Search Results (220)

Terrestrial laser scanners provide both geometric and radiometric information, and terrestrial laser scanning (TLS) intensity is influenced by surface optical properties and the angle of incidence. This study presents a piecewise polynomial Lambert–Beckmann (PPLB) model to enhance TLS intensity characterization for broad-leaved surfaces. The PPLB model incorporates the Beckmann law, thus enabling a data-driven estimation of transition angles and enhancing the fitting flexibility across diverse leaf surfaces. TLS measurements were conducted on adaxial and abaxial leaf surfaces of five tree species using a RIEGL VZ-400 scanner. The results indicated that the TLS intensity consistently declined as the angle of incidence increased; however, different species and surfaces showed distinct angular response patterns. Across all ten tested species–surface combinations, the PPLB model achieved low root mean square error values of 0.0095–0.0183 and yielded three physically meaningful parameters: specular reflection contribution ($k_s$), surface roughness ($m$), and transition angle (${\theta }_T$). Excluding the ${\theta }_T$ = 0° fitted result for the Golden Shower Tree abaxial surface, the estimated nonzero ${\theta }_T$ values ranged from 30° to 64°, indicating substantial variability in threshold angles among leaf surfaces beyond the commonly assumed value of 45°. These findings highlight the importance of incorporating surface-specific threshold angles for improved leaf characterization. Full article

Three-dimensional (3D) hyperspectral point clouds provide both surface geometry and spectral information, offering a promising tool for close-range surface characterization. However, reliable reflectance-related spectral measurement on complex surfaces remains challenging because camera-recorded spectral signals are strongly affected by non-uniform illumination, surface geometry, and the spectral response of the imaging system, while existing correction methods are often limited by Lambertian assumptions and narrow spectral capacity. In this work, we present a close-range 3D hyperspectral measurement framework with geometry-aware spectral correction that integrates a structured-light 3D measurement module with a hyperspectral imaging module. The system enables the acquisition of fused 3D hyperspectral data with a sphere-fitting RMS residual below 40 μm and a spectral resolution of 7 nm. To improve spectral correction on geometrically complex surfaces, we propose a physics-guided spectral correction model, termed 3D light-field spectral correction (3D-LFSC), which is inspired by the geometric dependence described by the bidirectional reflectance distribution function (BRDF) and uses measurable geometric information to model geometry-dependent spectral variation. Because the system adopts a cross-polarized illumination–detection configuration, the corrected spectra should be interpreted as diffuse-dominant apparent reflectance estimates under the fixed system configuration, rather than complete surface reflectance. Experiments on surfaces with different geometries and reflectance properties show that the proposed method improves spectral consistency by more than 10% compared with existing methods. The framework also demonstrates applicability to chromaticity-related analysis on facial surfaces, indicating its potential for close-range spectral measurement of complex biological surfaces. Full article

Understanding the variations in the bidirectional reflectance distribution function (BRDF) and albedo over snow surface under various conditions is important for interpreting the surface–atmosphere processes of the cryosphere, and the kernel-driven model is among the most popular methods to obtain this information for a comprehensive analysis. Recently, the RossThick-LiSparseReciprocal-Snow (RTLSRS) model was developed to better characterize the anisotropic reflectance of snow and shows strong potential for integration into operational remote sensing algorithms for snow BRDF/albedo retrieval. To comprehensively test the ability of the RTLSRS model to reproduce snow reflectance, the fitting accuracy to different multi-angular data derived from ground, tower, aircraft, and satellite platforms across the full optical wavelength range were demonstrated in this study. Special attention in this study was directed to analyzing the model performance under extreme illumination observation geometries, particularly with respect to the retrieval accuracy and stability under large Solar Zenith Angles (SZAs) and different Relative Azimuth Angles (RAAs). The model performance for silt-polluted snow surface with different concentrations is also assessed to provide necessary supplementation, relative to “pure” snow surface in the previous study. The main findings of this study are summarized as follows: (1) The RTLSRS model exhibits strong robustness under various SZAs; even when the SZA exceeds 80°, the model maintains high accuracy in BRDF reconstruction, with root mean square error (RMSE) values below 0.05. (2) The model also demonstrates satisfactory inversion capability when observations deviate from the principal plane (PP); the model can achieve fitting accuracy with R² approaching 0.5 and RMSE below 0.05 for MODIS data. (3) In the spectral range below 1300 nm, the RTLSRS model effectively reconstructs the scattering characteristics of snow surfaces with light impurity levels (<20 g/0.5 m²). (4) The spectral shape of snow reflectance remains consistent across different view zenith angles (VZAs) in general. However, the variations caused by different SZAs can be as high as 38.49% and such SZA-induced difference can result in WSA estimation discrepancy of up to 63.43%. This comprehensive assessment further affirms and demonstrates the applicability of the RTLSRS model for the first time in fitting observations across different platforms with various optical wavelengths and geometries, and provides an improved understanding to analyze BRDF variations for the user community. Full article

The rapid proliferation of resident space objects has made space situational awareness critically dependent on accurate characterization of non-cooperative targets using photometric light curves. This review provides a comprehensive examination of data-driven approaches for space object photometric prediction, synthesizing research across optical scattering characterization, shape and attitude inversion methodologies, and intelligent analysis techniques based on machine learning and deep learning. The evolution from traditional physics-based models to contemporary data-driven paradigms is systematically analyzed, revealing fundamental trade-offs between physical interpretability, computational efficiency, and predictive accuracy. Key findings indicate that while physical bidirectional reflectance distribution function (BRDF) models provide rigorous foundations, their computational demands and prior knowledge requirements limit operational applicability; conversely, deep learning has demonstrated superior predictive accuracy in existing comparative studies, although this conclusion is qualified by the absence of standardized public benchmarks, and it also suffers from interpretability deficits and simulation-to-reality generalization gaps. Critical research gaps are identified, including the absence of public benchmark datasets, inadequate handling of temporal multi-scale phenomena, and the persistent challenge of bridging simulated and real-world observations. Future directions should pursue physics-guided machine learning frameworks that integrate domain knowledge with data-driven capabilities, develop explainable artificial intelligence techniques tailored for photometric analysis, and establish standardized evaluation protocols to advance next-generation space object characterization essential for collision avoidance and space traffic management. Full article

The forest understory litter fraction $\left({\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}\right)$ is a critical indicator for evaluating the effectiveness of “understory erosion” control in red soil regions; however, its high-precision, large-scale monitoring remains challenging due to canopy occlusion. This study proposes an ${\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}$ inversion framework that integrates high-spatial-resolution Sentinel-2 imagery with multi-angular prior knowledge from MODIS BRDF products. First, a linear mapping model between multi-band reflectances at 0° and 45° view angles was constructed using 500 m MODIS MCD43A1 products $(R^2>0.8)$. This model was subsequently employed as a physical prior for anisotropic characterization and transferred to 10 m Sentinel-2 imagery to generate a long-term, dual-angle reflectance dataset. Subsequently, the four-scale geometric-optical model was utilized to decouple canopy and understory background signals, followed by quantitative ${\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}$ inversion using a pixel-based dimidiate model. Validation results confirmed the reliability of the framework $(R^2=0.74, RMSE=0.1073)$. Spatiotemporal evolution analysis indicated a significant upward trend in ${\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}$ across Changting County from 2016 to 2025, with over 90% of the area showing improvement. The proportion of high-coverage areas $({\mathrm{F}\mathrm{V}\mathrm{C}}_{\mathrm{y}}>0.75)$ increased from 10% to 38%, exhibiting a “high in the center, low in the periphery” spatial pattern that aligns closely with core ecological restoration zones. Stability and persistence analyses further revealed that 61.18% of the study area reached moderate-to-high stability, and 70% of pixels exhibited a “positive persistence-improvement” trend, highlighting a pronounced inertia-driven enhancement in ecological recovery. This study provides a refined technical pathway for assessing soil and water conservation benefits in red soil regions. Full article

The Advanced Meteorological Imager (AMI) on GEO-KOMPSAT-2A (GK-2A) lacks a 2.1 μm shortwave infrared channel, precluding the dark target surface reflectance estimation that other geostationary aerosol retrievals rely on. We propose an improved land aerosol optical depth (AOD) retrieval in which background surface reflectance (BSR) is derived entirely from pixel-level bidirectional reflectance distribution function (BRDF) inversion using the scaled Ross-Thick Li-Sparse (sRTLS) kernel model fitted to geostationary time-series observations. Unlike existing approaches, the algorithm inverts the BRDF independently at each retrieval channel without relying on spectral reflectance relationships or external surface reflectance products; it assumes a low-background AOD during an initial accumulation period and then iteratively refines both BRDF coefficients and AOD. Two aerosol models—generic and dust—are supported, with a geographic dust-zone mask activating two-model selection during spring. Validation against 74 Aerosol Robotic Network sites over 2023 yields R = 0.86, RMSE = 0.15, and bias = −0.02, compared with R = 0.59, RMSE = 0.25, and bias = −0.04 for the National Meteorological Satellite Center (NMSC) GK-2A AOD product. The largest improvements appear at AOD ≤ 0.1 (bias: +0.03 versus +0.11) and AOD > 0.8 (bias: −0.12 versus −0.85). The full March–May (MAM) evaluation yields bias = −0.06 across all 74 sites. As a separate parallel retrieval restricted to matchups inside the geographic dust-zone mask, the proposed algorithm (dust model included) gives bias = −0.03, which worsens to −0.11 when only the generic model is applied—nearly a fourfold increase. A comparison against Himawari-9/Advanced Himawari Imager (AHI)—a co-located geostationary sensor carrying a 2.3 μm shortwave infrared (SWIR) channel—shows that the proposed algorithm (R = 0.897) outperforms Himawari-9/AHI (R = 0.855) across all metrics, demonstrating competitive accuracy without relying on a SWIR channel. Full article

Land surface bidirectional reflectance distribution functions (BRDF) are critical for quantitative remote sensing but are significantly constrained by scale effects, limiting the interoperability of multi-resolution data and the accuracy of quantitative inversion, thereby rendering the investigation of BRDF multi-scale effects increasingly urgent. This study utilized UAV (Unmanned Aerial Vehicle)-based multi-angular observations and the RPV model to retrieve the BRDF of typical land covers, employing the Window Averaging Method to simulate multi-scale responses and systematically investigate the relationship between BRDF characteristics and spatial scale. The results indicate the following key findings: (1) The RPV (Rahman–Pinty–Verstraete) model demonstrated high robustness and inversion accuracy, yielding RMSE (Root Mean Square Error) below 0.06 and RRMSE (Relative RMSE) below 25% across all land covers, with the 840 nm band exhibiting superior performance. (2) Significant spatial scale effects were observed, where BRDF characteristics varied distinctively with scale but eventually stabilized at specific thresholds; specifically, the stabilization scales were identified as 1.3 m for bare soil, 1.5 m for tea plantations, 1 m for rice, and 2 m for forests. (3) The scale evolution of BRDF features exhibited a parallel trend with spatial heterogeneity, a correlation that enables the quantitative identification of optimal observation scales for different land cover types. Full article

With the growing global emphasis on space resources, the significance of space detection and surveillance technologies has escalated. Currently, space-based optical surveillance stands as the primary means for acquiring information on space objects. However, constrained by the diffraction limits of space telescopes, distant space objects are typically imaged as point sources. The resulting lack of sufficient spatial resolution renders traditional image-based recognition algorithms ineffective. In contrast, the Bidirectional Reflectance Distribution Function (BRDF) fully characterizes surface light scattering properties through four-dimensional features, significantly outperforming traditional two-dimensional spectral techniques in material identification. Consequently, leveraging BRDF signatures at varying phase angles has emerged as an effective approach for Space Object Identification. In this study, we developed an automated BRDF measurement system to characterize various typical aerospace materials and investigated the BRDF properties of mixed-material surfaces. A material composition ratio prediction model was constructed based on a One-Dimensional Convolutional Neural Network (1D-CNN). This model effectively extracts key features, including local slope variations and global waveform characteristics, from the BRDF curves. Experimental results demonstrate that the model achieves a maximum relative percentage error of 6.21%, implying a prediction accuracy for mixed-material composition ratios consistently exceeding 93.79%. Compared to image classification methods based on remote sensing imagery, the proposed approach offers higher computational efficiency, significantly reduced model complexity and computational cost, and enhanced robustness. This work provides essential data support for material identification by space-based telescopes and establishes an algorithmic and experimental foundation for intelligent space situational awareness systems. Full article

This study addresses key challenges in obtaining reliable infrared data for maritime ship observation and limitations of existing models, such as simplified reflectance assumptions and incomplete multi-band coverage. To improve modeling accuracy and computational efficiency, a high-precision Bidirectional Reflectance and Pseudo-random Vector Enhanced Reverse Monte Carlo Method (BP-ERMCM) is developed. By combining the Bidirectional Reflectance Distribution Function (BRDF), pseudo-random vector approaches, and improved ray-tracking algorithms with precomputed thermal radiation and MODTRAN’s atmospheric transfer model, BP-ERMCM provides multi-view infrared characteristic simulations across 3–5 μm and 8–12 μm bands. Simulations using a 3D ship model with 191 viewpoints reveal seasonal sensitivity, with summer peak intensity at 9.8 μm being 39.3% higher than in winter, and viewpoint dependency showing oblique overhead radiation 5.65 times greater than that from bow angles. Long-wave contours enhance target distinction, while mid-wave regions are dominated by reflection, increasing intensity at 3.8 μm by 56.1–85.7%. These findings highlight BP-ERMCM’s potential to inform infrared signature database construction, detector optimization, and maritime observation strategies. The findings underscore BP-ERMCM’s capability to enhance efficiency and accuracy, providing valuable insights for infrared databases, sensor selection, and maritime observation strategies, thereby advancing infrared signature analysis in maritime applications. Full article

The condition of technical surfaces strongly influences the functionality and lifetime of many components. In particular, the performance of aero-engines can be impaired by increased roughness of the turbine blade surfaces. In this work, an LED- and camera-based illumination sensor is presented for reflection-based characterisation of turbine blade surfaces, with a focus on rapid, wide-area assessment rather than direct roughness measurement. Traditional roughness measurements (e.g., profilometry, confocal microscopy) provide micrometre-scale height information but are limited in working distance and measurement volume, making complete surface coverage time-consuming. The proposed sensor acquires multi-illumination image data, from which an anisotropic BRDF (bidirectional reflectance distribution function) model is fitted on a per-pixel basis to obtain reflectance parameters. Independently, surface roughness parameters (Sa, Sq, Sz, Ssk, Sku) are measured using a confocal laser scanning microscope in accordance with ISO 25178 and used as reference data. Using two turbine blades with contrasting surface conditions (comparatively smooth vs. visibly rough), the study qualitatively investigates whether there are indications of relationships between BRDF model parameters and roughness characteristics. The results show weak relationships with height-based parameters (Sa, Sq, Sz), but clearer trends for distribution parameters (Ssk, Sku) and a good qualitative agreement between directional BRDF parameters and texture orientation. These findings indicate that the illumination sensor provides a complementary, reflectance-based approach for surface condition triage in MRO and QA contexts, highlighting regions that warrant more detailed roughness measurements. Extension of the approach to other component geometries and a comprehensive quantitative analysis of BRDF–roughness relationships are planned for follow-up studies. Full article

The absolute radiometric calibration of a satellite sensor is an essential process that determines the coefficients required to convert the radiometric quantities of satellite images. This procedure is crucial for ensuring the applicability and enhancing the reliability of optical sensors onboard satellites. This study performs the absolute radiometric calibration of the Compact Advanced Satellite 500-1 (CAS500-1) Advanced Earth Imaging Sensor System-C (AEISS-C), a low Earth orbit satellite developed independently by Republic of Korea for precise ground observation. Field campaign using a tarp, an Analytical Spectral Devices FieldSpecIII spectroradiometer, and a MicrotopsII sunphotometer was conducted. Additionally, reflectance-based vicarious calibration was performed using observational data and the MODerate resolution atmospheric TRANsmission model (version 6) radiative transfer model (RTM). Cross-calibration was also performed using data from the Sentinel-2 MultiSpectral Instrument, RadCalNet observations, and MODIS Bidirectional nReflectance Distribution Function (BRDF) products (MCD43A1) to account for differences in spectral response functions, viewing/solar geometry, and atmospheric conditions between the two satellites. From these datasets, two correction factors were derived: the Spectral Band Adjustment Factor and the BRDF Correction Factor. CAS500-1/AEISS-C acquires satellite imagery using two Time Delay Integration (TDI) modes, and the absolute radiometric calibration coefficients were derived considering these TDI modes. The coefficient of determination (R²) ranged from 0.70 to 0.97 for the reflectance-based vicarious calibration and from 0.90 to 0.99 for the cross-calibration. For reflectance-based vicarious calibration, aerosol optical depth was identified as the primary source of uncertainty among atmospheric factors. For cross-calibration, the reference satellite and RTMs were the primary sources of uncertainty. The results of this study will support the monitoring of CAS500-1/AEISS-C, which produces high-resolution imagery with a spatial resolution of 2 m, and can serve as foundational material for absolute radiometric calibration procedures for other CAS500 satellites. Full article

Multi-angular remote sensing plays a critical role in the study domains of ecological monitoring, climate change, and energy balance. The successful retrieval of the surface Bidirectional Reflectance Distribution Function (BRDF) and albedo from multi-angular remote sensing observations for various applications relies on the sensitivity of an appropriate BRDF model to both the number and the sampling distribution of multi-angular observations. In this study, based on selected high-quality multi-angular datasets, we designed three representative angular sampling schemes to systematically analyze different illuminating–viewing configurations of the retrieval results in a kernel-driven BRDF model framework. We first proposed an angular information index (AII) by incorporating a weighting mechanism and information effectiveness to quantify the angular information content for the angular sampling distribution schemes. In accordance with the principle that observations on the principal plane (PP) provide the most representative anisotropic scattering features, the assigned weight gradually decreases from the PP towards the cross-principal plane (CPP). The information effectiveness is determined based on the cosine similarity between the observations, effectively reducing the information redundancy. With such a method, we assess the AII of the different sampling schemes and further analyze the impact of angular distribution on both BRDF inversion and the estimation of snow surface albedo, including White-Sky Albedo (WSA) and Black-Sky Albedo (BSA) based on the RossThick-LiSparseReciprocal-Snow (RTLSRS) BRDF model. The main conclusions are as follows: (1) The AII approach can serve as a robust indicator of the efficiency of different sampling schemes in BRDF retrieval, which indicates that the RTLSRS model can provide a robust inversion when the AII value exceeds a threshold of −2. (2) When the AII value reaches such a reliable level, different sampling schemes can reproduce the BRDF shapes of snow across different bands to somehow varying degrees. Specifically, observations with smaller view zenith angle (VZA) ranges can reconstruct a BRDF shape that amplifies the anisotropic effect of snow; in addition, the forward scattering tends to be more pronounced at larger solar zenith angles (SZAs), while the variations in BRDF shape reconstructed from off-PP observations depend on both wavelength and SZAs. (3) The relative differences in both BSA and WSA grow with increasing wavelength for all these sampling schemes, mostly within 5% for short bands but up to 30% for longer wavelengths. With this novel AII method to quantify the information contribution of multi-angular sampling distributions, this study offers valuable insights into several main multi-angular BRDF sampling strategies in satellite sensor missions, which relate to most of the fields of multi-angular remote sensing applications in engineering. Full article

Row crops are regarded as a transitional type between continuous and discrete vegetation. Previous studies idealized row crops as periodic hedgerows with rectangular cross-sections. However, these models relied on oversimplified assumptions, failing to capture the intrinsic heterogeneity of canopy or its dynamic evolution over the life cycle. In 2020, we proposed a row crop model with a gradual decrease in leaf area volume density (LAVD) from the center of the row to the edge, partially overcoming these limitations. Building on this previous model, this paper introduces the leaf shape factor proposed by Mõttus et al. into the model. Three control parameters, a leaf width control parameter (β), leaf length control parameter (ψ), and leaf azimuth control parameter (e), are proposed to regulate the spatial distribution of LAVD. Additionally, an empirical exponential function from Watanabe et al. is adopted to describe the leaf zenith angle distribution, enabling the realistic calculation of the G-function and Γ-function in conjunction with leaf azimuth distribution. The LAVD is formulated at three hierarchical scales: individual scale, row scale, and scene scale. The model delivers two key advancements: enabling pronounced spatial heterogeneity and high tunability of the LAVD, and accurately simulating row crops throughout the life cycle, which bridges the row structure and continuous stages of row crops. Radiative transfer simulations are conducted to derive the bidirectional reflectance distribution function (BRDF), which is validated against the discrete anisotropic radiative transfer (DART) model. Comparisons across three growth stages demonstrated good consistency. Furthermore, this paper investigates the sensitivity of the BRDF to three control parameters (β, ψ, and e). The results indicate that changes in three parameters significantly affect the reflectance in the darkspot direction, leading to a maximum error of 22.6%. In carrying out remote sensing applications such as parameter inversion and yield estimation for row crops, the new model is recommended for more accurate BRDF simulations. Full article

Reliable power generation from solar cells is critical for spacecraft operation. High-energy laser irradiation poses a significant threat, as it can potentially cause irreversible damage to solar cells, which is difficult to detect remotely using conventional techniques such as radar or optical imaging. Spectral detection offers a potential approach through unique “spectral fingerprints,” but the spectral characteristics of laser-damaged solar cells remain insufficiently documented. This study investigates the scattering spectral characteristics of triple-junction GaAs (Gallium Arsenide) solar cells subjected to nanosecond pulsed laser irradiation to establish spectral signatures for damage assessment. GaAs solar cells were irradiated at varying energy densities. Bidirectional Reflectance Distribution Function (BRDF) spectra (400–1200 nm) were measured. A thin-film interference model was used to simulate damage effects by varying layer thicknesses, thereby interpreting experimental results. The results demonstrate that as the laser energy density increases from 0.12 to 2.96 J/cm², the number of absorption peaks in the visible range (400–750 nm) decreases from three to zero, and the oscillation in the near-infrared range vanishes completely, indicating progressive damage to the GaInP (Gallium Indium Phosphide) and GaAs layers. This study provides a spectral-based approach for remote assessment of laser-induced damage to solar cells, which is crucial for satellite health monitoring. Full article

The successful launch of Korea Multipurpose Satellite-3/Advanced Earth Imaging Sensor System (KOMPSAT-3/AEISS) on 18 May 2012 allowed the Republic of Korea to meet the growing demand for high-resolution satellite imagery. However, like all satellite sensors, KOMPSAT-3/AEISS experienced temporal changes post-launch and thus requires ongoing evaluation and calibration. Although more than a decade has passed since launch, the KOMPSAT-3/AEISS mission and its multi-year data archive remain widely used. This study focused on the cross-calibration of KOMPSAT-3/AEISS with Sentinel-2A/Multispectral Instrument (MSI) by comparing the radiometric responses of the two satellite sensors under similar observation conditions, leveraging the linear relationship between Digital Numbers (DN) and top-of-atmosphere (TOA) radiance. Cross-calibration was performed using near-simultaneous satellite images of the same region, and the Spectral Band Adjustment Factor (SBAF) was calculated and applied to account for differences in spectral response functions (SRF). Additionally, Bidirectional Reflectance Distribution Function (BRDF) correction was applied using MODIS-based kernel models to minimize angular reflectance effects caused by differences in viewing and illumination geometry. This study aims to evaluate the radiometric consistency of KOMPSAT-3/AEISS relative to Sentinel-2A/MSI over Baotou scenes acquired in 2022–2023, derive band-specific calibration coefficients and compare them with prior results, and conduct a side-by-side comparison of cross-calibration and vicarious calibration. Furthermore, the cross-calibration yielded band-specific gains of 0.0196 (Blue), 0.0237 (Green), 0.0214 (Red), and 0.0136 (NIR). These findings offer valuable implications for Earth observation, environmental monitoring, and the planning and execution of future satellite missions. Full article