Search Results (741)

Spaceborne detection of solar-induced chlorophyll fluorescence (SIF) requires extremely high radiometric accuracy, and the polarization characteristics of an ultra-wide swath spaceborne fluorescence ultraspectral camera directly affect the accuracy of SIF retrieval. This study takes an ultra-wide swath camera based on an off-axis three-mirror anastigmat telescope combined with a Littrow–Offner spectrometer as the research object. A full-field-of-view (FOV), full-spectral, pixel-by-pixel polarization testing system was established based on the Stokes–Muller formalism, achieving for the first time fine characterization and calibration of the pixel-level polarization properties of such a payload. The results show that: (1) polarization sensitivity (LPS) exhibits a strong linear positive correlation with wavelength (R² > 0.97), with good uniformity (fluctuation < 1%) across the full ±15° FOV; (2) the polarization sensitive axis (PSA) shows a symmetric distribution across the FOV and gradually approaches 90° as the wavelength increases, with a clear deviation in the short-wavelength bands and stabilization in the mid-to-long wavelength bands; (3) through multiple sets of cross-validation and Monte Carlo statistics, the calibration accuracy reaches 0.1%, and the system uncertainty is better than 0.05%. This study can provide data support and a reference basis for high-accuracy spaceborne SIF retrieval, payload polarization correction, and optical design optimization. Full article

South China is characterized by abundant and complex precipitation, with frequent typhoons, heavy rainfall, and pronounced extreme events, making it an ideal region for precipitation microphysics research. This study uses rainfall observations from an OTT Parsivel² (Parsivel) laser disdrometer and a Micro Rain Radar–2 (MRR–2) collected in Zhuhai during 2022–2023 to analyze the characteristics of stratiform rainfall (SR) and convective rainfall (CR). The results show that, although SR lasts longer, CR contributes much more to the total accumulated rainfall. In SR, samples with rain rate (RR) < 5 mm h⁻¹ account for about 27% of occurrences and contribute less than 10% of total rainfall, whereas in CR, samples with RR > 8 mm h⁻¹ represent only 7% of occurrences but contribute more than 45% of the accumulated rainfall. CR is characterized by a larger mass-weighted mean diameter (Dm), while SR shows a higher normalized intercept parameter (Nw). In SR, Dm increases with RR, whereas Nw changes little; in CR, both Dm and Nw increase with RR. Finally, by analyzing temporal/spatial collocated vertical rain profiles from MRR and Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR), the results show that CR exhibits larger RR, radar reflectivity and stronger vertical variability than SR, along with greater variations in Dm and log₁₀(Nw). Ground-based MRR also provides an independent vertical reference for evaluating DPR-derived precipitation structure and interpreting the consistency and discrepancies between satellite and ground-based observations. Although the results are not conclusive due to a limited number of events, both instruments capture distinct microphysical characteristics in the analyzed SR and CR cases, despite differences in their retrieved vertical DSD structures. Full article

The accurate estimation of pasture above-ground biomass (AGB) is critical for optimizing stocking rates and ensuring the sustainable use of Mediterranean pastures. This study developed empirical models to estimate fresh (AGB_fresh) and dry above-ground biomass (AGB_dry) using multispectral imagery acquired by Unmanned Aerial Vehicles (UAVs) in a Hedysarum coronarium pasture in Sicily, Italy. Field biomass was destructively sampled simultaneously with UAV surveys in 28 georeferenced plots during pre- and post-grazing phases over the 2023–2024 and 2024–2025 seasons. Data were collected with a DJI Mavic 3 Multispectral (for the 2024 test) and a DJI Matrice 300 + Altum-PT (for the 2025 test) and radiometrically calibrated to surface reflectance. Because two different multispectral sensors were used across years, an inter-sensor harmonization step was applied before vegetation-index calculation. Thirty-three vegetation indices were extracted as mean values within circular buffers of 1 m radius, centered on each sample plot to accommodate GNSS/georeferencing uncertainty. For each vegetation index, linear and exponential models were calibrated using 66% of the dataset and validated on the remaining 33% to predict fresh and dry above-ground biomass, and model performance was assessed using R² and RMSE. On the validation dataset, ARVI2 and EVI2 showed the highest explanatory power for AGB_fresh (R² = 0.89), with ARVI2 providing the lower RMSE (2047 g m⁻²). For AGB_dry, visible-band indices such as NGRDI and GRVI were among the best performers, reaching R² = 0.85 with RMSE = 1371 g m⁻². Visible-band greenness indices were among the most competitive predictors, whereas several conventional NIR-based indices showed only moderate performance. Overall, this UAV-based multispectral approach represents a promising and interpretable tool for biomass estimation in heterogeneous Mediterranean pastures, although further validation across additional seasons and sites is required to strengthen its transferability. Full article

Coastal urbanization is increasingly constrained by legacy land-use patterns and escalating climate risks, yet long-term morphological trajectories remain poorly quantified due to the absence of multispectral data in pre-satellite archives. This study introduces a scalable deep learning pipeline that bridges a century-scale domain gap through an attention-enhanced Pix2Pix colorization stage and a few-shot U-Net++ segmentation stage, enabling automated reconstruction of urban expansion from panchromatic historical aerial imagery (1920–1971) and digital aerial photographs (1997) to contemporary very-high-resolution satellite data (2024) in Les Sables-d’Olonne, France. The novelty of the approach lies in coupling generative colorization with epoch-specific fine-tuning to overcome radiometric and annotation bottlenecks that have historically prevented quantitative urban reconstruction from pre-satellite archives. The colorization stage achieved high spectral fidelity (PSNR 35.21 dB, SSIM 0.9762), and segmentation performed strongly on modern imagery (mIoU 0.9789). While the segmentation model performed strongly on modern imagery, direct transfer to historical data exhibited substantial domain shift due to radiometric discrepancies. Few-shot adaptation on year-specific calibration sets recovered reliable building footprints (mIoU 0.53–0.65) across the full timeline. Multi-scalar analysis of the reconstructed footprints revealed constrained anisotropic expansion: early saturation of the coastal historic core, followed by rapid inland peri-urbanization post-1971 driven by geographic barriers. This spatiotemporal shift has entrenched spatial lock-in, placing recent development in retro-littoral zones that are vulnerable to submersion and characterized by severe vegetation loss. The framework unlocks previously inaccessible historical archives for quantitative urban monitoring, providing critical insights into legacy effects of unconstrained growth and informing resilient coastal planning under climate change. Full article

The Ozone Monitor Suite-Nadir (OMS-N) onboard the FY-3F satellite is a key payload for global atmospheric ozone and trace gas detection. The data quality depends on the accuracy of ground calibration. This study presents a systematic ground calibration of OMS-N. The instrument operates over 250–500 nm, with a spatial resolution of 7 × 7 km² and a spectral resolution of 0.5–1 nm. Radiometric calibration was performed using an integrating sphere, spectral calibration using a tunable laser, and geometric calibration using a precision turntable. All tests were conducted under controlled environmental conditions (20 ± 3 °C and 50% ± 10% humidity). The absolute radiometric calibration uncertainty was below 2.33% for UV1/UV2 and 1.69% for VIS, with relative uncertainties ≤1.84%. The spectral wavelength error was ≤0.01 nm for the VIS channel and ≤0.03 nm for the UV1/UV2 channels, and the geometric positioning uncertainty was better than 0.1 pixels. All performance indicators met or exceeded the design requirements. These results provide technical support for the quantitative application of OMS-N data in atmospheric monitoring and establish a reference framework for the ground calibration of similar ultraviolet hyperspectral instruments. Full article

The orbital drift of the Fengyun-4B (FY-4B) satellite from 133°E to 105°E in early 2024 significantly altered its viewing geometry over China, providing a unique opportunity to evaluate the impact of satellite positioning on the accuracy of downward surface shortwave radiation (DSSR) retrievals. In this study, FY-4B DSSR products before and after the drift were systematically evaluated using a strictly matched common set of 141 first-order radiation stations from the China Meteorological Administration during the summer seasons of 2023 and 2024. The results show that the post-drift product achieved markedly improved satellite–ground consistency, with the correlation coefficient increasing from 0.93 to 0.95 and the RMSE decreasing by 11.8% from 111.5 to 99.58 W/m², while the mean bias remained close to zero. Spatially, the historical east–west disparity in retrieval accuracy was substantially mitigated, mainly because the westward orbital shift reduced the viewing zenith angle over western China and thereby weakened geometric distortions and atmospheric path-length errors. Further analyses across longitude, latitude, land cover, elevation, cloud regime, and diurnal cycle consistently indicate that the optimized viewing geometry was the dominant driver of the post-drift improvement, although residual errors remain in complex terrain and heterogeneous cloud conditions. These results demonstrate that the orbital shift to 105°E fundamentally enhanced the reliability of FY-4B DSSR products over China and provide useful guidance for future geostationary satellite deployment and radiation product application in solar energy assessment and numerical weather prediction. Full article

Accurate quantification of soil moisture (m_v) is of great scientific significance for regional hydrological modeling, meteorological forecasting, and drought and flood disaster monitoring. Although C-band SAR aboard the GF-3 satellite constellation supports large-scale retrieval, existing studies are mostly confined to local validation under simple surface conditions. Its retrieval performance across varied surface roughness (s), m_v, soil texture, and topography, as well as the synergistic retrieval ability of the satellite constellation, has not been fully investigated. Therefore, this study systematically evaluated four m_v retrieval strategies using quality-controlled satellite-ground synchronous observation data from 11 arid-to-humid experimental areas (378 plots) in China: Oh94 model inversion (Strategy I), calibrated Oh94 model inversion (Strategy II), calibrated Oh94 model inversion with prior constraints on m_v and s (Strategy III), and random forest inversion (Strategy IV). Subsequently, the measured satellite backscattering coefficients (${\sigma }_{\mathrm{obs}}^0$) were compared with model simulations (${\sigma }_{\mathrm{sim}}^0$), yielding initial biases of 2.08 dB, 0.78 dB, and −0.29 dB for VV, HH, and HV polarizations, respectively, and these biases were significantly reduced to −0.01 dB, 0.00 dB, and −0.06 dB after systematic deviation correction (SDC). Overall, the root-mean-square errors (RMSE) of m_v retrieval for Strategies I–IV were 0.092, 0.078, 0.058, and 0.046 cm³·cm⁻³, respectively, while those for s retrieval were 0.620, 0.578, 0.610, and 0.403 cm. Strategy IV achieved the highest m_v retrieval accuracy owing to the robust nonlinear predictive capacity of machine learning. Nevertheless, Strategy III exhibited superior transferability in spatially independent validation, with an RMSE of 0.054 cm³·cm⁻³, outperforming Strategy IV (0.065 cm³·cm⁻³). This demonstrates that Strategy III possesses a stronger generalization ability than purely data-driven models under domain shifts. By incorporating prior constraints, Strategy III effectively mitigated radiometric inconsistencies within the satellite constellation, and m_v retrieval biases among GF-3, GF-3B, and GF-3C converged stably within 0.021 cm³·cm⁻³, with RMSE ranging from 0.046 to 0.079 cm³·cm⁻³. This study validates the feasibility of synergistic m_v retrieval over bare surfaces using the GF-3 SAR constellation, providing critical technical support for large-area operational mapping. Full article

Unmanned aerial vehicle (UAV)-mounted thermal infrared (TIR) sensors occupy a useful middle ground between sparse in situ measurements, occasional aircraft-based campaigns, and coarse satellite products, enabling centimeter-scale thermal mapping under field conditions. Yet converting UAV thermal imagery into quantitative temperature products remains challenging because uncooled microbolometers are radiometrically drift-prone, thermal scenes often provide weak geometric texture, and surface temperature retrieval depends on scene-specific emissivity and atmospheric assumptions. This review focuses on quantitative UAV–TIR mapping rather than on the full range of drone thermal applications. It synthesizes the technical decisions that most strongly affect the reliability, comparability, and physical interpretability of UAV-derived temperature products, from radiometric data integrity and field calibration to RGB–TIR integration, physical correction, uncertainty propagation, and validation. We clarify the literature synthesis approach, compare field-deployable calibration and drift mitigation strategies, discuss application-specific uncertainty priorities, and derive a practical reporting checklist for reproducible studies. The review emphasizes how radiometric, geometric, and physical correction choices interact, and uses comparative tables to summarize recurring trade-offs, reporting gaps, and remaining research needs. Its aim is to clarify why UAV–TIR temperature products can differ across studies and which methodological details are needed for meaningful interpretation and comparison. Full article

This study evaluates mission-long inter-sensor radiometric calibration biases in Sensor Data Record (SDR) and/or Temperature Data Record (TDR) radiances from NOAA microwave sounders, including Advanced Technology Microwave Sounder (ATMS) (Suomi National Polar-orbiting Partnership or SNPP, NOAA-20, NOAA-21) and Advanced Microwave Sounding Unit-A (AMSU-A) (NOAA-19). Using four complementary validation techniques within the Inter-Sensor Radiometric Bias Assessment (iSensor-RCBA) system—32-day averaging, Community Radiative Transfer Model (CRTM) Double Difference (DD), Simultaneously Nadir Overpass (SNO), and sensor-DD via SNO—we characterize long-term performance. Results indicate that the SDR/TDR radiance quality remains stable and generally meets scientific requirements throughout their operational lifetimes with minimal anomalies; observed anomalies were infrequent and primarily correlated with calibration-table updates or spacecraft events or instrument degradation. Moreover, this research examines how radiometric calibration biases for the three ATMS instruments vary with Earth scene radiance or temperatures using the CRTM and SNO methods, as well as the radiance-dependency of inter-sensor calibration biases across the three instruments. Notably, due to its exceptional stability over 14 years, despite an approximate two-month data gap, the SNPP ATMS TDR and SDR datasets are recommended as the ideal reference to link legacy AMSU-A and Microwave Humidity Sounder (MHS) with Joint Polar Satellite System (JPSS), QuickSounder, and MetOp-Second Generation (MetOp-SG) microwave instruments. Beyond quantifying data quality, our multi-method framework with iSensor-RCBA effectively diagnosed critical issues, including a simulation error for CRTM ATMS radiance related to the CRTM spectral-response approximation and a NOAA-19 AMSU-A channel-8 performance anomaly. These findings confirm the long-term integrity of NOAA microwave sounder records and reinforce the value of integrated cross-sensor calibration assessments. Full article

The optical high spatial resolution component of the ESA Copernicus Earth Observation program is relying on the Sentinel-2 satellites. To secure the mission continuity, the Sentinel-2C unit was launched and has recently joined the Sentinel-2A and Sentinel-2B operational plan. The objective of the paper is to provide a status and a quantified assessment of the radiometric inter-operability of the latest unit with the constellation. The analyses reported here were performed using different vicarious methods during the commissioning phase of Sentinel-2C. Two of the methods were used for the first time with a Sentinel-2 satellite: lunar calibration and tandem inter-comparisons on selected surfaces. The results of the different methods are compared and the vicarious radiometric adjustment strategy is described. Finally, we discuss the impact of the different sources of uncertainty impacting the radiometric assessment. Full article

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FengYun-4B weather satellite provides critical upwelling atmospheric infrared radiance. To address the limitations of the previous sounder (FY-4A/GIIRS) in terms of spatial resolution and spectral coverage, FY-4B/GIIRS has increased the spatial resolution to 12 km and added more spectral channels in the long-wave band to enhance the observation details and information content of weather systems. To evaluate its baseline performance, a comprehensive preflight test campaign—encompassing spectral and radiometric assessments—was conducted in a thermal vacuum (TVAC) chamber. Spectral characterization via laser measurements confirmed the instrument spectral response function (ISRF) is highly consistent with the theoretical cardinal sine function (sinc). Gas-cell tests demonstrated that, after correcting for off-axis effect, the spectral calibration errors are on average less than 5 ppm, validated against Line-By-Line Radiative Transfer Model (LBLRTM) simulations. The radiometric calibration employed temperature-variable blackbodies for noise performance and radiometric accuracy assessments. The radiometric sensitivity, characterized by Noise Equivalent differential Radiance (NEdR), is less than 0.5 and 0.1 mW/(m²·sr·cm⁻¹) in the long-wave infrared (LWIR) and mid-wave infrared (MWIR) bands, respectively. To address the LWIR detector nonlinearity, an iterative polynomial fitting algorithm based on spectral responsivity invariance was implemented. This correction reduces the radiometric deviation from >1.0 K to ~0.2 K, meeting the 0.7 K accuracy requirement across a 180–315 K dynamic range. Conversely, the MWIR band exhibits high linearity but is limited by noise when observing low-temperature scenarios and can only meet the 0.7 K requirement within the range of 250 to 315 K. Full article

Infrared thermography provides key information for a wide range of diagnostic applications within built and natural environments. As thermal states are changing with ambient conditions, it is important to deploy thermal imaging systems and operators opportunistically. It is therefore an attractive proposition to make these systems more affordable and accessible. Low-cost thermal sensors generally produce low-resolution outputs. To increase data density across large subjects, diagnosticians may create image mosaics from multiple overlapping thermographs. The registration of individual inputs into large mosaics is aided by the acquisition of additional sensor data (photographs and depthmaps), which can provide critical spatial references. In many cases, the materials inherent to the modern built environment present challenges to traditional data registration workflows between multiple sensor streams. Mobile devices offer an opportunity to innovate in the creation of these mosaics, integrating rapid geospatial mapping functionality with radiometric thermography within a 3D context. In this paper the authors evaluate the FLIR One Pro thermal camera module along with iOS/iPhone specific rapid mapping capabilities, and present a methodology: (1) introducing a workflow for the integration of short-range (within 0.3–5 m capture distance) iPhone mobile sensor data into modeling pipelines; (2) introducing a calibration model enabling effective registration and fusion of multi-modal inputs from the iPhone mobile sensor stack and FLIR One thermographic module; and (3) detailing an alternative open-source methodology for the evaluation and translation of thermographic imagery for multi-sensor fusion. The end product of this pipeline is a 3D radiometric thermographic mosaic: a spatially continuous, textured surface model in which hundreds of individual low-resolution thermographs are fused into a single queryable output retaining full 16-bit temperature values at every point. All datasets have been made openly available and the two case studies used in this paper have been made accessible at full resolution for interactive 3D online viewing. Full article

Accurate absolute radiometric calibration is critical for ensuring the data quality of spaceborne Synthetic Aperture Radar (SAR) systems and supporting quantitative remote sensing applications. Absolute radiometric calibration generally relies on ground reference targets with known radar cross-section (RCS) deployed at dedicated calibration sites. Such ground-based calibration methods are costly and time-consuming, and calibration frequency is constrained by the distribution of calibration sites and the satellite revisit cycles. Additionally, for specialized SAR missions, such as deep space exploration, deploying calibration equipment on the observed extraterrestrial surface is infeasible. This study proposes a space-based absolute calibration concept using a small calibration satellite carrying a well-characterized reference (e.g., a passive reflector or an active transponder) and flying in formation with the SAR satellite. The relative motion ensures a side-looking acquisition geometry, enabling the SAR to image the accompanying target and derive calibration factors. The overall calibration process is divided into two stages: determination of an in-orbit calibration factor using the calibration satellite, followed by its transformation to accommodate ground imaging conditions. This method effectively isolates the radar system gain to characterize the intrinsic hardware response. Furthermore, by operating entirely in space, it avoids atmospheric and ground-clutter distortions, ensuring a fully space-based, end-to-end calibration process dominated primarily by sensor systematic errors. Moreover, it allows for more frequent and flexible calibration, eliminating reliance on ground calibration sites and infrastructure. The feasibility and advantages of the proposed concept are demonstrated through comprehensive simulations, covering orbit analysis, echo simulation, and image processing. Full article

Vicarious calibration is a technique that makes use of radiometrically stable targets such as dry lakebeds, desert sites, and open grasslands for the post-launch calibration of a satellite sensor. Top-of-the-atmosphere radiances or reflectances are provided from those sites for the calibration of a sensor. The reflectance of a remote sensing vicarious calibration site is measured by ratioing the signal from a ground target to the signal from a reference target, often a white panel made of PTFE whose reflectance is known. When physically mapping a vicarious calibration site prior to a satellite sensor overflight, there can be elapsed times between the two measurements as great as 10 min. The solar illumination can vary on time scales relevant to the time between measurements of a ground target and a reference panel, impacting the variance in the measured reflectance. In this work, we explore the impact of a temporal delay between two measurements taken outdoors on the Type A uncertainties in their ratios. A factor of 3 reduction in the Coefficient of Variation of the ratio taken simultaneously versus sequentially with delays on the order of 10 min was realized. Implications for protocols employed to measure the surface reflectance at sites used for the vicarious calibration of aircraft and satellite sensors are discussed. Full article

Targeting the long-term goal of synchronous acquisition of underwater terrain and material composition information, this study establishes a radiative transfer model for underwater hyperspectral LiDAR (UDHSL) and systematically verifies the effects of target reflectance, detection distance, and laser wavelength on backscattering echo intensity through controlled laboratory experiments. A wavelength-dependent water attenuation correction term incorporating absorption and scattering was introduced into the conventional LiDAR equation to derive a hyperspectral LiDAR radiative transfer equation applicable to underwater environments, and a normalized echo intensity processing method using window glass reflection as a reference was proposed. This study uses a custom-built UDHSL system (wavelength range: 450; detection range approximately 5–6 m). The echo intensity exhibits pronounced wavelength selectivity, peaking at 450–550 nm in clear water and shifting to 530–570 nm in turbid water. These experimental results are consistent with theoretical predictions of the radiative transfer model, validating its fundamental correctness and providing an experimental basis for radiometric calibration and underwater target reflectance retrieval of UDHSL systems. Full article