Understanding soil organic carbon (SOC) distribution and its environmental controls in permafrost regions is essential for achieving carbon neutrality and mitigating climate change. This study examines the spatial pattern of SOC and its drivers in the Headwater Area of the Yellow River (HAYR), northeastern Qinghai-Xizang Plateau (QXP), a region highly susceptible to permafrost degradation. Field investigations at topsoils of 86 sites over three summers (2021-2023) provided data on SOC, vegetation structure, and soil properties. Moreover, the spatial distribution of key permafrost parameters was simulated: temperature at the top of permafrost (TTOP), active layer thickness (ALT), and maximum seasonal freezing depth (MSFD) using the TTOP model and Stefan Equation. Results reveal a distinct latitudinal SOC gradient (high south, low north), primarily mediated by vegetation structure, soil properties, and permafrost parameters. Vegetation coverage and above-ground biomass showed positive correlation with SOC, while soil bulk density (SBD) exhibited a negative correlation. Climate warming trends resulted in increased ALT and TTOP. Random Forest analysis identified SBD as the most important predictor of SOC variability, which explains 38.20% of the variance, followed by ALT and vegetation coverage. These findings likely enhance the understanding of carbon storage controls in vulnerable alpine permafrost ecosystems and provide insights to mitigate carbon release under climate change.

Uncertainties in carbon storage estimates for disturbance-prone dryland ecosystems hinder accurate assessments of their contribution to the global carbon budget. This study examines the effects of land-use change on carbon storage in an African savanna landscape, focusing on two major land-use change pathways: agricultural intensification and wildlife conservation, both of which alter disturbance regimes. By adapting tree inventory and soil sampling methods for dryland conditions, we quantified aboveground and belowground carbon in woody vegetation (AGC and BGC) and soil organic carbon (SOC) across these pathways in two vegetation types (scrub savanna and woodland savanna). We used Generalized Additive Mixed Models to assess the effects of multiple environmental drivers on AGC and whole-ecosystem carbon storage (C-total). Our findings revealed a pronounced variation in the vulnerability of carbon reservoirs to disturbance, depending on land-use change pathway and vegetation type. In scrub savanna vegetation, shrub AGC emerged as the most vulnerable carbon reservoir, declining on average by 56% along the conservation pathway and 90% along the intensification pathway compared to low-disturbance sites. In woodland savanna, tree AGC was most affected, decreasing on average by 95% along the intensification pathway. Unexpectedly, SOC stocks were often higher at greater disturbance levels, particularly under agricultural intensification, likely due to the preferential conversion of naturally carbon-richer soils for agriculture and the redistribution of AGC to SOC through megaherbivore browsing. Strong unimodal relationships between disturbance agents, such as megaherbivore browsing and woodcutting, and both AGC and C-total suggest that intermediate disturbance levels can enhance ecosystem-level carbon storage in disturbance-prone dryland ecosystems. These findings underline the importance of locally tailored management strategies-such as in carbon certification schemes-that reconcile disturbance regimes in drylands with carbon sequestration goals. Moreover, potential trade-offs between land-use objectives and carbon storage goals must be considered.

Alpine grassland ecosystems play a crucial role in the global carbon (C) balance by contributing to the soil organic carbon (SOC) pool; thus, quantifying SOC stocks in these ecosystems is essential for understanding potential gains or losses in soil C under the threat of climate change and anthropogenic activities. Remote sensing plays a vital role in estimating SOC stocks; however, identifying reliable remote sensing proxies to enhance SOC prediction remains a challenge. Information on soil C cycling proxies can reveal how the balance between C inputs and outputs affects SOC. Therefore, these proxies could be effective indicators of SOC variations. In this study, we explored the potential of satellite-derived attributes related to soil C cycling proxies for predicting SOC stocks. We derived remote sensing indices such as gross primary production, soil respiration, soil moisture, land surface temperature, radiation, and soil disturbance and assessed the relationships between these indices and SOC stocks via partial least squares structural equation modeling (PLS-SEM). We evaluated the effectiveness of these indices in predicting SOC stocks, we compared PLS-SEM and quantile regression forest (QRF) models across different variable combinations, including static, intra-annual, and inter-annual information. The PLS-SEM results demonstrated the suitability of the derived remote sensing indices and their interactions in reflecting processes related to soil C balance. The QRF models, using these indices, achieved promising prediction accuracies, with a coefficient of determination (R2) of 0.54 and a root mean square error (RMSE) of 0.79 kg m-2 at the topmost 10 cm of soil. However, the prediction performance generally decreased with increasing soil depth, up to 30 cm. The results also revealed that adding intra- and inter-annual information to remotely sensed proxies did not increase the prediction accuracy. Our study revealed that gross primary production, soil respiration, soil moisture, land surface temperature, radiation, and soil disturbance are effective proxies for representing factors influencing soil C balance and mapping SOC stocks in alpine grasslands.

Frequent soil drying and wetting cycles significantly affect the mineralization processes of soil organic carbon (SOC) and total nitrogen (STN), impacting soil quality and contributing to nutrient loss. However, the effects of these dry-wet cycles on SOC and STN mineralization in dam soil are not well understood. This study simulated four consecutive wet-dry cycles under five soil moisture gradients of 0% (CK), 5%, 10%, 15%, and 100%, and 100%, across four cycles of 7, 14, 21, and 28 days, to investigate the effects on soil aggregates, enzyme activities, and the mineralization of SOC and STN. The results indicated that soil enzyme activity peaked after two dry-wet cycles and then began to decline. The dry-wet cycles reduced the proportion of soil macro-aggregates while also decreasing the proportions of small and micro-aggregates. In contrast, the 100% treatment conditions exhibited the opposite effect. Dry-wet cycles enhanced the mineralization rates of SOC and STN, with the average mineralization rates under the 10% soil moisture content being the highest-1.78 and 2.38 times greater than the CK treatment for SOC and STN, respectively. The impact of dry-wet cycles on SOC and STN mineralization through the enzyme pathway was greater than through the aggregate pathway. These research findings provide theoretical insights and scientific references for the efficient operation and ecological protection of sedimentation dams in the Loess Plateau.

Polyethylene mulching film, which is widely utilized in arid and semi-arid agriculture, leaves residual pollution. A novel approach to addressing this issue is microbial degradation. To screen the strains that degrade polyethylene efficiently and clarify the effect of degrading strains on the turnover of soil organic carbon, a polyethylene-degrading fungus PF2, identified as Trichoderma asperellum, was isolated from long-time polyethylene-covered soil. Strain PF2 induced surface damage and ether bonds, ketone groups and other active functional groups in polyethylene, with 4.15% weight loss after 30 days, where laccase plays a key role in the degradation of polyethylene. When applied to soil, the Trichoderma-to-soil weight ratios were the following: B1: 1:100; B2: 1:200; B3: 1:300 and B4: 1:400. Trichoderma asperellum significantly increased the cumulative CO2 mineralization and soil organic carbon mineralization in the B1 and B2 treatments compared with the control (B0). The treatments B1, B3 and B4 increased the stable organic carbon content in soil. An increase in the soil organic carbon content was observed with the application of Trichoderma asperellum, ranging from 27.87% to 58.38%. A positive correlation between CO2 emissions and soil organic carbon was observed, with the soil carbon pool management index (CPMI) being most correlated with active organic carbon. Trichoderma treatments improved the CPMI, with B3 showing the most favorable carbon retention value. Thus, Trichoderma asperellum not only degrades polyethylene but also contributes to carbon sequestration and soil fertility when applied appropriately.

Despite its crucial role in flood defense for downstream regions, the catastrophic breach of the Kakhovka Dam on June 6, 2023, along the Dnipro River in Ukraine caused extensive flooding and damage both upstream and downstream. In addition, the subsequent significant drying up of the dam reservoir poses serious challenges, including hindered electricity generation, compromised flood control measures, and disrupted aquatic ecosystems. This study aims to address knowledge gaps related to the event by employing multi-temporal change detection of pre- and post-event Sentinel-1 synthetic aperture radar (SAR) imagery, analyzed using the Google Earth Engine (GEE) platform, to map flood extent and impacts. Furthermore, we assessed the impacts of dam breaches on soil organic carbon (SOC) sequestration potential in both the drying reservoir region upstream and the flooded areas downstream. The results estimated the total area of the flood extent to be approximately 379.41 km2, with an overall accuracy (OA) of 94% and a Kappa index (K) of 0.89. Quantitative analysis revealed that 81.15 km2 of urban areas, 82.59 km2 of agricultural lands, and 215.56 km2 of herbaceous wetlands were submerged by floodwaters. Both flooding and reservoir drawdown from dam collapses can significantly affect soil organic carbon (SOC) sequestration rates in affected soils. The quantification of post-disaster impacts underscores the pressing need for restoration practices and sustainable management efforts to lessen the environmental impacts and enhance the recovery of the affected regions.

Wildfire strongly influences permafrost environment and soil organic carbon (SOC) pool. In this study, we reviewed the effects of fire severity, time after a fire, and frequency on SOC in boreal permafrost regions. This review highlighted several key points: the effect of wildfires on SOC increased with an increase of fire severity, and the amount of vegetation returned and surface organic matter replenished was less in a short term, which resulted in a significantly lower SOC content compared to that of before the fire. Within a short period after fire, the SOC in near-surface permafrost and the active layer decreased significantly due to the loss of above ground biomass, permafrost thaw, and increased microbial decomposition; as the years pass after a fire, the SOC gradually accumulates due to the contributions of litter layer accumulation and rooting systems from different stages of succession. The increase in fire frequency accelerated permafrost thawing and the formation of thermokarst, resulting in the rapid release of a large amount of soil carbon and reduced SOC storage. Therefore, the study on the effects of wildfires on SOC in the boreal permafrost region is of great significance to understanding and quantifying the carbon balance of the ecosystem.

Soil organic carbon (SOC) plays a vital role in the global carbon cycle and soil quality assessment. The Qinghai-Tibet Plateau is one of the largest plateaus in the world. Therefore, in this region, SOC density and the spatial distribution of SOC are highly sensitive to climate change and human intervention. Given the insufficient understanding of the spatial distribution of SOC density in the Qinghai-Tibet Plateau, this study utilized machine learning (ML) algorithms to estimate the density and distribution pattern of SOC density in the region. In this study, we first collected multisource data, such as optical remote sensing data, synthetic aperture radar) (SAR) data, and other environmental variables, including socioeconomic factors, topographic factors, climate factors, and soil properties. Then, we used ML algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), to estimate the topsoil SOC density and spatial distribution patterns of SOC density. We also aimed to investigate any driving factors. The results are as follows: (1) The average SOC density is 5.30 kg/m(2). (2) Among the three ML algorithms used, LightGBM showed the highest validation accuracy (R-2 = 0.7537, RMSE = 2.4928 kgC/m(2), MAE = 1.7195). (3) The normalized difference vegetation index (NDVI), valley depth (VD), and temperature are crucial in predicting the spatial distribution of topsoil SOC density. Feature importance analyses conducted using the three ML models all showed these factors to be among the top three in importance, with contribution rates of 14.08%, 12.29%, and 14.06%; 17.32%, 20.73%, and 24.62%; and 16.72%, 11.96%, and 20.03%. (4) Spatially, the southeastern part of the Qinghai-Tibet Plateau has the highest topsoil SOC density, with recorded values ranging from 8.41 kg/m(2) to 13.2 kg/m(2), while the northwestern part has the lowest density, with recorded values ranging from 0.85 kg/m(2) to 2.88 kg/m(2). Different land cover types showed varying SOC density values, with forests and grasslands having higher SOC densities compared to urban and bare land areas. The findings of this study provide a scientific basis for future soil resource management and improved carbon sequestration accounting in the Qinghai-Tibet Plateau.

Soil compaction and soil bulk density are key soil properties affecting soil health and soil ecosystem services like crop production, water retention and purification and carbon sequestration. The standard method for soil bulk density measurements using Kopecky rings is very labour intensive, time consuming and leaves notable damage to the field. Accurate data on bulk density are therefore scarce. To enable large-scale data collection, we tested a new portable gamma ray sensor (RhoC) for in situ field and dry bulk density measurements up to 1 m depth. In this first validation study, measurements with the RhoC-sensor were compared with classic ring sampling. Measurements were made in two agricultural fields in the Netherlands (a sandy clay loam and a sandy soil), with large variation in subsoil compaction. At 10 locations within each field, three soil density profiles were made. Each profile comprised six depth measurements (every 10 cm from 10 to 60 cm depth) using the RhoC-sensor and Kopecky rings, resulting in 30 pairwise profiles and 180 measurements in total per field. At an average soil density of 1.5 g/cm3, the relative uncertainty was 9% for the Kopecky rings and 15% for the RhoC-sensor. Because the RhoC-sensor is easy and quick to use, the higher relative uncertainty can easily be compensated for by making additional measurements per location. In conclusion, the RhoC-sensor allows a reliable quantitative in situ assessment of both field and dry bulk density. This provides the much-needed possibility for rapid and accurate assessment of soil compaction. The acquisition of this data supports the calculation of soil organic carbon stocks and is indispensable for (national) soil monitoring, to assess soil health and to inform sustainable land management practices for sustained or improved soil health and provision of soil ecosystem services, such as requested in the proposed EU Directive on Soil Monitoring and Resilience.

PurposeThe ecological damage caused by cut slopes in mountainous areas is serious, and ecological restoration is urgently needed. In this context, outside soil spray seeding (OSSS) combined with a frame beam is often used in mountainous areas of southwestern China. The aims of this study were (1) to determine the differences in soil organic carbon (SOC) and its fractions of cut slopes under different restoration methods and (2) to explore the factors influencing SOC and its fractions of cut slopes in this study area.Materials and methodsTwo cut slopes restored by different restoration methods (framed slope, using OSSS combined with a frame beam, FS; rimless slope, unassisted restoration, RS) were selected, and a nearby naturally developed slope that had not been cut was used as a reference (NS). The SOC, SOC fractions, and related soil parameters were investigated.Results and discussionCompared with RS, the available phosphorus, urease activity, amylase activity, microbial biomass carbon (MBC), and light-fraction organic carbon (LFOC) levels of FS were significantly higher. However, there were no significant differences in pH, bulk density, available nitrogen, saccharase activity, SOC, particulate organic carbon (POC), and readily oxidizable organic carbon (ROC) between FS and RS. Notably, the MBC contents of FS and RS were higher compared to that of NS, which may be due to the fact that the deep soil was exposed to the air after stripping the surface soil of the cut slopes, which facilitated the growth of aerobic microorganisms. The dissolved organic carbon (DOC) content of FS was lower than that of RS, most likely because of the higher MBC content of FS compared with RS. The main soil parameters influencing soil SOC and its fractions were available nitrogen, available phosphorus, and bulk density.ConclusionsDespite the implementation of ecological restoration measures, the SOC and its fractions of the cut slope did not fully recover, and there was a gap between the soil quality of FS and NS. Further research is needed to determine whether OSSS combined with frame beams is an effective ecological restoration method for cut slopes in this area.