Search Results (6,093)

High-resolution land cover classification is critical for monitoring environmental change and managing natural resources. This study presents an unsupervised framework with externally guided feature prioritization that integrates Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery at 10 m spatial resolution. A cloud-native export protocol in Google Earth Engine (GEE) enables the generation of consistent, cloud-free, and snow-free seasonal composites across Ontario, Canada. A comprehensive feature engineering pipeline combines spectral indices, radar backscatter metrics, terrain derivatives from digital elevation models (DEMs), and temporal statistics to create a rich multi-sensor input space. Dimensionality reduction is performed using Sparse Principal Component Analysis (SparsePCA) and mutual-information-based feature selection. Clustering is conducted using three complementary algorithms: centroid-based K-means, density-based Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and reachability-based Ordering Points To Identify the Clustering Structure (OPTICS). Final land cover labels are assigned via a majority-voting ensemble, with prediction ties resolved deterministically using OPTICS. OPTICS is particularly effective for modeling heterogeneous landscapes due to its ability to detect clusters of varying density without requiring a global threshold. This study is designed as a pilot-site methodological demonstration using three representative 2 km × 2 km regions in Ontario, rather than a full provincial-scale land cover product. The resulting classification maps are validated against reference land cover data, demonstrating the effectiveness and potential scalability of the proposed external-label guided unsupervised mapping approach. Full article

Machine-learning landslide-susceptibility maps are almost always judged by inventory-split skill (the area under the receiver-operating-characteristic curve, AUC, and Cohen’s κ), not by model-independent physical observation of where an event caused ground disturbance. For the May 2023 Emilia-Romagna event (>80,000 landslides; RER2023 inventory), we confront an open-data, event-conditioned susceptibility model (trigger rainfall is among its predictors) with a co-event disturbance footprint built from two satellites: phenology-matched Sentinel-2 change in the Normalized Difference Vegetation Index (ΔNDVI) and Normalized Burn Ratio (ΔNBR), and a 12-day Sentinel-1A C-band coherence-and-backscatter layer used as a cloud-independent coverage check (C-band 12-day decorrelation is the a priori expectation in this setting); neither enters the model. Apparent geomorphic plausibility depends critically on how the independent footprint is constructed. Against a raw co-event footprint (contaminated by flooding and agriculture), a model with AUC ≈ 0.945 is indistinguishable from a random mask. On a landslide-relevant footprint, the same model captures optically detectable disturbance at roughly twice the chance (bootstrap median 1.94×, 95% CI 1.25–2.72, excluding 1; ≈1.46× above a vegetation-phenology null), but the aggregate is driven by an upper-tail minority of 10 km blocks—the majority of blocks have per-block median lift 0.67× (below chance). The map is therefore a regional, not a per-place, statement. Results are a within-event consistency test; cross-event transferability is not claimed. Footprint construction is decisive and currently neglected. The open-source pipeline is released upon acceptance. Full article

Recurrent flooding along the Wabi Shabelle River has repeatedly displaced communities in Beledweyne, Somalia, prompting a 2020 government-led relocation policy intended to reduce long-term flood risk exposure. Whether such resettlement constitutes a durable change-detection method disaster risk reduction strategy in semi-arid East Africa remains empirically untested. We integrate ten years of Sentinel-1 SAR (259 scenes, 2015–2025), three global DEMs (Copernicus GLO-30, FABDEM, SRTM), CHIRPS precipitation, and BFAST changepoint analysis to map flood frequency at 10 m resolution. The Z-score showed the strongest coupling with 12-day cumulative precipitation (Pearson r = +0.338; block-bootstrap 95% CI [+0.13, +0.49], excluding zero) and strong agreement with the log-ratio method (r = +0.676), whereas the conventional fixed −17 dB threshold produced a physically implausible negative correlation (r = −0.248). These conclusions were stable across alternative thresholds. HAND from all three DEMs was positively associated with flood frequency (Spearman ρ ≈ +0.30); GLO-30 and FABDEM were near-equivalent in this low-relief setting (median pairwise difference, 0.13 m). BFAST detected 476,955 changepoints (49.9% post-2020 vs. 35.6% pre-2020), concentrated in high-flood-frequency pixels (Kolmogorov–Smirnov D = 0.854, p < 0.001). The mean flooded area fraction rose from 4.68% to 5.61%, a relative increase of +19.8% (95% CI 9.1–32.0); this remained significant after controlling for precipitation (+0.96 pp, p < 0.001) and excluding the extreme 2023 events (+0.81 pp). Because standard optical and multi-year surface water products are unsuitable for pixel-level validation in this turbid seasonal river, we demonstrate that SAR flood frequency is significantly higher within independently mapped JRC water corridors (median, 0.070 vs. 0.042; p < 0.001). These convergent lines of evidence are consistent with re-encroachment into hazardous floodplains, suggesting that structural relocation alone is unlikely to deliver durable flood risk reduction without parallel investment in tenure security, livelihoods, and inclusive governance (SDGs 11.5, 13.1). The reproducible, open-source SAR framework provides a transferable monitoring template for data-sparse Horn of Africa floodplains. Full article

Understanding the factors controlling tropospheric nitrogen dioxide (NO₂) variability is essential for improving air-quality assessment and environmental management in tropical regions. This study analyzed the spatial and interannual variability of tropospheric NO₂ in ten municipalities of Mato Grosso do Sul, Brazil, located within the Cerrado–Pantanal transition zone, during the period 2020–2024. Tropospheric NO₂ column densities were obtained from Sentinel-5P/TROPOMI observations and integrated with environmental and anthropogenic indicators, including fire density derived from the Brazilian National Institute for Space Research (INPE), land-use and land-cover data from MapBiomas, road density, and meteorological variables obtained from CEMTEC-MS. Descriptive statistics, Pearson correlation analysis, and multiple linear regression were applied to evaluate the relationships between NO₂ concentrations and the explanatory variables. The results revealed moderate spatial variability of tropospheric NO₂, with annual mean column densities ranging from 1.42 × 10⁻⁵ to 1.74 × 10⁻⁵ mol·m⁻². Higher concentrations were observed in municipalities characterized by greater urbanization and transport infrastructure, particularly Três Lagoas, Corumbá, and Ladário. Pasture area exhibited the strongest negative association with NO₂ concentrations (r = −0.81, p = 0.004), followed by agricultural area (r = −0.67, p = 0.034), whereas fire density showed a moderate positive relationship with NO₂ variability (r = 0.62, p = 0.056), highlighting the contribution of biomass burning to regional atmospheric pollution. Meteorological variables, especially precipitation and wind speed, also influenced NO₂ distribution through atmospheric removal and dispersion processes. These findings demonstrate that tropospheric NO₂ variability in Mato Grosso do Sul is controlled by the combined effects of land use, biomass burning, urban infrastructure, and meteorological conditions. The study provides new insights into the environmental drivers of atmospheric pollution in the Cerrado–Pantanal transition region and contributes to the development of monitoring and air-quality management strategies in tropical environments. Full article

The Kamchatka Peninsula is one of the most seismically active regions in the world and experienced the Mw 8.8 earthquake in 2025. To examine whether tectonic processes were associated with enhanced atmospheric warming while minimizing external interference, this study analyzed multi-level air temperature fields from the NCEP (National Centers for Environmental Prediction) reanalysis data. A tidal-force-based background framework was used to characterize the vertical evolution of air temperature excesses before and after the mainshock. Results showed that a pronounced warming excess emerged near the epicentral area prior to the earthquake. The warming excess exhibited the strongest intensity and amplitude in the near-surface layer and decayed progressively with altitude, a pattern that may be consistent with the vertical attenuation expected for tectonically related thermal excesses. Sentinel-5P SO₂ observations showed enhanced volcanic SO₂ concentrations from mid-July 2025, before the Mw 8.8 mainshock. The SO₂ enhancement was observed in the volcanic regions of the study area, suggesting that the SO₂ signal may reflect enhanced regional degassing activity while also being influenced by volcanic emissions, plume dispersion, and atmospheric transport. Sentinel-1A InSAR observations derived from SAR images acquired on 23 July and 4 August 2025 represent cumulative coseismic line-of-sight (LOS) deformation associated with the Mw 8.8 mainshock, rather than preseismic subsidence. Overall, the air temperature excesses and SO₂ enhancement may provide auxiliary evidence for possible changes in the regional volcanic-tectonic system before the earthquake, whereas the InSAR result provides coseismic deformation context. These findings highlight the potential value of vertical air temperature structure for investigating possible earthquake-related thermal signals, but further studies based on multi-year statistical analyses of various observational datasets and additional earthquake cases are still required. Full article

ICESat-2/ATLAS photon-counting LiDAR faces several challenges in regional-scale forest aboveground biomass (AGB) estimation. These challenges include sparse sampling, signal saturation, terrain effects, and limited model generalization. To solve these challenges, this study proposes a new synergistic multi-source remote sensing framework for regional-scale AGB estimation by integrating terrain-corrected ICESat-2 canopy height and forest-type heterogeneity. The framework combines structural, spectral, textural, topographic, and climatic information derived from multiple remote sensing datasets to improve biomass estimation accuracy and model robustness across different forest types. In this paper, multi-source datasets were integrated, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission (SRTM), WorldClim, and a terrain-corrected canopy height model (CHM). Subsequently, candidate features were derived such as spectral, textural, topographic, and climatic variables. In terms of the terrain-corrected CHM, canopy structural parameters were extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) ATL08 data after terrain correction based on a high-resolution DEM. Footprint-level AGB samples were first generated using ICESat-2-derived canopy structural parameters through four regression approaches, including Multiple linear regression, Stepwise multiple regression, Ridge regression, and Lasso regression. These generated AGB samples were then used as response variables for subsequent regional-scale modeling. To build accurate AGB estimation model, key features were first identified using correlation analysis. To account for forest structural heterogeneity, three models including random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were developed for regional AGB mapping. To evaluate the performance of the proposed AGB estimation model by integrating terrain-corrected canopy height and forest-type heterogeneity, this study conducted AGB estimation at the Harvard Forest (HARV) site in the United States. The experimental results show that forest-type-specific modeling improves model adaptability and robustness. Among the models (RF, XGBoost and SVM), RF achieved the best performance, with an average coefficient of determination of 0.694. The optimized model was applied to produce a 30 m resolution AGB map. The validation was conducted using airborne LiDAR-derived AGB referenced results. The validation shows that an overall coefficient of determination (R²) of 0.606 and a root mean square error (RMSE) of 16.53 Mg ha⁻¹. These results demonstrate that the proposed new synergistic AGB estimation framework, which integrates terrain-corrected ICESat-2 canopy height with forest-type-specific modeling, provides an accurate and reliable solution for regional-scale forest biomass mapping and carbon stock assessment. Full article

Temporary floodplain ponds (TFPs) are short-lived water bodies forming in microtopographic depressions after flood recession and represent an important but poorly quantified component of floodplain hydrology. This study investigated the spatial and temporal dynamics of TFPs and their relationship with vegetation patterns in the natural floodplain of the Biebrza River, Poland. High-resolution TerraSAR-X data and Sentinel-2 multispectral imagery were combined with field-based vegetation surveys and statistical modeling. Threshold-based SAR classification showed that TFPs occupied more than 32% of the floodplain surface shortly after spring flood recession and stored, on average, over 250 L m⁻² of surface water, but disappeared within one month. Random Forest classification demonstrated that combining SAR and multispectral data improved overall vegetation mapping accuracy from 64.5% to 81.7% (Kappa from 0.574 to 0.780). A global chi-square test revealed a strong association between vegetation patterns and TFP occurrence (χ² = 224.9, p < 0.001, Cramér’s V = 0.40). Multinomial logistic regression identified TFP depth as the strongest predictor of plant community distribution. Rorippo-Agrostietum, Caricetum gracilis and Glycerietum maximae increased with TFP depth, whereas Alopecuretum pratensis and Phalaridetum arundinaceae declined. These results show that TFPs act as a fine-scale hydrological filter structuring floodplain vegetation mosaics and that SAR–optical data fusion is effective for detecting these transient habitat patterns. Full article

Quantifying mountain forest carbon stocks and elucidating their spatially heterogeneous driving mechanisms are both critical for terrestrial carbon management under the global carbon neutrality agenda. Conventional single-source remote sensing approaches can neither fully exploit multi-source data synergies nor adequately resolve spatial heterogeneity in complex terrains. This study develops an integrated framework combining multi-source remote sensing classification, InVEST-based carbon estimation, and multiscale geographically weighted regression (MGWR) and applies it to Zixi County, a subtropical mountainous bamboo-abundant region in southeastern China. Sentinel-2 imagery, PlanetScope data, and DEM derivatives were fused with an optimized Random Forest classifier, achieving an overall accuracy of 0.8565 (Kappa = 0.7065). Carbon stocks were then estimated via the InVEST model. MGWR analysis (adjusted R² = 0.930, AICc = 594.032) substantially outperformed the global OLS model (adjusted R² = 0.795, AICc = 1717.450), confirming strong spatial non-stationarity across all drivers. Canopy density exhibited the strongest positive local effect (coefficient range: 0.343–0.768); slope position showed predominantly negative regulation with localized positive reversals (−0.778 to 0.270); elevation displayed a broad-scale positive gradient (0.133–0.140); and total vegetation cover exhibited bidirectional effects (−0.134 to 0.208) with pronounced east–west divergence. This framework not only provides a robust methodological reference for carbon stock assessment in complex mountain landscapes but also supports targeted forest management and carbon sequestration strategies through spatially explicit driver identification. Full article

This paper proposes a hybrid, interpretable machine learning framework for the preliminary screening of soil macronutrients using Sentinel-2 and AgroLens data. This study aims not to replace laboratory analysis, but to test the feasibility of obtaining a useful proxy signal for estimating nitrogen (N), phosphorus (P), and potassium (K) content using a limited set of remote sensing and agricultural features. The developed pipeline includes data auditing, leakage control, feature engineering, train-only normalization, group-aware partitioning, baseline/SOTA model comparison, hybrid regression modeling, SHAP interpretation, and uncertainty assessment. The experiment used 4471 AgroLens observations and 126 features derived from Sentinel-2 spectral aggregates, vegetation indices, temporal characteristics, and crop-related parameters. The evaluation indicated that the proposed approach consistently improves forecasting quality relative to baseline models under reduced-input conditions. Linear relationships between target variables ranged from 0.14 to 0.17, while nonlinear relationships reached 0.23. SHAP analysis revealed significant contributions from vegetation indices, crop-specific interactions, and Sentinel-2 spectral channels. The findings support the applicability of the proposed framework for preliminary monitoring, prioritizing field surveys, and decision support in digital agriculture. Although an additional AgroLens control segment was used to assess the robustness of the study, the study did not include independent external validation of the data collected across different geographic or agro-climatic conditions. Full article

Accurate characterization of flood dynamics in large river–lake systems remains challenging due to the difficulty of simultaneously capturing inundation extent and water surface elevation (WSE) variations under rapidly changing hydrological conditions. This study develops an integrated Synthetic Aperture Radar (SAR) and wide-swath interferometric altimetry framework to reconstruct the spatiotemporal evolution and storage dynamics of the 2024 flood event in the East Dongting Lake system, China. Sentinel-1 SAR imagery is utilized to derive high-resolution inundation extent, while the Surface Water and Ocean Topography (SWOT) mission, equipped with the Ka-band Radar Interferometer (KaRIn), provides two-dimensional WSE observations. To improve SAR-based flood extraction in heterogeneous floodplain environments, an Adaptive Spatially-Constrained Fuzzy C-Means (AS-FCM) algorithm is proposed by incorporating adaptive spatial regularization and structure-aware neighborhood weighting. Quantitative evaluation demonstrates that the proposed method achieves the highest performance among the evaluated conventional approaches, with an Overall Accuracy of 93.6%, an Intersection over Union of 0.89, and a Kappa coefficient of 0.87. The multi-temporal inundation sequence reveals a distinct flood evolution pattern characterized by rapid expansion during the rising stage and gradual recession during the post-peak period. SWOT-derived WSE observations exhibit strong agreement with synchronous in situ measurements after bias adjustment, with a correlation coefficient of 0.988. By integrating SAR-derived inundation extent with temporally matched water-level observations constrained by bias-adjusted SWOT and in situ gauge data, an empirical WSE–area relationship ($R^2=0.937$) is established to reconstruct daily flood dynamics and estimate cumulative water storage variation. The results indicate that the East Dongting Lake floodplain played an important buffering role during the 2024 flood event, with cumulative storage variation reaching approximately $10.7{\mathrm{km}}^3$ during the peak stage. Overall, the proposed framework demonstrates strong potential for flood monitoring and hydrological storage assessment in complex river–lake systems. Full article

High-accuracy parcel-level agricultural mapping is fundamental to precision agriculture. However, in fragmented agricultural regions of the Yangtze River Delta, identifying cropping patterns at the parcel level faces two compounding challenges: asynchronous multi-source observations and mixed-pixel effects in small parcels. When historical archive records are used as training labels, inter-annual cropping pattern changes further introduce label noise that undermines model reliability. To address these challenges and the label noise issue, we propose PAST (Parcel-level Asynchronous SpatioTemporal), a parcel-level cropping pattern classification framework comprising three stages: K-Shape-based label quality control, parallel dual-branch classification, and decision-level fusion. PAST employs a dual-branch architecture: the temporal branch achieves interpolation-free cross-modal phenological fusion of Sentinel-1 and Sentinel-2 data, while the image branch extracts canopy texture features from 0.8 m high-resolution imagery to partially address mixed-pixel interference. Experiments in a typical fragmented agricultural region of the Yangtze River Delta demonstrate that PAST achieves an overall F1 score of 0.926 and a small-parcel F1 score of 0.906, outperforming mainstream time-series baselines. These results confirm that combining K-Shape label quality control at the data level with a dual-branch interference-robust architecture at the model level provides a complete integrated three-stage pipeline for fine-grained crop mapping under weakly supervised historical archive label conditions. Full article

With an ever-growing recognition of Land Surface Temperature (LST) as a key Essential Climate Variable (ECV), it becomes utmost important to have such a variable at the fine spatial and temporal scales of urban spaces and dynamics. Sentinel-3 provides coarse LST (1 km, daily) based on thermal imagery acquired by its Sea and Land Surface Temperature Radiometer (SLSTR) as well as fine Spectral Directional Reflectances (SDRs, 300 m, every two days) synergically inferred from both SLSTR and the Ocean and Land Colour Instrument (OLCI), which gives the opportunity for using the latter as a predictor in the downscaling of the former. Herein, two scale-invariance-based architectures were developed: a single-timestamp (STS) model, trained with coarse data of the timestamp whose fine target it infers; and a multi-timestamp (MTS) one, trained with multiple timestamps. Note that while several Machine Learning (ML) models besides Linear Regression (LR) were considered for the MTS architecture, only LR was used for the STS one due to the limited amount of available data which the former require for hyperparameter tuning. The models were developed over four Danish Functional Urban Areas (FUAs) using SRD-derived indices and seasonal and geospatial predictors and validated against Landsat data. While Gradient Boosting (GB) achieved the best coarse-scale performance it corresponded to the worst fine-scale performer together with Random Forest (RF), indicating scale invariance breakdown. Tree-based models performed poorly due to extrapolation limitations, whereas Neural Net (NN) and LR proved more robust. After residual correction, single-timestamp LR achieved the best fine-scale performance, making it the most reliable and recommended architecture for operations. The overall results showed that, although ML models may better predict the target at their training scale, their performance may not significantly generalise at others, therefore revealing scale specificity. Furthermore, the results suggested that usage of the more general multi-timestamp architecture instead of the single one may deteriorate performance. Full article

Accurate maize distribution information is critical for crop-area statistics, food-security assessment, and agricultural monitoring, but large-scale maize-mapping remains difficult in regions with limited reference samples, heterogeneous crop calendars, and frequent optical data gaps. This study proposes a phenology-adaptive maize mapping framework based on Sentinel-2 time-series imagery and an Enhanced Red-edge NDVI (ENDVIre). ENDVIre was constructed from the Sentinel-2 red-edge 4 and red-edge 2 bands to enhance the spectral response of maize during the silking-to-grain-filling stage, when maize develops a dense canopy and high chlorophyll content but is often confused with soybean. The framework first reconstructed the NDVI time series using an upper-envelope-constrained Whittaker smoother to identify key phenological stages, including sowing–emergence, vigorous growth, and maturity–harvest. NDVI, ENDVIre, and LSWI were then integrated into an interpretable decision-tree model with phenology-aligned time windows to distinguish maize from soybean, rice, wheat, and other non-maize backgrounds. The method was evaluated in six representative maize-growing regions across the United States, Brazil, China, Kenya, and Ukraine, covering different crop calendars, field sizes, and agricultural systems. The mean overall accuracy, F1-score, and Kappa coefficient across the six regions reached 93.27%, 93.14%, and 0.8652, respectively. Cross-year experiments in a winter-wheat–summer-maize rotation region from 2020 to 2024 achieved overall accuracies of 89.80–96.80%, while spatial-transfer experiments in six independent regions achieved overall accuracies of 87.40–95.40%. A comparison with existing high-resolution maize products in the Huang-Huai-Hai Plain further showed that the proposed method better balanced omission and commission errors. These results indicate that ENDVIre-based phenology rules provide an interpretable and transferable solution for maize mapping under limited-sample conditions, although persistent cloud contamination and fragmented smallholder landscapes remain important challenges. Full article

Satellite Synthetic Aperture Radar (SAR) is an established technology for studying and monitoring archaeological landscapes, providing insights into surface morphology and the presence of near subsurface features. However, its application in large-scale archaeological prospection is limited by the lack of robust, automated methods for SAR data analysis. This study introduces a novel Deep Learning pipeline to automatically detect and segment archaeological settlement mounds, known as tells, in central Iraq on satellite SAR data. The pipeline leverages a state-of-the-art supervised method for instance segmentation, YOLOv8-Seg, and medium-resolution satellite SAR products, specifically the Copernicus Sentinel-1 Interferometric Wide Swath Mode Ground Range Detected and Copernicus Global 30-m Digital Elevation Model products. The model identifies tell sites with an Average Precision of $0.495\pm 0.010$ and a pixel-wise Intersection over Union of $0.361\pm 0.048$ over the test areas. Archaeological interpretation of the model’s inferences confirms its reliability in locating and segmenting archaeological sites, leading also to the identification of previously unmapped potential sites. After a main test in central Iraq, the proposed workflow demonstrates promising transferability to a nearby test area in Iran, although with a need for regional fine-tuning to account for inherent variations in feature morphology and environmental context. This research establishes a baseline for future Deep Learning applications in Synthetic Aperture Radar-based archaeological prospection. Full article

This study presents a systematic uncertainty quantification of the two-track InSAR three-dimensional (3D) deformation field of the 2025 Dingri earthquake (Mw 7.1). Using Sentinel-1 ascending and descending track data, a 3D coseismic displacement field was constructed via least-squares inversion. The results revealed that the earthquake produced a north–south-striking normal fault rupture, with the vertical component reaching a maximum subsidence of −403.3 mm and a maximum uplift of +621.1 mm, and the east–west component reaching a maximum westward displacement of −592.3 mm and an eastward displacement of +332.1 mm. Uncertainty analysis reveals a divergence between formal errors and actual accuracy: formal error propagation yields 1σ uncertainties of 1.09 mm and 1.38 mm for the vertical and east–west components, respectively; a realistic error budget based on Monte Carlo simulations indicates that the actual errors are approximately 13.8 mm for the vertical component and 17.2 mm for the east–west component, with systematic error contributions far exceeding random noise. Cross-validation against an independent Sentinel-1 processing chain supports the above error assessment: the correlation coefficient R for ascending track line-of-sight (LOS) displacement is 0.88, whereas it is 0.62 for the descending track; for the three-dimensional components, R reaches 0.88 for the vertical component and 0.59 for the east–west component, with discrepancies arising primarily from the greater sensitivity of the east–west component to processing strategies and observation geometry. This study demonstrates that formal error propagation underestimates the actual uncertainty of two-track InSAR inversion and that systematic error sources contribute far more than random noise does. Full article