Search Results (692)

This study evaluated satellite-based precipitation in Bolivia using a combination framework with four equations: relative error, geometric, weighted linear, and sigmoidal. These were compared to enhance daily GSMaP_Gauge V8 estimates. Considering elevation variability, the interpolation method KED proved critical to obtain proper rainfall patterns. The conventional method OK showed weak performance indicators (R² = 0.18, NSE = -4.19, PBias = 48.28%). In contrast, the KED approach restored spatial continuity and predictive capability nationwide (R² = 0.71, NSE = 0.68, KGE = 0.79, PBias = 11.24%, RMSE = 1.3 mm/day). Therefore, it provided a more consistent bias correction. On the other hand, sensitivity heat maps demonstrate that iterative algorithms require strict bounding. Specifically, iterating beyond optimal thresholds (N > 6 for relative error; N > 5 for geometric; N > 2 for weighted linear and sigmoidal) triggers exponential residual amplification. The optimized framework successfully bridges the gap between rain-gauge accuracy and gridded estimates in ungauged zones. The relative error showed best performance in the Amazon region, with geometric on the Altiplano plateau, and weighted linear in La Plata basin. These products yield reliable daily precipitation estimates for hydrological modeling, water-resource management, and climate change assessments in Bolivia. Finally, this approach can also be applied in other countries, identifying proper combination methods per hydrologic units. Full article

Deep learning has improved precipitation estimation and forecasting, but high predictive skill does not by itself ensure physically credible behavior, robustness under distribution shift, or reliable uncertainty. This critical narrative review examines physics-informed deep learning across quantitative precipitation estimation, radar and satellite nowcasting, short-range forecasting, numerical weather prediction post-processing, and selected Earth-system applications. We use a two-dimensional analytical framework—form of physical knowledge and point of model integration—to compare mechanisms that are often grouped under the same label but provide different levels of physical guarantee. Across the literature, useful physical information is strongly task- and scale-dependent. Physically meaningful inputs and transport-aware structures are most convincing when they represent processes that are both observable and dominant over the forecast horizon. Soft equation or consistency losses can reduce violations, but their effect depends on constraint validity, weighting, data quality, and precipitation regime. Hard parameterizations and output projections provide stronger guarantees for specified relations, although this does not necessarily translate into better precipitation forecasts when those relations are incomplete or scale-mismatched. Evidence for cross-region robustness, extreme-event reliability, and operational maturity remains comparatively limited. A key evidence gap is whether these benefits persist under matched ablations and transfer across regions, sensors, and precipitation regimes. Full article

Physics-based fast radiative transfer models (RTMs), such as the Community Radiative Transfer Model (CRTM), face increasing computational demands from growing satellite data volumes and model complexity. We developed a physics-informed Liquid Neural Network emulator for CRTM (CRTM-LNN) that draws on continuous dynamical-system concepts while retaining physical interpretability. The framework combines an Ordinary Differential Equation (ODE)-inspired Liquid Neural Network for layer-by-layer optical-depth modeling with an analytic, differentiable radiative-transfer solver. This design preserves the explicit optical-depth-to-radiance pathway and incorporates physical constraints directly into the forward calculation. In the implementation evaluated in this study, the LNN uses discrete hidden-state updates on the fixed European Centre for Medium-Range Weather 91-layer (ECMWF91L) grid under clear-sky, absorption-only assumptions. Evaluation using Infrared Atmospheric Sounding Interferometer (IASI) observations and ECMWF91L forecast profiles shows that CRTM–LNN closely reproduces the reference CRTM simulations, with global, channel-mean brightness-temperature biases below 0.1 K in magnitude. Regional bias magnitudes nevertheless reach approximately 0.2–0.4 K for selected channels and latitude bands. Correlations with CRTM exceed 0.97 for cumulative optical depth, transmittance, and weighting functions in regimes with cumulative optical depth below five. It accelerates forward calculations by up to 18-fold and efficiently generates Jacobians through automatic differentiation. Compared with a conventional multilayer perceptron CRTM emulator, CRTM-LNN produces smoother and more physically consistent Jacobians, with improved vertical localization and fewer spurious oscillations. Absolute centroid-pressure errors for temperature Jacobians are reduced across all evaluated channels, ranging from 1.77 to 5.01 hPa for CRTM-LNN versus 5.88–8.17 hPa for CRTM–MLP, although the magnitude of improvement varies by channel. These results highlight the potential of the integrated CRTM–LNN framework, which combines ODE-driven neural emulation with physics-based radiative-transfer modeling, to provide a scalable foundation for atmospheric retrievals, satellite data assimilation, and near-real-time radiative-transfer applications. Current limitations and opportunities for extension to more complex radiative processes are also discussed. Full article

An empirical model of the TU Muscae binary star system has been developed by a study of 23 high resolution SWP spectrophotometric images that were obtained with the International Ultraviolet Explorer (IUE) satellite telescope including some that were downloaded from the NASA/MAST IUE Archive. The images are well distributed in Keplerian orbital phase thereby permitting a simultaneous fitting of the C IV wind-line profile by the SEI method and the light curve for the blanketed continuum (1450–1490 Å) bandpass by means of a program developed by the author. The result is a set of parameters characterizing the physical and geometric properties of the wind envelopes surrounding the stars. Surprisingly, there is no evidence for a P Cygni profile or strong, distinguishable shock front in the system, as has been found for similar investigations of EM Carinae and HD 159176. This is probably a result of the contact nature of the binary and the high-temperature environment of such a shock. That is, most of the carbon ions in the shock are more highly ionized. Based on the parameters for the SEI fit to the C IV profile, the value for the ionization fraction of C IV in the wind was calculated to be 10⁻⁴. With this value, the mass loss rate, Ṁ, calculated from two independent equations, was found to be about 10⁻⁶ solar masses per year (M_ʘ/yr). The UV line blanketing in the 1500 to 1600 Ångstrom bandpass was found to be erratically variable with orbital phase and time, indicating a variable amount of fast moving, dense clouds in the winds and/or a great amount of turbulence. The meaning of rotational velocities for the stars is problematic and depends on what point on the photospheres one is considering. Full article

Accurate estimation of regional forest carbon storage is essential for assessing forest carbon sink capacity and supporting climate change mitigation. This study developed a UAV-LiDAR-assisted multi-source remote sensing approach to estimate forest AGB and carbon storage in Fuzhou City. UAV-LiDAR data acquired close to satellite overpasses were used to generate temporally matched plot-scale AGB samples through individual-tree segmentation, tree height–DBH estimation, and allometric equations. After screening, 204 samples were retained. Four predictor combinations (Landsat, SAR, Landsat + SAR, and Landsat + SAR + other factors) were combined with SWR and CatBoost to build eight AGB models after RF-RFE feature selection. The optimal model was applied to analyze forest carbon storage changes from 2015 to 2023. CatBoost generally outperformed SWR, with lower RMSE, MAE, and rRMSE. The CatBoost model integrating Landsat, SAR, and other factors performed best (RMSE = 19.83 t·hm⁻², MAE = 17.02 t·hm⁻², rRMSE = 21.73%). Forest carbon storage increased from 32.43 × 10⁶ t in 2015 to 36.76 × 10⁶ t in 2023, alongside increases in forest area and carbon density. These findings suggest the potential of UAV-LiDAR for temporally matched AGB sampling and regional forest carbon storage estimation in subtropical areas, contributing to relevant SDGs. Full article

Modular satellite architectures can shorten development cycles and support flexible mission configurations; however, their thermal management is complicated by inter-module coupling, variable operating conditions, and stringent temperature limits. This study proposes a Thermal Digital Datasheet (TDD) framework that provides a standardized representation of module-level thermal information for data exchange, rapid system analysis, and consistent characterization across suppliers. The TDD organizes the passive thermal properties, active control capabilities, and operational constraints of each module in a unified format. To populate the datasheet with physically consistent parameters, a physics-constrained algebraic identification method is developed using excitation-based thermal response data and a structured least-squares formulation derived from the energy-balance equations. The resulting TDD representation is then integrated with a graph neural network (GNN), in which the modular interconnection topology is represented explicitly as a graph for network-wide temperature prediction. On the simulated modular satellite dataset, TDD-GNN achieves a mean absolute error of 0.096 °C and an $R^2$ of 0.962 for one-step temperature-increment prediction, maintaining high accuracy over autoregressive horizons of up to 2 h. In an end-to-end evaluation in which the thermal parameters of each test configuration are independently identified before GNN inference, the model retains $R^2=0.959$, demonstrating robustness to realistic parameter-identification errors. Perturbation-based sensitivity analysis further shows that the learned parameter ranking is consistent with the expected thermal behavior. These simulation results indicate that the proposed framework can support computationally efficient thermal-state prediction for modular satellite systems. Full article

Soil water erosion is one of the most serious environmental problems worldwide, with major consequences for agricultural output, food security, and terrestrial ecosystems, particularly in the Mediterranean basin. This study compares modelled soil-loss conditions across Andalusia, Spain (87,268 km²), between the 2018 and 2025 reference years using a cloud-based implementation of the Revised Universal Soil Loss Equation (RUSLE) in Google Earth Engine (GEE). The framework couples daily precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) at its native information scale of approximately 5.5 km, 10 m satellite imagery, and a machine learning-derived, year-specific soil-erodibility update on a common 30 m output grid. The regional mean annual soil loss in 2025 (45.86 t ha⁻¹ yr⁻¹) was 24.98% higher than in 2018 (36.70 t ha⁻¹ yr⁻¹). The 2025 reference year also showed a 12.28% higher R-factor and a 7.83% higher C-factor. The area under Severe erosion (>50 t ha⁻¹ yr⁻¹) increased from 16,330 to 20,636 km² (+26.37%). Exact signed Shapley attribution on the common erodible support assigned +7.43, +3.52, +0.23, and −2.70 t ha⁻¹ yr⁻¹ to R, C, P, and K, respectively. These results describe a marked contrast between two modelled reference years without establishing a continuous trend or causal change, and demonstrate a transparent framework for regional erosion screening to support sustainable land-use planning and soil-conservation strategies. Full article

This work presents the first complete system for automated six degrees of freedom (6DOF) satellite pose estimation from spatially resolved, ground-based, adaptive optics (AO)-corrected imagery, addressing a key challenge in Space Domain Awareness (SDA). The approach mitigates the need for human labeling by directly regressing satellite orientation and position from blurry, noisy, and deeply shadowed imagery. A multi-stage deep neural network pipeline localizes the satellite, predicts pose, and optionally applies temporal filtering. Networks are trained exclusively on fully synthetic imagery generated from a CAD model, yet generalize effectively to real data, bridging the Sim2Real domain gap. On 137 real, human-labeled test images of Seasat, the model achieved a mean rotation error of $5^{°}$ and a mean image-plane translation error of 21 cm. Slant range error was quantitatively evaluated on synthetic data due to unknown real-sensor parameters. Qualitative evaluation of additional real Seasat imagery rated 177 of 199 predicted poses as “ground truth equivalent” or “high-confidence match,” with zero catastrophic failures. The system was extended to seven degrees of freedom (7DOF) for satellites with articulating components and demonstrated on real Hubble Space Telescope (HST) imagery, achieving ${5.5}^{°}$ rotation error, 51 cm image-plane translation error, and $8^{°}$ symmetry-adjusted solar array error on a 249-frame pass with causal temporal filtering. Across 586 real test images from Seasat and HST (captured over multiple decades under diverse conditions) the system consistently performed well. Full 6DOF performance was quantified on a high-fidelity wave optics (HFWO) synthetic test set of Seasat, where the model achieved ${8.4}^{°}$ mean rotation error, 34 cm image-plane translation error, and 1.4% line-of-sight range error at $r_0=6$ cm and 1031 km range. In a limited 200-image benchmark, the model demonstrated 48% lower mean rotation error than a single human labeler while operating ∼800× faster. It required <40 h and a single A100 GPU to generate data and train. The approach was also demonstrated for ARGOS, a smaller satellite with highly symmetric geometry. An exploratory General Image-Quality Equation-based image quality metric (AO-IQ) was introduced as an empirical correlate for pose accuracy. General-purpose models like GPT-4o and Depth Anything V2 failed across most SDA tasks, but rapid gains in vision-language models warrant continued monitoring. These results establish a new operational baseline for practical, real-time satellite pose estimation from AO SDA imagery. Full article

Research Highlights: This study quantifies aboveground biomass for Oreopanax oroyanus and Escallonia resinosa in an Andean relict forest and examines their conservation relevance related to the scale of emissions voluntarily reported by small corporate emitters. Background and Objectives: Andean relict forests face severe fragmentation and degradation. This research evaluates carbon stocks in the Bosque de Zárate Reserved Zone (Peru) and explores how these findings may inform climate mitigation and conservation initiatives by examining their potential alignment with emissions voluntarily reported by Peruvian firms participating in a carbon disclosure system. Materials and Methods: A total of 27 plots were evaluated between 3034 and 3200 m a.s.l., tree height and diameter (DBH ≥ 10 cm) were measured for key species, and biomass was estimated using a pantropical allometric equation. Landsat imagery (1985–2025) was analyzed to assess long-term vegetation conditions, while Dynamic World land cover and Sentinel-1 radar (2018–2025) were used to assess forest cover and canopy structure changes. Voluntarily reported emissions of Peruvian firms participating in the “Carbon Footprint Peru” system (2012–2024) were analyzed to contextualize the forest results in the potential corporate interest in climate mitigation in Peru. Results: Total aboveground carbon stock for the altitudinal belt in the study area was 919.4 Mg C (18.6 Mg C ha⁻¹), equivalent to 3374.2 Mg CO₂, with Escallonia resinosa accounting for approximately 71% of the estimated stock. Multi-decadal satellite observations indicated persistent forest cover within the evaluated belt, while analysis of voluntarily reported corporate emissions identified numerous service-sector firms with annual emissions below 100 Mg CO₂ eq, providing context for the potential scale of future conservation-financing initiatives. Conclusions: Relict forests offer relevant localized carbon storage linked to other ecosystem services. Providing field-based carbon data may support the development of locally relevant community-led initiatives meaningful to climate-financing initiatives. However, the existing carbon stock does not by itself represent a source of carbon credits, and carbon capture-specific studies would need to be implemented to fully assess the mitigation capacity of these ecosystems. Full article

The paper proposes two attitude motion determination algorithms based on an extended Kalman filter dealing with asynchronous measurements of the star tracker and angular velocity sensor. The satellite control system tracks a complex angular velocity profile during the Earth-point stabilization of the remote sensing camera axis. The star tracker measurement accuracy decreases with higher satellite angular velocity; when a certain angular velocity value threshold is exceeded, the star tracker measurements might be unavailable. These star tracker aspects, along with the variable bias of the angular velocity sensor, are addressed by the developed algorithms. The first algorithm uses kinematic relations; it estimates the attitude quaternion and the angular velocity sensor bias. It does not require information on satellite inertia parameters and on current control torque; it is characterized by low computational burden, though the angular velocity estimation accuracy is limited by the standard deviation of the sensor random noise. The second algorithm is based on both kinematic and dynamic motion equations: it estimates the attitude quaternion, angular velocity sensor bias, and angular velocity vector as well. The satellite tensor of inertia, reaction wheels’ parameters, and history of control inputs are required for the state vector estimation. The performance of these algorithms is compared under the scenario of Earth-point stabilization attitude motion, taking into account different levels of angular velocity measurement errors. It is obtained that the algorithm based on kinematic equations only is characterized by lower stabilization and estimation accuracies compared to the algorithm based on both kinematic and dynamic motion equations, though the latter is significantly more computationally complex. The influence of the state vector estimation errors on the Earth-point stabilization accuracy is studied. The paper contribution is an algorithm performance study considering star-tracker accuracy degradation with angular velocity during the Earth-point flyby. Full article

Reference evapotranspiration (ET₀) is essential for irrigation management, but its spatial estimation is limited by sparse meteorological observations. Satellite products such as MOD16 potential evapotranspiration (PET) provide spatial continuity, yet they are not directly comparable to FAO-56 ET₀ due to structural differences in model parameterization. This study proposes a topography-constrained framework to convert MOD16 PET into FAO-consistent ET₀. The approach was tested in two heterogeneous regions of northern Italy (Piemonte and Veneto) using ground-based ET₀ derived from the FAO Penman–Monteith equation (2010–2022). PET–ET₀ transformation coefficients, estimated via station-wise linear regression, showed no significant temporal drift over the study period and strong spatial structure. Among the tested topographic predictors, elevation was retained as the main topographic proxy for modelling the spatial variability of the correction coefficients. The locally calibrated correction reduced the systematic overestimation of raw MOD16 PET and improved agreement with station-based ET₀ in both regions. Its performance was comparable to an IDW interpolation benchmark, although IDW slightly outperformed the topography-based model in Piemonte. A cross-region test showed that the correction reduced MOD16 PET errors when transferred between Piemonte and Veneto, but residual bias remained. The proposed framework should therefore be interpreted as a parsimonious regional topographic correction approach that requires local calibration and validation before application to other areas. Full article

Over the past three decades, five satellites have succeeded one another on the reference orbit, building the longest continuous climate record of global sea level measurements. Its continuity is ensured thanks to tandem flights between consecutive satellites. In this paper, we demonstrate that the first satellite of the Sentinel-6 series (Sentinel-6 Michael Freilich) has enabled the detection of a processing anomaly in the Jason-1/2/3 ground segment. An inconsistency in the altimeter range reconstruction has been identified, causing its underestimation by 3.65 mm. At certain latitudes, determined by the satellite’s orbital velocity, the altimeter range shows no effect from the anomaly. For the reference orbit, the range is not affected at the poles, around the equator and, for ascending tracks, at 40° S. All Jason Geophysical Data Record (GDR) versions prior to GDR-G are impacted by the described processing anomaly. While a full reprocessing of the Jason data with the GDR-G standard is pending, this paper presents a latitudinal empirical correction to be applied to Jason datasets generated with GDR-F and earlier ground segments. This correction, to be applied on the altimeter range, is derived from one month of patched Jason-3 data and is intended for reference orbit only. Additionally, SWOT Nadir ground processing is also affected by the same processing error and has been corrected from the GDR-S2 version onward. Finally, our analysis shows a negligible impact of this processing anomaly on Jason Level-2-derived products, models and metrics. Full article

High-temporal-resolution vertical total electron content (VTEC) inversion is important for ionospheric delay correction in maritime GNSS applications, but offshore single-station observations often suffer from limited satellite geometry, clustered ionospheric pierce points, and noise-sensitive least-squares (LSs) solutions. This study proposes a Virtual-Observation-Based Tikhonov Regularization (TVO) method for stabilizing ill-conditioned least-square VTEC inversion. TVO links the regularization factor to the condition number of the normal-equation matrix and selectively constrains higher-order spatial-gradient parameters while preserving background VTEC and receiver-bias terms. Experiments using the European mid-latitude station OBE4 and 17 surrounding stations on 1 July 2021 show that short epoch intervals and increased model complexity aggravate ill-conditioning, especially for the full quadratic model at 30 s. Compared with LS, TVO reduces the average RMS difference relative to the GIM-interpolated VTEC reference by 56.30% across the four VTEC models for the 17 stations. Maritime validation using South China Sea buoy data collected from 19 to 25 May 2025 further shows that TVO suppresses local discontinuities and amplitude anomalies, reducing the overall RMS difference relative to the GIM-interpolated VTEC reference from 26.07 TECU to 14.74 TECU. These results suggest that TVO can improve the numerical stability of maritime single-station VTEC inversion under constrained observation geometry. Full article

This study investigates the low-latitude ionospheric response over the Eastern Hemisphere during two successive geomagnetic storms on 12–13 November 2025. BDS-GEO observations from 20 GNSS stations, CODE GIM data, Swarm satellite observations, and simulations from the TIEGCM and HWM14 models were integrated to investigate regional ionospheric disturbances, single-station responses, and Equatorial Ionization Anomaly (EIA) evolution. During the first storm, with SYM-H reaching −254 nT, EIA intensification and poleward expansion beyond ±20° magnetic latitude were observed, with VTEC approaching 100 TECU at stations over Australia and rTEC exceeding 80% over Australia and the adjacent Pacific Ocean. Swarm observations showed TEC decreases within the EIA crest region and TEC increases in the surrounding areas. In contrast, the second storm, with SYM-H reaching −154 nT, produced disturbances with lower amplitudes, mainly characterized by localized positive TEC anomalies near the magnetic equator within 100°E–180°E, together with negative TEC anomalies in the surrounding low-latitude regions. The first storm was associated with southward IMF Bz reaching −54 nT and electrodynamic uplift related to PPEF, which contributed to the superfountain effect, whereas the second storm was influenced by residual disturbed neutral winds, reduced O/N₂ ratios at low latitudes, and the preconditioned ionospheric state inherited from the first storm. These results demonstrate that successive geomagnetic storms can produce different ionospheric responses in terms of intensity, spatial morphology, and driving mechanisms, highlighting the event dependence and regional variability of low-latitude ionospheric storm responses. BDS-GEO observations offer distinct advantages for monitoring localized ionospheric disturbances over the Eastern Hemisphere. Full article

This paper provides a systematic synthesis of recent developments in physics-informed neural networks (PINNs) applied to aerospace engineering, with an emphasis on their role in physically consistent surrogate modeling, forward simulation, and inverse parameter estimation. Using a PRISMA-based methodology, the study surveys peer-reviewed works published between 2017 and 2025 across aviation- and space-related domains, including aerodynamics, structural mechanics, aeroelasticity, propulsion, control, structural health monitoring, satellite-orbit prediction, space-debris collision avoidance, and spacecraft radiation-impact modeling. The analysis shows that embedding governing equations, boundary conditions, and observational data into composite loss functions enables PINNs to improve predictive consistency, reduce dependence on dense simulation or experimental datasets, and support parameter identification under sparse or noisy measurements. Attention is given to architectural variants such as XPINNs, cPINNs, gPINNs, operator-learning approaches, and hybrid PINN-CFD/FEM formulations, as well as to training strategies based on adaptive sampling, domain decomposition, transfer learning, and dynamic loss weighting. Reported benefits include reduced approximation error, improved convergence in selected high-gradient or multiphysics problems, and enhanced interpretability compared with purely data-driven models. At the same time, the review identifies persistent open challenges, including scalability to large aerospace domains, sensitivity to loss-weighting and collocation strategies, limited robustness under noise and uncertainty, high computational cost, and the lack of standardized aerospace benchmarks. Overall, the review highlights PINNs as a promising but still developing framework for fast, interpretable, and physically consistent modeling of aircraft and spacecraft systems. Full article