Search Results (3,809)

Canopy urban heat island (CUHI), defined as the air temperature difference between the urban near-surface atmosphere and surrounding rural areas, exhibits diurnal variability and affects urban thermal environments and well-being. However, the nonlinear effects of urban morphology on daytime and nighttime CUHI remain insufficiently understood. Taking the Yangtze River Delta (YRD) as the study area, this study integrates remote sensing, building morphology, and ground-based meteorological data to characterize urban morphology and canopy urban heat island intensity (CUHII). Extreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), and dependence plots were used to quantify and interpret nonlinear morphology–CUHII relationships. The models showed moderate explanatory ability, indicating that the selected morphology indicators explained only part of CUHII variability. Nighttime CUHII was stronger than daytime CUHII, with average values of 0.848 °C and 0.431 °C, respectively, and high-value areas concentrated in Shanghai, northern Zhejiang, and southern Jiangsu. During the daytime, the Aggregation Index (AI) was the most important selected morphology variable and was positively associated with CUHII, suggesting that compact built-up patterns may enhance heat accumulation by increasing heat absorption and limiting ventilation. At night, the Splitting Index (SPLIT) ranked first, with higher values generally associated with weaker CUHII, possibly reflecting greater spatial openness and reduced continuity of built-up surfaces. The nonlinear transition ranges of AI and SPLIT further reveal diurnal asymmetry in morphology–CUHII relationships. These findings support time-specific urban heat mitigation while avoiding attribution of overall CUHII variability solely to urban morphology. Full article

The U.S. Geological Survey collects remote sensing data to support national scientific assessments of natural resources, hazards, and landscape change. Spectrometers and spectroradiometers are essential for gathering point-based spectral measurements used in applications such as uncrewed aerial systems (UAS) multispectral image calibration, validation, and analysis. Evaluating how different instruments perform in laboratory and field environments helps determine whether they provide consistent, interoperable measurements. Such verification can expand access to spectral ground data during UAS operations by allowing scientists to use alternative instruments when budgets, logistics, or field conditions limit options. We propose and test a methodological framework for evaluating spectrometers for measurement consistency during UAS multispectral vegetation mapping applications. There are three central evaluation components to the framework: laboratory, field, and relative to UAS multispectral imagery. By evaluating the instruments in both relatively controlled and uncontrolled environments, we thoroughly examine measurement consistency and when/why measurements may differ. We opportunistically selected two instruments for a case study in a coastal marsh setting: a compact laboratory spectrometer we modified for field use and a field-ready spectroradiometer. The instruments produced consistent measurements in both environments. We found differences between the field spectra and UAS spectra that likely reflect the perspectives of ground vs. aerial data and indicate that further radiometric calibration may be needed. Full article

Polygonal patterned ground (PPG) is an important remote-sensing indicator of permafrost dynamics, yet its high-resolution distribution in alpine permafrost regions remains unknown. In this study, we developed an ensemble modeling framework to map PPG in the Three-River Source Region (TRSR), northeastern Qinghai–Tibet Plateau. A total of 4200 PPG occurrence samples and multi-source environmental variables were used to train four meta models: Random Forest (RF), Support Vector Machine (SVM), Maximum Entropy (MaxEnt), and the BIOCLIM package. Model performance was evaluated using the area under the curve (AUC) and true skill statistics (TSS). An AUC-weighted ensemble model was constructed from the best-performing models. RF showed the highest accuracy, with an AUC of 0.97 and TSS of 0.85, followed by SVM and MaxEnt. The final ensemble map achieved an overall accuracy of 93.7% and a kappa coefficient of 0.91 based on validation with high-resolution satellite imagery. The mapped PPG area was 59,082 km², accounting for 25.76% of the permafrost area and 16.01% of the TRSR. PPG was mainly distributed in the Yangtze River Source Region, with limited distribution in the Yellow River Source Region and no occurrence in the Lancang River Source Region. Compared with a previous Northern Hemisphere-scale PPG distribution product, our map reduced the estimated PPG area by 21.79% and improved local spatial detail. Variable importance analysis indicated that solar radiation, elevation, freeze–thaw indices, active-layer thickness, topography, soil moisture, and ground ice jointly controlled PPG distribution. PPG mainly occurred at elevations of 4400–5000 m, on gentle slopes of 0–3°, and in areas with 30–40% ground ice content. We also found that PPG can serve as a reliable geomorphic proxy for ice-rich and thermally sensitive permafrost in alpine regions. These findings highlight the utility of PPG for permafrost dynamic assessment and associated eco-hydrological impacts in alpine permafrost regions. Full article

This study presents a comprehensive statistical characterization of cloud vertical structure and phase occurrence over Limassol, Cyprus, using 18 months of continuous ground-based remote sensing observations from the Cyprus Cloud Aerosol and Radiation Experiment (CyCARE) campaign (October 2016–March 2018). The Eastern Mediterranean represents a climatologically complex, understudied subtropical region characterized by strong seasonal variability and frequent exposure to diverse aerosol mixtures, including mineral dust, biomass-burning smoke, marine particles, and anthropogenic pollution. Utilizing the standardized Cloudnet target classification framework, synergistically combining lidar and cloud radar observations, 1,393,659 vertical profiles are analysed, finding that 35% contained hydrometeors. Within these profiles, ice-phase targets occurred in 83.8% of cloud-containing profiles, followed by mixed-phase (33.5%) and liquid-phase (29.2%) targets, with precipitation detected in 32% of cases (these percentages are not mutually exclusive). Cloud occurrence exhibited pronounced seasonality: 57.7% in winter, 23.9% in spring, 17.8% in autumn, and virtually absent in summer (0.6%). Among five mutually exclusive cloud-layer classifications, pure ice clouds were the most frequent (38.4%), narrowly ahead of mixed-phase clouds (36.2%); combined, mixed-phase and mixed-phase precipitating layers together (41.0%) constituted the most frequent phase family overall. These results establish an 18-month observational baseline for evaluating climate model parameterizations, validating satellite-derived cloud products, and guiding future aerosol–cloud interaction studies in this climate-sensitive region. Full article

Conventional image acquisition requires an electrically powered image sensor to be placed directly behind the camera lens, constraining camera placement. To overcome this issue, we introduce M3-RGB as an incoherent-light fiber imaging system in which a multicore, multimode optical fiber passively relays lens images to a remotely located image sensor. Unlike conventional approaches, M3-RGB is designed to operate directly on incoherent light and requires no electrical power or active components at the sensing interface. Because propagation through the fiber yields spatially scrambled patterns, a neural network is used to reconstruct the original scene by exploiting the spatial locality preserved by the multicore structure. In a controlled optical bench setup, where a liquid crystal display monitor displays road-scene images, we construct a paired dataset of scrambled and ground-truth images and quantitatively evaluate reconstruction performance across different fiber core counts, fiber lengths, and calibration settings, utilizing the peak signal-to-noise ratio and structural similarity index measure as performance metrics. By decoupling imaging electronics from the sensing point, this passive remote image relay approach may expand sensor placement options for potential applications such as all-around perception for mobile robots and autonomous vehicles, surveillance, and inspection in confined spaces. Evaluations in real outdoor environments constitute future work. Full article

On-orbit geometric calibration is essential for maintaining the geometric positioning performance of optical remote sensing cameras throughout their operational lifetime. Existing calibration approaches primarily rely on ground control points (GCPs) or stellar observations, which are often constrained by reference availability, observation conditions, and operational requirements, particularly for GEO wide-field imaging systems. To address these limitations, this paper proposes an Earth-limb-constrained framework for on-orbit geometric calibration of GEO wide-field area-array cameras. The proposed framework establishes geometric constraints by relating the observed Earth limb to reference Earth limb geometry derived from the camera imaging geometry and the WGS-84 reference ellipsoid. A unified geometric calibration model is developed by introducing an equivalent camera-to-inertial attitude representation, and terrain elevation information from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is further incorporated as a local geometric constraint during calibration parameter estimation. The calibration parameters are estimated by minimizing the elevation residuals of multiple Earth limb observations through nonlinear optimization. The proposed framework is validated through both simulation and real GEO on-orbit experiments. Simulation results under different attitude-error settings demonstrate accurate parameter recovery and stable convergence, while experiments using thirteen GEO image scenes show an approximately 69% improvement in geometric positioning accuracy. An additional ablation experiment confirms that incorporating SRTM terrain-elevation information further improves the calibration performance. These results demonstrate the effectiveness and practical applicability of the proposed framework for GEO wide-field area-array cameras. By reducing the dependence on GCPs and dedicated stellar observations, the proposed framework provides a practical approach for the long-term geometric performance maintenance of GEO optical remote sensing systems. Full article

With the increasing demand for miniaturization, multi-channel RF transceiver capability, and high-speed digital processing in unmanned aerial vehicles, satellite remote sensing, and anti-jamming communication systems, conventional board-level discrete integration schemes face significant limitations in terms of interconnect length, parasitic effects, volume and weight, and electromagnetic compatibility. This paper proposes a high-density integrated RF microsystem packaging scheme based on a 12-layer ABF organic substrate. Within a package size of 37.5 mm × 37.5 mm, the microsystem integrates a digital processing chip, two DDR3 memories, two Flash memories, two broadband RF transceiver chips, and passive components, thereby realizing a 4-receiver/4-transmitter MIMO architecture. Compared with a conventional discrete PCB-based integration scheme, the proposed microsystem reduces the board-level occupied area by approximately 70% and decreases the weight by more than 30%. To address the non-reworkable nature of the in-package DDR3 address/command/clock links and their sensitivity to high-speed signal integrity, a field-circuit co-simulation method combining three-dimensional electromagnetic S-parameter extraction with IBIS models is adopted to optimize the transmission-line impedance and termination parameters under a fly-by topology. The results show that by optimizing the DDR3 clock-line impedance from the conventional 100 Ω differential impedance to 80 Ω differential impedance, and the address/control-line impedance from the conventional 50 Ω single-ended impedance to 40 Ω single-ended impedance, together with 40 Ω and 120 Ω terminations, respectively, overshoot and ringing can be effectively suppressed. The DDR3 eye width is improved from 0.89 ns to 0.92 ns. To address impedance discontinuities in the vertical interconnects of RF channels, a refined structure combining enlarged antipads and accompanying ground vias is proposed. As a result, the worst-case return loss of the RF channel is improved from below 16 dB to 19.69 dB, the maximum insertion loss is reduced from above 0.5 dB to 0.35 dB, and the worst-case inter-channel isolation is improved from below 60 dB to 73.42 dB. To mitigate thermal coupling and localized thermal isolation caused by thickness differences among multiple chips, a locally recessed copper heat spreader is designed, reducing the junction-to-case thermal resistance of the RF chip from 1.25 °C/W to 0.50 °C/W, corresponding to a reduction of approximately 60%. Preliminary hardware-in-the-loop frequency-hopping tests based on the proposed microsystem demonstrate that the system can achieve an analog frequency-hopping rate exceeding 4000 hops/s and a hybrid frequency-hopping rate exceeding 10,000 hops/s. The results indicate that the proposed packaging scheme provides a feasible engineering implementation path for high-density, multi-channel RF microsystem design. Full article

Here, we study melting glacier surface albedo using satellite hyperspectral imagery from the Environmental Mapping and Analysis Program (EnMAP). The proposed broadband albedo (BBA) retrieval algorithm is based on radiative transfer theory and includes atmospheric and topography corrections. The comparison with ground and other satellite snow and ice albedo products is presented. Ways to improve current satellite snow and ice albedo retrieval algorithms are discussed. While the EnMAP-derived BBA is highly correlated with BBA retrieved from other spaceborne instrumentation and algorithms, the various modern snow and ice satellite BBA products can differ by more than 5–10% for snow and, especially, bare ice. Bare ice optical heterogeneity is high from variable roughness conditions and impurity content. Bare ice BBA uncertainties exceed requirements needed for highly accurate assessment of glacier climatic effects. Full article

Accurate monitoring of surface water quality remains challenging due to pronounced spatial heterogeneity and limited ground observations. Satellite remote sensing offers scalable solutions, yet the extent to which environmental drivers enhance predictive performance, particularly in complex river–lake systems, remains insufficiently understood. This study develops a parallel comparative river–lake modeling framework to estimate permanganate index (CODmn), total phosphorus (TP), and total nitrogen (TN) by integrating satellite spectral data with climatic and land-use variables. Four model configurations were evaluated: spectral predictors alone (M₁), spectral predictors combined with climate variables (M₂), spectral predictors combined with land-use information (M₃), and full integration of all predictors (M₄). LightGBM models were optimized using Bayesian hyperparameter tuning (Optuna) and trained over rivers (January 2021–July 2025) and lakes (January 2021–December 2024) datasets in the Dongliao Basin, China. Spectral predictors alone (M₁) provided robust performance for CODmn (R² = 0.78), with marginal improvement when land-use variables were included (M₂) in rivers (R² = 0.80). In contrast, nutrient predictions showed stronger dependence on environmental covariates. TN predictions improved substantially with land-use inputs (M3) (R² = 0.75 in rivers and 0.63 in lakes with M₂), with further gains with full integration (M4) in lakes (R² = 0.66). TP predictions exhibited marked improvements with land-use variables in rivers (R² = 0.76) and with full integration in lakes (R² = 0.72). Model interpretability analysis using SHAP revealed that spectral features dominate CODmn estimation, while climatic and watershed characteristics exert greater influence on TN variability. Seasonal analysis indicated that hydrological drivers dominate during wet seasons, while land-use effects and internal biogeochemical processes become more important in dry seasons. The proposed framework advances predictive accuracy and process understanding, supporting more effective monitoring and management of water quality in complex river–lake systems. Full article

Reservoir impoundment can reactivate pre-existing landslides and reorganize slope topography, thereby changing seepage conditions and subsequent deformation. However, crack mapping, geomorphic interpretation, and hydrodynamic diagnosis are still often treated as separate tasks. This study investigates the Hemenkou (HMK) landslide in the Wudongde Reservoir area, China, using multi-scale space–air–ground observations, including multi-temporal optical satellite images, unmanned aerial vehicle (UAV) photogrammetry, pyramid scene parsing network (PSPNet)-based crack segmentation, global navigation satellite system (GNSS) monitoring, and convergent cross mapping (CCM). The remote sensing record shows a progressive damage sequence: cracks were mainly restricted to the upper source area in 2012, crown cracking intensified and propagated downslope by December 2020, and the UAV survey of 10 June 2024 revealed a mature tension-crack network concentrated in Zone II. ResNet-50-PSPNet achieved the best crack-extraction performance among the tested models, with Precision = 0.9120, Recall = 0.9041, F1 = 0.9081, and IoU = 0.8316. The mapped cracks are dominated by short, narrow, northeast–southwest-oriented tension cracks. GNSS monitoring reveals strong spatial heterogeneity, with stepwise deformation concentrated in Zone II. CCM provides strong directional evidence for the influence of reservoir water-level fluctuation on Zone II deformation, whereas the weaker rainfall signal is consistent with a secondary reinforcing role. The apparent increase in the rainfall-related CCM signal from 2021 to 2023 is consistent with progressive crack expansion and potentially enhanced hydraulic connectivity in Zone II. Taken together, these observations support the interpretation that post-deformation topography, particularly the tension-crack network and disturbed toe, may organise preferential seepage pathways and increase the sensitivity of the landslide to reservoir drawdown. The study provides an integrated remote sensing and monitoring framework for process-based interpretation of reservoir landslides. Full article

The use of remote sensing data in hydrological applications has increased, especially in regions with limited ground-based observations. Satellite precipitation products (SPPs) provide extensive temporal and spatial coverage but may contain biases that can affect their performance in hydrological simulations. This study evaluates the performance of four near-real-time SPPs for daily streamflow modeling in the Tulijá River Basin (TRB), Mexico: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now), and the Early Run and Late Run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). The SPPs were first compared with meteorological station precipitation data and subsequently bias-corrected using the Linear Scaling (LS) method. The CEQUEAU hydrological model simulated streamflow using three precipitation datasets: meteorological stations, original SPPs, and bias-corrected SPPs. For simulations using observed precipitation, the model was calibrated for 1991–2014 and validated for 1968–1990; for SPP-based simulations, calibration and validation were performed for 2003–2011 and 2012–2014, respectively. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R²). The results show that CEQUEAU performance varies by precipitation dataset. Simulations using observed precipitation yielded NSE values close to 0.70 during both calibration and validation, whereas the original SPPs yielded NSE values below 0.18, including negative values. After bias correction, IMERG-Early and IMERG-Late yielded NSE values of approximately 0.55 during both periods. These findings highlight the importance of analyzing the performance of near-real-time SPPs in hydrological applications, especially in tropical regions with complex topography. Full article

High-resolution remote sensing understanding requires models to preserve small spatial evidence, account for acquisition-dependent appearance, and separate genuine geographic change from nuisance variation. We introduce GeoGATE, a geo-sensor-guided framework that combines typed acquisition conditioning, budget-constrained adaptive token acquisition, metadata-compatible evidence retrieval, and reliability-aware temporal reasoning. LoRA adaptation and NF4 quantization support efficient training and deployment. On the VRSBench test split, GeoGATE reaches 53.4 BLEU-1, 36.8 BLEU-2, 18.2 BLEU-4, 56.4 Acc@0.5, 82.3 VQA, 25.1 METEOR, and 42.6 ROUGE-L, outperforming the controlled GeoGATE (Base) configuration across captioning, question answering, and grounding. Component ablations associate adaptive slicing most strongly with localization, retrieval with language and VQA, and language model adaptation with all reported tasks. NF4 reduces measured video memory from 24.5 GiB to 7.2 GiB with only minor metric changes. These experiments support the single-image language and grounding components. Dedicated cross-sensor and bi-temporal benchmarks are not reported; the corresponding modules are therefore presented as architectural extensions rather than validated performance claims. Full article

Wide-area monitoring and accurate assessment of potential geohazards (PGHs) based on remote sensing will provide a crucial foundation for geohazard prevention and mitigation. Current remote sensing methods for PGH identification and evaluation require extensive manual effort and lack intelligence throughout the process. To effectively integrate multisource remote sensing data, we propose an automated method for identifying and assessing PGHs across a wide area. This approach integrates InSAR deformation, high-resolution optical remote sensing, terrain, and vector data of ground features to enable automated delineation of unstable zones, automatic identification of potentially threatened objects (PTOs), automatic screening of PGHs, and risk assessment. The proposed method is tested in the Hequ–Baode–Pianguan (HBP) region of Shanxi province. Using the DS-InSAR technique, we process 94 Sentinel-1 SAR images covering the HBP region from 2020 to 2024 to estimate surface stability. We automatically detect the boundaries of 161 active deformation areas (ADAs) in HBP. A deep learning model based on DeepLabV3+ processes optical remote sensing images of the study area at 0.5 m resolution to automatically identify all PTOs. By integrating terrain data and spatial relationships among PTOs and ADAs, we develop an algorithmic model to identify 90 PGHs and classify them into external-threat, internal-threat, and internal-external-threat geohazard zones. Finally, a risk matrix is created for an automatic geohazard risk assessment, producing results for all PGHs in the study area. This developed method will support wide-area screening and prioritization of potential geohazards on the Loess Plateau and improve PGH investigation capabilities. Full article

Major floods following prolonged drought can substantially alter ground cover, soil stability and pasture condition, yet longer-term trajectories of rangeland recovery remain poorly understood. This study assessed land condition at 62 monitoring sites in the Flinders River catchment, north-west Queensland, following the 2019 flood, with re-assessment in 2024. Land condition was evaluated using the A–B–C–D framework alongside rainfall, satellite-derived bare ground, land type, distance to drainage and flood-extent data. In March 2019, 79% of sites were classified as C or D, reflecting the combined influence of prolonged drought and flood disturbance. By 2024, 30 of 62 sites (48%) had improved by at least one class, and median condition shifted from class C to B, though change was spatially variable. Initial land condition was negatively correlated with net change (ρ = −0.47, p < 0.001, n = 62). Rainfall showed no significant association with land condition or net change among sites, whereas dry-season bare ground was significantly associated across multiple temporal windows. These findings indicate that land condition change following drought–flood disturbance is likely associated with site-level factors, particularly residual pasture structure and soil surface condition, and highlight the value of combining field and satellite data for rangeland monitoring following extreme climate events. Full article

We analyzed AIRS CH₄ volume mixing ratio (VMR) Standard L3 v6/v7 IR-Only Daily products and ground-based measurements from 16 stations of the Network for the Detection of Atmospheric Composition Change (NDACC) at 24 pressure levels from 1000 to 1 mbar. We assessed the dependence of maximum AIRS sensitivity on latitude. At high latitudes, the zone of maximum sensitivity is closer to the surface, at 700–500 mbar; in mid-latitudes, it is 500–250 mbar; and in tropical and subtropical regions, good initial agreement between satellite and ground-based data is observed at 400–200 mbar for both AIRS product versions. At the vast majority of pressure levels and all comparison sites, a unidirectional negative drift in the difference between satellite and ground-based measurements (i.e., discrepancy drift) was observed. Drift coefficients were calculated for each statistically supported pressure level. Two regions of maximum drift were identified: one in the lower atmosphere (925–850 mbar) and another near 50 mbar. The smallest drift was observed at 400–200 mbar. As the main result of the study, we developed and applied correction factors for all 23 AIRS v6 and v7 levels. Using these coefficients led to much better agreement between long-term methane trends from ground-based and satellite measurements and to higher correlation coefficients across all comparison sites. Full article