1400 m asl, 1401–1800 m asl and &gt;1801 m asl were surveyed and served for data collection. A model for predicting SOC was developed through stepwise regression analysis based on vegetation information and altitude as independent variables with the field data on SOC. For vegetation information, we used the normalized difference vegetation index (NDVI) measured through remote sensing (RS). The mean SOC stock up to 100 cm depth was increased with increasing altitude and were in the order of 69.66 ± 19.86, 85.27 ± 17.53, 95.68 ± 7.90 and 148.41 ± 71.39 million g ha−1 (million gram per hectare) for &lt;1000, 1001–1400, 1401–1800 and &gt;1801 m_asl, respectively. The result showed that NDVI was a good predictor for SOC estimation. The model predicted SOC stock between 57 and 152 million g ha−1 with a mean of 93 million g ha−1, which was close to the SOCs from field inventory. Therefore, RS could be used to precisely map the SOC stock in the chir pine forests of the Himalayas through NDVI and provide information to policymakers for forest management.

Mapping of Soil Organic Carbon Stocks Based on Aerial Photography in a Fragmented Desertification Landscape

Northern China’s agropastoral ecotone has been a key area of desertification control for decades, and digital maps of its soil organic carbon (SOC) stocks are needed to reveal the gaps between the actual SOC levels and baseline to support land degradation neutrality (LDN) under the Sustainable Development Goals. However, reliable soil information is scarce, and accurate prediction is hindered by the fragmented landscape, which is a dominant characteristic of desertified land. To improve the patchiness identification and accuracy of SOC prediction, we conducted field surveys and collected low-altitude aerial images along the desertification degrees (severe and extremely severe, moderate, slight) in the Horqin Sandy Land. Linear regressions were performed on the relationships between the normalized difference vegetation index and the fractional vegetation cover (FVC) extracted from aerial images, and regression kriging was applied to predict SOC stocks based on the soil-forming factors (vegetation, climate, and topography). Our prediction and cross-validation showed that the fragmented structure and prediction accuracy of SOC stocks were both greatly improved for desertified land. The FVC (R2c = 0.94) and evapotranspiration (R2c = 0.86) had significant positive effects on SOC stocks, respectively, with indirect and direct causal relationships. Our results could provide soil information with better patchiness and accuracy to help policymakers determine the future LDN status in this fragmented desertification landscape. As drone technology becomes more available, it will fully support digital mapping of soil properties.

Spatial modelling approach and accounting method affects soil carbon estimates and derived farm-scale carbon payments

Improved farm management of soil organic carbon (SOC) is critical if national governments and agricultural businesses are to achieve net-zero targets. There are opportunities for farmers to secure financial benefits from carbon trading, but field measurements to establish SOC baselines for each part of a farm can be prohibitively expensive. Hence there is a potential role for spatial modelling approaches that have the resolution, accuracy, and estimates to uncertainty to estimate the carbon levels currently stored in the soil. This study uses three spatial modelling approaches to estimate SOC stocks, which are compared with measured data to a 10 cm depth and then used to determine carbon payments. The three approaches used either fine- (100 m × 100 m) or field-scale input soil data to produce either fine- or field-scale outputs across nine geographically dispersed farms. Each spatial model accurately predicted SOC stocks (range: 26.7–44.8 t ha−1) for the five case study farms where the measured SOC was lowest (range: 31.6–48.3 t ha−1). However, across the four case study farms with the highest measured SOC (range: 56.5–67.5 t ha−1), both models underestimated the SOC with the coarse input model predicting lower values (range: 39.8–48.2 t ha−1) than those using fine inputs (range: 43.5–59.2 t ha−1). Hence the use of the spatial models to establish a baseline, from which to derive payments for additional carbon sequestration, favoured farms with already high SOC levels, with that benefit greatest with the use of the coarse input data. Developing a national approach for SOC sequestration payments to farmers is possible but the economic impacts on individual businesses will depend on the approach and the accounting method.

Spectral and Soil Quality Index for Monitoring Environmental Rehabilitation and Soil Carbon Stock in an Amazonian Sandstone Mine

Rehabilitation is the key factor for improving soil quality and soil carbon stock after mining operations. Monitoring is necessary to evaluate the progress of rehabilitation and its success, but the use of repeated field surveys is costly and time-consuming at a large scale. This study aimed to monitor the environmental/soil rehabilitation process of an Amazonian sandstone mine by applying spectral indices for predicting soil organic carbon (SOC) stock and comparing them to soil quality index. The studied area has different chronological rehabilitation stages: initial, intermediate, and advanced with 2, 10, and 12 years of onset rehabilitation activities, respectively. Non-rehabilitated (NR) and two native forest areas (RA) were used as controls. Soil samples were analyzed for physical, chemical, and biological attributes. After determination of Normalized Difference Vegetation Index and Bare Soil Index, simple regression analysis comparing these indices with SOC stock showed a good fit (R2 = 0.82). Rehabilitated areas presented higher soil quality index (~1.50-fold) and SOC stock (~10.6-fold) than NR; however, they did not differ of RA. The use of spectral indices was effective for monitoring the soil quality in this study, with a positive correlation between the predicted SOC stock and the calculated soil quality index.

The effectiveness of digital soil mapping with temporal variables in modeling soil organic carbon changes

The effectiveness of using changes in environmental conditions to explain the spatiotemporal variability in soil organic carbon (SOC) with digital soil mapping (DSM) requires investigation. In this study, temporal variables representing temporal patterns of climate, vegetation, and land cover factors were explored. Models to predict SOC stocks were developed using a random forest algorithm and data from China during two periods (the 1980s and 2010s). We forecasted and hindcasted the developed models and assessed their temporal projections against temporally independent data. Models were developed for both periods using different sets of variables (with/without temporal variables), and their temporal projections were compared. The important temporal variables were identified by applying the recursive feature elimination algorithm. The results showed that the performances of temporal projections for the 1980s and 2010s were improved by approximately 17% and 47%, respectively, when temporal variables were included in the models. Spatially, the maps of changes in SOC stocks derived from the models that included temporal variables presented stronger associations with temporal changes in climate, vegetation, and land cover than those derived from the models that did not include temporal variables. This work highlights that variation in SOC stocks can be linked to temporal patterns of environmental factors. The findings also provide evidence that the application of temporal patterns of environmental factors to DSM models can be useful for the large-scale prediction of changes in SOC.

Factors controlling soil organic carbon stocks in hardwood floodplain forests of the lower middle Elbe River

We assessed key relationships of soil organic carbon (SOC) stocks to hydromorphic features, soil texture, C/N ratio, pH, and forest age in hardwood floodplain forests. Floodplain ecosystems play a significant role in the global carbon cycle, particularly due to their SOC storage potential. Interactions between heterogeneous micro-relief and anthropogenically affected vegetation and the hydrologic situation of floodplains make predicting SOC stocks difficult without field validation. Especially within hardwood floodplain forests, the controlling factors for SOC storage remain understudied, whereas these floodplain types can be the largest reservoir for carbon and have a substantial SOC sequestration potential. To determine the controlling factors for SOC storage, we selected 38 hardwood floodplain forest and 11 floodplain grasslands of the lower middle Elbe River and categorized them according to vegetation type (grassland, young forest, or old forest) and hydrologic situation (low-lying or high elevated and active or former floodplain). Profile SOC stocks were determined to a depth of 1 m, and topsoil SOC stocks were related to vegetation and soil characteristics, particularly pedological traits (e.g., hydromorphic features).Forest SOC stocks ranged between 99 and 149 t ha−1. SOC stocks decreased with depth throughout all categories and were unaffected by vegetation type within the same hydrologic situation. Vegetation parameters such as age, basal area, or leaf litter carbon stock had no direct effect on SOC stocks. An active connection to the river had the strongest effect on SOC stocks, with former floodplain sites storing 33% less SOC than the active sites. Within the active floodplains, low sites stored 50% more SOC than high sites. This effect was mainly controlled by relief-affected features such as flooding duration and fine texture, which also were the strongest univariate predictors for SOC stocks (R2 = 0.39 and 0.63). A multiple linear regression showed that fine texture, pH, C/N ratio and forest age can be used to explain 86% of variance in SOC stocks.We conclude that proxies for relief and sedimentation (i.e., hydromorphic features, flooding duration, soil texture, C/N ratio, and pH) are the strongest factors controlling SOC stocks in hardwood floodplain forests. Both can be related to allochthonous carbon inputs, increasing SOC stabilization through the accumulation of fine soil particles, and decreasing aerobic carbon mineralization potential through oxygen scarcity, while vegetation plays only a subordinate role.