Search Results (110)

Based on summer ground-based microwave radiometer (MWR) observations over North China, this study systematically evaluates the accuracy of temperature and humidity profile products derived from the Vertical Atmospheric Sounding System (VASS) onboard the FY-3E satellite. The VASS products are compared with the numerical weather prediction (NWP) background fields as well as the ERA5 and CMA-RA 1.5 (CRA) reanalysis datasets. The results show that both ERA5 and CRA exhibit stable and reliable performance in representing temperature and humidity fields under both clear and cloudy conditions over North China. The temperature root mean square error (RMSE) generally ranges from 1.6 K to 2.6 K at different height levels (from 0 km to 10 km), while the RMSE of absolute humidity is approximately 0.4–2.7 g/m³. These results further confirm the reliability of CRA under both clear and cloudy conditions in this region. In contrast, the errors of the VASS products show pronounced variations with height, station, and weather conditions. A clear systematic underestimation of temperature is found at 1–3 km, with a mean bias of about −3.44 K. Humidity is also significantly underestimated in the boundary layer, with a mean bias of approximately −5.91 g/m³. Both temperature and humidity errors decrease rapidly with increasing height. Clear inter-station differences are also identified. Temperature errors show boundary-layer overestimation in Beijing, while Xingtai and Dingzhou exhibit systematic underestimation throughout most of the profile, with mean biases reaching −4.1 K and −3.3 K, respectively. Boundary-layer humidity underestimation is more pronounced in Xingtai and Dingzhou (approximately −6.6 g/m³) than in Beijing (−4.0 g/m³). Weather-based analysis indicates that clouds have a significant impact on the accuracy of the VASS products. Under cloudy conditions, the near-surface temperature mean bias shifts from overestimation under clear skies to underestimation. The magnitude of humidity underestimation under cloudy conditions is approximately twice that under clear conditions. Further comparison shows that the error characteristics of the NWP background fields in the lower and middle troposphere are partly similar to those of the VASS products. This suggests that the current retrieval algorithm still has limited capability to correct background field biases under complex weather conditions. These results provide scientific support for the selection of application scenarios and the optimization of retrieval algorithms for FY-3E/VASS temperature and humidity profile products, and they also support the reliable use of domestic reanalysis datasets in regional studies. 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

Accurate and interpretable air quality prediction remains a critical challenge for environmental health management due to complex, nonlinear interactions among emissions, meteorology, and atmospheric chemistry. This study presents a hybrid physics informed and multimodal deep learning framework for city-scale air quality and health risk prediction. The framework combines a Gaussian plume dispersion model with a residual CNN-LSTM network that learns data driven corrections while preserving physical consistency. Multimodal open datasets, including ground based pollutant sensors, meteorological records, and satellite derived aerosol and temperature features, are jointly fused to improve spatiotemporal fidelity. An Exposure Health Index module further links predicted pollutant fields with respiratory morbidity indicators, providing a quantitative bridge between atmospheric variability and health outcomes. Using open source datasets from Riyadh, Jeddah, and Dammam, the proposed approach achieves up to 25% lower mean absolute error and $R^2$ values above 0.85 compared with physics only and purely data driven baselines. Explainability analyses using SHAP and spatial attention highlight physically plausible drivers and confirm feature relevance. The results demonstrate that physics guided residual learning can unify deterministic dispersion modeling and multimodal inference, providing a transparent, scalable, and reproducible foundation for air quality forecasting and health risk assessment. Full article

The rate constant k_OH for the reaction of 1,1,1,3,3,3-hexafluoroprop-2-imine with OH radicals was measured relative to two reference compounds, CH₃F and CH₃CHF₂, to be k_OH = (4.2 ± 1.1) × 10⁻¹⁴ cm³ molecule⁻¹ s⁻¹ at 295 K. This implies an atmospheric lifetime with respect to consumption by OH of 0.75 years. Reaction with Cl atoms yielded k_Cl = (7.9 ± 1.7) × 10⁻¹⁶ cm³ molecule⁻¹ s⁻¹ at 295 K, and reaction with O₃ has an upper limit of k_O3 < 4 × 10⁻²³ cm³ molecule⁻¹ s⁻¹, so that the atmospheric consumption by Cl and O₃ is negligibly slow. Absolute infrared cross sections of the imine yield a radiative efficiency of 0.34 W m⁻² ppb⁻¹, which is corrected to 0.23 W m⁻² ppb⁻¹ for the effects of atmospheric lifetime. The imine’s corresponding 100-year global warming potential is 64 ± 19. This value is an upper limit, given that heterogenous atmospheric removal paths, such as hydrolysis in water droplets, are not included. Full article

Land surface temperature (LST) is essential for studying land–atmosphere energy exchange, the impact of climate change, and its influence on crop yields and hydrology. Although satellite remote sensing provides large-scale LST data, existing spatiotemporal fusion methods face challenges. Traditional algorithms have difficulty with heterogeneous surfaces, and deep-learning models often produce blurred details and inaccurate temperatures, which limits their use in high-precision applications. This study addresses these issues by developing a Deep-Learning Spatial and Temporal Fusion Model (DLSTFM) for Landsat-8 and MODIS LST imagery in Griffith, Australia. DLSTFM employs a dual-branch structure: one branch is dedicated to dual-temporal fusion, and the other branch is dedicated to multi-source feature fusion. Key innovations include the Spatial Adaptive Feature Modulation (SAFM) module, which performs adaptive multi-scale feature fusion, and the Temperature Adaptive Correction Module (TCM), which makes pixel-wise adjustments using reference data. Experiments demonstrate that DLSTFM significantly outperforms traditional methods and existing deep-learning fusion methods. DLSTFM achieves clearer surface features and a mean absolute temperature error of approximately 2.1 K. The model also demonstrated excellent generalization performance in another test area (Ardiethan) without retraining, showcasing its substantial practical value for high-accuracy LST fusion. Full article

Aerobic exercise with eicosapentaenoic acid (EPA) may enhance cognition via cerebrovascular pathways. We tested whether mild hyperbaric oxygen (HBO; 1.41 atmospheres absolute [ATA], approximately 30% O₂) adds to gains in cognitive processing capacity (throughput) versus normobaric normoxia (1.0 ATA, approximately 21% [20.9%] O₂). Healthy young adults (n = 16) performed cycling exercise at 60–70% VO_2peak for 60 min, twice weekly, for 4 weeks per environment with a 1-week washout; EPA (2170 mg·day⁻¹) was taken during each 4-week training phase (total 8 weeks) and was paused during the washout. An EPA-only control (n = 8) was included for supplementary analysis. The primary outcome was throughput (correct·min⁻¹; T1–T4); secondary outcomes were interference indices (I1: stroop interference, I2: reverse-stroop interference). Effects were estimated using linear mixed models [environment, time, environment × time; AR(1), REML] and Hedges’ g_av; accuracy used generalized estimating equations. Throughput improved mainly with time (T1–T2 p < 0.001; T4 p = 0.017; T3 p = 0.055), with no environment or interaction effects. I1/I2 showed no significant change, and one task exhibited an accuracy ceiling. Under safe, feasible conditions (≤1.41 ATA), aerobic exercise improved processing capacity (throughput) independently of environmental oxygenation level. The absence of detectable additive effects should be interpreted cautiously under conservative settings. Full article

The absolute radiometric calibration of a satellite sensor is an essential process that determines the coefficients required to convert the radiometric quantities of satellite images. This procedure is crucial for ensuring the applicability and enhancing the reliability of optical sensors onboard satellites. This study performs the absolute radiometric calibration of the Compact Advanced Satellite 500-1 (CAS500-1) Advanced Earth Imaging Sensor System-C (AEISS-C), a low Earth orbit satellite developed independently by Republic of Korea for precise ground observation. Field campaign using a tarp, an Analytical Spectral Devices FieldSpecIII spectroradiometer, and a MicrotopsII sunphotometer was conducted. Additionally, reflectance-based vicarious calibration was performed using observational data and the MODerate resolution atmospheric TRANsmission model (version 6) radiative transfer model (RTM). Cross-calibration was also performed using data from the Sentinel-2 MultiSpectral Instrument, RadCalNet observations, and MODIS Bidirectional nReflectance Distribution Function (BRDF) products (MCD43A1) to account for differences in spectral response functions, viewing/solar geometry, and atmospheric conditions between the two satellites. From these datasets, two correction factors were derived: the Spectral Band Adjustment Factor and the BRDF Correction Factor. CAS500-1/AEISS-C acquires satellite imagery using two Time Delay Integration (TDI) modes, and the absolute radiometric calibration coefficients were derived considering these TDI modes. The coefficient of determination (R²) ranged from 0.70 to 0.97 for the reflectance-based vicarious calibration and from 0.90 to 0.99 for the cross-calibration. For reflectance-based vicarious calibration, aerosol optical depth was identified as the primary source of uncertainty among atmospheric factors. For cross-calibration, the reference satellite and RTMs were the primary sources of uncertainty. The results of this study will support the monitoring of CAS500-1/AEISS-C, which produces high-resolution imagery with a spatial resolution of 2 m, and can serve as foundational material for absolute radiometric calibration procedures for other CAS500 satellites. Full article

In inland waters, Atmospheric Correction (AC), including Adjacency Effect (AE) correction, is a major challenge for water quality retrieval using optical satellite data. This study evaluated three image pre-processing options for turbidity retrieval in the St. Lawrence River using Sentinel-2 (S2) and Landsat 8/9 (L8/9) imagery with the Light Gradient Boosting Machine (LightGBM) model: (1) No pre-processing, i.e., use of Top-of-Atmosphere (TOA) reflectance, (2) AC pre-processing, obtaining water-leaving reflectance (R_w) from AC for the Operational Land Imager lite (ACOLITE)’s Dark Spectrum Fitting (DSF) technique, and (3) AE pre-processing, correcting for the AE using T-Mart before obtaining R_w from DSF. Results demonstrated that AE pre-processing outperformed the other two options. For L8/9, AE pre-processing reduced the Root Mean Square Error (RMSE) and improved the median symmetric accuracy (ε) by 48.8% and 19.0%, respectively, compared with AC pre-processing, and by 48.5% and 50.7%, respectively, compared with No pre-processing. For S2, AE pre-processing performed better than AC pre-processing and also outperformed No pre-processing, reducing RMSE by 28.4% and ε by 50.8%. However, No pre-processing yielded the lowest absolute symmetric signed percentage bias (|β|) among all pre-processing options. Analysis indicated that AE pre-processing yielded superior performance within 0–300 m from shore than other options, where the AE influence is strongest. Turbidity maps generated using AE pre-processing were smoother and less noisy compared to the other pre-processing options, particularly in cloud-adjacent regions. Overall, our findings suggest that incorporating AE correction through T-Mart improves the performance of the LightGBM model for turbidity retrieval from both L8/9 and S2 imagery in the St. Lawrence River, compared to the alternative pre-processing options. Full article

Extracted peatlands experience strong hydrological fluctuations due to drainage, vegetation succession, and climatic variability, yet long-term, high-frequency groundwater data remain scarce in Northern Europe. Our dataset presents two years (June 2023–May 2025) of 30-min groundwater table depth (WTD) measurements from six wells installed across contrasting Greenhouse Gass Emission Site Types (GEST 5, 6, 15, 20) in the Kaigu peatlands, central Latvia. Each well was equipped with an automatic pressure transducer (TD-Diver, van Essen Instruments) recording absolute pressure (m H₂O). The dataset also includes metadata on coordinates, installation elevation, well construction, and manual control measurements. All values are unprocessed, i.e., they represent original logger outputs without atmospheric or elevation correction, enabling users to apply their own calibration or referencing methods. This is the first openly available high-frequency extracted peatland groundwater pressure dataset from the Baltic region and provides a foundation for hydrological modelling and rewetting designs. Full article

In this work, we tested the performance of automated atmospheric PM_2.5 monitoring instruments and contrasted the data from automated measurements with those from filter-based reference measurements. The tested instruments include four brands of beta attenuation instruments (two were made in China, D1 and D2; the other two were imported from other countries, I1 and I2) and one brand of a light scattering instrument (also imported from another country, I3). The automated monitoring data were corrected based on the reference tests. The total testing period lasted 18 months. The objective of this work is to evaluate the influences of environmental factors on the performance of different automated instruments, and to improve the accuracy of the automated instruments by using a correction method. The results showed that contrasted with the reference tests, the absolute errors (MAE, mean absolute error; SD, standard deviation; and RMSE, root mean square error) of the automated monitoring instruments werehigher for temperature (T ≤ 10 °C), humidity (60% ≤ RH < 80%), and PM_2.5 concentrations (PM_2.5 ≥ 75 μg/m³). Meanwhile, the relative errors (CV, coefficient of variation; and NRMSE, normalized root mean square error) of the automated monitoring instruments were higher for humidity (RH > 80%) and PM_2.5 concentrations (PM_2.5 < 15 μg/m³). For winter data, it proved challenging to pass the reference test, which was based on a linear regression between 24-h average automated monitoring data and the integrated filter-based PM_2.5 data (aka the KBR test). Before corrections, the pass rates of D1, D2, I1, I2, and I3 in the rolling KBR tests are 57.7%, 51.3%, 41.1%, 21%, and 90.2%, respectively. After corrections, the rates increase to 79.6%, 86.6%, 81.8%, 58.9%, and 91.8%, respectively. The coefficient corrections (corrections of system errors) have made the most prominent contribution to improving the pass rates of the winter samples. The quarterly correction method can significantly improve the data accuracy of automated monitoring instruments. Full article

Soil moisture serves as a critical factor in the hydrological cycle, affecting plant growth, ecosystem health, and groundwater reserves. Current methods for monitoring and predicting it fail to account for the complexities introduced by climatic variations and other influencing factors, such as the effects of atmospheric interference and data gaps, leading to reduced prediction accuracy. To address these challenges, this study introduces a novel soil moisture prediction model based on remote sensing and deep learning, utilizing the Adaptive Group Radial Lasso Regularized Basis Function Networks (AGRL-RBFN) optimized by the Hierarchical Correlated Spider Wasp Optimizer (HCSWO) and incremental learning (IL) techniques. The proposed method for monitoring soil moisture utilizes hyperspectral and soil moisture data from a 2017 campaign in Karlsruhe, encompassing variables such as datetime, soil moisture percentage, soil temperature, and remote sensing spectral bands. The proposed methodology begins with comprehensive preprocessing of historical remote sensing data to fill gaps, reduce noise, and correct atmospheric disturbances. It then employs a unique seasonal mapping and grouping technique, enhanced by the AdaK-MCC method, to analyze the impact of climatic changes on soil moisture patterns. The model’s innovative feature selection approach, using HCSWO, identifies the most significant predictors, ensuring optimal data input for the AGRL-RBFN model. The model achieves an impressive accuracy of 98.09%, a precision of 98.17%, a recall of 97.24%, and an F1-score of 98.95%, outperforming existing methods. Furthermore, it attains a mean absolute error (MAE) of 0.047 in gap filling and a Dunn Index of 4.897 for clustering. Although successful in many aspects, the study did not investigate the relationship between soil moisture levels and specific crops, which presents an opportunity for future research aimed at enhancing smart agricultural practices. Furthermore, the model can be refined by integrating a wider range of datasets and improving its resilience to extreme weather conditions, thereby providing a reliable tool for climate-responsive agricultural management and water conservation strategies. Full article

Precise point positioning–real-time kinematic (PPP-RTK) enables users to achieve rapid centimeter-level absolute positioning accuracy within a few epochs. The interpolation of ionospheric delay corrections at the user end, extracted from reference stations, constitutes a key aspect of the process, which depends not solely on the precision of the interpolation model. This study investigates the recommended number of selected reference stations and proposes a method to mitigate the potential loss of observations due to missing ionospheric corrections. According to the experimental results, the number of reference stations should be determined based on the reference network size. Under normal conditions (terrain is relatively flat and the atmospheric conditions are inactive) where reference stations are approximately evenly distributed in all directions, and using low-order surface interpolation model, for networks with 50 km spacing, four or five reference stations are recommended, while for 100 km networks, six or seven stations are enough to calculate precise corrections. Adding more stations beyond these thresholds provides limited improvement in interpolation accuracy and increases the communication load. In addition, an interpolation basis recovery algorithm is proposed to preserve otherwise excluded satellite observations through intelligent handling of correction data gaps at individual reference stations. Experimental validation demonstrates that the recovered ionospheric delay corrections obtained through the algorithm deviate from the ground-truth interpolated values of no more than ±1 cm, an accuracy level deemed adequate for PPP-RTK applications. Furthermore, approximately 3% of the observations, which would otherwise have been discarded due to the missing corrections from a specific reference station, are retained by the algorithm. Full article

Copper is typically acknowledged as a critical mineral and one of the vital components of various of today’s fast-growing green technologies. Porphyry copper systems, which are an important source of copper and molybdenum, typically consist of large volumes of hydrothermally altered rocks, mainly around porphyry copper intrusions. Mapping hydrothermal alteration zones associated with porphyry copper systems is one of the most important indicators for copper exploration, especially using advanced satellite remote sensing technology. This paper presents a sophisticated remote sensing-based method that uses ASTER satellite imagery (SWIR bands 4 to 9) to identify hydrothermal alteration zones by combining the discrete wavelet transform (DWT) and the median absolute deviation (MAD) algorithms. All six SWIR bands (bands 4–9) were analyzed independently, and band 9, which showed the most consistent spatial patterns and highest validation accuracy, was selected for final visualization and interpretation. The MAD algorithm is effective in identifying spectral anomalies, and the DWT enables the extraction of features at different scales. The Urmia–Dokhtar magmatic arc in central Iran, which hosts the Zafarghand porphyry copper deposit, was selected as a case study. It is a hydrothermal porphyry copper system with complex alteration patterns that make it a challenging target for copper exploration. After applying atmospheric corrections and normalizing the data, a hybrid algorithm was implemented to classify the alteration zones. The developed classification framework achieved an accuracy of 94.96% for phyllic alteration and 89.65% for propylitic alteration. The combination of MAD and DWT reduced the number of false positives while maintaining high sensitivity. This study demonstrates the high potential of the proposed method as an accurate and generalizable tool for copper exploration, especially in complex and inaccessible geological areas. The proposed framework is also transferable to other porphyry systems worldwide. Full article

This research addresses the challenge of bias in Remotely Sensed Rainfall (RSR) datasets used for hydrological planning in Uganda’s data-scarce, ungauged catchments. Four bias correction methods, Quantile Mapping (QM), Linear Transformation (LT), Delta Multiplicative (DM), and Polynomial Regression (PR), were evaluated using daily rainfall data from four gauged stations (Gulu, Soroti, Jinja, Mbarara). QM consistently outperformed other methods based on statistical metrics, e.g., for National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA_CPC) RSR data at Gulu, Root-Mean-Square Error (RMSE) was reduced from 29.20 mm to 19.00 mm, Mean Absolute Error (MAE) reduced from 22.44 mm to 12.84 mm, and Percent Bias (PBIAS) reduced from −19.23% to 1.05%, and improved performance goodness-of-fit tests (KS = 0.03, p = 1.00), while PR, though statistically strong, failed due to overfitting. A bias correction framework was developed for ungauged catchments, using predetermined bias factors derived from observed station data. Validation at Arua (tropical savannah) and Fort Portal (tropical monsoon) demonstrated significant improvements in RSR data when the bias correction framework was applied. At Arua, bias correction of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data reduced RMSE from 49.14 mm to 21.41 mm, MAE from 45.74 mm to 17.38 mm, and PBIAS from −59.83% to −8.18%, while at Fort Portal, bias correction of the CHIRPS dataset reduced RMSE from 28.35 mm to 15.02 mm, MAE from 25.28 mm to 11.35 mm, and PBIAS from −46.2% to 4.74%. Our research concludes that QM is the most effective method, and that the framework is a tool for improving RSR data in ungauged catchments. Recommendations for future work includes machine learning integration and broader regional validation. Full article

This study addresses the critical need for radiometrically accurate and consistent hyperspectral data as the remote sensing community moves towards a hyperspectral world. Previous calibration efforts on Hyperion, the first on-orbit hyperspectral sensors, have exhibited temporal stability and absolute accuracy limitations. This work has developed and validated a novel cross-calibration methodology to address these challenges. Also, this work adds two other hyperspectral sensors, DLR Earth Sensing Imaging Spectrometer (DESIS) and Earth Surface mineral Dust Source Investigation instrument (EMIT), to maintain temporal continuity and enhance spatial coverage along with spectral resolution. The study established a robust approach for calibrating Hyperion using DESIS and EMIT. The methodology involves several key processes. First is a temporal stability assessment on Extended Pseudo Invariant Calibration Sites (EPICS) Cluster13–Global Temporal Stable (GTS) over North Africa (Cluster13–GTS) using Landsat Sensors Landsat 7 (ETM+), Landsat 8 (OLI). Second, a temporal trend correction model was developed for DESIS and Hyperion using statistically selected models. Third, absolute calibration was developed for DESIS and EMIT using multiple vicarious calibration sites, resulting in an overall absolute calibration uncertainty of 2.7–5.4% across the DESIS spectrum and 3.1–6% on non-absorption bands for EMIT. Finally, Hyperion was cross-calibrated using calibrated DESIS and EMIT as reference (with traceability to ground reference) with a calibration uncertainty within the range of 7.9–12.9% across non-absorption bands. The study also validates these calibration coefficients using OLI over Cluster13–GTS. The validation provided results suggesting a statistical similarity between the absolute calibrated hyperspectral sensors mean TOA (top-of-atmosphere) reflectance with that of OLI. This study offers a valuable contribution to the community by fulfilling the above-mentioned needs, enabling more reliable intercomparison, and combining multiple hyperspectral datasets for various applications. Full article