Abstract. There is increasing interest in global dynamic soil information with changes in soil properties mapped over time and at high spatial resolution. Thanks to long-term, multi-temporal, and fine- and medium-resolution satellite missions such as Landsat, MODIS, Copernicus Sentinel and similar, it is possible to produce globally consistent predictions of key soil variables that match other 10–30 m spatial resolution global data sets. This paper describes data preparation, modeling, and production of OpenLandMap-soildb: global dynamic predictions of soil organic carbon content, soil organic carbon density, bulk density, soil pH in H2O, soil texture fractions (clay, sand and silt) and USDA subgroup soil types (USDA soil taxonomy subgroups) at 30 m spatial resolution based on spatiotemporal Machine Learning (Quantile Regression Random Forest with output predictions showing the mean plus the 68 % probability lower and upper prediction intervals). To train the models, a large compilation of soil samples imported from legacy soil projects was used: 216 000 soil samples with soil carbon density (kg m−3), 408 000 soil samples with soil carbon content (g kg−1), 272 000 soil samples with soil pH in H2O, 363 000 soil samples with clay, silt and sand content (%) and 134 000 samples with bulk density oven dry (t m−3). Soil carbon and soil pH were mapped with 5-year time-intervals; soil texture fractions, bulk density, and soil types were mapped for recent years only. The cross-validation results indicate Root Mean Square Error (RMSE) of 17.7 (kg m−3; 0.486 in log-scale) and Concordance Correlation Coefficient (CCC) of 0.88 for SOC density, RMSE of 51.3 (g kg−1; 0.574 in log-scale) and CCC of 0.87 for SOC content, RMSE of 0.15 (t m−3) and CCC of 0.92 for bulk density of fine-earth, RMSE of 0.51 and CCC of 0.91 for soil pH, RMSE of 8.4 % and CCC of 0.87 for soil clay content, and RMSE of 12.6 % and CCC of 0.84 for soil sand content respectively. The most important variables for predicting soil organic carbon density (kg m−3) were: soil depth, Landsat-based uncalibrated Gross Primary Productivity (GPP), Normalized Difference Vegetation Index (NDVI) and CHELSA bioclimatic indices. The global distribution of soil pH can be primarily explained by the CHELSA Aridity Index (long-term), annual precipitation, and salinity grade. The global stocks for 2020–2022+ period for 0–30 cm depth interval are estimated at 461 Pg (Peta grams); the results further indicate that, in the last 25 years, the world has lost at least 11 Pg of SOC in the top soil. Suggestions are made on how to set up global permanent monitoring stations to accurately track land degradation and enable land restoration projects. The training data set is available at https://doi.org/10.5281/zenodo.4748499 (Hengl and Gupta, 2025), while the resulting data products can be accessed at https://doi.org/10.5281/zenodo.15470431 (Consoli et al., 2025) and https://world.soils.app (OpenGeoHub Foundation, 2026). Both datasets are released under a CC-BY license.

Abstract. Soil organic carbon (SOC) is an important component of the global carbon cycle and a vital indicator of ecosystem health, playing key roles in agricultural productivity and climate change mitigation. To trace the spatiotemporal dynamics of SOC in China, a high-resolution (1 km) Soil Organic Carbon Density (SOCD) dataset for the 0–20 and 0–100 cm depths spanning the period from 1985 to 2020 is produced in this study. By integrating Landsat archives, topographic and meteorological data, and 11 743 soil profile measurements, we produced the SOCD dataset from 1985–2020 in China using the Random Forest ensemble learning approach. Specially, a climate zoning strategy was developed to account for the significant environmental heterogeneity across China. The validation of our SOCD estimated results with 0–20 cm depth with independent testing samples showed strong agreement with R2=0.63 and RMSE=2.03 (kg C m−2) for 0–20 cm SOCD estimation and R2=0.62 and RMSE=6.16 (kg C m−2) for 0–100 cm. Moreover, our SOCD estimated results with 0–20 cm depth are aligned well with independent samples (R2=0.76, RMSE=1.75 kg C m−2) and Xu's dataset (R2=0.68, RMSE=1.70 kg C m−2). Furthermore, the validation of our SOCD estimated results with 0–100 cm depth with independent measurements from Dong et al. (2024a) showed strong agreement (R2=0.50, RMSE=4.93 kg C m−2). Furthermore, our SOCD product exhibits high consistency with existing global datasets (HWSD, SoilGrids250 m, and GSOCmap), showing the best fit with SoilGrids250 m (R2=0.74, RMSE=1.03 kg C m−2). Comparisons of model predictions to independent datasets from the 1980s, 2000s, and 2010s in China reveal substantial connections and demonstrate strong performance over time. The estimated SOCD products, along with the compiled raw soil profile observations for both 0–20 and 0–100 cm depths, are openly available via Figshare (https://doi.org/10.6084/m9.figshare.27290310.v2) (Dong et al., 2024b).

Relationship between plant diversity and soil organic carbon density in inland salt marsh wetlands of Sugan Lake

IntroductionUnderstanding the synergies and trade-offs among tree, understory, and soil carbon pools is critical for optimizing forest carbon sinks. However, the mechanisms regulating these relationships, particularly how they differ between natural and planted forests, remain unclear. This study aims to deconstruct these complex interactions in subtropical forests to provide a scientific basis for enhancing ecosystem carbon storage.MethodsBased on data from 440 plots covering six major forest types in subtropical China, we employed linear mixed-effects models (LMMs) to quantify universal and context-dependent driver effects. We then used structural equation models (SEMs) to test and compare the mechanistic pathways of carbon allocation in natural versus planted forests.ResultsOur LMMs revealed a universal trade-off, with tree layer carbon density (TCD) strongly suppressing understory carbon density (UCD) (β = -0.22, P < 0.001) while synergistically promoting soil organic carbon density (SOCD) (β = 0.36, P < 0.001). SEM analysis (natural forests: CFI = 0.986, RMSEA = 0.064; planted forests: CFI = 0.960, RMSEA = 0.076) revealed divergent regulatory mechanisms. In natural forests, tree diversity directly buffered the suppressive effect of TCD on UCD via a significant positive path (β = 0.22, P < 0.01). This buffering pathway was absent in planted forests, leading to an amplified TCD-UCD trade-off (β = -0.54, P < 0.001).DiscussionOur findings demonstrate that forest carbon allocation is governed by a vertical trade-off and an above-belowground synergy, with tree diversity acting as a key modulator. Compared with complex natural forests, the carbon allocation mechanism in planted forests is simplified with more acute trade-offs. We conclude that enhancing structural and species diversity in plantations is a critical pathway for synergistically optimizing the entire ecosystem’s carbon sink capacity.

Wetland restoration enhances soil carbon sequestration in lake ecosystems: Integrating multi-source remote sensing and optimized ensemble machine learning to map soil organic carbon density

Urban green spaces play a vital role in climate change mitigation through carbon sequestration and storage. However, accurately quantifying their carbon sink capability remains challenging due to complex vertical structures and spatial heterogeneity. This study proposes a comprehensive inventory framework integrating multi-source LiDAR (UAV and Backpack) with a phenology-based complementary strategy to quantify carbon dynamics across three nested scales: green space types, plant communities, and species. Two key indicators—Carbon Sequestration Efficiency (CSE) and Carbon Density (CD)—were used to evaluate both the dynamic and static aspects of carbon sink function. The results reveal a clear asynchrony between CSE and CD across scales. No single plant type performed best in both dimensions, indicating a trade-off between growth efficiency and biomass accumulation. Hierarchical clustering identified distinct plant groups with divergent carbon sink strategies, supporting nuanced vegetation selection. The dual-indicator and dual-platform approach proposed in this study advances our existing understanding of the carbon sequestration capacity of urban green spaces and provides a robust methodological foundation for data-driven low-carbon urban ecological planning.

Highland urban wetlands are primarily located in high-altitude, low-temperature regions, possessing unique ecological and regulatory functions. Following ecological restoration and supplemented by artificial interventions, wetlands in Xining City play a significant role in carbon sequestration, oxygen release, and water purification. Against the backdrop of China’s “carbon peak and carbon neutrality” goals, investigating carbon cycling processes in high-altitude urban wetlands has emerged as a current research focus. However, systematic exploration integrating the three elements-“high-altitude,” “urban ecosystems,” and “sediment carbon pools”-remains largely unexplored. This study examines the sediment carbon pools in Xining’s wetlands—specifically Haihu (semi-artificial wetland), Ninghu (artificial wetland), and Beichuanhe (riverine wetland)—within the Hangsui River Wetland Park. Key findings include: (1) The short-term sedimentation rate was determined to be 0.50–0.75 cm·a−1 based on the “inflection point” of biogenic elements in wetland sediments. (2) Significant differences (p &lt; 0.05) in average TOC content were observed across different wetland sediments during distinct periods. Sediment TOC content is higher under plant cover than without plant cover. (3) The mean sediment carbon density ranged from 6.58 ± 1.38–13.02 ± 3.91 g·cm−2, with sediment organic carbon burial rates between 0.67–1.65 g·cm−2·a−1. (4) The sediment carbon stock in the wetland was 20,856.09 Mg·C.

A regression model was built for the relationship between carbon density, temperature, and precipitation in the terrestrial ecosystem of Anhui Province in the base year （2010）. Based on the actual changes in climate and land cover from 2010 to 2020, the carbon density of various land types in 2020 was calculated while the changes in carbon storage were simulated quantitatively in the province under the dual driving forces of nature and human activities. Using the four future climate scenarios proposed by CMIP6 and the future land cover simulated by the PLUS model, the carbon storage of terrestrial ecosystems in Anhui Province in 2030 and 2060 was predicted. The results indicate that： ① This multiple linear regression equation （R2=0.886 75） better confirmed the existing research conclusions that the interannual temperature rise accelerated soil organic carbon decomposition, and the increase in precipitation promoted vegetation biomass accumulation in the transition zone from warm temperate to northern subtropical regions in eastern China. ② From 2010 to 2020, the overall temperature in Anhui Province slightly decreased, and precipitation slightly increased, resulting in an increase of 89.81 Tg in carbon storage caused by climate change. The main trend of land cover change was the outward expansion of construction land, which occupied surrounding arable land, resulting in a reduction of 6.11 Tg in carbon storage. As a result, the carbon storage of terrestrial ecosystems in Anhui Province increased from 2 097.07 Tg in 2010 to 2 180.77 Tg in 2020, with an average annual carbon sequestration rate per unit area （calculated as C） of 59.76 g·（m2·a）-1. ③ From 2020 to 2030, under the SSP1-2.6 （low emission） scenario, the overall temperature in Anhui Province decreased narrowly, and precipitation increased, resulting in a slight increase in carbon density in all six land types. Driven by both climate change （91.6% contribution） and human activities （8.4% contribution）, carbon storage increased by 142.84 Tg over the next 10 years, reaching 2 323.61 Tg in 2030, with an average annual carbon sequestration rate per unit area （calculated as C） of 101.96 g·（m2·a）-1. In the SSP2-4.5 （medium emission）, SSP3-7.0 （competition）, and SSP5-8.5 （high emission） scenarios, there were varying degrees of warming, resulting in a corresponding decrease in carbon density and carbon storage by 2030. The SSP3-7.0 （competition） scenario had the smallest relative decrease. ④ From 2030 to 2060, due to the further increase in temperature under the four scenarios, carbon density in all six land types was predicted to decrease. In addition, relatively high-density carbon sequestration farmland would continue to be significantly transformed into low-density carbon sequestration towns, resulting in a significant decrease in carbon storage in all four scenarios. In the SSP3-7.0 （competitive） scenario in 2060, carbon storage was relatively highest compared to that in the other three scenarios.

The estimation of carbon storage and quantitative analysis of the impact of climate and land use changes on carbon storage are of great significance for explaining the hydrological and ecological response mechanisms of a watershed and achieving carbon neutrality. This study focused on the Yiluo River, an important first-order tributary of the Yellow River, using the RHESSys model to estimate soil, plant, litter, and total carbon storage in the watershed. A multiple regression analysis model was constructed to quantify the contributions of climate change and land use change to carbon storage. The results showed that： The Nash efficiency coefficients（NSE） of the RHESSys model were above 0.65 during both the calibration and validation periods, and the R2 values were greater than 0.7, indicating that the model performed well in simulating the Yiluo River Basin. The spatial distribution of carbon density in the Yiluo River Basin decreased from west to east and from upstream to downstream. Over the period from 2000 to 2023, both the soil carbon and total carbon in the Yiluo River Basin showed a significant upward trend, with soil carbon density increasing by 4.48 kg·m-2 and total carbon density increasing by 4.25 kg·m-2. Moreover, plant carbon density slightly decreased, while litter carbon density slightly increased, but the interannual variations were not significant. The contrabution rates of impacts of meteorological factors and land use change on total carbon storage were 51% and 49%, respectively. Litter carbon was most sensitive to meteorological factors, especially changes in minimum temperature, whereas land use change had a more significant effect on soil and plant carbon, with changes in forest area having the most pronounced impact.

The spatiotemporal variation in soil organic carbon density （SOCD） is of great significance for improving global carbon cycle management and the local ecological environment, so it is necessary to study the spatiotemporal distribution and influencing factors of regional SOCD. This study collected a total of 614 soil profiles from 1980 to 2021 in Anhui Province, analyzed the temporal and spatial changes of SOCD in different geographical regions and land use in Anhui Province, and used the structural equation model （SEM） to explore the influencing factors of SOCD. The results showed that： ① In 1980 and 2021, the spatial distribution of SOCD in Anhui Province was as follows： south Anhui hilly region&gt;west Anhui mountain region&gt;riverine plain&gt;Jianghuai hilly downland&gt;Huaibei plain. From 1980 to 2021, SOCD increased in Huaibei plain and Jianghuai hilly downland but decreased in the riverine plain, west Anhui mountain region, and south Anhui hilly region. The highest SOCD content among land use types in 1980 and 2021 was grass land. Compared with that in 1980, the SOCD content in uplands will increase the most in 2021, while the SOCD content in grass lands will decrease the most. ② The results of structural equation model analysis showed that SOCD was positively correlated with average annual precipitation, land use, fertilizer application, NDVI, and terrain and negatively correlated with average annual temperature. In 1980, the terrain in the 0-30 cm soil layer in Anhui Province was the main factor affecting the spatial distribution of SOCD, and the land use in the 30-100 cm soil layer was the main factor affecting the spatial distribution of SOCD, while the average annual temperature in the 0-100 cm soil layer in Anhui Province in 2021 was the main factor affecting the spatial distribution of SOCD. The results provide scientific basis for the development of soil carbon sink management measures in Anhui Province.

This study investigates the impact of landscape patterns on ecosystem services in the Ruoergai Plateau from 1990 to 2020. Ecosystem services play a crucial role in maintaining the balance of natural ecosystems and facilitating socio-economic development. Using the InVEST model, it quantifies and assesses carbon storage, soil conservation, water yield, and habitat quality. Correlation analysis is employed to explore the interrelationships and constraints among different ecosystem services, while stepwise regression analysis and bivariate spatial autocorrelation are utilized to investigate the impact mechanisms of landscape patterns on ecosystem services. Results indicate that the Ruoergai Plateau has experienced changes and transitions in land use, with grasslands being the primary type showing a decreasing trend. Landscape patterns have significantly altered, with a mitigation of fragmentation observed. Overall, ecosystem services show a declining trend initially followed by an increase. Carbon stock showed a decreasing trend from 1990 to 2010, with a significant increase from 2010 to 2020 and an increase of 0.41 × 108 t. The average carbon density and stock in the study area in 2020 reached 78.48 t.hm− 2 and 3.64 × 108 t, which were mainly concentrated in the wetland and forested land, distributed in the eastern and southwestern parts of the study area. There exist varied trade-offs and coordination among ecosystem services across different regions and temporal scales, while demonstrating a certain correlation with landscape indices. These findings enhance our understanding of how landscape pattern dynamics shape ecosystem service functions, providing valuable insights for regional ecological management and sustainable development.

Abstract. Organic carbon stored in continental margin sediments might be at risk by widespread mobile bottom fishing, potentially leading to reductions of organic carbon stocks, increased ocean acidification, additional atmospheric carbon dioxide emissions and a reduction of the buffering capacity of the ocean. Spatially explicit studies that have been conducted to inform marine management have so far looked at organic carbon stocks that have already been affected by mobile bottom fishing. Here, we focus instead on areas on the Norwegian continental margin that are currently not fished, based on fishing data covering the years 2009–2020. Using these data and spatial prediction methods, we estimate that the surface sediment layer (0–2 cm) in unfished areas covering 765 600 km2 contains 139.2 Tg of organic carbon. Based on data from a meta-analysis of demersal fishing impacts on organic carbon density and estimated reductions in sediment thickness due to fishing-induced erosion, we estimate that 18.7 Tg (1.9–33.5 Tg) of organic carbon might be lost due to mobile bottom fishing in a scenario where each grid cell is fished evenly over the entire area and down to the full depth of the surface layer. Approximately one third of this vulnerable organic carbon is currently located in existing area-based protection measures. Additional protection could be guided by hotspots of vulnerable organic carbon, which are mainly found in the Barents Sea. We argue that the protection of vulnerable organic carbon that is at high risk of being lost e.g. in areas becoming accessible to fishing due to sea ice retreat such as in the northern Barents Sea should be given a high priority.

Biodiversity-carbon relationships in urban wilderness: Evidence for synergies, trade-offs, and low-intervention strategies.

Abstract Integumentary fossils have improved understanding of dinosaur physiology, appearance and ecological niches. Fossil melanin and fossil melanosome organelles that produced melanin have made it possible to reconstruct dinosaur colour patterns, evidencing fundamental but previously elusive behaviours like camouflage. However, the colouration of several important groups, including sauropods, is still unknown. Here, we propose the first evidence of colouration in a sauropod based on potential melanosome-bearing epidermal scales. The fossil skin originates from juvenile diplodocids from the Mother’s Day Quarry of the Morrison formation in Montana, USA. Scanning electron microscopy reveals two fossilized epidermal layers in the scales that vary in microbody and carbon density. Two distinct microbodies are grouped together and dispersed within the potential outermost epidermal layer. The first are oblong-shaped and interpreted as melanosomes. The nature of the second disc-shaped microbody is unclear, but their flat shape is reminiscent of platelet melanosomes, though they are smaller in size.

Abstract Unvegetated intertidal sediments are increasingly recognized as contributors to coastal carbon storage, yet their organic carbon burial potential remains poorly constrained. This study examines spatial and temporal patterns of carbon accumulation in unvegetated intertidal flats of Ōhiwa Harbor, New Zealand, using surface sediments and three radiocarbon‐dated cores spanning up to ∼7,700 yrs. Within the harbor, five distinct sedimentary facies were identified, each displaying unique sediment characteristics and patterns of organic carbon burial. Mud‐rich, low‐energy facies, including rippled and bioturbated muds, consistently showed higher organic carbon density and burial rates compared to sandy, more dynamic facies. Estimated carbon stocks in the upper meter of sediment range from 44 to 120 t C ha −1 , comparable to or exceeding those of many vegetated coastal habitats. Temporal changes in facies distribution driven by estuarine processes and variations in sediment supply led to significant long‐term fluctuations in organic carbon burial. These results demonstrate that organic carbon storage in unvegetated intertidal flats is highly heterogeneous and controlled by the persistence of fine‐grained depositional environments. A facies‐based framework offers a process‐driven approach to assessing and managing blue‐carbon potential in estuarine systems increasingly altered by climate and land‐use change.

Optimal nitrogen fertilizer management increased soil organic carbon density by increasing particulate organic matter proportion and plant-derived carbon in topsoil

Effects of LUCC changes on ecosystem service supply and trade-off-synergy relationships in the Bohai Rim region.

Effects of stand structural changes on vegetation carbon sequestration capacity during secondary succession in subtropical evergreen-deciduous broadleaved mixed forests in central China.

A significant amount of global greenhouse gas (GHG) emissions comes from Indonesia, largely driven by deforestation and land degradation. As a developing nation, it is also dealing with the growing pressures of urban expansion. This study assesses the distribution of carbon stock in Parepare City, South Sulawesi, Indonesia. Notably, Parepare City has not yet experienced extensive land-use transformations, retaining substantial carbon stock, which positions it as a proactive case study for preventing future carbon loss amidst ongoing urbanization. Using the InVEST Carbon Storage and Sequestration model with SPOT 7 satellite imagery (2016) and global carbon density data, the research quantifies carbon storage across various land use/land cover (LULC) types. Analysis reveals natural ecosystems, particularly mixed forests and fields, hold the highest carbon storage potential. The total estimated carbon stock in Parepare City is 1,456,909.41 Mg C. These findings emphasize the urgent need for climate-responsive land management, including forest conservation, and urban greening, to enhance local carbon sinks and support Indonesia's climate change mitigation goals. This assessment provides crucial insights for urban planners and policymakers to balance growth with ecosystem conservation for a susta00inable future.

Abstract Changing climate is altering the amount of carbon that can be sustained in forest ecosystems. Increasing heat and drought is already causing increased mortality and decreased regeneration in some locations. These changes have implications for landscape carbon storage with ongoing climate change. We used a climate analogs approach to project aboveground forest carbon density under +2˚C warming above pre-industrial climate for western US forests. We calculated analogs for current climate and under +2˚C warming and associated carbon density for each time period. We found that in most ecoregions, maximum carbon density values are projected to decline and the interquartile range of carbon density values is projected to narrow. Using mean carbon density values, we project a 796 Tg decline in landscape carbon storage across the western US. As tree mortality increases, the transition from live to dead carbon will increase fuel buildup and fire hazard in many ecosystems. Greater fire hazard and increased susceptibility to insects from drought could cause carbon density changes to occur more rapidly than our climate-only projections. This may have substantial implications for forest-based carbon offset projects.