Search Results (140)

Traditional meteorological data spatial interpolation methods often rely on linear or static assumptions, which are inadequate for complex terrain and fail to exploit continuous spatiotemporal variation information. This paper proposes a Spatiotemporal Graph Network with Adaptive Temporal Decay (DG) that integrates a learnable graph convolution module and a temporal attenuation mechanism, enabling accurate precipitation estimation for target stations or regions at consecutive time steps. The method is evaluated using daily precipitation data from nine stations in Longnan City, Gansu Province, China, along with ERA5 (0.25°) and GPCP (0.5°) gridded reanalysis products. In the station-to-station interpolation scenario, DG significantly outperforms ordinary Kriging (OK), reducing the average RMSE from 1.4 mm/day to 1.2 mm/day, with a 28.6% improvement at mountainous stations. The DG model also exhibits superior performance in grid-to-station interpolation, achieving an average RMSE of 1.9 mm/day (OK: 2.5 mm/day). On heavy precipitation days (≥20 mm/day), DG reduces the RMSE nearly by half (11.7 mm/day) compared to OK (23.2 mm/day). A temporal-only LSTM baseline and three ablation variants (spatial-only OSI, temporal-only OTI and dgcn-only OD) are also compared, and DG consistently outperforms them, confirming the essential role of spatiotemporal integration. Additional baselines including IDW and Co-Kriging further validate the superiority of DG. The proposed method offers a promising new approach for high-precision spatiotemporal interpolation of meteorological elements in complex terrain. Full article

High-resolution spatiotemporal sea ice concentration (SIC) estimates are essential for Arctic navigation and ice analysis, but existing observational products are often too coarse, and physics-based models are computationally expensive. This study proposes a data-driven framework that couples Light Gradient Boosting Machine (LightGBM) temporal prediction with Kriging-based spatial interpolation to reconstruct SIC fields over the Northern Sea Route sector. LightGBM is trained on a grid-based SIC time series with engineered features representing persistence, seasonality, and short-term variability, enabling multi-horizon forecasting across large spatial grids. The predicted SIC fields are then refined using Ordinary Kriging (OK) and Co-Kriging (CK) with Gaussian and spherical semi-variogram models. Prediction performance is evaluated using root mean square error, and interpolation accuracy is assessed through cross-validation. Results show that, for high-latitude regions and resolutions finer than 0.25° × 0.25°, OK with a spherical semi-variogram achieves lower interpolation errors than CK and Gaussian-based alternatives. By sequentially coupling temporal learning and spatial refinement, the proposed framework improves temporal continuity, spatial structure, and error quantification, providing high-resolution SIC information suitable for large-scale Arctic ice analysis and navigation support. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

Soil salinization constrains the sustainability of irrigated oasis agriculture in arid regions. Using repeated post-harvest monitoring of 125 fixed cropland sites in Bachu County, southern Xinjiang, from 2018 to 2021, this study investigated the short-term spatiotemporal variability of topsoil total salt content (TSC) and pH. Descriptive statistics, one-way ANOVA with Tukey’s HSD test, Universal Kriging interpolation, class-transition analysis, hotspot recurrence, centroid migration, and principal component analysis were used to characterize temporal variation, spatial structure, and environmental gradient associations. TSC showed a mitigation–rebound sequence, decreasing to 4.88 ± 5.21 g kg⁻¹ in 2020 and increasing to 6.90 ± 5.93 g kg⁻¹ in 2021, whereas pH increased first and then declined. Salinity remained consistently concentrated in downstream cropland, while pH showed weaker and more year-dependent zonal differentiation. Class-transition analysis revealed marked salinity reorganization in 2021, mainly driven by conversion from lower-salinity classes to moderately and severely saline classes. Severe-salinity hotspots were temporally intermittent but spatially recurrent in the downstream zone, whereas high-pH hotspots were short-lived and mainly confined to the upstream zone. PCA further showed that TSC and pH were aligned with different environmental gradient combinations. Overall, the four-year sequence should be interpreted as short-term interannual variability rather than a robust long-term sequence. These results indicate that TSC and pH should not be treated as interchangeable indicators in oasis cropland assessment, and they provide a transferable basis for zone-specific salinity monitoring and management, with priority given to persistent downstream sink areas. Full article

Water age reflects water sources, storage, and pathways, and regulates the solute retention and dissolution associated with biogeochemical processes, highlighting its hydrological and ecological importance. However, accurate water age estimation in tracer-aided models depends heavily on the quality and spatio-temporal resolution of precipitation isotopic signals. This study investigates how distributed rainfall δ²H signals affect the simulation of young water fraction (F_yw) via the Storage Age Selection (SAS) model in topographically complex subtropical mountain catchments. Eight precipitation δ²H scenarios were generated using two temporal approaches (stepwise and sinewave) and four spatial interpolation methods: (1) raw data, (2) reversed effective recharge elevation method (rERE), (3) linear regression with elevation (ER), and (4) regression-kriging (RK). Later on, the time-variant SAS model was calibrated against observed stream water δ²H collected from the year 2022 to the year 2024. Results show that the SAS model consistently produced similar F_yw estimates for catchments (8%~40%) across all eight scenarios, demonstrating strong robustness to input uncertainty and validating the dominant role of catchment characteristics in regulating water age. The combined stepwise temporal and rERE spatial approach provided better agreement with observed stream δ²H, particularly in the eastern, steeper catchments, yielding superior model efficiency along with better constrained uncertainty. This study highlights the sensitivity of age-tracking models to precipitation isotopic inputs and provides practical guidance for selecting an interpolation strategy in data-limited mountainous environments. Full article

Zenith Tropospheric Delay (ZTD) and its associated atmospheric water vapor information constitute essential environmental variables for Earth observation (EO)-based atmospheric monitoring and environmental variable retrieval. High-quality ZTD products are therefore of great importance for the post-processing, refinement, and reconstruction of atmospheric environmental variables at regional scales. Among existing observation techniques, Global Navigation Satellite System (GNSS) measurements provide high-precision ZTD estimates and have become an important means for retrieving tropospheric delay and water vapor. However, the sparse and uneven spatial distribution of GNSS stations limits their direct applicability for continuous environmental monitoring. Reanalysis-based products, such as ERA5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), offer EO big data with excellent spatiotemporal continuity but suffer from pronounced systematic biases compared to precision GNSS retrievals, restricting their direct use in high-accuracy regional applications. To address these limitations, this study proposes a Residual Correction Kriging method for ZTD (RK ZTD) that integrates GNSS ZTD and ERA5 ZTD grids through a multi-source data fusion framework. High-precision GNSS ZTD is treated as reference data, and the differences between GNSS ZTD and ERA5 ZTD at modeling stations are defined as residuals to characterize the systematic bias in ERA5 ZTD grids. A Kriging interpolation algorithm is then employed to model the spatial distribution of these residuals and generate residual correction grids. By superimposing the interpolated residual grids onto the ERA5 ZTD grids, a refined and high-precision regional ZTD product is reconstructed. Experiments were conducted using observations collected in 2023 from 36 GNSS stations in the Netherlands, including 10 modeling stations and 26 independent validation stations, together with concurrent ERA5-derived ZTD grids. The results demonstrate that the proposed RK ZTD model provides spatially robust and high-precision ZTD products across the study region. The RK ZTD achieves a Root Mean Square Error (RMSE) of 5.70 mm, representing improvements of 58.4% and 35.4% compared with the original ERA5 ZTD (13.69 mm) and the GNSS-Kriging ZTD (8.82 mm), respectively. Moreover, the absolute bias is reduced to 0.41 mm, in contrast to 5.15 mm for the ERA5 ZTD, indicating that systematic biases are effectively mitigated. Spatial and seasonal analyses further confirm that the proposed method maintains stable performance across all seasons and significantly alleviates interpolation inaccuracies caused by sparse GNSS stations, even under extreme weather conditions such as Storm Ciarán, proving its value for advanced Earth environmental science applications. Full article

Ground-level ozone pollution poses significant risks to public health and ecosystems and remains a major environmental challenge worldwide. Accurate forecasting is difficult due to the nonlinear formation mechanisms of ozone and its strong dependence on meteorological conditions. This study proposes a Wind Speed and Direction-Based Dynamic Spatiotemporal Graph Attention Network (WSDST-GAT) for multi-step hourly ground-level ozone prediction. The model integrates a wind-aware dynamic graph to represent anisotropic pollutant transport and a Transformer-based temporal encoder to capture long-range dependencies. Meteorological variables are incorporated to enhance physical interpretability and predictive robustness. A co-kriging module is further employed to reconstruct continuous spatial ozone fields with quantified uncertainty. Using hourly observations from 35 monitoring stations in Beijing, WSDST-GAT achieves a Coefficient of Determination of 0.957, with a Mean Absolute Error of 5.25 μg/m³, and a Root Mean Square Error of 9.58 μg/m³. The prediction intervals demonstrate strong reliability with a Prediction Interval Coverage Probability of 94.01% and a Prediction Interval Normalized Average Width of 0.174. These results indicate that the proposed framework provides an accurate and physically informed solution for ozone forecasting and air quality management. Full article

Understanding the spatiotemporal dynamics of medicinal plant distributions and their quality responses under climate change is essential for formulating forward-looking conservation and utilization strategies. In response to the increasing depletion of wild resources of Alpinia officinarum Hance, one of the ‘Ten Major Guangdong Medicinal Materials’, this study developed an integrated modeling platform incorporating nine algorithms. These included generalized linear models, machine learning techniques, and a MaxEnt model optimized using ENMeval (Regularization Multiplier (RM) = 3, Feature Class (FC) = LQH). The platform was applied to simulate habitat suitability evolution under current climatic conditions (1970–2000) and for two future periods (2050s: 2041–2060; 2090s: 2081–2100) across four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). Furthermore, Co-kriging interpolation was coupled to conduct a comprehensive quality zoning based on the dual “ecological-chemical” dimension. Analysis of key environmental factors revealed that the distribution of A. officinarum is primarily constrained by hydrothermal conditions, with a suitable annual temperature ranges from 19.96 to 29.05 °C and a dry-season precipitation requirement between 56.64 and 185.65 mm. Model projections indicate that future warming does not promote habitat expansion; instead, it drives a latitudinal shift in the suitability centroid toward lower latitudes. The cumulative effects of different emission pathways vary markedly: the high-emission scenario (SSP585) triggers severe habitat contraction by the 2090s, while habitat loss under the SSP370 scenario remains relatively manageable. By overlaying the spatially heterogeneous distribution of galangin, this study delineated southeastern Yunnan, southeastern Guangxi, southwestern Guangdong, and northern Hainan as core “integrated quality regions”. These findings not only reveal the sensitivity and vulnerability of A. officinarum Hance to climate change but also provide spatially explicit guidance for in situ germplasm conservation and the selection of high-quality cultivation bases. Full article

Advanced time series interpolation techniques used for estimating marine environmental factors encounter challenges regarding their usability, practical implementation, and reproducibility outside of marine science laboratories. This study aimed to interpolate NIFS Serial Oceanographic Observations and develop a system for analyzing complex factors in offshore and coastal fishing ground formation in South Korea. Additionally, the study explored the potential for integration of spatiotemporally discontinuous in situ data with continuously available satellite data through interpolation methods. Specifically, daily sea temperature and salinity data were generated through conventional time series interpolation techniques such as linear, cubic spline, and STL + PCHIP, and spatial interpolation techniques such as IDW, kriging, and natural neighbor were used to construct monthly raster data. The generated data were compared with the output of the GOFS3.1 model, and statistical indices such as MAE, RMSE, R², and Pearson or Spearman correlation coefficients were used to evaluate the accuracy and reproducibility. Cubic spline temporal and kriging spatial interpolation methods demonstrated strong performance for the sea temperature data; however, the interpolation performance for the salinity data exhibited limited effectiveness owing to unique local variability. This study introduces techniques for transforming discontinuous in situ observational data into high-resolution data and demonstrates that the integrated use of in situ data can enhance our understanding of the fishing ground formation mechanisms and ecosystem-based fishery management. Full article

To address sparse meteorological data and the “smoothing effect” over complex terrain, this study proposes a spatiotemporal model based on a Diffusion Graph Convolutional Network (DG model). Focusing on Quanzhou, China, and using 2020–2024 data from 198 stations, the model integrates diffusion graph convolution and residual learning to capture nonlinear meteorological patterns. Ensemble experiments (100 iterations) demonstrate that the DG model significantly outperforms Ordinary Kriging and the KCN baseline in stability and accuracy. Specifically, it improves mountainous temperature prediction by 23.4% (40.0% vs. KCN) through terrain-adaptive weighting, effectively reproducing physical distribution characteristics. Furthermore, the model reduces inherent ERA5 reanalysis bias by integrating historical station data while maintaining background consistency. Validated against spatial-only (OSI) and temporal-only (OTI) variants, the DG model offers a robust approach for high-resolution meteorological reconstruction in complex terrain. Full article

Traffic state estimation (TSE) is crucial for intelligent transportation systems, as it provides unobserved parameters for traffic management and control. In this paper, we propose a novel physics-guided graph neural network for TSE that integrates traffic flow theory into an estimation framework. First, we constructed wave-informed anisotropic temporal graphs to capture the time-delayed correlations across the road network, which were then merged with spatial graphs into a unified spatiotemporal structure for subsequent graph convolution operations. Then, we designed a four-layer diffusion graph convolutional network. Each layer is enhanced with squeeze-and-excitation attention mechanism to adaptively capture dynamic directional correlations. Furthermore, we introduced the fundamental diagram equation into the loss function, which guided the model toward physically consistent estimations. Experimental evaluations on a real-world highway dataset demonstrated that the proposed model achieved a higher accuracy than benchmark methods, confirming its effectiveness in capturing complex traffic dynamics. Full article

Soil organic carbon (SOC) is a key indicator for assessing pasture degradation. This study presents an integrated, field-based approach to analyzing SOC dynamics in pastures of Rio de Janeiro state (Brazil). Unlike methods based exclusively on remote sensing or modeling, our analysis is based on 350 georeferenced soil samples collected by Embrapa Solos and complemented by historical land use data, providing robust and reliable empirical evidence. Statistical methods (ANOVA, Tukey test), geostatistical interpolation (kriging), and unsupervised clustering (k-means) were used to characterize the spatiotemporal distribution of SOC. The results revealed patterns linked to both topographic and anthropogenic drivers, enabling the objective delineation of degraded versus non-degraded pastures. SOC levels below 40 g/kg in areas under 300 m elevation were strongly associated with degradation due to intensive use. In contrast, degradation at higher altitudes was primarily linked to sloping terrain more prone to water erosion. This methodological approach demonstrates the potential of combining field data with data mining tools to detect degradation patterns and inform targeted land management. The findings reaffirm SOC as a vital indicator of soil quality and highlight the importance of sustainable pasture practices in conserving carbon stocks and mitigating climate change. The proposed threshold-based method offers a practical foundation for diagnosing degraded pastures and identifying priority areas for restoration. Full article

Satellite remote sensing is an important approach for monitoring lake water environments. However, in regions with frequent cloud and rainfall, optical remote sensing imagery often suffers from extensive data gaps caused by cloud cover, rainfall, and sun glint, which severely limit its continuity and reliability for long-term monitoring. To address this issue, this study uses Lake Taihu—a typical eutrophic lake located in a cloudy and rainy region—as a case study and systematically compares four representative gap-filling methods: Kriging Interpolation, Savitzky–Golay (SG) Filtering, Data Interpolating Empirical Orthogonal Functions (DINEOF), and the Data Interpolating Convolutional Auto Encoder (DINCAE). The results show that traditional methods retain some accuracy under low missing-data conditions (for Kriging: R = 0.84, RMSE = 7.85 μg/L; for SG Filtering: R = 0.88, RMSE = 6.67 μg/L), but tend to produce over-smoothing or distorted estimations in cases of extensive gaps or highly dynamic environments. In contrast, both DINEOF and DINCAE capture the spatiotemporal variability of chlorophyll-a more effectively, maintaining relatively high accuracy and robustness even when the missing rate exceeds 60% (for DINEOF: R = 0.84, RMSE = 6.91 μg/L; for DINCAE: R = 0.79, RMSE = 8 μg/L). Based on the optimal algorithm, a seamless long-term dataset of chlorophyll-a concentration covering Lake Taihu can be constructed, providing a solid data foundation for eutrophication trend analysis and algal bloom early warning. This study demonstrates the effectiveness of integrating statistical and deep learning approaches for lake water color remote sensing data reconstruction, offering important implications for enhancing continuous monitoring of lake water environments and supporting ecological management decisions. Full article

Lakes, as key freshwater reserves and ecosystem cores, supply human water, regulate climate, sustain biodiversity, and are vital for global ecological balance and human sustainability. Lake Chaohu, as a crucial ecological barrier in the middle and lower reaches of the Yangtze River, faces significant environmental challenges to regional sustainable development due to water quality deterioration and consequent eutrophication issues. To address the limitations of conventional monitoring techniques, including insufficient spatiotemporal coverage and high operational costs in lake water quality assessment, this study proposes an enhanced Informer model optimized by the Whale Optimization Algorithm (WOA) for predictive analysis of concentration trends of key water quality parameters—dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN)—across multiple time horizons (4 h, 12 h, 24 h, 48 h, and 72 h). The results demonstrate that the WOA-optimized Informer model (WOA-Informer) significantly improves long-term water quality prediction performance. Comparative evaluation shows that the WOA-Informer model achieves average reductions of 9.45%, 8.76%, 7.79%, 8.54%, and 11.80% in RMSE metrics for 4 h, 12 h, 24 h, 48 h, and 72 h prediction windows, respectively, along with average improvements of 3.80%, 5.99%, 11.23%, 17.37%, and 23.26% in R² values. The performance advantages become increasingly pronounced with extended prediction durations, conclusively validating the model’s superior capability in mitigating error accumulation effects and enhancing long-term prediction stability. Spatial visualization through Kriging interpolation confirms strong consistency between predicted and measured values for all parameters (DO, CODMn, TP, and TN) across all time horizons, both in concentration levels and spatial distribution patterns, thereby verifying the accuracy and reliability of the WOA-Informer model. This study successfully enhances water quality prediction precision through model optimization, providing robust technical support for water environment management and decision-making processes. Full article

This paper presents a proposed LSTM-based vertical-gradient prediction combined with three-dimensional kriging that enables reconstruction of greenhouse 3D temperature fields under sparse-sensor deployments while capturing temporal dynamics and spatial correlations. In northern China, winter solar greenhouses rely on standardized structures and passive climate-control strategies, which often lead to non-uniform thermal conditions that complicate precise regulation. To address this challenge, 24 sensors were deployed, and their time-series data were used to train a long short-term memory (LSTM) model for vertical temperature-gradient prediction. The predicted values at multiple heights were fused with in situ observations, and three-dimensional ordinary kriging (3D-OK) was applied to reconstruct the spatiotemporal temperature field. Compared with conventional 2D monitoring and computationally intensive CFD, the proposed approach balances accuracy, efficiency, and deployability. LSTM–Kriging validation showed Trend + Residual Kriging had the lowest RMSE (0.45558 °C) and bias (−0.03148 °C) (p < 0.01), outperforming Trend-only RMSE (3.59 °C) and Kriging-only RMSE (0.48 °C); the 3D model effectively distinguished sunny and rainy dynamics. This cost-effective framework balances accuracy, efficiency, and deployability, overcoming limitations of 2D monitoring and CFD. It provides critical support for adaptive greenhouse climate regulation and digital-twin development, directly advancing precision management and yield stability in CEA. Full article