Search Results (177)

Understanding vegetation phenology responses to climate change is essential for predicting ecosystem dynamics, especially in mountainous transition zones, such as the Qinling Mountains, where climatic and ecological gradients are pronounced. To quantify these complex interactions, we combined high spatiotemporal resolution remote sensing data (30 m, 8-day) with CMFD climate datasets from 2010 to 2020. We leveraged a rigorous analysis of covariance (ANCOVA) framework to simultaneously test the spatial heterogeneity of phenological baselines and the temporal convergence of trends across vegetation types. Results revealed that the spatial pattern of the start of the growing season (SOS) exhibited highly significant heterogeneity (p < 0.001), primarily governed by vegetation composition and altitudinal gradients—a phenomenon we define as a spatial baseline constraint effect. In contrast, the interannual SOS trends (slopes) showed no significant differences among vegetation types (p = 0.685), indicating a temporal convergence effect. This regional synchrony, characterized by a consistent shift toward earlier SOS of approximately −0.8 to −0.9 days yr⁻¹ at low and mid-elevations, was largely driven by rising spring temperatures (R² ≈ 0.20). Crucially, the end of the growing season (EOS) displayed weak climatic sensitivity, revealing an asymmetric phenological response to temperature changes. Our findings demonstrate that vegetation phenology in the Qinling Mountains is jointly controlled by spatial baseline constraint and temporal trend convergence. This dual-mechanism framework provides new insights into the highly structured stability and resilience of mountainous ecosystems under regional warming. Full article

The resilience of arid ecosystems to climate change hinges on their carbon-water dynamics. This study investigates the spatiotemporal patterns of ecosystem water use efficiency (WUE) and its resilience in the ecologically vulnerable Yili-Balkhash Basin, a critical watershed in Central Asia. Contrary to a basin-wide trend of increasing WUE, we identify a significant decline in the WUE of high-productivity forest ecosystems. We demonstrate that this decline stems from a fundamental decoupling between the drivers of carbon (GPP) and water (ET) cycles during drought periods. While GPP shows a positive response to atmospheric aridity (vapor pressure deficit), likely driven by co-varying high radiation and temperature, ET remains primarily controlled by soil moisture and surface thermal conditions. This driver asynchrony results in ET-dominated control over WUE across 65.8% of the basin, rendering forests particularly vulnerable. Machine learning-based attribution reveals that ecosystem resilience is not determined by long-term drought legacy but by the combined effects of immediate thermal stress and a one-month ecological memory. Our findings highlight an emerging vulnerability of high-productivity forest ecosystems to atmospheric aridity and underscore the necessity of process-based frameworks for assessing ecosystem stability under a changing climate. Full article

Understanding the spatiotemporal characteristics of residents’ leisure travel distances (hereafter referred to as “RLTD”) and their underlying influencing factors is pivotal to reducing leisure travel costs and enhancing travel experiences. However, scholars have yet to identify leisure travel behavior and quantify RLTD accurately, and the nonlinear effects of the built environment on such distances remain underexplored. Therefore, this study, selecting Guangzhou as the case, employed multi-source data to measure RLTD and utilized a random forest model to explore the nonlinear relationship between the built environment and RLTD. Our findings are as follows. (1) Leisure activities among Guangzhou residents are dominated by short- and medium-distance travel (<10 km). Furthermore, RLTD exhibits significant spatiotemporal heterogeneity: on weekdays, it follows a zonal pattern where distances increase from the urban core to the periphery; conversely, on weekends, low-RLTD areas show a multi-center agglomeration pattern. (2) Proximity to central business districts (CBD) and large commercial centers, as well as optimal parking facility provision, emerge as the strongest predictors of RLTD on both weekdays and weekends. (3) All built environment variables exert nonlinear effects on RLTD, with distinct thresholds between weekdays and weekends. Additionally, a noticeable interaction effect is observed between the “distance to CBD” variable and other covariates. This study implies that when designing targeted interventions to promote residents’ leisure travel experience, policymakers should account for the temporal variations in how the built environment complexly influences RLTD. Full article

This study models the spatiotemporal heterogeneity of extreme rainfall over the Korean Peninsula using the generalised additive extreme value models (EVGAM) to address the limitations of traditional stationary approaches under climate change. Analyzing 30 years of daily precipitation data (1995–2024), we conducted a comparative analysis between typhoon-inclusive and non-typhoon scenarios to isolate the meteorological drivers of extremes. The results revealed distinct covariate dependencies: while spatial location (latitude and longitude) governs rainfall variability in non-typhoon conditions, elevation emerged as the critical determinant for the scale parameter during typhoon events, highlighting the role of orographic effects. Furthermore, the shape parameter exhibited multi-decadal oscillations corresponding to climate variability indices. To ensure local accuracy, a dual fitting strategy was implemented, supplementing EVGAM with standalone generalized extreme value (GEV) estimation for stations exhibiting poor goodness-of-fit. The resulting 50-year and 100-year return level maps quantify regional risks, identifying the southern coast as a high-vulnerability zone driven by typhoons, while inland basins benefited from orographic shielding. This comprehensive framework provides a robust scientific basis for adaptive water resource management and infrastructure design. Full article

The Pearl River Basin (PRB) is a humid subtropical system where frequent floods and recurrent droughts challenge water management. GRACE and GRACE Follow-On provide basin-scale constraints on terrestrial water storage anomalies (TWSA), yet their coarse native resolution limits applications at regional scales. We employ a downscaled TWSA product derived via a joint inversion that integrates GRACE/GFO observations with the high-resolution spatial patterns of WaterGap Global Hydrological Model (WGHM). Validation against GRACE/GFO shows that the downscaled product outperforms WGHM at basin and pixel scales, with consistently lower errors and higher skill, and with improved terrestrial water flux (TWF) estimates that agree more closely with water balance calculations in both magnitude and phase. The TWSA in the PRB exhibits strong seasonality, with precipitation (P) exceeding evapotranspiration (E) and runoff (R) from April to July and storage peaking in July. From 2002 to 2022, the basin alternates between multi-year declines and recoveries. On the annual scale, TWSA covaries with precipitation and runoff, and large-scale climate modes modulate these relationships, with El Niño and a warm Pacific Decadal Oscillation (PDO) favoring wetter conditions and La Niña and a cold PDO favoring drier conditions. extreme gradient boosting (XGBoost) with shapley additive explanations (SHAP) attribution identifies P as the primary driver of storage variability, followed by R and E, while vegetation and radiation variables play secondary roles. Drought and flood diagnostics based on drought severity index (DSI) and a standardized flood potential index (FPI) capture the severe 2021 drought and major wet-season floods. The results demonstrate that joint inversion downscaling enhances the spatiotemporal fidelity of satellite-informed storage estimates and provides actionable information for risk assessment and water resources management. Full article

Soil temperature (ST) is a key regulator of crop growth, microbial activity, and soil biogeochemical processes, making its accurate estimation critical for agricultural monitoring. Focusing on the Huang-Huai-Hai (HHH) Plain, a major grain-producing region of China, we developed a monthly ST prediction framework for two depths (0–5 cm and 5–15 cm) using Random Forest and recursive feature elimination with cross-validation. Based on ~3000 in situ records (2003–2020) and 19 geo-environmental covariates, we generated 1 km monthly cropland ST maps and examined their spatiotemporal dynamics. The models achieved consistently high accuracy (R² ≥ 0.80; RMSE ≤ 1.9 °C; MAE ≤ 1.1 °C; NSE ≥ 0.8, Bias ≤ ±0.3 °C). Feature selection revealed clear month-to-month shifts in predictor importance: environmental variables dominated overall but followed a U-shaped pattern (decreasing then increasing importance), soil properties became more influential in spring–summer, and topography gained importance in autumn–winter. Interannually, cropland ST declined during 2003–2012 (−0.60 °C/decade at 0–5 cm; −0.52 °C/decade at 5–15 cm) but increased more rapidly during 2012–2020 (1.04 and 0.84 °C/decade, respectively). Seasonally, the largest amplitudes occurred in spring–summer (±0.5 °C at 0–5 cm; ±0.4 °C at 5–15 cm), with there being moderate fluctuations in autumn (±0.1 °C) and negligible changes in winter. These temporal dynamics exhibited pronounced spatial heterogeneity shaped by latitude, elevation, and soil type. Collectively, this study produces high-resolution monthly maps and a transparent variable-selection framework for cropland ST, providing new insights into soil thermal regimes and supporting precision agriculture and sustainable land management in the HHH Plain. Full article

Prenatal ozone (O₃) exposure may trigger systemic inflammation and oxidative stress. These effects could contribute to adverse pregnancy outcomes. We conducted a prospective cohort study involving 235 pregnant women in Ningxia, China. Maternal O₃ exposure during pregnancy and prior to conception was assessed using high-resolution spatiotemporal models. Multivariable logistic and linear regression analyses were employed to evaluate the associations between O₃ exposure and adverse pregnancy outcomes. Mediation and interaction models were further applied to examine the potential modifying roles of gestational diabetes mellitus (GDM) and inflammatory biomarkers. In multivariable analyses adjusted for maternal and environmental covariates, higher prenatal O₃ exposure was significantly associated with an increased risk of preterm birth (PTB) (OR = 1.24, 95% CI: 1.05~1.45, p = 0.010) and low birth weight (LBW) (OR = 1.29, 95% CI: 1.09~1.54, p = 0.004). Similarly, elevated maternal SAA and CRP levels were positively associated with these adverse pregnancy outcomes (p < 0.05). Notably, higher TNF-α levels were inversely associated with the risks of PTB (OR = 0.15, 95% CI: 0.03~0.85, p = 0.032) and LBW (OR = 0.05, 95% CI: 0.01~0.39, p = 0.005). IL-17A levels were inversely associated with neonatal length-for-age Z scores (β = −0.28, 95% CI: −0.55~−0.01, p = 0.043). Our findings suggest that prenatal O₃ exposure is associated with increased risks of PTB and LBW. Alterations in systemic inflammatory markers and metabolic dysfunction during pregnancy were related to adverse pregnancy outcomes and fetal growth deficits, but they did not mediate these associations, with O₃ remaining an independent predictor after adjustment. Full article

Obtaining evapotranspiration (ET) estimates at high spatiotemporal resolution is a fundamental prerequisite for clarifying the patterns and controlling factors of agricultural water consumption in drylands. However, most existing ET products are provided at daily or coarser spatial–temporal scales, which limits the ability to capture short-term variations in crop water use. This study developed a novel hourly 10-m ET estimation method that combines remote sensing with machine learning techniques. The approach was evaluated using agricultural sites in two arid regions: the Heihe River Basin in China and the Bekaa Valley in Lebanon. By integrating hourly eddy covariance measurements, Sentinel-2 reflectance data, and ERA5-Land reanalysis meteorological variables, we constructed an XGBoost-based modeling framework for hourly ET estimation, and incorporated the SHapley Additive exPlanations (SHAP) method for model interpretability analysis. Results demonstrated that the model achieved strong performance across all sites (R² = 0.86–0.91, RMSE = 0.04–0.05 mm·h⁻¹). Additional metrics, including the Nash–Sutcliffe efficiency coefficient (NSE) and percent bias (PBIAS), further confirmed the model’s robustness. Interpreting the model with SHAP highlighted net radiation (Rn), 2-m temperature (t2m), and near-infrared reflectance of vegetation (NIRv) as the dominant factors controlling hourly ET variations. Significant interaction effects, such as Rn × NIRv and Rn × t2m, were also identified, revealing the modulation mechanism of energy, vegetation status and temperature coupling on hourly ET. The study offers a practical workflow and an interpretable framework for generating high-resolution ET maps, thereby supporting regional water accounting and land–atmosphere interaction research. Full article

The Qinling Mountains, situated in the climatic transition zone between northern and southern China, represent a critical region for climate and ecological studies due to their unique transitional characteristics and the rising frequency of extreme climate events. As net primary productivity (NPP) is a key indicator of ecosystem stability, clarifying its response to extreme climate events is essential for understanding ecological resilience in this region. In this study, daily observational data from 123 meteorological stations (1960–2023) were used to derive eight extreme temperature and precipitation indices. Combined with MODIS NPP data (2001–2023), we applied Theil–Sen slope estimation, Mann–Kendall significance testing, ridge regression, Pearson correlation analysis, and Moran’s I spatial autocorrelation to systematically investigate the spatiotemporal dynamics and driving mechanisms of NPP. The main findings are as follows: (1) From 2001 to 2023, the mean annual NPP in the Qinling region was 558.43 ± 134.27 gC·m⁻²·year⁻¹, showing a significant increasing trend of 5.44 gC·m⁻²·year⁻¹ (p < 0.05). (2) Extreme temperature indices exhibited significant changes, whereas among the precipitation indices, only the number of days with daily precipitation ≥ 20 mm (R20) showed a significant trend, suggesting that extreme temperatures exert a stronger influence in the region. (3) Correlation analysis indicated that temperature-related indices were generally positively correlated, precipitation-related indices displayed even stronger associations, and covariation existed among extreme precipitation events of varying intensities. Moreover, precipitation indices demonstrated relatively stable spatial distributions, while temperature indices fluctuated considerably. (4) Absolute contribution analysis further revealed that the number of days with daily minimum temperature below the 10th percentile (TN10p) contributed up to 3.53 gC·m⁻²·year⁻¹ to annual NPP variation in the Henan subregion, whereas maximum rainfall over five consecutive days (Rx5day) exerted an overall negative effect on NPP (−0.77 gC·m⁻²·year⁻¹). By integrating long-term meteorological observations with remote sensing products, this study quantitatively evaluates the differential impacts of extreme climate events on vegetation within a climatic transition zone, offering important implications for ecological conservation and adaptive management in the Qinling Mountains. Full article

Visible near-infrared (VNIR) spectroscopy offers a cost-effective solution to quantify the spatiotemporal dynamics of soil organic carbon (SOC), especially in the context of rapid advances in spectra-based local modeling approaches using large-scale soil spectral libraries. And yet, direct temporal transferability of VNIR spectroscopic modeling (applying historical models to new spectral data) and its capability to monitor temporal changes in SOC remain underexplored. To address this gap, this study uses the LUCAS Soil dataset (2009 and 2015) from France to evaluate the effectiveness of localized spectral models in detecting SOC changes. Two local learning algorithms, memory-based learning (MBL) and GLOBAL-LOCAL algorithms, were adapted to integrate spectral and soil property similarities during local training set selection, while also incorporating LUCAS 2009 soil measurements (clay, silt, sand, CEC) as covariates. These adapted local learning algorithms were then compared against global partial least squares regression (PLSR). The results demonstrated that localized models substantially outperformed global PLSR, with MBL achieving the highest accuracy for croplands, grasslands, and woodlands (R² = 0.72–0.79, RMSE = 4.73–20.92 g/kg). Incorporating soil properties during the local learning procedure reduced spectral heterogeneity, leading to improved SOC prediction accuracy. This improvement was particularly pronounced after excluding organic soils from grasslands and woodlands, as evidenced by 13.3–21.1% decreases in the RMSE. Critically, for SOC monitoring, spectrally predicted SOC successfully identified over 70% of samples experiencing significant SOC changes (>10% loss or gain), effectively capturing the spatial patterns of SOC changes. This study demonstrated the potential of localized spectral modeling as a cost-effective tool for monitoring SOC dynamics, enabling efficient and large-scale assessments critical for sustainable soil management. Full article

Soil organic matter (SOM) is essential for ecosystem health and agricultural productivity. Accurate prediction of SOM content is critical for modern agricultural management and sustainable soil use. Existing digital soil mapping (DSM) models, when processing temporal data, primarily focus on modeling the changes in input data across successive time steps. However, they do not adequately model the relationships among different input variables, which hinders the capture of complex data patterns and limits the accuracy of predictions. To address this problem, this paper proposes a novel deep learning model, 2-Channel Network (2C-Net), leveraging sequential multi-temporal remote sensing images to improve SOM prediction. The network separates input data into temporal and spatial data, processing them through independent temporal and spatial channels. Temporal data includes multi-temporal Sentinel-2 spectral reflectance, while spatial data consists of environmental covariates including climate and topography. The Multi-sequence Feature Fusion Module (MFFM) is proposed to globally model spectral data across multiple bands and time steps, and the Diverse Convolutional Architecture (DCA) extracts spatial features from environmental data. Experimental results show that 2C-Net outperforms the baseline model (CNN-LSTM) and mainstream machine learning model for DSM, with R² = 0.524, RMSE = 0.884 (%), MAE = 0.581 (%), and MSE = 0.781 (%)². Furthermore, this study demonstrates the significant importance of sequential spectral data for the inversion of SOM content and concludes the following: for the SOM inversion task, the bare soil period after tilling is a more important time window than other bare soil periods. 2C-Net model effectively captures spatiotemporal features, offering high-accuracy SOM predictions and supporting future DSM and soil management. Full article

Background: Psoriasis is a chronic immune-mediated disease affecting approximately 43 million individuals worldwide. While previous studies provide certain insights, there remains different conclusions and a lack of a comprehensive analysis regarding the burden of psoriasis. In response to ongoing therapeutic advances and a growing patient population, this study utilizes the Global Burden of Disease (GBD) 2021 estimates to characterize the spatiotemporal evolution of the psoriasis burden from 1990 through 2021. By integrating these biological, geographic, and socioeconomic determinants, this study aims to inform more targeted and effective health policy planning. Methods: To track changes over time, the Estimated Annual Percentage Change (EAPC) was determined using a linear regression model. In addition, a frontier analysis was utilized to investigate the link between psoriasis burden and socio-demographic progress. Furthermore, geographically weighted regression was used for the spatial econometric assessment of EAPC, age-standardized rates (ASRs), and Human Development Index (HDI) covariance structures across nation-states. Results: Between 1990 and 2021, the global burden of psoriasis increased consistently, with ASRs exhibiting a positive correlation with the Socio-demographic Index (SDI). High-SDI regions reported the highest burden, while high–middle-SDI regions experienced the steepest rise. Conclusions: This study reveals an increasing global psoriasis burden (1990–2021) through systematic analyses, indicating distinct regional progression patterns. These findings advocate for geographically tailored strategies to alleviate healthcare system pressures. Full article

This study aims to guide the management and service of highways towards a more efficient and intelligent direction, and also provides intelligent and green data support for achieving sustainable development goals. The forecasting of traffic flow at highway stations serves as the cornerstone for spatiotemporal analysis and is vital for effective highway management and control. Despite considerable advancements in data-driven traffic flow prediction, the majority of existing models fail to differentiate between directions. Specifically, entrance flow prediction has applications in dynamic route guidance, disseminating real-time traffic conditions, and offering optimal entrance selection suggestions. Meanwhile, exit flow prediction is instrumental for congestion and accident alerts, as well as for road network optimization decisions. In light of these needs, this study introduces an enhanced heterogeneous spatiotemporal graph network model tailored for predicting highway station traffic flow. To accurately capture the dynamic impact of upstream toll stations on the target station’s flow, we devise an influence probability matrix. This matrix, in conjunction with the covariance matrix across toll stations, updated graph structure data, and integrated external weather conditions, allows the attention mechanism to assign varied combination weights to the target toll station from temporal, spatial, and external standpoints, thereby augmenting prediction accuracy. We undertook a case study utilizing traffic flow data from the Chengdu-Chengyu station on the Sichuan Highway to gauge the efficacy of our proposed model. The experimental outcomes indicate that our model surpasses other baseline models in performance metrics. This study provides valuable insights for highway management and control, as well as for reducing traffic congestion. Furthermore, this research highlights the importance of using data-driven approaches to reduce carbon emissions associated with transportation, enhance resource allocation at toll plazas, and promote sustainable highway transportation systems. Full article

Multivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields. To effectively characterize such data in geostatistical frameworks, valid and practical covariance models are essential. In this work, we propose several classes of multivariate spatio-temporal covariance matrix functions to model underlying stochastic processes whose discrete temporal margins correspond to well-known autoregressive and moving average (ARMA) models. We derive sufficient and/or necessary conditions under which these functions yield valid covariance matrices. By leveraging established methodologies from time series analysis and spatial statistics, the proposed models are straightforward to identify and fit in practice. Finally, we demonstrate the utility of these multivariate covariance functions through an application to Kansas weather data, using co-kriging for prediction and comparing the results to those obtained from traditional spatio-temporal models. Full article

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article