Spatial-temporal Variations Research Articles

Spatial-temporal distributions of phytoplankton shifting, chlorophyll-a, and their influencing factors in shallow lakes using remote sensing

Understanding phytoplankton dynamics is crucial for assessing water ecosystem health. However, traditional in-situ investigations often fall short of capturing the spatial-temporal variations of phytoplankton community structure and biomass on large scales. This study introduces a novel approach that harnesses the power of machine learning models coupled with Sentinel-2 satellite data to predict phytoplankton shifting and chlorophyll-a (Chla) in three large shallow lakes in central China from 2016 to 2021. We employed an array of machine learning algorithms, including the extreme gradient boosting method (XGBoost), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) to retrieve Chla and phytoplankton groups clustered by hierarchical cluster analysis based on the Bray-Curtis similarity index (HCA). The influences of environmental factors, including meteorology and nutrients, on phytoplankton dynamics were then explored. Our results identified four distinct phytoplankton groups, each exhibiting unique structural characteristics according to the HCA. The RF and XGBoost, utilizing top-of-atmosphere reflectance, provided the most accurate estimations of phytoplankton shifting and Chla, respectively. The red edge-red ratio emerged as a key variable in both models, underlining the significance of red edge bands in phytoplankton monitoring. We mapped spatial-temporal patterns of phytoplankton group occurrence and average Chla concentration across the three lakes. Our findings indicated that eutrophication extended periods of cyanobacteria domination and algal blooms, particularly in bays and nearshore areas. Redundancy analysis and classification and regression tree model highlighted temperature as a significant driver of phytoplankton shifting, while nutrient levels exhibited stronger influences on Chla. Our study underscores the potential of integrating machine learning models with Sentinel-2 data to enhance the monitoring and prediction of phytoplankton dynamics in shallow lakes. This approach offers valuable insights for the early detection of algal blooms and informed management strategies, contributing to the preservation and sustainable management of aquatic ecosystems.

Correction: Nong et al. Spatial-Temporal Variations and Driving Factors of the Coupling and Coordination Level of the Digital Economy and Sustainable Rural Development: A Case Study of China. Agriculture 2024, 14, 849

In the original publication [...]

Characteristics of Water Environment and Its Influencing Factors in Zhari Namco Basin, Xizang

Zhari Namco is situated in the alpine grassland belt of northwestern Xizang with a fragile ecological environment. As the third-largest lake in Xizang, there has been a long-term lack of research data concerning its basin water environment. In an effort to elucidate the surface water environment characteristics of the basin and the factors influencing them, an extensive investigation was conducted from August 2021 to June 2022, encompassing periods of high flow, low flow, and base flow. Further, the study also involved comprehensive assessments of the water chemistry characteristics and spatial-temporal variation in lake sampling sites of the basin that were not significant by using mathematical statistics, hydrochemical analysis, correlation analysis, and principal component analysis. The findings revealed the following： ① The water in the Zhari Namco Basin exhibited an alkaline nature, with dominant ionic compositions in the lake comprising Na+, SO42-, and Cl-, whereas the rivers were primarily characterized by Ca2+, HCO3-, and SO42-. ② The main pollutants exceeding established standards included sulfates, arsenic, chlorides, and total phosphorus. The study identified significant spatiotemporal variations in water quality. Temporally, the exceedance of sulfates, arsenic, and total phosphorus was most pronounced during high-flow periods, followed by that during low-flow and base flow periods, with chloride levels showing less temporal variation. Spatially, river water quality surpassed that of the lakes, with arsenic, total phosphorus, TDS, sulfate, chloride, K+, and Na+ concentrations in lakes 1 to 2 orders of magnitude higher than those in rivers. Water qualities exceeding the established standard were primarily found in the lake, with less spatial variations within the lake itself. ③ Hydrochemical processes within the basin were found to be primarily influenced by natural phenomena, including evaporation-concentration and rock weathering. Various elements entered the lakes via surface runoff, where they continuously accumulated under the influence of evaporation-concentration processes, ultimately leading to exceedances. ④ Temporal variations in water quality were primarily attributed to increased elemental loss and intensified evaporation during high-flow periods. The spatial discrepancies in water quality were predominantly a consequence of the differing hydrodynamic conditions between flowing water bodies and enclosed water bodies.

Estimation of Regional Background Ozone in Henan Province from 2019 to 2021

Clarifying the regional transmission and local generation contributions of ozone （O3） is important for controlling O3 pollution. To quantify the regional background and spatial-temporal variations of O3, a comprehensive study was conducted using multiple methods including principal component analysis （PCA） and TCEQ, with Henan Province as a case study. Based on monitoring data from 59 national sites in Henan Province during 2019-2021, four methods were employed to estimate the regional background of O3. Method 1 was the traditional method, performing O3 univariate-multisite PCA analysis. Method 2 was a multivariate-single-site PCA analysis considering nitrogen dioxide and meteorological conditions as constraints. Method 3 combined PCA and multiple linear regression （MLR） to determine regional background contributions, drawing on the idea of source apportionment. Method 4 was the TCEQ method that used the lowest measured O3-8h concentration as the regional background. The estimation results showed that Methods 1 and 2 were basically equal, and Methods 3 and 4 were approximately 37-60 μg·m-3 lower than Method 1. From 2019 to 2021, the changes in regional background ρ（O3-8h） estimated by Methods 1-4 were 1.6, -13.4, 5.9, and -3.5 μg·m-3, respectively. The average estimations derived from multiple methods showed that the regional background ρ（O3-8h） in Henan Province from 2019 to 2021 concentrations were 82.0, 79.0, and 79.7 μg·m-3, accounting for 75.9%, 76.4%, and 78.7% of the total regional O3-8h, respectively. The regional background O3-8h estimated by the four methods showed obvious seasonal changes, characterized by summer > spring > fall > winter.

Spatial–temporal variation and correlation analysis of ecosystem service values and ecological risks in winter city Shenyang, China

Ecosystems in winter cities are complex and fragile, experiencing significant changes due to climate variations and human construction activities. Previous studies on the assessment of overall ecosystem service value (ESV) and ecological risk index (ERI) in winter cities are scarce. In this study, we constructed ESV and ERI measurement models using land use data in 2000, 2010, and 2020 using the improved value per unit area factor method and the landscape pattern index method, respectively, to reveal their spatial and temporal change characteristics. Geographic detectors were used to explore the driving roles of natural and artificial factors on the changes of ESV and ERI. The combination in ESV and ERI can then provide a more quantitative and accurate basis for policy decisions, identify priority areas for urban ecological restoration, and reduce the risk to ecosystems. The results of the study show that the total ESV of Shenyang city decreased from 273.97 × 108 CNY to 270.38 × 108 CNY during 2000–2020. Although the decrease is not large, the ESV changes structurally with the advancement of urbanization. During the 20 years, the construction land with the lowest ecological service function continues to expand, increasing by 354 km2, the grassland decreased by 215.9 km2, and the arable land decreased by 196.6 km2. The ecological service function of the water area is the strongest, with an increase of 51.3 km2 in the water area, ensuring that there is no significant decline in ESV. The size of the ERI is Very high, High, and Medium value zones remained relatively stable, while the size of the Very Low-value zone decreased by 12.78% and the size of the Low-value zone increased by 13.21%. The interaction factors that contributed most to the changes in ESV and ERI were annual evapotranspiration (EVP)/ Normalized Difference Vegetation Index (NDVI) and Annual sunshine hours (SSD)/ Digital Elevation Model (DEM) , respectively. There was a spatial correlation between ESV and ERI. The areas with the highest ESV supply capacity and at the same time facing severe ecological risks to the landscape pattern are distributed in the northeastern hilly lands. This area should be prioritized to develop planning and control measures to prevent further erosion of forest lands and grasslands and reduce ecological risks. These results provide a theoretical basis for ensuring ecological security and sustainable development in winter cities.

Significant Increase in African Water Vapor over 2001–2020

Atmospheric water vapor is not only a key element of the global hydrological cycle but also the most abundant greenhouse gas. The phase transition and transportation of water vapor are essential for maintaining global energy balance and regulating hydrological processes. However, due to insufficient meteorological observational data, climate research in Africa faces significant limitations despite its substantial contribution to changes in global precipitable water vapor (PWV). In this study, we used MODIS near-infrared (NIR) PWV products and Berkeley temperature data to depict the spatial–temporal variability in PWV across Africa from 2001 to 2020. The results reveal a significant increasing trend in PWV over Africa, with an increase of 0.0158 cm/year. Nearly 99.96% of Africa shows an increase in PWV, with 88.95% of these areas experiencing statistically significant changes, particularly in central regions of Africa. The increase in PWV is more pronounced in high-value months compared to low-value months. The equatorial region of the Congo Basin exhibits higher PWV, which gradually decreases as latitude increases. Despite significant warming (0.0162 °C/year) in Africa, there is no consistent positive correlation between temperature and water vapor. A positive relationship between PWV and temperature is observed in western Africa, while a negative relationship is noted in eastern and southern Africa on an annual scale. Additionally, an increasing trend in precipitation (4.6669 mm/year) is observed, with a significant positive correlation between PWV and precipitation across most of Africa, although this relationship varies by month. These findings provide valuable insights into the comprehension of the hydrothermal variation in Africa amidst climate warming.

Ocean weather, biological rates, and unexplained global ecological patterns.

As on land, oceans exhibit high temporal and spatial temperature variation. This "ocean weather" contributes to the physiological and ecological processes that ultimately determine the patterns of species distribution and abundance, yet is often unrecognized, especially in tropical oceans. Here, we tested the paradigm of temperature stability in shallow waters (<12.5 m) across different zones of latitude. We collated hundreds of in situ, high temporal-frequency ocean temperature time series globally to produce an intuitive measure of temperature variability, ranging in scale from quarter-diurnal to annual time spans. To estimate organismal sensitivity of ectotherms (i.e. microbes, algae, and animals whose body temperatures depend upon ocean temperature), we computed the corresponding range of biological rates (such as metabolic rate or photosynthesis) for each time span, assuming an exponential relationship. We found that subtropical regions had the broadest temperature ranges at time spans equal to or shorter than a month, while temperate and tropical systems both exhibited narrow (i.e. stable) short-term temperature range estimates. However, temperature-dependent biological rates in tropical regions displayed greater ranges than in temperate systems. Hence, our results suggest that tropical ectotherms may be relatively more sensitive to short-term thermal variability. We also highlight previously unexplained macroecological patterns that may be underpinned by short-term temperature variability.

Vegetation’s Dynamic Changes, Spatial Trends, and Responses to Drought in the Yellow River Basin, China

Drought is a complex and recurrent natural disaster that can have devastating impacts on economies, societies, and ecosystems around the world. In light of climate change, the frequency, duration, and severity of drought events worldwide have increased, and extreme drought events have caused more severe and irreversible damage to terrestrial ecosystems. Therefore, estimating the resilience of different vegetation to drought events and vegetation’s response to damage is crucial to ensuring ecological security and guiding ecological restoration. Based on meteorological and remote-sensing datasets from 1982 to 2022, the spatial distribution characteristics and temporal variability of vegetation were identified in the Yellow River Basin (YRB), the dynamic changes and recurrence periods of typical drought events were clarified, and the driving effects of different drought types on vegetation were revealed. The results indicated that (1) during the research period, the standardized vegetation water-deficit index (SVWI) showed a downward trend in the YRB, with a 99.52% probability of abrupt seasonal changes in the SVWI occurring in January 2003; (2) the characteristic values of the grid trend Zs were −1.46 and 0.20 in winter and summer, respectively, indicating a significant downward trend in the winter SVWI; (3) the drought with the highest severity (6.48) occurred from September 1998 to February 1999, with a recurrence period of 8.54 years; and (4) the growth of vegetation was closely related to drought, and as the duration of drought increased, the sensitivity of vegetation to drought events gradually weakened. The research results provide a new perspective for identifying vegetation’s dynamic changes and responses to drought, which is of great significance in revealing the adaptability and potential influencing factors of vegetation in relation to climate.

Seasonal Temperature Distributions and Variations in Salt Marshes: Field Investigation and Numerical Simulation

AbstractSoil temperature regulates biogeochemical processes and is a key environmental factor affecting salt marsh ecosystems. Previous studies on soil temperature and heat transport in intertidal marshes predominantly focused on short‐term changes, leaving seasonal variations unclear. This study conducted a yearlong field and modeling investigation to examine temporal and spatial temperature variations in a creek‐marsh section under estuarine and meteorological influences. The results showed that inundating tidal water propagated the seasonal water temperature signal to marsh soils, especially at low elevations, thereby modulating air temperature‐induced soil temperature variations and distributions. The response of soil temperature to air and tidal water temperatures exhibited a damped and delayed pattern. In contrast, tide‐induced porewater circulations near the creek facilitated the temperature responses. A regression model incorporating a Gamma distribution function was developed to quantify the delayed and cumulative thermal effects of tidal water and air on shallow soil temperatures. The model coefficients varied along the creek‐marsh section, capturing the seasonal regulation of periodic tidal inundation on soil temperature in the low‐elevation marsh and near the creek. Sensitivity analyses indicated that relative sea level rise lowered yearly‐averaged temperatures for the marsh platform by enhancing latent heat export. Surface water warming increased the marsh temperatures at lower elevations. This study demonstrates that the creek‐marsh topography, warming, and relative sea level rise jointly affect soil temperature dynamics, advancing our understanding of temperature‐dependent biogeochemical processes in marsh ecosystems.

Resolution of Hydroacoustic Noise Signals under Conditions of Spatial–Temporal Variability of Ocean Environment

Resolution of Hydroacoustic Noise Signals under Conditions of Spatial–Temporal Variability of Ocean Environment

Modeling the evaporation and drying of two-component slurry droplets

In this paper, we present a mathematical model and numerical simulation for the evaporation and drying of a liquid droplet containing suspended solids, relevant to processes such as spray drying and spray pyrolysis in the food and pharmaceutical industries. The model comprises three stages: first is the evaporation of the liquid droplet consisting of solid particles, followed by the second stage starting with the formation of a porous crust around a wet-core region and, finally, the third stage with sensible heating of the dry particle. Using a finite difference method with a moving grid, we account for the moving interface between the crust and wet core. Our model incorporates spatial temperature variations and is validated against experimental data on colloidal silica droplet drying, showing good agreement. We examine model assumptions and analyze the impact of drying conditions on drying rate and final particle morphology. Along with the temperature and velocity of the drying gas, we also find that the shape of suspended solid particles inside the droplet and assuming continuum flow of vapor through the crust influence drying quality. Finally, we develop a regime map to predict whether the final particle will be solid or hollow based on operating conditions.

Investigation on phytoplankton dynamics and its environmental correlates in the Xiangjiaba channel-type reservoir

Mega reservoirs, such as the Three Gorges Reservoir on the Yangtze River, have garnered significant attention due to their environmental impacts. However, the ecological ramifications of upstream cascade reservoirs remain understudied, despite their potential influence on the Yangtze River ecosystem. The Xiangjiaba Reservoir (XJB), presents a unique case for understanding the ecological impacts of large channel-type reservoirs marked with considerable water depth and extensive runoff, which lies between the characteristics of rivers and lakes. This study delves into the phytoplankton community dynamics and associated environmental factors within the XJB. Through field surveys and laboratory analysis, we explore the drivers of phytoplankton distribution and their temporal shifts. Our findings revel significant changes in phytoplankton dominance among various phyla. Bacillariophyta and Chlorophyta dominated throughout the year, while Cryptophyta prevailed in spring and Xanthophyta peaked in autumn, indicating a unique ecological feature of XJB. The reservoir’s water quality was found to be moderate. The average chlorophyll-a exhibited significant spatial–temporal variations, peaking at 26 μg/L at the mainstream-tributary confluence. Our investigation reveals a tenfold increase in algal abundance since the reservoir’s inception, accompanied by a significant transition from a Bacillariophyta-dominated community to a diverse, multi-phylum system. This shift has been significantly influenced by hydrodynamic conditions, with water stratification favoring flagellated algae like Chlorophyta and Cryptophyta. Notably, differences in phytoplankton composition between XJB and the Three Gorges Reservoir were linked to the latter’s pronounced vertical mixing. The slower ecological responses of the phytoplankton community to hydrodynamic changes underscore the importance of long-term, continuous monitoring to maintain the reservoir’s ecological balance. Our research offers insights into the ecological impacts of reservoir construction, highlighting the role of hydrodynamics in reservoir ecosystems and aiding in understanding reservoir functioning, water quality management, and biodiversity conservation.

TEMDI: A Temporal Enhanced Multisource Data Integration model for accurate PM2.5 concentration forecasting

Accurate forecasting of PM2.5 concentration is crucial for implementing effective protective measures and mitigating the adverse health impacts of air pollution. To address the complex spatial propagation dynamics and temporal variations of PM2.5, we developed the Temporal Enhanced Multisource Data Integration (TEMDI) model. This innovative approach combines spatial modeling by a Graph Neural Network (GNN) to capture the intricate spatial propagation patterns based on multi-source data fusion, and a novel Time Series Enhancement (TSE) module that includes Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Units (GRUs), and a self-attention mechanism to adequately manage the time series’ short-term and long-term trends. Our results demonstrate TEMDI’s superior performance, achieving exceptionally high Probability of Detection (POD) rates of 96.15%, 80.28%, and 71.86% for forecast horizons of 3, 36, and 72 h, respectively. Furthermore, our feature importance analysis reveals that multi-scale features extracted by the EEMD component become increasingly crucial as the prediction horizon extends. The TEMDI model’s ability to provide accurate, reliable PM2.5 forecasts and its enhanced interpretability position it as a valuable tool for guiding environmental policy and management decisions to safeguard public health.

Dynamic Spatiotemporal Evolution and Driving Mechanisms of Vegetation in the Lower Reaches of the Tarim River, China

Analyzing the changes in vegetation under different factors is crucial for ecological protection in arid areas. The spatial-temporal variations of vegetation in the lower reaches of the Tarim River (LRTR) from 2000 to 2020, were analyzed using the Theil-Sen estimator and the Mann-Kendall test. The future trends of NDVI are projected to use the Hurst exponent method. The driving mechanisms of vegetation changes were analyzed using the GeoDetector method and multivariate residual analysis. The NDVI values in the LRTR significantly increased during the study period, indicating good vegetation recovery. The overall vegetation level remains poor and was primarily concentrated around the riverine areas. There is still a risk of vegetation degradation in most areas of the future LRTR. Compared to climate change, vegetation was more affected by human activities. Human activities have helped restore the riparian vegetation and prevented the degradation of vegetation far from the river. Therefore, distance from river channels is the strongest explanatory factor (q = 0.078) for vegetation changes, followed by precipitation, and temperature, while changes in slope have minimal impact on vegetation. Statistics have found that when two factors are combined, their impact on vegetation change is stronger. These findings are beneficial for identifying vegetation evolution patterns in LRTR and providing theoretical support for the government to carry out ecological restoration.

Structure-aware sign language recognition with spatial–temporal scene graph

Continuous sign language recognition (CSLR) is essential for the social participation of deaf individuals. The structural information of sign language motion units plays a crucial role in semantic representation. However, most existing CSLR methods treat motion units as a whole appearance in the video sequence, neglecting the exploitation and explanation of structural information in the models. This paper proposes a Structure-Aware Graph Convolutional Neural Network (SA-GNN) model for CSLR. This model constructs a spatial–temporal scene graph, explicitly capturing motion units’ spatial structure and temporal variation. Furthermore, to effectively train the SA-GNN, we propose an adaptive bootstrap strategy that enhances weak supervision using dense pseudo labels. This strategy incorporates a confidence cross-entropy loss to adjust the distribution of pseudo labels adaptively. Extensive experiments validate the effectiveness of our proposed method, achieving competitive results on popular CSLR datasets.

Aircraft-derived CH4 emissions from surface and in-situ mining activities in the Alberta oil sands region

The identification and reduction of methane sources is considered an important part of the fight to stem greenhouse gas (GHG) emissions to the atmosphere. One of the largest industrial contributors to national GHG emissions in Canada is the Alberta Oil Sands Region. To quantify and investigate the spatial distribution and temporal variability of methane emissions from this region, airborne measurements were conducted in 2017 and 2018 with three aircraft. 59 flights were conducted in total to assess emissions for both open-pit and in-situ facilities, in both cold and warm seasons. Derived emission rates were higher than those reported in national inventories by 30%–96% depending on the facility. In-situ facilities had emission rates an order of magnitude lower than surface mining operations and differed significantly from inventory estimates. No statistical differences in CH4 emissions between cold and warm seasons were observed, substantiating the use of simple upscaling to annual emissions within inventories. Rather than confirming a reported decrease in emissions between 2013 and 2018, the measurements suggest essentially no change from the 18 t h−1 for the region observed in 2013. Overall, the results suggest that current methods of CH4 emission determination within the oil sands region, for use in reporting, require improvement.

Long-term investigation of spatial–temporal variations, risk assessments, and controlling factors of persistent organic pollutants (HCHs, DDTs, and PAHs) and heavy metals in coastal regions along the Chinese Bohai Sea

Long-term investigation of spatial–temporal variations, risk assessments, and controlling factors of persistent organic pollutants (HCHs, DDTs, and PAHs) and heavy metals in coastal regions along the Chinese Bohai Sea

Multi-Year Continuous Observations of Ambient PM2.5 at Six Sites in Akure, Southwestern Nigeria

The spatial–temporal variations of fine particulate matter (PM2.5) in Akure, a city in southwestern Nigeria, are examined based on multi-year continuous observations using low-cost PM2.5 sensors at six different sites. The average annual concentration of PM2.5 across these sites was measured at 41.0 µg/m3, which surpassed both the Nigerian national air quality standard and the World Health Organization air quality guideline level. PM2.5 levels were significantly higher during the dry season (November–March), often exceeding hazardous levels (over 350 µg/m3), than during the wet season. The analyses of trends in air mass trajectories and satellite data on fire occurrences imply that the transport of dust and accumulation of PM2.5 originating from local/regional open burning activities played crucial roles in increased PM2.5 concentrations during the dry season. Further, site-to-site variations in the PM2.5 levels were observed, with relatively high concentrations at less urbanized sites, likely due to high local emissions from solid fuel combustion, waste burning, and unpaved road dust. Diurnal patterns showed morning and evening peaks at less urbanized sites, accounting for an estimated 51–77% of local emissions. These results highlight the importance of local emission sources in driving spatial–temporal PM2.5 variations within the city and the need for targeted mitigation strategies to address the significant air pollution challenges in Akure and similar regional cities in West Africa.

Implications of ocean warming and acidification on heavy metals in surface seawater of the Bohai Sea

At present, a clear dependency of the dynamics upon temperature and pH has not been established for many heavy metals (HMs), so making it difficult to project and quantify the impact of ocean warming and acidification on metal biogeochemistry in future scenarios. To understand the responses of HMs to future ocean warming and acidification, we estimated the spatial-temporal variations and pollution status of six dissolved HMs (i.e., Cu, Zn, Pb, Cd, Hg, and As) in surface seawater throughout the Bohai Sea during 2012–2014. The results showed that the average concentrations of Cu, Zn, Pb, Cd, Hg, and As in seawater of the Bohai Sea were between 2.01–3.18, 10.47–15.58, 0.85–2.31, 0.25–0.55, 0.05–0.13, and 1.24–1.98 μg L-1, respectively. Spatially, the average concentrations of the studied HMs generally decreased from the three bays towards the central area, except for Hg which was relatively high in the central Bohai Sea in some cases. This implied that, in addition to continental inputs, there may be other processes affecting the distribution pattern of Hg, such as cyclonic or anticyclonic gyres, benthic fluxes between surface and bottom layers, and some marine planktonic and microbial activities. The pollution assessments of six HMs in seawater revealed that the major risk pollutants were Pb and Hg across the Bohai Sea. Analyses of the local and interactive effects of temperature and pH on HMs showed that the interactive effect of changing temperature and pH on HMs is much more complex than a direct temperature/pH relationship with HMs. Altogether, the results suggested that future ocean warming and acidification will significantly influence the concentrations of dissolved HMs in seawater of the Bohai Sea, but with different relationships.

Spatial–Temporal Variations in Soil Organic Carbon and Driving Factors in Guangdong, China (2009–2023)

Spatial–temporal variation in soil organic carbon is an important factor for national targets to mitigate climate change and land degradation impacts. In this research, we took Guangdong Province of China as the study area, evaluated the spatial–temporal distributions of soil organic carbon using data from three China Geochemical Baseline projects (conducted in 2009, 2016, and 2023, respectively), and quantified the main driving factors of spatial–temporal variations in soil organic carbon using the random forest algorithm, further predicting the density and inventories of soil organic carbon. The results demonstrate that the mean value of SOC in Guangdong in 2009 was 0.81%; in 2016 it was 1.13%; and in 2023 it was 1.02%. The inventories of soil organic carbon (0–30 cm) in Guangdong Province were 0.61 Pg in 2009, 0.74 Pg in 2016, and 0.62 Pg in 2023. Soil in Guangdong acted as a carbon sink from 2009 to 2023 as a whole, and the most important driving force behind spatial–temporal variations in soil organic carbon was temperature, followed by precipitation and vegetation cover.