Soil Organic Carbon Density Research Articles

Spatial distribution of soil organic carbon and its response to forest growth and soil layer in Cunninghamia lanceolata plantations in mid-subtropical China

To investigate the effects of stand age and soil layer on the temporal and spatial distribution of soil organic carbon (SOC) in Cunninghamia lanceolata plantations, five experimental plots and 15 soil sampling sites were established in five stands of different ages (3, 6, 9, 15, and 25 years) in Huitong, Hunan Province. Tree diameter at breast height, tree height, canopy density and elevation in sample plots, and soil organic carbon concentrations (SOCC), soil enzyme activity, soil pH, and soil bulk density at 0–15, 15–30, 30–45 cm soil layers were measured during stand development. The change in SOC distribution with stand age and soil layer, and soil enzyme activity were investigated using ANOVA, LSD test, and inter-subject effect test and simulation analysis. The results showed that the SOC distribution in Cunninghamia lanceolata plantations had spatiotemporal heterogeneity and surface aggregation. The SOC increased with increasing stand age and decreased with increasing soil depth. Furthermore, SOCC and SOC density (SOCD) were strongly correlated with environmental factors; however, the interaction between forest growth and soil layer reduced the variation of SOC distribution. Forest growth and soil enzyme activity were important factors that affect the temporal and spatial distribution of SOC. Therefore, the management measures to promote the growth of the plantation and improve soil enzyme activity can enhance the SOC storage capacity from the angle of balancing ecological and economic benefits, and contribute to the achievement of the goal of “carbon neutrality”.

A novel method incorporating large rock fragments for improved soil bulk density and carbon stock estimation

AbstractSoil bulk density (BD) is a principal component in estimating the density of soil nutrients and elements including carbon (C). Current literature states that in soils with rock fragment (RF) content ≥3% of the total sample volume, substantial differences in estimated soil organic carbon density (SOCD) are found, depending on the soil BD calculation method chosen, potentially affecting the accuracy of soil nutrient and C inventories. In many soil surveys, soil BD is not measured directly, or the core method is used as the sole determinant of soil BD, potentially neglecting the soil volume dilution effect of RFs larger than the diameter of the cores used. This study uses the core and quantitative pit methods at 10 forest sites in Ireland to determine the BD and RF mass and volume to a depth of 40 cm. The authors examine how large RFs impact BD and subsequently affect the estimated SOCD values by comparing against reference values from established soil sampling and BD calculation methods. The analysis reveals significant variations in the estimated SOCD values when the RF volume in the soil sample exceeds 8% of the total sample volume. A novel method, hereafter named “core‐scaling,” combines core and pit sampling methods to account for large RF mass and volume in BD calculations. This study suggests that using the core‐scaling method provides results that are strongly correlated with the pit method, thus offering an alternative that can also provide accurate SOCD estimates in soils with a high RF content.

Quantifying soil organic carbon (SOC) density and stock in the Urmodi River watershed of Maharashtra, India: implications for sustainable land management

Soil Organic Carbon (SOC) plays a vital role in the global carbon cycle, affecting soil fertility and agricultural sustainability. Our study focused on areas with low SOC, where increasing its levels could enhance soil health and carbon management. We used an earth science approach to analyze SOC density and stock in different land classes of the Urmodi River watershed in Maharashtra, India. Using GIS, we estimated SOC from soil samples collected up to 20 cm depth, and produced maps of SOC, SOC density (SOCD), and SOC stock. Our results showed that natural vegetation areas, such as closed and open forests, had a higher nutritional status of SOC and SOC stock, indicating the importance of land cover history on soil health. We found the average SOC to be 1.4 g/kg−1, SOCD to be 57.36 kg/m−2, SOC stock to be 3.46 ton/ha−1. We also depicted the relationship between elevation and SOCD using a scatterplot, revealing the distribution of SOC across different elevations. Our study demonstrates the use of modern geoscientific analysis tools to understand the physical and chemical properties of soil, which can be useful in assessing soil health. Overall, our results provide valuable insights into the distribution of SOC and its relationship with other geo-chemical aspects at a regional scale.

Zonal patterns and driving factors of soil organic carbon density in Zhejiang Province, China

The complex topography and intensive land use and land cover change (LUCC) might shift the spatial pattern of soil organic carbon (SOC) density. However, the effects of LUCC caused by development on the distribution and driving factors of SOC density remain unclear. As one of the most developed regions in China, Zhejiang Province has experienced intensive land use change and has various vegetation types, making it an ideal location to analyse the zonal pattern and driving factors of SOC density in terrestrial ecosystems. To do that, a dataset was built with data collected from existing literature. The average SOC density in Zhejiang Province was 69.75 Mg ha−1, ranging widely from 6.44 to 301.90 Mg ha−1 depending on land and vegetation types. We found that forests had the highest mean SOC density (78.30 Mg ha−1), while cropland had the lowest (45.69 Mg ha−1). Meanwhile, the SOC density of unmanaged land was significantly higher than that of managed land (P < 0.001). Additionally, SOC density varied greatly with longitude and altitude, showing a decreasing zonal pattern from west to east and from high to low altitude. The RF model indicated that vegetation type, stand age, mean annual temperature (MAT), soil pH, and mean annual precipitation (MAP) were the five most important factors affecting SOC density in this area, but the effects differed between managed and unmanaged lands. These results aid with understanding the dynamics of SOC pools in terrestrial ecosystems and inform policy-making to mitigate global climate change. These findings provide valuable insights into the dynamics of SOC pools in terrestrial ecosystems, enabling more effective policymaking to mitigate the impact of global climate change.

Effects of Nitrogen Deposition on Leaf Litter Decomposition and Soil Organic Carbon Density in Arid and Barren Rocky Mountainous Regions: A Case Study of Yimeng Mountain

The ecological impact of nitrogen (N) deposition has gained significance since the advent of the industrial revolution. Although numerous studies have examined the impact of N deposition on soil organic carbon (SOC), certain arid and barren rocky mountainous regions, which experience more pronounced N limitations, have been overlooked. This study was conducted in the Yimeng Mountains, examining eight treatments created by four N addition levels (0 kg N ha−1 yr−1, 50 kg N ha−1 yr−1, 100 kg N ha−1 yr−1 and 200 kg N ha−1 yr−1) and two tree species (Quercus acutissima Carruth. and Pinus thunbergii Parl.). The research revealed variations in the effect of N addition on leaf litter decomposition and SOC density (SOCD) between different tree species. Notably, N addition stimulated the decomposition of leaf litter from Quercus acutissima Carruth. However, the decomposition of Pinus thunbergii Parl. leaf litter was enhanced at N addition levels below 100 kg N ha−1 yr−1, while it was hindered at levels exceeding 100 kg N ha−1 yr−1. In the Quercus acutissima Carruth. forest, the N addition levels of 50 kg N ha−1 yr−1, 100 kg N ha−1 yr−1 and 200 kg N ha−1 yr−1 resulted in decreases in SOCD by 10.57%, 22.22% and 13.66%, respectively, compared to 0 kg N ha−1 yr−1. In the Pinus thunbergii Parl. forest, the N addition levels of 50 kg N ha−1 yr−1, 100 kg N hm−2 ha−1 and 200 kg N ha−1 yr−1 led to increases in SOCD by 49.53%, 43.36% and 60.87%, respectively, compared to 0 kg N ha−1 yr−1. Overall, N addition decreases the SOCD of Quercus acutissima Carruth., but it increases the SOCD of Pinus thunbergii Parl., attributed to the alteration in soil enzyme stoichiometry and nutrient cycling by N addition. This study fills a theoretical gap concerning leaf litter decomposition and SOC sequestration in arid and barren rocky mountainous regions under global climate change.

Effects of cropland reclamation on soil organic carbon in China's black soil region over the past 35 years.

The long-term use of cropland and cropland reclamation from natural ecosystems led to soil degradation. This study investigated the effect of the long-term use of cropland and cropland reclamation from natural ecosystems on soil organic carbon (SOC) content and density over the past 35 years. Altogether, 2140 topsoil samples (0-20 cm) were collected across Northeast China. Landsat images were acquired from 1985 to 2020 through Google Earth Engine, and the reflectance of each soil sample was extracted from the Landsat image that its time was consistent with sampling. The hybrid model that included two individual SOC prediction models for two clustering regions was built for accurate estimation after k-means clustering. The probability hybrid model, a combination between the hybrid model and classification probabilities of pixels, was introduced to enhance the accuracy of SOC mapping. Cropland reclamation results were extracted from the land cover time-series dataset at a 5-year interval. Our study indicated that: (1) Long-term use of cropland led to a 3.07 g kg-1 and 6.71 Mg C ha-1 decrease in SOC content and density, respectively, and the decrease of SOC stock was 0.32 Pg over the past 35 years; (2) nearly 64% of cropland had a negative change in terms of SOC content from 1985 to 2020; (3) cropland reclamation track changed from high to low SOC content, and almost no cropland was reclaimed on the "Black soils" after 2005; (4) cropland reclamation from wetlands resulted in the highest decrease, and reclamation period of years 31-35 decreased when SOC density and SOC stock were 16.05 Mg C ha-1 and 0.005 Pg, respectively, while reclamation period of years 26-30 from forest witnessed SOC density and stock decreases of 8.33 Mg C ha-1 and 0.01 Pg, respectively. Our research results provide a reference for SOC change in the black soil region of Northeast China and can attract more attention to the area of the protection of "Black soils" and natural ecosystems.

Relationship between carbon pool changes and environmental changes in arid and semi-arid steppe—A two decades study in Inner Mongolia, China

As the arid and semi-arid grassland with the most extensive distribution area in northern China, the carbon stored in Inner Mongolia (IM) grassland is highly susceptible to environmental changes. With the global warming and drastic climate changes, exploring the relationship between carbon pool changes and environmental changes and their spatiotemporal heterogeneity is necessary. This study estimates the carbon pool distribution of IM grassland during 2003–2020 by combining the measured below ground biomass (BGB) dataset, measured soil organic carbon (SOC) dataset, multi-source satellite remote sensing data products, and random forest regression modeling method. It also discusses the variation trend of BGB/SOC and its correlation with critical environmental factors, vegetation condition factors and drought index. The results show that the BGB/SOC in IM grassland was stable during 2003–2020, with a weak upward trend. The correlation analysis reveals that high temperature and drought environment were unfavorable for developing vegetation roots and would lead to a decrease in BGB. Furthermore, temperature rise, soil moisture decrease, and drought adversely effected grassland biomass and SOC in areas with low altitude, high SOC density, suitable temperature and humidity. However, in areas with relatively poor natural environments and relatively low SOC content, SOC was not significantly affected by environmental deterioration and even showed an accumulation trend. These conclusions provide directions for SOC treatment and protection. In areas where SOC is abundant, it is important to reduce carbon loss caused by environmental changes. However, in areas with poor SOC, due to the high carbon storage potential of grasslands, carbon storage can be improved through scientifically managing grazing and protecting vulnerable grasslands.

Effects of raster resolution on quantifying farmland soil organic carbon stock in various landforms of a complex topography, China

Raster resolution has a significant impact on quantifying farmland soil organic carbon (SOC) stock. However, it remains poorly understood to set an appropriate raster resolution when converting vector data to raster data, especially for complex topography and its various landforms. To address this issue, Fujian Province of China was selected as the study area, a large region of complex topography that included three landforms: hill-mountain, valley-basin and plain-platform. Based on 235,309 sampling sites and interpolation of simple kriging combining with terrain information (SK_T), we developed a detailed vector dataset at a scale of 1: 50,000, and then it was converted into a series of raster datasets with different resolutions. Furthermore, we evaluated the accuracy of the conversion and quantified the effect of raster resolution on the estimation of farmland SOC stock in the complex topography and its various landforms by calculating variations in an index value (VIV) for farmland area (AREA), SOC density (SOCD) and SOC storage (SOCS). Results showed that VIV decreased with finer resolution on the whole. However, coarser raster resolutions did not always lead to lower accuracy. Specifically, conversions at 0.1 km resolution for both AREA and SOCS in Fujian's complex topography were acceptable (5% ˂ VIV ≤ 10%), but others were deemed unacceptable at other resolutions (VIV ˃ 10%). For SOCD, it was considered error-free (VIV ≤ 1%) and satisfactory (1% ˂ VIV ≤ 5%) at 0.1 and 0.2 km resolution conversions, acceptable at 0.5, 2.0, 2.5 km but unacceptable at 0.8, 1.0, 1.5 km resolutions. As viewed from its various landforms, hill-mountain was the landform that had the greatest accuracy loss in the conversion for AREA and SOCS, followed by valley-basin and then plain-platform, while the opposite is true for SOCD. The major factors contributing to these differences among landforms were patch density and spatial variation. In addition, resolution setting during conversion and the generalization of maps contributed to the uncertainties of SOC stock estimation based on raster datasets. Therefore, our work provides a guideline for researchers to identify a landform-specific appropriate raster resolution in converting vector to raster for farmland SOC stock estimation in areas like subtropical China or other places with similar complex topographies.

Northeast China holds huge wetland soil organic carbon storage: an estimation from 819 soil profiles and random forest algorithm

As a huge natural carbon storage, wetlands play an important role in the global carbon cycle. However, the spatial pattern and storage of soil organic carbon (SOC) in wetland ecosystems remain largely uncertain due to large spatial heterogeneity and insufficient field observations. In this study, we predict the spatial pattern of SOC density and estimated SOC storage in wetlands of Northeast China based on 819 field samples and multiple geospatial data using random forest algorithm. The SOC density of wetlands at different depths was affected differently by environmental factors and the SOC density in the surface layer (0–30 cm) was more susceptible to climatic change. The correlation coefficients (r) between the SOC density predicted by the random forest model and the measured SOC density were 0.86, 0.77 and 0.73 in 0–30, 30–60 and 60–100 cm soil depths, respectively. Our estimation showed that Northeast China holds huge wetland SOC storage in the amount of 3.40 ± 0.13 Pg C. The average wetland SOC density was 44.30 ± 1.72 kg C m−2, which decreased gradually from north to south in the study area. The wetland SOC density in Northeast China decreased with soil depth, and the influence of environmental factors on wetland SOC gradually decreased. Surface wetland SOC may be more sensitive to global climate change. Our results examined the relationship between wetland SOC and environmental factors, which benefits the understanding of the responses of wetland SOC to climate change.

Assessing the carbon sequestration potential and identifying influential factors of cultivated soils in Northeast China

Cultivated lands play a crucial role in terrestrial carbon cycle, and enhancing soil organic carbon (SOC) sequestration in these areas can help effectively mitigate the rise of atmospheric CO2 concentration. In this study, topsoil (0–20 cm) saturated SOC and its density of cultivated soil in Northeast China were mapped using a boosted regression trees (BRT) model. The distribution of the SOC sequestration potential was also calculated based on the difference between saturated SOC and SOC density in ArcGIS. Nine environmental factors including climate, topography and lengths of cultivation data (LCD) and 197 soil samples are used. A 10-fold cross-validation technique is applied to derive four statistical indices - mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R2) and Lin's consistent correlation coefficient (LCCC) to verify the model performance. The model explains 81% and 85% of the spatial variation of saturated SOC and SOC density, respectively. Mean annual temperature and mean annual precipitation are key factors controlling SOC density and saturated SOC distribution. In addition, LCD showed a similar spatial pattern to SOC sequestration potential, influencing the distribution of SOC density and saturated SOC in the study area. We recommend LCD as an important factor to consider in saturated SOC and SOC density predictions, especially in the farmland ecosystem with a long reclamation history. Accurate mapping SOC sequestration potential and identifying environmental factors will help manage land use and promote soil quality evaluation and improve soil carbon sequestration in this region.

The Assessment of Soil Organic Carbon Pools in Different Soils Using Four Fractionation Methods

ABSTRACT The sensitivity of SOC (soil organic carbon) fractions can provide information on soil organic matter proxies and kinetically defined components related to their chemical composition and oxidability. The purpose of this study was to assess the stability of SOC on five soil types (Arenosols, Chernozems, Fluvisols, Vertisols and Solonetz) and three land uses (cropland, meadow and forest). To obtain SOC functional fractions chemical, physical and density fractionating methods were used as follows: SOC susceptible to oxidation with 333 mM KMnO4 (SOCKMnO4S), SOC with varying degrees of oxidation resistance by the modified Walkley-Black method (SOCwb1, SOCwb2, SOCwb3, SOCwb4), particulate SOC (SOCpar); mineral-associated SOC (SOCmas), aggregate-associated size classes of the SOC (SOC >2000, SOC2000–250, SOC250–53, SOC <53). The relative contribution of fractions in SOC was as follows: SOCKMnO4S (7.9–42.5%), SOCwb1 (35.6%) > SOCwb2 (27.5%) > SOCwb4 (22.7%) > SOCwb3 (14.2%), SOCmas (56.6–76.4%) > SOCpar (23.6–43.4%), SOC >2000 (8.2–44.8), SOC 2000–250 (27.4–34.9), SOC 250–53 (13.4–27.4%), SOC <53 (5.4–28.8%). Fractions SOCpar, SOCwb4, SOC >2000 and SOC <53 showed sensitivity to land use, whereas SOCmas, SOC KMnO4S, SOCwb3 showed variations among soil types. The fraction SOCwb1 indicated sensitivity to both land use and soil type. Considering obtained functional fractions of SOC it was possible to determine discriminators between particular soil types or indicators of land use changes due the anthropogenization.

Digital mapping of soil organic carbon density in China using an ensemble model

The soil organic carbon stock (SOCS) is considered as one of the largest carbon reservoirs in terrestrial ecosystems, and small changes in soil can cause significant changes in atmospheric CO2 concentration. Understanding organic carbon accumulation in soils is crucial if China is to meet its dual carbon target. In this study, the soil organic carbon density (SOCD) in China was digitally mapped using an ensemble machine learning (ML) model. First, based on SOCD data obtained at depths of 0–20 cm from 4356 sampling points (15 environmental covariates), we compared the performance of four ML models, namely random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and artificial neural network (ANN) models, in terms of coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values. Then, we ensembled four models using Voting Regressor and the principle of stacking. The results showed that ensemble model (EM) accuracy was high (RMSE = 1.29, R2 = 0.85, MAE = 0.81), so that it could be a good choice for future research. Finally, the EM was used to predict the spatial distribution of SOCD in China, which ranged from 0.63 to 13.79 kg C/m2 (average = 4.09 (±1.90) kg C/m2). The SOC storage amount in surface soil (0–20 cm) was 39.40 Pg C. This study developed a novel, ensemble ML model for SOC prediction, and improved our understanding of the spatial distribution of SOC in China.

Quantifying changes in soil organic carbon density from 1982 to 2020 in Chinese grasslands using a random forest model.

China has the second-largest grassland area in the world. Soil organic carbon storage (SOCS) in grasslands plays a critical role in maintaining carbon balance and mitigating climate change, both nationally and globally. Soil organic carbon density (SOCD) is an important indicator of SOCS. Exploring the spatiotemporal dynamics of SOCD enables policymakers to develop strategies to reduce carbon emissions, thus meeting the goals of "emission peak" in 2030 and "carbon neutrality" in 2060 proposed by the Chinese government. The objective of this study was to quantify the dynamics of SOCD (0-100 cm) in Chinese grasslands from 1982 to 2020 and identify the dominant drivers of SOCD change using a random forest model. The results showed that the mean SOCD in Chinese grasslands was 7.791kg C m-2 in 1982 and 8.525kg C m-2 in 2020, with a net increase of 0.734kg C m-2 across China. The areas with increased SOCD were mainly distributed in the southern (0.411kg C m-2), northwestern (1.439kg C m-2), and Qinghai-Tibetan (0.915kg C m-2) regions, while those with decreased SOCD were mainly found in the northern (0.172kg C m-2) region. Temperature, normalized difference vegetation index, elevation, and wind speed were the dominant factors driving grassland SOCD change, explaining 73.23% of total variation in SOCD. During the study period, grassland SOCS increased in the northwestern region but decreased in the other three regions. Overall, SOCS of Chinese grasslands in 2020 was 22.623 Pg, with a net decrease of 1.158 Pg since 1982. Over the past few decades, the reduction in SOCS caused by grassland degradation may have contributed to soil organic carbon loss and created a negative impact on climate. The results highlight the urgency of strengthening soil carbon management in these grasslands and improving SOCS towards a positive climate impact.

Carbon Sequestration Characteristics Under Natural Vegetation Restoration in Ziwuling Area of the Loess Plateau

The aim of this study was to analyze the carbon sink effect under natural vegetation restoration and the influence of changes in vegetation community characteristics on ecosystem carbon density in ecologically fragile areas of the Loess Plateau. In this study, the changes in carbon sequestration of a vegetation-soil system under eight successional stages[slope cropland, abandoned cropland for 10 years, abandoned cropland for 20 years, Sophora davidii (Franch.) Skeels., Betula platyphylla Suk., Pinus tabulaeformis Carr., Quercus wutaishanic Mary+P. tabulaeformis Carr mixed forests, and Q. wutaishanic Mary] in Ziwuling area over 150 restoration periods were investigated using the common method of spatial and temporal substitution. This study also discussed the relationship between changes in vegetation community characteristics and vegetation-soil system carbon density. The results showed that the community coverage of the investigated vegetation fluctuated from 85% in the slope cropland stage to 100% in the arbor stage. The number of species, Margalef index, Shannon-Wiener index, Pielou index, and Simpson index initially increased rapidly, then declined slowly until becoming stable, and reached a peak in the middle of the succession (B. platyphylla Suk.). The biomass and carbon density of vegetation components (above-ground biomass, below-ground roots, and litter) increased exponentially during the succession, i.e., increased slowly before B. platyphylla Suk. but increased significantly in B. platyphylla Suk. and P. tabulaeformis Carr.(P<0.05). The biomass and carbon density reached the maximum values of 27858.08 g·m-2 and 13232.51 g·m-2, respectively, in Q. wutaishanic Mary+P. tabulaeformis Carr mixed forests and tended to be stable in the late succession stage. Soil organic carbon density showed a power function relationship with vegetation restoration, with the greatest increase in the stages of abandoned cropland for 10 years and B. platyphylla, but no significant changes in the subsequent stages (P>0.05). In the early succession stage, the carbon density of the farmland ecosystem was the lowest (4395.70 g·m-2), whereas the other seven stages increased by 55.54%, 40.37%, 69.96%, 202.48%, 326.35%, 357.43%, and 351.07%, respectively, compared with the farmland ecosystem. Community coverage, Margalef index, Shannon-Wiener index, above-ground biomass, root biomass, and litter biomass were significantly positively correlated with vegetation-soil system carbon density (P<0.05). The carbon sink effect of long-term natural restoration in Ziwuling Region was significant, and the carbon density of the vegetation-soil system under interspecific competition tended to be stable in the late succession stage. Dynamic changes in the vegetation community structure and plant diversity during the succession process increased vegetation carbon density and soil carbon density. This study helps to clarify the carbon sink effect of natural vegetation restoration in ecologically fragile areas of the Loess Plateau and provides a theoretical basis for promoting natural forest conservation and achieving carbon neutrality.

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.

Long-term intensive management reduced the soil quality of a Carya dabieshanensis forest

The evaluation of soil quality can provide new insights into the sustainable management of forests. This study investigated the effects of three types of forest management intensities (non-management (CK), extensive management (EM), and intensive management (IM)), and five management durations (0, 3, 8, 15, and 20 years) on the soil quality of a Carya dabieshanensis forest. Further, minimum data sets (MDS) and optimized minimum data sets (OMDS) were established to evaluate the soil quality index (SQI). A total of 20 soil indicators representing its physical, chemical, and biological properties were measured for the 0–30 cm layer. Using one-way ANOVA and principal component analysis (PCA), the total data set (TDS), the minimum data set (MDS), and optimized minimum data set (OMDS) were established. The MDS and OMDS contained three (alkali hydrolyzed nitrogen (AN), soil microbial biomass nitrogen (SMBN), and pH) and four (total phosphorus (TP), soil organic carbon (SOC), AN, and bulk density (BD)) soil indicators, respectively. The SQI derived from the OMDS and TDS exhibited a stronger correlation (r = 0.94, p < 0.01), which was suitable for evaluating the soil quality of the C. dabieshanensis forest. The evaluation results revealed that the soil quality was highest during the early stage of intensive management (IM-3), and the SQI of each soil layer was 0.81 ± 0.13, 0.47 ± 0.11, and 0.38 ± 0.07, respectively. With extended management times, the degree of soil acidification increased, and the nutrient content decreased. Compared with the untreated forest land the soil pH, SOC, and TP decreased by 2.64–6.24%, 29.43–33.04%, and 43.63–47.27%, respectively, following 20 years of management, while the SQI of each soil layer decreased to 0.35 ± 0.09, 0.16 ± 0.02 and 0.12 ± 0.06, respectively. In contrast to extensive management, the soil quality deteriorated more rapidly under longer management and intensive supervision. The OMDS established in this study provides a reference for the assessment of soil quality in C. dabieshanensis forests. In addition, it is suggested that the managers of C. dabieshanensis forests should implement measures such as increasing the amount of P-rich organic fertilizer and restoring vegetation to increase soil nutrient resources for the gradual restoration of soil quality.

Digital assessments of soil organic carbon storage using digital maps provided by static and dynamic environmental covariates

AbstractUnderstanding the vertical and lateral distribution of soil organic carbon (SOC) and soil organic carbon density (SOCD) is indispensable for soil use and environmental management because of their vital role in soil quality assessments. Primarily, they are needed in calculating soil organic carbon storage (SOCS). The objective of this research was to provide digital maps of SOC and SOCD variation as well as their uncertainties at multiple standardized depths (H1: 0–5, H2: 5–15, H3: 15–30, H4: 30–60 and H5: 60–100 cm) using a parsimonious model with optimized terrain‐related attributes and satellite‐derived data. SOCS were evaluated at soil subgroup levels. An area of about 808 km2 with varying elevation, plant cover and lithology from the Miandoab region, West Azerbaijan Province, Iran was selected as a case study area. A total of 386 soil samples were collected from 104 profiles comprising various soil genetic horizons. A continuous spline function was then fitted to the target properties in advance of creating a dataset at five standard depth intervals (following the GlobalSoilMap project). These were then grouped into three classes including top (H1), middle (H2, H3 and H4) and bottom (H5) depths to ease interpretation. Static and dynamic covariates (30‐m resolution) were derived from a digital elevation model (DEM) and a suite of Landsat‐8 spectral imageries, respectively. Four candidate models including stepwise multiple linear regression (SMLR), random forest (RF), cubist (CU) and extreme gradient boosting (XGBoost) Tree were tested in this study. Finally, the digital maps at 30‐m resolution of SOC and SOCD and their uncertainties were prepared using the best‐fit model and the bootstrapping method, respectively. Four soil subgroups (Gypsic Haploxerepts, Typic Calcixerepts, Typic Haploxerepts and Xeric Haplocalcids) were identified across the study area. The covariates had variable contributions on the evaluated models. The XGBoost Tree model generally outperformed other models for prediction of SOC and SOCD (R2 = 0.60, on average). Regardless of soil subgroups, the uncertainty analysis showed that the SOCD map had a low prediction interval range value indicating high accuracy. Additionally, the highest SOCS and SOCD was observed at the top followed by middle and bottom depths in the study area. All subgroups exhibited a decreasing trend of SOCD with increasing depth. A similar trend was also observed for SOCS. The highest SOCD (on average) was observed in Gypsic Haploxerepts (4.71 kg C/m2) followed by Typic Calcixerepts (4.46 kg C/m2), Typic Haploxerepts (4.45 kg C/m2) and Xeric Haplocalcids (4.40 kg C/m2). Overall, the SOCS normalized by area within soil order boundaries was greater in Inceptisols than Aridisols across the study area. The findings of this study provide critical information for sustainable management of soil resources in the area for agricultural production and environmental health in the Miandoab region of Iran.

Soil organic carbon storage in Liaohe River Estuary Wetlands under restoration and multiple management strategies, based on landscape patterns

IntroductionManagement of coastal wetlands has resulted in extensive conservation of this natural resource; however, changes in carbon storage function are not yet known. There is a direct link between landscape and soil carbon storage. Predicting future changes in the landscape and carbon storage in coastal wetlands is important for developing wetland management policies.MethodHere, remote sensing and physical methods were used to measure and calculate the landscape and surface soil carbon stocks of the Liaohe River Estuary Wetland (LREW). The changes in the landscape and soil carbon stocks under three scenarios: natural development, strict protection, and culture pond transfer, were then predicted using the PLUS model.ResultThe results indicate that the surface soil organic carbon storage was 2107.97×103 t, while soil organic carbon density decreased from land to sea. Anthropogenic activity was found to be the main driver of the current landscape evolution. However, the impact of sea level rise is increasing. By 2030, considerably more storage will be gained under the culture pond transfer scenario than at present.DiscussionOur results reveal that some of the methods of ecological restoration may diminish the carbon storage capacity of coastal wetlands. Making full use of areas with high carbon storage potential may be an effective wetland carbon sink management strategy. Governments should consider more comprehensively for a better carbon pool when developing restoration strategies.

Deep tillage enhanced soil organic carbon sequestration in China: A meta-analysis

The increasing atmospheric carbon dioxide (CO2) causes serious environmental problems, soil organic carbon (SOC) sequestration is suggested as an efficient strategy to offset global CO2 emissions. Deep tillage (DT) can change soil properties and the input and loss of OC, and then influence SOC sequestration across the whole soil profile. However, the effect of DT on SOC sequestration relative to conventional tillage (CT) under different environmental factors and management practices is unclear in China. In this study, we evaluated the response of SOC stock to DT based on collected field data (447 observations) with a meta-analysis. DT significantly increased SOC stock by 7.36% over CT, with a greater increase for subsoiling (8.76%) than for deep ploughing (DP) (5.85%). Subsoiling enhanced SOC across the 0–40 cm soil layer and the greatest increase occurred at 0–10 cm layer (11.41%); DP enhanced SOC stock at 10–40 cm layer, but had no effect on SOC of 0–10 cm layer. DP and subsoiling did not affect SOC storage below 40 cm depth. According to subgroup analysis, the high rainfall, fine soil texture, residue retained, double cropping, and high rate of N fertilization greatly promoted SOC stock compared with the low rainfall, sandy texture, residue removal, monoculture, and low N fertilizer input under DT. DT significantly enhanced SOC storage under low initial SOC (<15 g kg−1), but did not affect SOC when the initial SOC exceeded 15 g kg−1 compared with CT. However, the experiment duration, initial SOC and bulk density (BD) did not significantly affect the response of SOC to DT. Overall, this study has supplied a reference for SOC management in mitigating climate change, and the specific environmental factors and management practice should be considered when utilizing DT to enhance SOC sequestration in different regions of China.

Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping

In the context of increasing soil degradation worldwide, spatially explicit soil information is urgently needed to support decision-making for sustaining limited soil resources. Digital soil mapping (DSM) has been proven as an efficient way to deliver soil information from local to global scales. The number of environmental covariates used for DSM has rapidly increased due to the growing volume of remote sensing data, therefore variable selection is necessary to deal with multicollinearity and improve model parsimony. Compared with Boruta, recursive feature elimination (RFE), and variance inflation factor (VIF) analysis, we proposed the use of modified greedy feature selection (MGFS), for DSM regression. For this purpose, using quantile regression forest, 402 soil samples and 392 environmental covariates were used to map the spatial distribution of soil organic carbon density (SOCD) in Northeast and North China. The result showed that MGFS selected the most parsimonious model with only 9 covariates (e.g., brightness index, mean annual temperature), much lower than RFE (22 covariates), VIF (30 covariates), and Boruta (76 covariates). The repeated validation (50 times) showed that the MGFS derived model performed better (R2 of 0.60, LCCC of 0.74, RMSE of 13.80 t ha−1) than these using full covariates, Boruta, RFE and VIF (R2 of 0.48–0.57, LCCC of 0.64–0.72, RMSE of 14.24–15.79 t ha−1). Despite the similar performance of the uncertainty estimate (PICP), the model using MGFS and RFE had the lowest global uncertainty (0.86) as indicated by the uncertainty index. In addition, MGFS had the best computation efficiency when considering the steps of variable selection and map prediction. Given these advantages over Boruta, RFE and VIF, MGFS has a high potential in fine-resolution soil mapping practices, especially for these studies at a broad scale involving heavy computation on millions or billions of pixels.