Search Results (465)

Expansive soils are rich in hydrophilic clay minerals, and repeated wetting–drying cycles can induce deformation that threatens infrastructure safety. Therefore, accurate monitoring of soil moisture (SM) dynamics is essential for understanding hydro-mechanical processes and assessing related geohazards. In this study, spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is applied to expansive SM monitoring, and an Attention-Enhanced Spatio-Temporal Graph Convolution Network (ASTGCNet) is proposed for SM retrieval. The Texas coastal region, where Beaumont clay is widely distributed, was selected as the study area. The ASTGCNet-derived SM showed consistency with the Soil Moisture Active Passive (SMAP) reference product, with an overall correlation coefficient of 0.92, an RMSE of 0.035 m³/m³, and a bias of 0.006 m³/m³. Validation against in situ observations showed that ASTGCNet provided more accurate SM estimates than the Cyclone Global Navigation Satellite System (CYGNSS) L3 SM product. Extended triple collocation analysis further indicated that ASTGCNet achieved the lowest standard deviation of 0.020 m³/m³ and the highest signal-to-noise ratio of 7.33. Compared with non-expansive soils, expansive soils exhibited stronger water absorption and moisture retention behavior. By integrating GNSS vertical displacement observations, the retrieved SM revealed a nonlinear SM–deformation response that was mainly observed in shallow expansive soils. Drying-induced SM decreases corresponded to pronounced subsidence, while subsequent wetting led to ground rebound; this behavior was not clearly observed in non-expansive soils. This study demonstrates the potential of GNSS-R for expansive SM monitoring and provides new insights into the coupling between SM dynamics and deformation. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

Snow density influences Arctic climate, ecosystems, and surface energy exchange, yet spatially continuous observations remain limited. This study constructed an ERA5-supervised snow-density dataset for 60–90° N by collocating Advanced Microwave Scanning Radiometer 2 (AMSR-2) Level-1R brightness temperatures with ECMWF Reanalysis v5 (ERA5) snow density, Soil Moisture Active Passive (SMAP) surface roughness, and auxiliary variables. Ten models were evaluated using 29 observation days spanning September 2022–February 2023 under a chronological training–validation–test split. Extra Trees achieved the best overall performance, with a root mean square error of 18.54 kg m⁻³ and an R² of 0.87, while the bidirectional gated recurrent unit (BiGRU) was the strongest deep-learning model. Feature-attribution and ablation analyses showed that microwave brightness temperatures contained predictive information, although geographic and auxiliary variables also contributed substantially. The evaluated models could reproduce ERA5-referenced Arctic snow-density patterns, but their performance partly reflected regional information. Moreover, ERA5 showed limited consistency with station-based Northern Hemisphere Snow Water Equivalent estimates. Consequently, the reported metrics quantify agreement with ERA5 rather than accuracy against independently observed snow density. Temporally coincident and spatially independent field validation remains necessary in the future. Full article

Surface soil moisture (SM) is the keystone variable of terrestrial ecohydrology. Yet, the rapid diversification and development of deep learning architectures for satellite SM estimation have outpaced practitioners’ capacity to select among them. This review synthesizes 37 deep learning studies from the SMAP era (2015–2026) across five architecture families (MLP and physics-informed neural networks [MLP/PINN], long short-term memory [LSTM] and gated recurrent unit [GRU] networks, convolutional neural networks [CNN], convolutional LSTM and graph neural networks [GNN], and Transformer-based models) to establish an architecture–task-matching framework that links each family to its dominant estimation niche. The analysis reveals consistent specializations: MLP/PINN models achieve competitive surface SM retrieval from satellite inputs; recurrent networks extend SMAP temporally (RMSE ≤ 0.035  ${\mathrm{m}}^3\hspace{0.17em}{\mathrm{m}}^{-3}$); CNN disaggregates SMAP to 1 km (reported unbiased root-mean-square error (ubRMSE) approaching 0.04  ${\mathrm{m}}^3\hspace{0.17em}{\mathrm{m}}^{-3}$); ConvLSTM and GNN address spatiotemporal gap-filling (low reported ubRMSE 0.022  ${\mathrm{m}}^3\hspace{0.17em}{\mathrm{m}}^{-3}$); and Transformers enable global multi-source fusion and decadal climate-scenario projection. Across all families, four physics-DL integration modes (hard architectural constraints, soft loss-function penalties, physics-as-input feature engineering, and physics-ML hybrid output fusion) consistently yield RMSE reductions of 8–50% relative to data-driven baselines. These findings provide a practitioner-oriented framework that is applicable to ecohydrological monitoring of plant water stress, agricultural drought, early flood warnings, and land–atmosphere coupling. Full article

Soil moisture (SM) remote sensing is widely used for agricultural drought monitoring, yet most applications still emphasize static moisture states rather than event-scale wetting responses. We developed an interpretable Earth observation (EO) framework to evaluate precipitation–SM product consistency and diagnose wetting processes across China’s croplands. Multi-source precipitation and SM products, including ERA5-Land, Soil Moisture Active Passive (SMAP), Soil Moisture of China by in situ data (SMCI), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Grid-based Precipitation dataset for Mainland China (CHM_PRE), were assessed using lagged consistency between rainfall forcing and relative soil moisture increments. The selected pairing was then used to model daily wetting increments at three depths with eXtreme Gradient Boosting (XGBoost), Shapley additive explanations (SHAPs), generalized additive models (GAMs), and quantile regression (QR). ERA5-Land precipitation paired with ERA5-Land SM showed the strongest reanalysis-constrained event-scale consistency (peak mean r = 0.43 at a 1-day lag), providing an internal-consistency baseline for comparison with independent satellite-derived combinations rather than an absolute accuracy ranking. EO-derived wetting signals showed depth-dependent lags, with a 1-day surface response and an approximately 2-day delayed profile signal at 28–100 cm; this pattern should not be interpreted as direct evidence of rapid physical infiltration to 100 cm. Precipitation transition thresholds followed a U-shaped dependence on antecedent wetness, with higher rainfall requirements under extremely dry and near-saturated states. These findings indicate that event-scale EO diagnostics can characterize product consistency, lagged wetting responses, and state-dependent precipitation thresholds, while same-system and deep-layer interpretations remain constrained by reanalysis coupling and model-assisted root-zone products. Full article

A newly developed downscaling algorithm produces a 400 m resolution soil moisture (SM) product from native 36 km Soil Moisture Active Passive (SMAP) observations. The objective of this research is to demonstrate that this downscaled SM product provides enhanced field-scale discrimination of SM variability compared with the existing 9 km SMAP product in the San Luis Valley in Colorado. We demonstrate that the 400 m product exhibits greater sensitivity to differences in irrigation type, crop type, evapotranspiration (ET) and planting/harvesting dates. The high-spatial-resolution SM correlates well with Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) precipitation, and Moderate Resolution Imaging Spectroradiometer (MODIS) ET. MODIS ET correlates better with the 400 m product than the 9 km product (0.457 versus 0.392) and better discerns differences between crop types. We also find that SM and precipitation have a stronger correlation in crops requiring less irrigation and that sprinkler-irrigated fields have lower ET and SM than flood-irrigated fields. Our rotated empirical orthogonal function (REOF) analysis shows that unlike the 9 km product, the 400 m soil moisture product can detect field-scale trends consistent with seasonal water management. Both products detect basin-wide changes, including SM responses to snowmelt and increasing SM at higher elevations in recent years. Rotated principal component analysis of the 400 m SM product and MODIS ET revealed seasonal patterns concentrated over irrigated fields, lower values during dry years, and increased runoff following winters with high snow water equivalent. Full article

With the widespread deployment of industrial systems and Internet of Things (IoT) devices, multivariate time series anomaly detection has become increasingly important for ensuring system reliability and operational safety. However, accurately detecting anomalies in complex industrial environments remains challenging because existing approaches often fail to jointly model evolving inter-variable dependencies and multi-scale temporal patterns. To address these challenges, this paper proposes WaveGraphFormer (WGF), a unified framework for multivariate time series anomaly detection. The proposed method introduces a lightweight dynamic graph learning module to capture time-varying dependencies among variables and employs discrete wavelet transform (DWT) to extract multi-scale temporal-frequency features. In addition, a graph-guided residual modulation mechanism is designed to facilitate joint spatio-temporal representation learning. Experiments conducted on five public benchmark datasets (SWaT, WADI, SMAP, SMD, and MSL) demonstrate that WGF consistently achieves competitive performance in terms of F1-score and AUC compared with several state-of-the-art baselines. Ablation studies further validate the effectiveness of each component in the proposed framework. These results highlight the potential of WGF to provide reliable anomaly detection for complex industrial monitoring systems and establish a foundation for future research on adaptive spatio-temporal-frequency modeling. Full article

Field-scale soil moisture (SM) estimation from Sentinel-1 C-band SAR alone is challenged by vegetation, roughness, and spatial heterogeneity. This study proposes a Stacked Additive Boosting-based Model (SABM) that combines Sentinel-1 SAR, Sentinel-2 optical, and ancillary geophysical features with temporal lag SM features (SM_lag1, SM_lag2) derived from a station’s own antecedent in situ record, exploiting SM persistence at 12-day Sentinel-1 repeat intervals; the framework is accordingly intended for instrumented sites with historical SM observations rather than as a satellite-only retrieval method for ungauged locations. Validated at Little Washita Watershed, Oklahoma, USA (21 stations, 2016–2023), lag features improved temporal hold-out validation from R² = 0.6255, RMSE = 0.0535 m³ m⁻³ to R² = 0.7771, RMSE = 0.0413 m³ m⁻³ (23% RMSE reduction) and improved accuracy at held-out, instrumented stations from mean R² = −0.6024 to R² = 0.5134 across five station-level hold-out folds. Extended Triple Collocation indicated lower point-scale error for SABM (ETC RMSE = 0.0347 m³ m⁻³) than SMAP L3 Enhanced (0.0419 m³ m⁻³) at 13 of 20 stations at a nominal ~900× finer native pixel resolution (10 m Sentinel-1 pixel spacing vs. ~9 km SMAP footprint), though the effective support scale of the extracted features is coarser due to spatial averaging of the input features, and the ground reference’s own error term could not be reliably resolved at most stations, a limitation attributable to SABM’s use of antecedent ground observations as predictors. With cross-site transfer to REMEDHUS, Spain yielded R² = 0.6729 without retraining, though this accuracy was attributable primarily to SM persistence rather than transferred SAR/optical relationships. Boruta–SHAP identified soil pH as the top-ranked predictor by SHAP importance, though follow-up ablation indicates that this ranking substantially reflects a replaceable, station-identity-correlated proxy rather than an indispensable physical driver; SHAP dependence patterns for seasonal forcing, vegetation density, and SAR polarisation ratio were physically coherent. These findings demonstrate that temporal lag features substantially enhance retrieval accuracy at instrumented, model-unseen stations, providing a robust and interpretable framework for operational field-scale SM monitoring at sites with antecedent soil moisture records. Full article

Satellites are critical components of modern space information systems. During long-term on-orbit operation, satellite telemetry often exhibits multi-scale temporal dynamics, heterogeneous channel behavior, and time-varying inter-variable dependencies, which pose substantial challenges to anomaly detection. Existing methods remain limited in adaptively representing anomaly patterns across temporal scales, jointly modeling temporal evolution and dynamic asymmetric channel dependencies, and preventing over-generalized reconstruction of anomalous inputs. To address these limitations, this paper proposes MSGMamba, a multi-scale graph state space model for satellite telemetry anomaly detection. First, a multi-scale temporal patch decomposition and gated fusion mechanism partitions telemetry sequences into patches of different granularities and adaptively integrates their representations at each temporal position, enabling the joint modeling of short-term transients and relatively slow-varying patterns. Second, a graph–sequence alternating propagation mechanism couples selective state space updates with dynamic graph interaction. At each temporal patch, a directed and asymmetric dependency graph with self-connection priors is generated from the temporally encoded features, allowing temporal evolution and time-varying cross-channel dependencies to be modeled within a unified framework. Third, an orthogonal memory-augmented anomaly discrimination mechanism introduces an orthogonality-constrained memory bank to reduce redundancy among nominal prototypes and constrain the reconstruction space. A dual-pathway anomaly score further combines signal-space reconstruction error with encoder–memory discrepancy to improve the separability of nominal and anomalous samples. Experiments on the SMAP, MSL, and EIRSAT-1 datasets show that MSGMamba outperforms representative baseline methods in terms of average PA-F1 and AFF-F1. Full article

Freeze–thaw (F/T) processes play a critical role in the regulation of soil hydrothermal dynamics, land–atmosphere energy exchange, and ecosystem functioning. The spaceborne global navigation satellite system reflectometry (GNSS-R) has shown great potential for land surface F/T state detection; however, its monitoring capability remains limited by spatial resolution, revisit interval, observation coverage, and complex land surface conditions. In this study, Tianmu-1 (TM-1) multi-GNSS-R observations were used to detect daily land surface F/T states over the mid-to-high latitudes of the Northern Hemisphere. First, surface reflectivity observations from multi-GNSS, including the Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), Galileo, and GLONASS, were fused using a weighted averaging method based on the number of specular reflection points. Then, TM-1 multi-GNSS-R reflectivity was used as the primary remote-sensing input, while vegetation water content (VWC), surface roughness, and snow cover information were introduced as auxiliary environmental variables. The Soil Moisture Active Passive (SMAP) F/T product was used to provide supervised reference labels for developing Bayesian-optimized extreme gradient boosting (XGBoost) models for F/T state classification. Evaluation against SMAP F/T reference labels showed that the multi-GNSS fusion model achieved an area under the curve (AUC) of 0.853 and an overall accuracy of 77.3% without incorporating snow cover information, outperforming the single-GNSS models. After incorporating snow cover information, the AUC increased to 0.959, and the overall accuracy reached 89.3%. Shapley additive explanations (SHAP) analysis further showed that snow cover made the largest contribution to the final model output, suggesting that its improvement effect may reflect both physical snow-related surface information and seasonal contextual information. An independent point-based comparison with in situ observations from the international soil moisture network (ISMN) showed that the TM-1 F/T classification accuracy reached 85.2% after incorporating snow cover information, which was comparable to that of the SMAP product. These results demonstrate that TM-1 multi-GNSS-R observations have promising potential for detecting land surface F/T states during the autumn–winter freezing development period, and that integrating multi-GNSS-R reflectivity with snow cover information can substantially improve classification performance and spatial consistency within the available observation period. Full article

Soil moisture (SM) plays a pivotal role in regulating terrestrial energy-water exchanges and exerts substantial influence on agricultural productivity. In this study, a high-resolution soil moisture (HRSM) dataset (16 m) was generated by integrating multi-source remote sensing data from SMAP, HJ-2, Sentinel-2, and Gaofen-6, together with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model. The data assimilation (DA) method was implemented for assimilating HRSM within the Ensemble Kalman Filter (EnKF) framework using the Noah-MP model at a spatial resolution of 1 km. To enhance the spatial detail of SM, HRSM and its relative uncertainties derived from the three-cornered hat (TCH) method were used to update the observation error and Kalman gain in the EnKF framework, thereby improving SM profile estimates at a 16 m resolution. The performance of the DA method was evaluated against in situ measurements during the spring drought period in central Yunnan Province, China. The results show that assimilating HRSM (DA_HRSM) significantly improves surface and root-zone SM estimates in the Noah-MP model. The simulated SM from the DA_HRSM method demonstrates lower relative uncertainty. Compared to the assimilation of SMAP SM, the DA_HRSM method provides higher-resolution spatial features of SM and enhances spatial heterogeneity across 20 irrigation districts. The DA_HRSM method effectively captured the spring drought in central Yunnan, demonstrating good agreement with the Palmer Drought Severity Index (PDSI). The result highlights the advantages of incorporating high-resolution SM data into agricultural and drought monitoring systems. Full article

Mixed-matrix membranes (MMMs) incorporating porous organic fillers into high-performance polyimides were developed to investigate the influence of free volume and molecular architecture on gas transport. Four structurally rigid, intrinsically porous fillers (TFAP-Trp, Is-Trp, TFAP-TPB, and Is-TPB) were incorporated into a range of polymer matrices (P84^®, Matrimid^®, Pi-DAPOH, Pi-DAROH, Pi-HABAc, Pi-DAM, and PIM-1), enabling the development of a matrix-independent methodology for estimating intrinsic filler permeabilities for five gases (He, O₂, N₂, CH₄, and CO₂). This comprehensive multi-matrix, multi-gas study reveals a strong correlation between filler fractional free volume (FFV), BET surface area, and gas permeability, with isatin-based fillers exhibiting particularly high CO₂ permeability. Filler incorporation generally resulted in substantial permeability enhancements (100–350%) while maintaining selectivity, often with only minor losses or even favorable improvements in CO₂/CH₄ and He/CH₄ separation performance. Several MMMs, particularly those based on Pi-DAPOH and Pi-DAROH polyimides, approached or exceeded the Robeson upper bound. Analysis of permeability as a function of gas kinetic diameter further elucidated clear structure–property relationships, confirming that filler-induced disruption of polymer chain packing and the creation of additional transport pathways are the primary factors governing separation performance. Overall, these findings demonstrate that rationally designed porous organic fillers provide a robust and broadly applicable strategy for mitigating the permeability–selectivity trade-off in polymer membranes and enhancing gas separation efficiency. Full article

Coarsely gridded Land Surface Models (LSMs) often smooth over sub-grid spatial heterogeneity and non-linear surface soil moisture dynamics during extreme-precipitation events. This study introduces a clustering-based Soil Moisture Active Passive (SMAP) residual framework, evaluating the spatiotemporal discrepancies between 3 km SMAP Level 2 (SMAP-L2) retrievals and 9 km SMAP Level 4 (SMAP-L4) data-assimilation products within the Yanco study region during the extreme March 2021 floods in New South Wales, Australia. By applying k-means clustering to the residual time series, we partitioned the landscape into three distinct hydrological response patterns: a Low-Residual Baseline ($64.5\%$), a Persistent Positive Anomaly ($20.7\%$) indicative of unmodeled inundation, and a Transient Negative Anomaly ($14.8\%$) representing rapid drainage. Consequently, $35.5\%$ of the usable analysis area exhibited temporal trajectories that diverged significantly from model expectations, highlighting profound geographic heterogeneity in surface wetting and retention that cannot be captured by uniform precipitation inputs alone. Benchmarking the satellite-derived time series against the Yanco in situ network provided critical context for cross-scale variations, illustrating general agreement in overarching temporal trends despite the inherent scale mismatch. Ultimately, this approach leverages residual dynamics as a scalable spatial diagnostic, offering a robust, data-driven method to map localized flood responses that are typically obscured by broad-scale model parameters. Full article

High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m³ m⁻³ at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m³ m⁻³. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m³ m⁻³. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. Full article

This study evaluates the potential of L-band passive microwave data for monitoring soil moisture (SM) in boreal and temperate forests using SMAP and SMOS AM and PM overpasses. SMAP and SMOS Level 3 SM products were first assessed for spring and summer seasons. SMOS showed lower accuracy (r² = 0.04–0.24, ubRMSE = 0.09–0.13 m³/m³), while SMAP performed better (r² = 0.18–0.62, ubRMSE = 0.05–0.07 m³/m³) across sites and overpasses. Given the larger number of SMAP TB observations at a fixed incidence angle and greater temporal coverage over the study area, SMAP was selected for SM estimation using ML models. Feature importance analysis identified brightness temperature (TB) as the most influential variable, followed by vegetation water content (VWC), air and soil temperatures, and the microwave polarization difference index (MPDI). Soil and air temperatures were interchangeable during AM overpasses, whereas PM overpasses showed distinct differences, likely due to thermal absorption by dense vegetation. Using optimal features, SM was estimated with CatBoost, Gradient Boosting (GB), Random Forest (RF), and Principal Component Regression (PCR), using stratified shuffle split (SSS) and leave-one-year-out cross-validation (LOYOCV). In SSS, CatBoost achieved slightly higher accuracy than the other ensemble models (AM: r² = 0.73; PM: R² = 0.74), while PCR yielded substantially lower accuracy across both overpasses. LOYOCV showed closer rankings among models, with CatBoost ranking highest overall (r² = 0.58 for AM and 0.54 for PM). Results highlight the feasibility of improved SM estimation in forests using L-band TB, VWC, temperature variables, and MPDI. Full article