Search Results (1,024)

Terraces constitute a critical form of land surface modification, serving both as essential agricultural resources and effective soil and water conservation measures. However, their morphological diversity, pronounced scale variation, and ambiguous geometric boundaries pose substantial challenges for automated extraction from high-resolution remote sensing imagery. Existing CNN- and Transformer-based methods still face difficulties in simultaneously preserving fine spatial details and modeling long-range contextual information. To address these limitations, this study proposes TerraceNet, a hybrid CNN–Transformer architecture with an encoder–dual-decoder design. The framework employs a ConvNeXt encoder to extract multi-scale features, which are subsequently aggregated by a dynamic fusion feature pyramid network (DF-FPN) and fed into two parallel decoders: a boundary decoder dedicated to fine-grained edge localization and a multi-scale Transformer decoder that incorporates boundary priors for global context modeling and final segmentation. Experimental results on a 2 m resolution GF-1 satellite imagery dataset from the Wuding River Basin in the Loess Plateau demonstrate that TerraceNet an IoU of 77.61% and an F1-Score of 87.39%. These results validate the effectiveness of the proposed architecture for extracting morphologically diverse terraces in complex terrain. Full article

As climate change intensifies heat extremes, the Urban Heat Island (UHI) effect amplifies local thermal stress. Assessing the UHI using robust observational data, whether ground- and/or satellite-based, is essential for climate risk assessment and evidence-based urban adaptation. Therefore, this study aims to provide a comprehensive climatological assessment of air temperature patterns and UHI intensity across the Lisbon Metropolitan Area (LMA) over a 26-year period (2000–2025). The methodology employs a dense, high-quality integrated network of in-situ weather stations from the Portuguese Institute for Sea and Atmosphere (IPMA) and the National Water Resources Information System (SNIRH). To bridge critical gaps in traditional climate assessments, this research implements a dual-perspective approach that combines the high temporal resolution of MSG-SEVIRI and the spatial precision of MODIS Land Surface Temperature (LST). This framework accurately captures the lag effects between surface heating and atmospheric response. Validation results demonstrate that satellite-derived LST is a robust proxy for monitoring the nocturnal UHI, with differences generally below 1 °C compared with near-surface air temperature observations (T2m). However, daytime LST significantly overestimates atmospheric temperatures, with deviations of 2–8 °C due to solar radiation and urban geometry. The selection of rural reference stations constitutes a critical methodological factor, as a baseline shift can alter perceived UHI intensities by more than 3 °C. Despite these sensitivities, the results unequivocally confirm a persistent and spatially heterogeneous UHI effect in Lisbon, which intensifies during extreme heat events by up to an additional 4 °C. Analysis of the 2003 and 2018 heatwaves reveals surface LST anomalies exceeding 10 °C and urban–rural thermal differentials reaching up to 7 °C under conditions of suppressed maritime breezes. These nocturnal anomalies are particularly pronounced in densely built-up areas, limiting thermal dissipation and preventing physiological recovery. Integrating multi-sensor satellite data with in-situ validation provides a new benchmark for climate risk assessments, delivering the reliable, reproducible data required to strengthen long-term urban resilience under increasingly frequent extreme heat events. Full article

Accurate field-scale surface soil moisture (SSM) measures are important for agricultural water management. Conventional satellite SSM products remain too coarse for within-field applications. Here, we developed sequence-aware deep learning models for growing-season SSM estimation by fusing data from Sentinel-1 C-band SAR, harmonized Landsat-8/Sentinel-2 (HLS), and local ancillary datasets. We assembled a multi-source dataset on Sentinel-1 overpass time for 2016–2024 (May–September), yielding 1469 samples and 65 features per sample, including SAR and optical features, meteorological data, soil texture and bulk density, topography, crop labels, irrigation-likelihood flag, and irregular-time-step encoding. We compared long short-term memory (LSTM) and temporal convolutional neural network (TCN) architectures together with attention-augmented variants, including feature attention (FA), temporal attention (TA), and the combined feature–temporal attention (FTA). Models were trained and tested on seven years of data and were validated based on a temporal generalization using combined data of a wet year (2016) and a dry year (2023). The best model, FTA-TCN, achieved R² = 0.851, RMSE = 0.024 m³.m⁻³, and MAE = 0.020 m³.m⁻³ on the withheld validation years, outperforming the base LSTM (R² = 0.422; RMSE = 0.053 m³.m⁻³; MAE = 0.043 m³.m⁻³) and the base TCN (R² = 0.746; RMSE = 0.034 m³.m⁻³; MAE = 0.022 m³.m⁻³). Shapley additive explanations (SHAP) analysis indicated that antecedent precipitation and short-term rainfall accumulations were dominant forcings, while soil texture, elevation, incidence angle, and vegetation indices modulated SSM variability. Satellite-derived features accounted for ~28.5% of aggregated SHAP importance. Overall, the results show that dual-attention temporal convolution can capture field-scale SSM dynamics across wet and dry seasons when satellite signals are coupled with local soil-meteorological-management context. Full article

With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s new-generation satellite-borne dual-frequency precipitation radar observations to investigate summer rainstorms in Nanjing, China, during 2017–2024. Results reveal pronounced spatiotemporal heterogeneity, with higher rainfall intensities concentrated over urban and adjacent areas. During the study period, rainstorm intensity and duration increased by 7.44% and 38.63%, respectively, while the affected area decreased by 8.18%, indicating a transition toward more localized yet more intense rainfall events. Environmental analyses suggest that large-scale thermodynamic conditions and regional topographic forcing provide a favorable background for convection development, while local urban thermal effects may further modulate rainfall enhancement. Three-dimensional radar detection of an illustrative rainstorm event indicates an inverted-cone vertical structure, suggesting a mixed convective-stratiform precipitation structure involving both warm-rain and ice-phase processes. An Explainable Bayesian-Optimized XGBoost (EBOX) model further identifies near-surface air temperature and specific humidity as the primary environmental factors associated with rainstorm occurrence and development. Overall, this study highlights the value of integrating satellite remote sensing with explainable artificial intelligence to improve understanding of urban extreme rainfall and provide new insights into how climate change, topography, and urbanization jointly shape precipitation extremes in rapidly urbanizing monsoon regions. Full article

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article

Post-disaster scenarios such as tsunamis require rapid terrain assessment that cannot wait for the next satellite synthetic aperture radar (SAR) revisit, yet a readily deployable system remains lacking. We present an off-the-shelf K-band drone-borne dual circular polarimetric (DCP) SAR and a processing pipeline for on-demand terrain classification. Compared with fully polarimetric (FP) SAR, DCP requires only a single transmit polarization and two receive channels, providing a wider swath than FP for the same acquisition, while still separating odd-bounce and even-bounce scattering mechanisms, which dual linear polarimetric modes with the same channel count provide with greater ambiguity due to their sensitivity to target orientation angle. To compensate for platform motion, we implemented RTK global navigation satellite system (GNSS) guided time-domain backprojection (TDBP) with phase gradient autofocus (PGA), yielding an 11.98 dB improvement in peak amplitude. We then applied single-target wire calibration to correct a measured 8.91 dB inter-channel complex gain difference between co-polarization and cross-polarization. As a result, H/α decomposition of the calibrated DCP data classifies canonical reflectors, artificial structures, gravel roads, vegetation, and a pond surface. These field experiments extend compact polarimetric H/α decomposition to drone-borne SAR data for terrain discrimination, establishing a practical pathway toward rapid post-disaster terrain assessment. Full article

Global Navigation Satellite System Reflectometry Interferometric Synthetic Aperture Radar (GNSS-R InSAR) offers all-weather, all-day observation capabilities and high temporal resolution, enabling elevation deformation monitoring with a single satellite. However, in hazardous regions, such as tailings dam slopes, measuring the deformation of a greater number of target points is essential for a more accurate assessment of geological hazard risks. Since navigation satellite signals are not originally designed for imaging purposes, their inherent narrow bandwidths result in low spatial resolution and limited target recognition capabilities, rendering them inadequate for such scenarios. To address these limitations, this paper investigates a GNSS-R InSAR deformation measurement architecture utilizing dual-frequency BeiDou-3 (BDS-3) signal fusion. Specifically, a coherent spectrum fusion method is introduced to effectively expand the signal bandwidth, thereby significantly enhancing range resolution and target identification capabilities. Building upon this, deformation measurements are conducted to achieve more refined and detailed monitoring. Full article

The atmospheric weighted mean temperature ($T_m$) is critical for converting a zenith wet delay ($ZWD$) to precipitable water vapor ($PWV$). However, the existing $T_m$ models still have certain limitations: Those driven by surface-measured parameters achieve high accuracy but depend heavily on in situ instruments, incurring high costs and lacking forecasting capability. Empirical models avoid measured data but fail to capture short-term $T_m$ variations, leading to lower accuracy. Daily weather forecast data—which are low-cost, readily available, and reflective of short-term changes—offer a promising alternative. This study develops a gridded $T_m$ model named CGTV-Tm, which couples temperature and water vapor pressure, using ERA5 reanalysis data over China (2019–2023). The model can be driven by daily weather forecast data. A dual vertical correction method is also proposed to improve performance. Validation against 2024 ERA5 and radiosonde data shows that CGTV-Tm achieves RMSEs of 2.38 K (vs. ERA5) and 2.64 K (vs. radiosonde), significantly outperforming the Bevis (3.61 K, 3.67 K), PTm (3.19 K, 2.94 K), and CGT-Tm (2.71 K, 3.08 K) models. When driven by daily weather forecast data, CGTV-Tm achieves an RMSE of 2.90 K, improving accuracy by 29.6% and 21.2% over the state-of-the-art empirical models GPT3 and HGPT2, respectively. These results demonstrate that CGTV-Tm not only surpasses traditional linear $T_m$ models that rely solely on surface temperature but also, by using weather forecast data, it removes dependence on in situ instruments, offering a superior low-cost solution for real-time GNSS (Global Navigation Satellite System) $PWV$ retrieval. Full article

For small-satellite imagery, traditional image points derived from the direct back-projection of laser altimetry points onto the imagery often exhibit deviations of tens of pixels or more, owing to the relatively limited attitude and orbit determination accuracy of small-satellite platforms. Moreover, the diversity of imaging sensors, variations in image resolution, and inherently weak image geometric configurations further complicate the accurate acquisition of image-space coordinates for laser altimetry points. To facilitate the application of laser altimetry data for geometric positioning across multi-satellite, multi-sensor, and multi-resolution small-satellite imagery, this study proposes a measurement method for laser altimetry points tailored to small-satellite images and establishes a combined geometric positioning model that integrates virtual control points, laser altimetry points, and image-matching tie points. The framework comprises four key procedural components: (1) an image-point-measurement strategy for laser altimetry points; (2) the construction of a laser altimetry data-assisted geometric positioning model for small-satellite imagery; (3) the solution of the geometric positioning model using a total least squares approach based on the partial-EIV (errors-in-variables) models; and (4) a comprehensive accuracy assessment conducted under multiple image-combination scenarios, including single-satellite single-stereo, single-satellite multi-stereo, dual-satellite single-stereo, and multi-satellite multi-stereo imagery configurations. Experimental validation is carried out using Jilin-1 small-satellite panchromatic images (KF01A, GF02A, and GF02B) acquired over the Henan region of China. The experimental results demonstrate that, with the laser altimetry point-measurement method and the combined geometric positioning model, the vertical positioning accuracy is substantially improved across all tested image-combination scenarios. These findings further confirm the capability in enhancing the vertical geometric positioning performance of stereoscopic small-satellite imagery characterized by multi-satellite platforms, multi-sensors, and multi-resolutions over terrain conditions similar to those tested. Full article

Achieving scalable monitoring of Alternate Wetting and Drying (AWD) for methane mitigation in rice cultivation depends on establishing field benchmarks for drainage behavior and demonstrating that satellite observations can reliably detect corresponding changes in water status. We analyzed about two million high-frequency in situ water-level observations from hundreds of sensors deployed in rice fields across the Philippines and Japan to quantify drainage duration from near-surface conditions to 15 cm below the soil surface and to test the sensitivity of open-access PALSAR-2 dual-polarization L-band SAR to vertical water-level variations. Across 564 drainage events, the median drainage duration was 19.0 h, and only 0.9% of events exceeded 240 h, indicating that drainage happens generally within a day. Seasonal differences were evident in Pangasinan, while small Chiba and Cagayan samples suggested exploratory longer-duration patterns; multiple drainage events occurred in 48.0% of Philippine dry-season fields but only 21.6% of wet-season fields. PALSAR-2 data showed a statistical significance in detecting inundation at Mid crop growth stage with cross-polarization band, but the significant overlap induces challenges in operational applications. These results provide empirical benchmarks for AWD-related drainage dynamics while showing that dual-polarization PALSAR-2 alone is unlikely to support robust field-scale monitoring of rice-field water status. Full article

Ultra-shallow and confined water environments are challenging to survey with conventional towed side-scan sonar (SSS) due to limited access and positioning uncertainties. This study introduces a portable, battery-powered acoustic survey system that integrates a pole-mounted dual-frequency side-scan sonar (600/1600 kHz) with RTK GNSS (Real-Time Kinematic Global Navigation Satellite System), deployable from a small inflatable boat. The system was validated in two settings: an inland lake and a marina. Field trials demonstrated reliable acquisition of high-resolution sonar imagery and effective detection of both natural and anthropogenic seabed features, including small and low-reflectivity objects. The high-frequency channel (1600 kHz) produced superior image quality and interpretability compared to the lower frequency. While there are limitations associated with fixed sonar mounting and limited altitude control, the system offers high mobility, rapid deployment, and operational safety. This approach represents a practical, cost-effective solution for high-resolution acoustic remote sensing in ultra-shallow water settings where traditional survey methods are ineffective or impractical. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

Natural lakes in urbanizing regions face compounding climatic and anthropogenic pressures. Despite their socio-ecological importance, the dual vulnerability of these urban lakes to both long-term areal shrinkage and the shifting frequencies of extreme water events remains a critical research gap, often overlooked in favor of large, remote lake systems. We investigated surface area dynamics, extreme events, and climatic attribution of 7320 natural lakes across China’s five major urban agglomerations (Jing-Jin-Ji, Yangtze River Delta, Greater Bay Area, Chengdu-Chongqing, and Middle Yangtze) from 2001 to 2023. Using a satellite area product, we assessed long-term trends via Seasonal-Trend decomposition by Loess (STL). Regional climate shifts were detected via multi-scale Standardized Precipitation–Evapotranspiration Index (SPEI) breakpoint analysis, and climate attribution was performed by correlating detrended lake areas with SPEI. Results show 59.4% of lakes exhibit significant trends, with shrinkage (50%) vastly outpacing expansion (9.4%), most severely in Jing-Jin-Ji (−0.28%/year). Despite all agglomerations transitioning toward wetter conditions (2008–2013), extreme event responses diverged markedly regionally. Climate-driven lakes (14.5%) displayed stronger shrinkage and greater sensitivity to extremes than lakes with low climate sensitivity, particularly in Jing-Jin-Ji and Chengdu-Chongqing. These findings reveal pronounced spatial heterogeneity in urban lake vulnerability, providing an evidence base for sensitivity-stratified management strategies. Full article

Pine wilt disease (PWD) can spread rapidly after the disease occurs and often causes large-scale death of the pine. Therefore, the timely identification of infected trees is critical for forest conservation and effective disease management. However, early infected trees are difficult to distinguish in satellite remote sensing images. Their visual differences from healthy trees and complex background features are often subtle, and existing image-processing methods do not fully exploit heterogeneous information. To address this problem, we constructed the Naro dataset for satellite-based PWD detection and proposed SGM-RTDETR based on Real-Time Detection Transformer (RT-DETR). The proposed model consists of a Semantic–Visual Fusion Module (SVFM) and a Disease Feature Refinement Module (DFRM). In SVFM, ExG, VARI, and GLI are concatenated with RGB imagery to form a six-channel visual input, which enhances the spectral differences between diseased and non-diseased targets. In addition, textual prior knowledge is introduced into the decoder input through a Stackelberg game-based visual–text fusion strategy. This strategy helps the encoded memory features maintain clearer disease-related semantics in complex backgrounds. DFRM then performs channel recalibration, feature refinement, and residual enhancement on the fused memory features to better extract fine-grained disease cues in remote sensing scenes. Experiments on the Naro dataset show that SGM-RTDETR achieves 80.75% mAP@0.5 and 35.43% mAP@0.5:0.95, which is 2.74 percentage points higher than RT-DETR-L on mAP@0.5:0.95. Overall, the results indicate that the dual-module structure improves the precision and robustness of PWD detection in satellite remote sensing images. Full article

This work proposes a multiscale spatial and temporal approach to assess the impacts of the ionosphere and neutrosphere (neutral atmosphere including both tropospheric and stratospheric) through an independent analysis of each component on Precise Point Positioning (PPP) accuracy and stability during selected representative geomagnetic events of Solar Cycle 25. Geomagnetically quiet and disturbed days were selected using the Kp index, with 21 multi-GNSS stations distributed across latitude bands. Kinematic PPP processing was performed using APPPOLO software (v1.0) with ionosphere-free dual-frequency combinations, precise products, and robust filtering, totaling 924 solutions. Results show improvements in geometry and satellite availability with multi-GNSS, achieving discrepancies within 0–10 cm in more than 89% of the solutions. The VMF3 model confirmed the deterministic behavior of ZHD and the latitudinal variability of ZWD, with increased stability in multi-GNSS solutions. Greater degradation was observed at high latitudes under disturbed geomagnetic conditions, particularly for GPS-only processing. Residual analysis indicated elevation-dependent effects and constellation-related differences. The analysis of ionospheric irregularities using ROTI revealed that PPP degradation is strongly associated with spatial distribution and satellite geometry, with enhanced effects at high latitudes and low elevation angles. Full article