Search Results (90)

High-resolution TIR missions require sustained and well-characterized radiometric accuracy to support applications such as land surface temperature retrieval, drought monitoring, and surface energy budget analysis. To address this need, we develop an operational and automated ground-based vicarious radiometric calibration framework for TIR sensors and demonstrate its performance using the Wide-swath Thermal Infrared Imager (WTI) onboard Gaofen-5 01A (GF-5A). Three arid Gobi calibration sites were selected by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, Shuttle Radar Topography Mission (SRTM)-derived topography, and WTI-based radiometric uniformity metrics to ensure low cloud cover, flat terrain, and high spatial homogeneity. Automated ground stations deployed at Golmud, Dachaidan, and Dunhuang have continuously recorded 1 min contact surface temperature since October 2023. Field-measured emissivity spectra, Integrated Global Radiosonde Archive (IGRA) radiosonde profiles, and MODTRAN (MODerate resolution atmospheric TRANsmission) v5.2 simulations were combined to compute top-of-atmosphere (TOA) radiances, which were subsequently collocated with WTI imagery. After data screening and gain-stratified regression, linear calibration coefficients were derived for each TIR band. Based on 189 scenes from February–July 2024, all four bands exhibit strong linearity (R-squared greater than 0.979). Validation using 45 independent scenes yields a mean brightness–temperature root-mean-square error (RMSE) of 0.67 K. A full radiometric-chain uncertainty budget—including contact temperature, emissivity, atmospheric profiles, and radiative transfer modeling—results in a combined standard uncertainty of 1.41 K. The proposed framework provides a low-maintenance, traceable, and high-frequency solution for the long-term on-orbit radiometric calibration of GF-5A WTI and establishes a reproducible pathway for future TIR missions requiring sustained calibration stability. 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

The reflectance-based vicarious calibration approach uses measurements at well-understood test sites to provide top-of-atmosphere reference reflectance values suitable for inter-calibration approaches and does not require coincident views. The challenge is that results from such data may suffer from high variability from day to day. Data from high-quality sensors, such as the imaging spectrometers on the International Space Station (ISS) platform, provide an opportunity to use improved fine spectral information about the test sites with various sun/sensor geometries and site surface and atmospheric conditions to improve the test sites’ characterization. The results here are based on data from the DLR Earth Sensing Imaging Spectrometer (DESIS) instrument installed on the ISS since 2018 combined with output from the Radiometric Calibration Network (RadCalNet) site at Railroad Valley Playa (RRV) to decouple the effects of sun/sensor geometry from the RadCalNet predictions. The approach here uses the precessing orbit of the ISS to allow similar sensor view zenith angles at varying sun angles over short periods that limit the impact of any sensor changes and highlight the bi-directional effects of the surface reflectance and atmospheric conditions. DESIS data collected at (i) similar solar angles but varying view angles, (ii) similar sensor angles and varying solar angles, and (iii) similar scatter angles are compared. The DESIS results indicate that the top-of-atmosphere reflectance spectra for RRV at similar solar zenith angles but with varying sensor viewing angles provide more consistent data than those with varying solar zenith but with similar sensor viewing angles. In addition, comparisons of reflectance spectra of the site performed in terms of the sensor view scatter angle show good agreement, indicating that a directional reflectance correction would be straightforward and could offer a significant improvement in the use of RadCalNet data. The work shows that observations from imaging spectroscopy data from DESIS, and eventually Earth Surface Mineral Dust Source Investigation (EMIT), Surface Biology and Geology (SBG), and the climate-quality sensor CLARREO Pathfinder (CPF), provide the opportunity for the development of a model-based, SI-traceable prediction of at-sensor radiance over the RRV site that would serve as the basis for similar site characterizations with error budgets valid for arbitrary view and illumination angles. Full article

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 (NOAA-20) satellite was launched on 18 November 2017. The on-orbit calibration of the NOAA-20 VIIRS visible and near-infrared (VisNIR) bands has been very stable over time. However, NOAA-20 operational M1 (a dual gain band with a center wavelength of 0.41 µm) sensor data records (SDR) have exhibited persistent scene-dependent striping over clear-sky ocean (high gain, low radiance) since the beginning of the mission, different from other VisNIR bands. This paper studies the root causes of the striping in the operational NOAA-20 M1 SDRs. Two potential factors were analyzed: (1) polarization effect-induced striping over clear-sky ocean and (2) imperfect on-orbit radiometric calibration-induced striping. NOAA-20 M1 is more sensitive to the polarized lights compared to other NOAA-20 short-wavelength bands and the similar bands on the Suomi NPP and NOAA-21 VIIRS, with detector and scan angle-dependent polarization sensitivity up to ~6.4%. The VIIRS M1 top of atmosphere radiance is dominated by Rayleigh scattering over clear-sky ocean and can be up to ~70% polarized. In this study, the impact of the polarization effect on M1 striping was investigated using radiative transfer simulation and a polarization correction method similar to that developed by the NOAA ocean color team. Our results indicate that the prelaunch-measured polarization sensitivity and the polarization correction method work well and can effectively reduce striping over clear-sky ocean scenes by up to ~2% at near nadir zones. Moreover, no significant change in NOAA-20 M1 polarization sensitivity was observed based on the data analyzed in this study. After the correction of the polarization effect, residual M1 striping over clear-sky ocean suggests that there exists half-angle mirror (HAM)-side and detector-dependent striping, which may be caused by on-orbit radiometric calibration errors. HAM-side and detector-dependent striping correction factors were analyzed using deep convective cloud (DCC) observations (low gain, high radiances) and verified over the homogeneous Libya-4 desert site (low gain, mid-level radiance); neither are significantly affected by the polarization effect. The imperfect on-orbit radiometric calibration-induced striping in the NOAA operational M1 SDR has been relatively stable over time. After the correction of the polarization effect, the DCC-based striping correction factors can further reduce striping over clear-sky ocean scenes by ~0.5%. The polarization correction method used in this study is only effective over clear-sky ocean scenes that are dominated by the Rayleigh scattering radiance. The DCC-based striping correction factors work well at all radiance levels; therefore, they can be deployed operationally to improve the quality of NOAA-20 M1 SDRs. Full article

The Planet SuperDove sensors produce eight-band, three-meter resolution images covering the blue, green, red, red-edge, and NIR spectral bands. Variations in spectral response in the data used to perform atmospheric correction combined with low signal-to-noise over ocean waters can lead to visible striping artifacts in the downstream ocean-color products. It was determined that the striping artifacts could be removed from these products by filtering the top of the atmosphere radiance in the red and NIR bands prior to selecting the aerosol models, without sacrificing high-resolution features in the imagery. This paper examines an approach that applies this filtering to the respective bands as a preprocessing step. The outcome and performance of this filtering technique are examined to assess the success of removing the striping effect in atmospherically corrected Planet SuperDove data. Full article

In the optical satellite on-orbit imaging quality estimation system, the calculation of Modulation Transfer Function (MTF) is not fully standardized, and the influence of atmosphere is often simplified, making it difficult to obtain completely consistent on-orbit MTF measurements and comparisons. This study investigates the effects of various factors—such as edge angle, edge detection methods, oversampling rate, and interpolation techniques—on the accuracy of MTF calculations in the commonly used slanted-edge method for on-orbit MTF assessment, informed by simulation experiments. A relatively optimal MTF calculation process is proposed, which employs the Gaussian fitting method for edge detection, the adaptive oversampling rate, and the Lanczos (a = 3) interpolation method, minimizing the absolute deviation in the MTF results. A method to quantitatively analyze the atmospheric scattering and absorption MTF is proposed that employs a radiative transfer model. Based on the edge images of GF-2 satellite, images with various atmospheric conditions and imaging parameters are simulated, and their atmospheric scattering and absorption MTF is obtained through comparing the MTFs of the ground and top atmosphere radiance. The findings reveal that aerosol optical depth (AOD), viewing zenith angle (VZA), and altitude (ALT) are the primary factors influencing the accuracy of GF-2 satellite on-orbit MTF measurements in complex scenarios. The on-orbit MTF decreases with the increase in AOD and VZA and increases with the increase in ALT. Furthermore, a collaborative analysis of the main influencing factors of atmospheric scattering and absorption MTF indicates that, taking the PAN band of the GF-2 satellite as an example, the atmospheric MTF values are consistently below 0.7905. Among these, 90% of the data are less than 0.7520, with corresponding AOD conditions ranging from 0 to 0.08, a VZA ranging from 0 to 50°, and an ALT ranging from 0 to 5 km. The results can provide directional guidance for the selection of meteorological conditions, satellite attitude, and geographical location during satellite on-orbit testing, thereby enhancing the ability to accurately measure satellite MTF. Full article

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments on the recently launched NOAA-21 (previously known as JPSS-2) satellite. The VIIRS, like its predecessors on the SNPP and NOAA-20 satellites, provides daily global coverage in 22 spectral bands from 412 nm to 12 μm. The geometrically and radiometrically calibrated observations are the basis for many operational applications and scientific research studies. A total of 14 of the 22 bands are reflective solar bands (RSBs), covering photon wavelengths from 412 nm to 2.25 μm. The RSBs were radiometrically calibrated prelaunch and have been regularly calibrated on orbit through the onboard solar diffuser (SD) and scheduled lunar observations. The on-orbit SD’s reflectance change is determined by the onboard solar diffuser stability monitor (SDSM). We review the calibration algorithms and present the early mission performance of the NASA N21 VIIRS RSBs. Using the calibration data collected at both the yaw maneuver and regular times, we derive the screen transmittance functions. The visible and near-infrared bands’ radiometric gains have been stable, nearly independent of time, and so were the radiometric gains of the shortwave-infrared bands after the second mid-mission outgassing. Further, we assess the Earth-view striping observed in the immediate prior collection (Collection 2.0) and apply a previously developed algorithm to mitigate the striping. The N21 VIIRS RSB detector signal-to-noise ratios are all above the design values with large margins. Finally, the uncertainties of the retrieved Earth-view top-of-the-atmosphere spectral reflectance factors at the respective typical spectral radiance levels are estimated to be less than 1.5% for all the RSBs, except band M11 whose reflectance factor uncertainty is 2.2%. Full article

Monitoring atmospheric concentrations of greenhouse gases (GHGs) like carbon dioxide and methane in near real time and with good spatial resolution is crucial for enhancing our understanding of the sources and sinks of these gases. A novel approach can be proposed using a constellation of small satellites equipped with miniaturized spectrometers having a spectral resolution of a few nanometers. The objective of this study is to describe expected results that can be obtained with a single satellite named Uvsq-Sat NG. The SolAtmos end-to-end simulator and its three tools (IRIS, OptiSpectra, and GHGRetrieval) were developed to evaluate the performance of the spectrometer of the Uvsq-Sat NG mission, which focuses on measuring the main GHGs. The IRIS tool was implemented to provide Top-Of-Atmosphere (TOA) spectral radiances. Four scenes were analyzed (pine forest, deciduous forest, ocean, snow) combined with different aerosol types (continental, desert, maritime, urban). Simulated radiance spectra were calculated based on the wavelength ranges of the Uvsq-Sat NG, which spans from 1200 to 2000 nm. The OptiSpectra tool was used to determine optimal observational settings for the spectrometer, including Signal-to-Noise Ratio (SNR) and integration time. Data derived from IRIS and OptiSpectra served as input for our GHGRetrieval simulation tool, developed to provide greenhouse gas concentrations. The Levenberg–Marquardt algorithm was applied iteratively to fine-tune gas concentrations and model inputs, aligning observed transmittance functions with simulated ones under given environmental conditions. To estimate gas concentrations (CO₂, CH₄, O₂, H₂O) and their uncertainties, the Monte Carlo method was used. Based on this analysis, this study demonstrates that a miniaturized spectrometer onboard Uvsq-Sat NG is capable of observing different scenes by adjusting its integration time according to the wavelength. The expected precision for each measurement is of the order of a few ppm for carbon dioxide and less than 25 ppb for methane. Full article

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching $R^2$ = 0.92 (LAI), $R^2$ = 0.72 (CCC) and 0.68 (CWC), $R^2$ = 0.94 (FAPAR), and $R^2$ = 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between $4\%$ and $16\%$ ($R^2$ between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level. Full article

This paper presents the initial vicarious radiometric calibration results for Landsat 9 OLI using a combination of ground-based techniques and test sites located in Nevada, California, and South Dakota, USA. The field data collection methods include the traditional reflectance-based approach and the automated Radiometric Calibration Test Site (RadCaTS). The results for top-of-atmosphere spectral radiance show an average ratio (OLI/ground measurements) of 1.03, 1.01, 1.00, 1.02, 1.02, 1.01, 0.98, and 1.01 for Landsat 9 OLI bands 1–8, which is within the design specification of ±5% for spectral radiance. The results for top-of-atmosphere reflectance show an average ratio (OLI/ground measurements) of 0.99, 0.99, 1.00, 1.02, 1.01, 1.02, 1.00, and 1.00 for Landsat 9 OLI bands 1–8, which is within the design specification of ±3% for top-of-atmosphere reflectance. Full article

Cloud formations often obscure optical satellite-based monitoring of the Earth’s surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (ML) methods within the remote sensing domain has significantly improved performance for a wide range of EO tasks, including cloud detection and filtering, but there is still much room for improvement. A key bottleneck is that ML methods typically depend on large amounts of annotated data for training, which are often difficult to come by in EO contexts. This is especially true when it comes to cloud optical thickness (COT) estimation. A reliable estimation of COT enables more fine-grained and application-dependent control compared to using pre-specified cloud categories, as is common practice. To alleviate the COT data scarcity problem, in this work, we propose a novel synthetic dataset for COT estimation, which we subsequently leverage for obtaining reliable and versatile cloud masks on real data. In our dataset, top-of-atmosphere radiances have been simulated for 12 of the spectral bands of the Multispectral Imagery (MSI) sensor onboard Sentinel-2 platforms. These data points have been simulated under consideration of different cloud types, COTs, and ground surface and atmospheric profiles. Extensive experimentation of training several ML models to predict COT from the measured reflectivity of the spectral bands demonstrates the usefulness of our proposed dataset. In particular, by thresholding COT estimates from our ML models, we show on two satellite image datasets (one that is publicly available, and one which we have collected and annotated) that reliable cloud masks can be obtained. The synthetic data, the newly collected real dataset, code and models have been made publicly available. Full article

Utilizing data from the Tropical Rainfall Measuring Mission (TRMM) satellite’s precipitation radar (PR) and lightning imaging sensor (LIS), this study explores the spatiotemporal distributions of thunderstorm and lightning structures over the Qinghai–Tibet Plateau (QTP), an aspect that has not been explored previously. The structural aspects are crucial when considering the impact of thunderstorm and lightning activity in the atmospheric processes. Thunderstorms over the QTP show clear spatial variations in both vertical height and horizontal extension. In the southern region, the average heights of 20 dBZ and 30 dBZ echo tops typically exceed 11.2 and 9.3 km, respectively. Meanwhile, in the eastern part, the average coverage areas for reflectivity greater than 20 dBZ and 30 dBZ consistently surpass 1000 and 180 km², respectively. The spatial distribution of thunderstorm vertical development height relative to the surface aligns more closely with the horizontal extension, indicating stronger convection in the eastern QTP. The thunderstorm flash rate shows an eastward and northward prevalence, while the thunderstorm flash density peaks in the western and northeastern QTP, with a minimum in the southeast. Furthermore, in the eastern QTP, lightning duration, spatial expansion, and radiance are more pronounced, with the average values typically exceeding 0.22 s, 14.5 km, and 0.50 J m⁻² sr⁻¹ μm⁻¹, respectively. Monthly variations reveal heightened values during the summer season for thunderstorm vertical extension, areas with reflectivity greater than 30 dBZ, and lightning frequency. Diurnal variations highlight an afternoon increase in thunderstorm vertical and horizontal extension, lightning frequency, duration, and spatial scale. From a statistical perspective, under weak convective conditions, lightning length exhibits a positive correlation with thunderstorm convection intensity, contrasting with the opposite relationship suggested by previous studies. This article further analyzes and discusses the correlations between various thunderstorm and lightning structural parameters, enhancing our understanding of the distinctive features of thunderstorm and lightning activities in the QTP. Full article

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation. Full article

The polarization crossfire (PCF) suite carried onboard the Chinese GaoFen-5B satellite is composed of a Particulate Observing Scanning Polarimeter (POSP) and a Directional Polarimetric Camera (DPC), which can provide multi-angle, multi-spectral, and polarization data. In this paper, the influence of polarization and the directionality of reflectance in open oceans on the inversion of chlorophyll a (Chla) concentrations are investigated, from 410 nm to 670 nm. First, we exploit a vector radiative transfer model to simulate the absolute and relative magnitudes of the water-leaving radiance signal (I) and the parallel polarization radiance (PPR) to the top-of-atmosphere (TOA) radiation field. The simulation results show that the PPR can enhance the relative contribution of the water-leaving signal, especially in sunglint observation geometry. The water-leaving signal for PPR exhibits significant directional and spectral variations relative to the observation geometries, and the maximum value of the water-leaving signal for PPR occurs in the backscattering direction. In addition, the sensitivity of the PPR to the Chla concentration is sufficient. The synthetic datasets are utilized to develop retrieval algorithms for the Chla concentrations based on the back-propagation neural network (BPNN). The inversion results show that the PCF strategy improves the accuracy of Chla retrieval, with an RMSE of 0.014 and an RRMSE of 6.57%. Thus, it is an effective method for retrieving the Chla concentration in open oceans, by utilizing both the directionality and polarization of the reflectance. Full article

Closed-form Method for Atmospheric Correction (CMAC) is software that overcomes radiative transfer method problems for smallsat surface reflectance retrieval: unknown sensor radiance responses because onboard monitors are omitted to conserve size/weight, and ancillary data availability that delays processing by days. CMAC requires neither and retrieves surface reflectance in near real time, first mapping the atmospheric effect across the image as an index (Atm-I) from scene statistics, then reversing these effects with a closed-form linear model that has precedence in the literature. Five consistent-reflectance area-of-interest targets on thirty-one low-to-moderate Atm-I images were processed by CMAC and LaSRC. CMAC retrievals accurately matched LaSRC with nearly identical error profiles. CMAC and LaSRC output for paired images of low and high Atm-I were then compared for three additional consistent-reflectance area-of-interest targets. Three indices were calculated from the extracted reflectance: NDVI calculated with red (standard) and substitutions with blue and green. A null hypothesis for competent retrieval would show no difference. The pooled error for the three indices (n = 9) was 0–3% for CMAC, 6–20% for LaSRC, and 13–38% for uncorrected top-of-atmosphere results, thus demonstrating both the value of atmospheric correction and, especially, the stability of CMAC for machine analysis and AI application under increasing Atm-I from climate change-driven wildfires. Full article