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Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation.

PurposeAccurately assessing soil organic carbon (SOC) content is vital for ecosystem services management and addressing global climate challenges. This study undertakes a comprehensive bibliometric analysis of global estimates for SOC using remote sensing (RS) and machine learning (ML) techniques. It showcases the historical growth and thematic evolution in SOC research, aiming to amplify the understanding of SOC estimation themes and provide scientific support for climate change adaptation and mitigation.Materials and MethodsEmploying extensive literature database analysis, bibliometric network analysis, and clustering techniques, the study reviews 1,761 articles on SOC estimation using RS technologies and 490 articles on SOC employing both RS and ML technologies.Results and DiscussionThe results indicate that satellite-based RS, particularly the Landsat series, is predominant for estimation of SOC and other associated studies, with North America, China, and Europe leading in evaluations with Africa is having low evaluations adopting RS technology. Trends in the research demonstrate an evolution from basic mapping to advanced topics such as carbon (C) sequestration, complex modeling, and big data utilization. Thematic clusters from co-occurrence analysis suggest the interplay between technology development, environmental surveys, soil properties, and climate dynamics.ConclusionThe study highlights the synergy between RS and ML, with advanced ML techniques proving to be critical for accurate SOC estimation. These findings are crucial for comprehensive ecosystem SOC estimation, informed environmental management and strategic decision-making.

Natural and anthropogenic disturbances are important drivers of tree mortality, shaping the structure, composition, and biomass distribution of forest ecosystems. Differences in disturbance regimes, characterized by the frequency, extent, and intensity of disturbance events, result in structurally different landscapes. In this study, we design a model‐based experiment to investigate the links between disturbance regimes and spatial biomass patterns. First, the effects of disturbance events on biomass patterns are simulated using a simple dynamic carbon cycle model based on different disturbance regime attributes, which are characterized via three parameters: μ (probability scale), α (clustering degree), and β (intensity slope). 856,800 dynamically stable biomass patterns were then simulated using combined disturbance regime, primary productivity, and background mortality. As independent variables, we use biomass synthesis statistics from simulated biomass patterns to retrieve three disturbance regime parameters. Results show confident inversion of all three “true” disturbance parameters, with Nash‐Sutcliffe efficiency of 94.8% for μ, 94.9% for α, and 97.1% for β. Biomass histogram statistics primarily dominate the prediction of μ and β, while texture features have a more substantial influence on α. Overall, these results demonstrate the association between biomass patterns and disturbance regimes. Given the increasing availability of Earth observation of biomass, our findings open a new avenue to understand better and parameterize disturbance regimes and their links with vegetation dynamics under climate change. Ultimately, at a large scale, this approach would improve our current understanding of controls and feedback at the biosphere‐atmosphere interface in the present Earth system models. Plain Language Summary Forest dynamics are shaped by different disturbances, which are challenging to monitor and predict. Identifying individual disturbance occurrences and their impact on forest carbon stocks (biomass) is complex. However, our study deciphers the characteristics of disturbance occurrence, that is, disturbance regime, from biomass pattern. We characterized this regime across three dimensions: extent (μ), frequency (α), and intensity (β). Through a 200‐year landscape experiment, we explored the synthetic dynamically stable biomass under different disturbance regimes. Statistical features from biomass simulations revealed distinct spatial patterns, forming a connection between these patterns and the disturbance regime parameters via machine learning. Notably, specific biomass pattern statistics influence distinct disturbance regime parameters: μ and β are linked to histogram stats, while α is tied to texture statistics. This approach establishes a framework to diagnose disturbance regimes from biomass patterns, offering a way to incorporate these regimes into Earth system models. Key Points We investigate the link between disturbance regimes and spatial patterns of aboveground biomass emerging from diverse primary productivity The proposed framework allows for inferring disturbance probability, size and intensity from spatial features in aboveground biomass Disturbance regimes from high‐res Earth observations can enhance carbon cycle dynamics prediction from interannual to longer time scales

Carbon capture and storage (CCS) is an essential greenhouse gas removal (GGR) technology used to achieve negative emissions in bioenergy plants using biomass feedstock (Bio-CCS). In this study, the climate mitigation potential of a novel GGR technology consisting in the production of renewable-derived plastics from municipal solid waste (MSW) refuse has been evaluated. This novel GGR technology allows for carbon storage, for variable periods, in stable materials (plastics), and thus overcomes the technical limitations of CCS. A time-dependent carbon cycle assessment has been conducted based on the Absolute Global surface Temperature change Potential (AGTP) metric. This new method to assess carbon emissions is presented against a traditional life cycle assessment (LCA). The production of renewable-derived plastics proves to be an effective GGR technology for both landfill- and incineration-dominant countries in Europe. The results obtained encourage the implementation of renewable-derived plastics in Integrated Assessment Models (IAMs) to assess their global potential in forecasting scenarios to achieve the ambitious climate change targets set in the European Union. Thanks to this study, a novel approach toward a green and sustainable economy has been established. This study will help to fill the gaps between bioenergy and renewable materials production.

An atmospheric CH4 box model coupled to a global carbon cycle box model is used to constrain the carbon emission associated with the PETM and assess the role of CH4 during this event. A range of atmospheric and oceanic emission scenarios representing different amounts, rates, and isotopic signatures of emitted carbon are used to model the PETM onset. The first 3 kyr of the onset, a pre‐isotope excursion stage, is simulated by the atmospheric release of 900 to 1100 Pg C CH4 with a δ13C of −22 to −30‰. For a global average warming of 3°C, a release of CO2 to the ocean and CH4 to the atmosphere totalling 900 to 1400 Pg C, with a δ13C of −50 to −60‰, simulates the subsequent 1‐kyr isotope excursion stage. To explain the observations, the carbon must have been released over at most 500 years. The first stage results cannot be associated with any known PETM hypothesis. However, the second stage results are consistent with a methane hydrate source. More than a single source of carbon is required to explain the PETM onset.

Constraining the carbon isotopic changes associated with the end‐Triassic mass extinction is key to understanding the causes of the extinction and dynamics of recovery from it. Yet the pattern and timing of δ13C variation surrounding the extinction remain poorly constrained. Here we present close to 1000 new δ13C measurements from six newly sampled sections in Italy. We observe a sharp negative excursion in δ13Ccarb coincident with the disappearance of the Triassic fauna, and two positive excursions above it. The negative δ13Ccarb excursion in these sections does not occur in δ13Corg suggesting a possible diagenetic origin. In contrast, the interval of elevated δ13C occurs in both carbonate and organic carbon, suggesting that it is likely to be a primary feature. The positive excursions in the Lombardy Basin (southern Alps) and Mt. Cefalo (southern Apennines) appear to be time correlative on the basis of their position above the disappearance of characteristically Triassic biota. However, it is less certain that they are time correlative with positive excursions in other sections worldwide, as few options exist that honor both bio‐ and chemostratigraphy. Nonetheless, similarity to other events that are interpreted as global, as well as carbon cycle considerations, suggest that the isotopic enrichment is best interpreted to reflect a shift in the isotope composition of the global surface carbon reservoir. Our data indicate that perturbation of the global carbon cycle was not confined to the immediate vicinity of the extinction interval, but rather persisted for substantial length of geologic time afterwards. Key Points 13C enrichment occurs above last Triassic fossils in Italian sections Co‐variation of d13Ccarb and d13org indicates a primary origin for enrichment Enrichment reflects elevated organic carbon burial in aftermath of extinction

The North Atlantic is characterized by strong seasonality in mixed layer depths, resulting in winter recharge of surface layer nutrients and the spring phytoplankton bloom. This is the classical textbook model of seasonal cycles of oceanic biogeochemical processes, but in fact the North Atlantic is the exception rather than the rule. In much of the temperate and subpolar regions of the basin, the vernal accumulation of biomass is accompanied by a marked drawdown of inorganic carbon in the water column and pulses of particle flux to the seafloor. In the classical model, the decline of the CO$_{2}$ is balanced by accumulation of biogenic carbon and particle export. The main export mechanisms include sinking of ungrazed but possibly senescent phytoplankton and zooplankton grazing and egestion. Carbon budgets based on observations from the Joint Global Ocean Flux Study North Atlantic Bloom Experiment and Bermuda Atlantic Time Series cannot be closed using the elements of the classical model. That is, the CO$_{2}$ drawdown cannot be balanced by biomass accumulation and exports estimated by sediment traps. There are at least three possible routes toward reconciliation: (i) trap estimates are in error and systematically biased; (ii) spatial variability aliasses the observations making budgeting impossible without recourse to coupled three-dimensional models; and/or (iii) the classical model must be abandoned and replaced by a concept in which the accumulation and export of dissolved organic carbon assumes a major role in the North Atlantic carbon balance. At Bermuda, where the most complete data set exists, the weight of the evidence favours the first and third possibilities.

The vertical flux and transformation of biogenic particles are important processes in the oceanic carbon cycle. Changes in the magnitude of the biological pump can occur in the north eastern Atlantic on both a seasonal and interannual basis. For example, seasonal variations in vertical flux at 47 degrees N 20 degrees W are linked to seasonal ocean productivity variations such as the spring bloom. The size and organic and inorganic content of phytoplankton species, their development and succession also play a role in the scale and composition of the biological pump. The majority of flux is in the form of fast sinking aggregates. Bacteria and transparent exopolymer particle production by phytoplankton have been implicated in aggregate production and mass flux events. Zooplankton grazing and faecal pellet production, their size and composition and extent of their vertical migration also influence the magnitude of vertical flux. Aggregates are formed in the upper ocean, often reaching a maximum concentration just below the seasonal thermocline and can be a food resource to mesozooplankton as well as to the high concentrations of attached bacteria and protozoa. Attached bacteria remineralize and solubilize the aggregate particulate organic carbon. The degree of particle solubilization is likely to be affected by factors controlling enzyme activity and production, for example temperature, pressure or concentration of specific organic molecules, all of which may change during sinking. Attached bacterial growth is greatest on particulate organic matter collected at 500 m which is the depth where studies of $^{210}$Po reveal that there is greatest break-up of rapidly sinking particles. Break-up of particles by feeding zooplankton can also occur. The fraction of sinking POC lost between 150-3100 m at one station in the north eastern Atlantic could supply about 90% of the bacterial carbon demand. Some larger, faster sinking aggregates escape solubilization and disaggregation in the upper 1000 m and arrive in the deep ocean and on the deep-sea bed. Seasonally varying rates of sedimentation are reflected at the deep-sea floor by deposition of phytodetrital material in summer. Approximately 2-4% of surface water primary production reaches the sea floor in 4500 m depth at 47 degrees N 20 degrees W after a sedimentation time of about 4-6 weeks. In this region, concentrations of chloroplastic pigments increased in summer by an order of magnitude, whereas seasonal changes in activity or biomass parameters were smaller. Breakdown of the generally strongly degraded organic matter deposited on deep-sea sediments is mainly accomplished by bacteria. Rates of degradation and efficiency of biomass production depend largely on the proportion of biologically labile material which decreases with advancing decay. It is likely that different levels of organic matter deposition influence the bioturbation rates of larger benthos, which has an effect on transport processes within the sediment and presumably also on microbial degradation rates.

This paper focuses upon lipid biomarkers as tracers of the biological carbon cycle and our efforts to derive and validate `molecular tools' for oceanography and palaeoceanography as part of the 1989-1991 UK-JGOFS Biogeochemical Ocean Flux Study (BOFS). Biomarker concentrations and composition in water column particulates and bottom sediments in the North Atlantic show a strong correspondence to seasonal and interannual patterns of productivity. Biomarkers document the rapidity with which vertical flux processes operate in the high latitude North Atlantic: for example, the massive sedimentation of phytodetritus following a coccolithophorid bloom in the Iceland Basin and its subsequent resuspension, and the benthic biological response to this pulse of biologically available carbon. Sedimentary biomarker distributions indicate that organic material decomposition in sediments is dominated by processes at or near the sediment water interface and that downmixing of labile material into the sediments is largely controlled by advective rather than diffusive-like processes.

Productivity in the ocean is viewed from the perspective of its three components: biomass, yield (new production), and the rate of production. The three components are ordered along a time scale and a fundamental time scale is defined for the rate of production at the diel (24 h). Two optical properties, the absorption by phytoplankton and particle scattering, are presented and it is argued that they provide a means to unite the rate of production with biomass and yield at the diel scale.