Eradiate is a new open-source 3D radiative transfer model designed to provide highly accurate results for various applications in Earth observation and atmospheric science. Its Monte Carlo ray tracing radiometric kernel is derived from Mitsuba 3, a research-oriented rendering system, and therefore benefits from many technological advances made in the computer graphics community. This foundation unlocks a path to improving radiative transfer simulation accuracy by facilitating the integration of models and numerical techniques developed by scientific communities that are otherwise compartmentalized. Eradiate currently covers the [250 nm , 3 & micro;m] spectral region and offers advanced 3D surface modelling features, as well as a state-of-the-art 1D atmospheric model (plane-parallel and spherical-shell) and polarization support. Designed for modern scientific Python programming workflows, it is intended for use in interactive Python sessions such as Jupyter notebooks. Eradiate is thoroughly tested and validated against various radiative transfer benchmarks, ensuring its suitability for calibration and validation tasks. This paper introduces Eradiate from historical, scientific, and architectural perspectives. It elaborates on its feature set and showcases a variety of applications, with scenes ranging from simple 1D plane-parallel setups to complex, fully resolved 3D vegetated canopies and even large regions on Earth.

Because of the critical role they play in Earth observation (EO) workflows, radiative transfer models (RTM) must undergo thorough validation campaigns that will help guarantee that their output is representative of the physical reality. In this study, we test the ability of the Eradiate RTM to simulate the reflectance of a manufactured target given SI-traceable measurements of its shape and optical properties. To address issues identified in similar prior work, we select a material with reflective features that can be accurately modeled using a straightforward data-driven approach. We produce an artificial target design that is easy to manufacture with good precision. The material and artifact are characterized optically using an SI-traceable 3-D goniospectrophotometer, and the artifact is characterized geometrically in an SI-traceable facility. The geometry of the artificial target and the optical properties of the material are used to build a numerical experiment with Eradiate that simulates the optical characterization of the artificial target. The simulation includes the propagation of uncertainties on the input parameters. We compare the simulated and measured data and analyze the performance of this method using three different metrics. Our findings demonstrate consistent performance across all considered illumination and sensor pointing configurations. Simulations deviate from measurements by less than 2%, and more than 80% of measured and simulated data points agree.

Accurate hyperspectral simulations are critical for the vicarious calibration of next-generation space-based sensors and for ensuring the long-term consistency of climate data records. This study presents a refined methodology to generate simulated radiometric calibration references over bright desert pseudo-invariant calibration sites, specifically designed to meet the stringent accuracy requirements of hyperspectral observations. Building on metrology principles, and in the absence of SI-traceable references, the approach leverages simulated reflectance over stable desert targets as a community-accepted calibration reference. Key advancements include improved surface reflectance modelling using the Rahman–Pinty–Verstraete model and the Combined Inversion of Surface and Aerosol (CISAR) algorithm, enhanced atmospheric property characterization from multiple state-of-the-art datasets, and the use of the Eradiate Monte Carlo-based radiative transfer model. These refinements reduce uncertainty in simulated top-of-atmosphere reflectance, achieving an accuracy within ±3% in high-transmittance spectral regions. Validation against both multispectral and hyperspectral satellite data (i.e., EMIT, EnMAP, and PRISMA) confirms the robustness of the methodology. This work establishes a reliable framework for hyperspectral sensor calibration and intercalibration, addressing the pressing need for traceable, high-fidelity reference data in Earth observation.

This paper investigates how atmospheric vertical profiles of pressure, temperature, and concentration affect molecular absorption calculation. This sensitivity analysis is performed in preparation to the upcoming TRUTHS mission, anticipated to provide hyperspectral TOA BRF records with a radiometric accuracy better than 1%. Two methods for characterizing the atmospheric vertical profile are compared: rescaling the H2O and O3 concentrations of an AFGL U.S. Standard vertical profile, and using customized profiles based on CAMS data. The study investigates the effects of those methods on multi-spectral observations in key spectral regions affected, respectively, by water vapour, ozone and methane, as well as on hyperspectral observations covering the visible to SWIR region. Results show that when molecular transmittance exceeds 97%, the choice of method has minimal impact, with less than 1% uncertainty. When the molecular transmittance decreases from 97% to 75%, the corresponding uncertainty on the TOA BRF simulation increases from 1% up to 5%. For transmittance below 75%, using CAMS data for vertical profile characterization is recommended. The study also highlights how pressure and temperature profiles influence Rayleigh optical thickness estimation, particularly affecting TOA BRF in the blue spectral region.

Observations acquired by the SPOT-VEGETATION and PROBA-V missions offer a unique opportunity to improve our understanding of the climate, providing global and continuous data over the land surface over 20 years. The possibility of generating a long-term climate data record from the entire archive, stored on the Mission Exploitation Platform (MEP), is here explored. For this purpose, in the framework of the ESA-funded SPAR@MEP project, the Combined Inversion of Surface and Aerosols (CISAR) algorithm has been applied to the SPOT-VGT and PROBA-V archive, following the harmonization of the observations according to the Fidelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) principles. CISAR has been applied to the full 20-year harmonized archive over key areas, as well as to one year of global acquisition from PROBA-V, processed at 5 km resolution, to derive aerosol single-scattering properties and surface reflectance. The retrieval is evaluated in terms of consistency among the three sensors and against reference datasets, including ground-based observations, models, and other sensor products. This activity has revealed the importance of characterizing the radiometric uncertainty for every processed pixel.

Surface Bidirectional reflectance distribution function (BRDF) is a key intrinsic geophysical variable depending only on the characteristics of the observed medium. It is therefore the most suitable measurand to support the definition of fiducial reference measurements (FRM). Field acquisition of surface reflectance data relies on substantial assumptions and simplifications, often without accounting for their impact. For example, the BRDF is a theoretical concept and can never be measured in the field. In contrast, the hemispherical conical reflectance factor (HCRF), which is the measurand obtained during field campaigns, is impacted by all scene elements and is not intrinsic to the surface. This study analyses the impact of four parameters (atmospheric scattering, measurement device field of view cropping, acquisition duration, non-Lambertian reference panels) on HCRF estimation. Simulations are performed on a 3D vegetation scene, using the new radiative transfer model Eradiate. It is found that among the aforementioned parameters, atmospheric scattering alone leads to a relative root-mean-square error (RRMSE) of more than 10% between HCRF and reference Bidirectional reflectance factor (BRF).

The Combined Inversion of Surface and AeRosols (CISAR) algorithm for the joint retrieval of surface and aerosol single scattering properties has been further developed in order to extend the retrieval to clouds and overcome the need for an external cloud mask. Pixels located in the transition zone between pure cloud and pure aerosol are often discarded by both aerosol and cloud algorithms, despite being essential for studying aerosol–cloud interactions, which still represent the largest source of uncertainty in climate predictions. The proposed approach aims at filling this gap and deepening the understanding of aerosol properties in cloudy environments. The new CISAR version is applied to Sentinel-3A/SLSTR observations and evaluated against different satellite products and ground measurements. The spatial coverage is greatly improved with respect to algorithms processing only pixels flagged as clear sky by the SLSTR cloud mask. The continuous retrieval of aerosol properties without any safety zone around clouds opens new possibilities for studying aerosol properties in cloudy environments.

Radiative transfer models of the Earth’s atmosphere play a critical role in supporting Earth Observation applications such as vicarious calibration. In the solar reflective spectral domain, these models usually account for the scattering and absorption processes in the atmosphere and the underlying surface as well as the radiative coupling between these two media. A range of models is available to the scientific community with built-in capabilities making them easy to operate by a large number of users. These models are usually benchmarked in idealised but often unrealistic conditions such as monochromatic radiation reflected by a Lambertian surface. Four different 1D radiative transfer models are compared in actual usage conditions corresponding to the simulation of satellite observations. Observations acquired by six different space-borne radiometers over the pseudo-invariant calibration site Libya-4 are used to define these conditions. The differences between the models typically vary between 0.5 and 3.5% depending on the spectral region and the shape of the sensor spectral response.

This paper addresses future developments and associated challenges of radiative transfer models that might be beneficial to support calibration and validation activities of hyperspectral imaging products. Comments and questions should be directed to the OSA Conference Papers staff (tel: +1 202.416.6191, e-mail: cstech@osa.org).

European UVN satellite missions deliver global measurements for air quality and climate applications from Low Earth Orbit (LEO) satellites since over two decades. Currently we have in the morning data from GOME-2 on the three MetOp satellites and in the early afternoon data from OMI/Aura and TROPOMI/Sentinel-5 Precursor. The temporal barrier imposed by LEO satellites, providing only one daily observation, can be broken using Geostationary Equatorial Orbit (GEO) satellites. The Sentinel-4 (S4) mission on-board the MTG-S GEO satellite will focus on monitoring of trace gas column densities and aerosols over Europe with an hourly revisit time, thereby covering the diurnal variation of atmospheric constituents. We present the algorithm, verification, and processor work being performed as part of the ESA Sentinel-4 Level 2 (S4-L2) project responsible for developing the operational S4-L2 products: O3 total and tropospheric column, NO2 total and tropospheric column, SO2, HCHO, CHOCHO columns, aerosol and cloud properties as well as surface reflectance.

Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in the optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and it presents a general framework to evaluate them based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those for which it is expected that the techniques will perform well and those for which some complexity about the site may provide a more severe test. Overall, all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small or too large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as optimal estimation, the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the retrieval forward model (e.g. potentially mixed land–water surfaces or aerosol optical properties outside the family of assumptions), some algorithms fail to provide a retrieval, while others do but with a quantitatively unreliable uncertainty estimate. The discussion suggests paths forward for the refinement of these techniques.

The CISAR (Combined Inversion of Surface and AeRosols) algorithm is exploited in the framework of the ESA-SEOM CIRCAS (ConsIstent Retrieval of Cloud Aerosol Surface) project, aiming at providing a set of atmospheric (cloud and aerosol) and surface reflectance products derived from S3A/SLSTR observations using the same radiative transfer physics and assumptions. CISAR is an advance algorithm developed by Rayference originally designed for the retrieval of aerosol single scattering properties and surface reflectance from both geostationary and polar orbiting satellite observations. It is based on the inversion of a fast radiative transfer model (FASTRE). The retrieval mechanism allows a continuous variation of the aerosol and cloud single scattering properties in the solution space. Traditionally, different approaches are exploited to retrieve the different Earth system components, which could lead to inconsistent data sets. The simultaneous retrieval of different atmospheric and surface variables over any type of surface (including bright surfaces and water bodies) with the same forward model and inversion scheme ensures the consistency among the retrieved Earth system components. Additionally, pixels located in the transition zone between pure clouds and pure aerosols are often discarded from both cloud and aerosol algorithms. This “twilight zone” can cover up to 30% of the globe. A consistent retrieval of both cloud and aerosol single scattering properties with the same algorithm could help filling this gap. The CIRCAS project ultimately aims at overcoming the need of an external cloud mask, letting the CISAR algorithm discriminate between aerosol and cloud properties. This would also help reducing the overestimation of aerosol optical thickness in cloud contaminated pixels. The surface reflectance product is delivered both for cloud-free and cloudy observations. Results from the processing of S3A/SLSTR observations will be shown and evaluated against independent datasets.

Copernicus is the European Union's Earth Observation and Monitoring programme, delivering free access to operational and historical environmental data to support applications in a wide range of societal benefit areas. To allow meaningful long-term environmental monitoring and robust decision-making, it is essential to ensure that satellite-retrieved products are of high quality and consistency. This paper describes the outputs of an international workshop on the radiometric calibration validation of the Copernicus Sentinel-2A and Sentinel-2B Multi-Spectral Instrument. A wide range of vicarious methodologies have been applied independently and then compared per type of target. All methods agree on the good radiometric performance of both Sentinel-2A and Sentinel-2B with respect to the mission requirements as well as on evidence of a slight bias between the two instruments. Comparisons of all these results are discussed to highlight the benefits and advantages of the methods as well as to propose potential improvements either for the methods themselves and/or for the comparison exercise.

Meteosat First-Generation satellites have acquired more than 30 years of observations that could potentially be used for the generation of a Climate Data Record. The availability of harmonized and accurate a Fundamental Climate Data Record is a prerequisite to such generation. Meteosat Visible and Infrared Imager radiometers suffer from inaccurate pre-launch spectral function characterization and spectral ageing constitutes a serious limitation to achieve such prerequisite. A new method was developed for the retrieval of the pre-launch instrument spectral function and its ageing. This recovery method relies on accurately simulated top-of-atmosphere spectral radiances matching observed digital count values. This paper describes how these spectral radiances are simulated over pseudo-invariant targets such as open ocean, deep convective clouds and bright desert surface. The radiative properties of these targets are described with a limited number of parameters of known uncertainty. Typically, a single top-of-atmosphere radiance spectrum can be simulated with an estimated uncertainty of about 5%. The independent evaluation of the simulated radiance accuracy is also addressed in this paper. It includes two aspects: the comparison with narrow-band well-calibrated radiometers and a spectral consistency analysis using SEVIRI/HRVIS band on board Meteosat Second Generation which was accurately characterized pre-launch. On average, the accuracy of these simulated spectral radiances is estimated to be about ±2%.

This document forms the deliverable D5.8 to report on the climate data record (CDR) of aerosol optical thickness (AOT) as retrieved from the MVIRI fundamental climate data record (FCDR) [RD 1, RD 2, RD 3] using the Combined Inversion of Surface and AeRosol (CISAR) Algorithm [RD 4]. The primary objective of this data record is to assess and demonstrate how the recalibrated and uncertainty-quantified MVIRI FCDR can support improved retrieval of geophysical parameters. Of particular interest is the impact of in-flight reconstructed and spectrally degrading spectral response functions.

PROBA-V mission was conceived, and launched by ESA in 2013, to ensure continuity to the SPOT - VGT longterm data series of global daily land observations, started in 1998, bridging the gap to Sentinel-3. Nowadays, thanks to the successful and smooth operations of the PROBA- V satellite, a >20 years consistent data record of land surface reflectances, is available to the scientific community. This long-term dataset has clear relevance for trend analysis, anomaly detection and climate related applications. Despite this obvious interest, the exploitation of this dataset is still limited and mostly focused to near-real time applications, using the SPOT - VGT archive as a background average to detect anomalies, e.g., in NDVI time series. In order to fill this gap, and to exploit the full potential of this dataset, ESA has funded a new project called CISAR@MEP, whose primary goal is to set the stage for the generation of a global 20 years long-term data record of surface reflectance and aerosol properties from analysis of SPOT-VGT and PROBA-V observations. For this purpose, a recently developed algorithm, called CISAR and developed by Rayference, will be used, allowing the joint retrieval of aerosol and surface reflectance properties, together with the associated retrieval uncertainties. This new long-term data record will eventually fulfill current Global Climate Observing System (GCOS) requirements for the generation of Essential Climate Variables (ECVs), such as surface albedo and aerosol optical thickness, being 5% and 10%, respectively. Such data set can support the needs of both the atmospheric and land surface research communities and it can contribute to Copernicus operational service and ESA Climate Change Initiatives (CCI) activities. The CISAR@MEP project started during Q1 2019 with expected 2 years duration. The aim of this paper is to present this ESA funded project, to illustrate the objectives and the methodology and to provide some preliminary results. Furthermore, the technological implementation approach will be detailed, which makes full use of the PROBA-V Mission Exploitation Platform (MEP), a scalable processing environment, providing direct access to both SPOT-VGT and PROBA-V full mission archive together with the required resources and tools.

This report addresses the assessment of the Fundamental Climate Data Records (FCDRs) using the methodologies developed under WP2 for mutual and relative consistency and stability (trend artefacts and step changes) both within and across sensor-series. Where possible the FCDR uncertainty information will be validated. Each sensor team has found it necessary to assess stability in different ways, because of the different context of different sensor series.

This paper presents the simultaneous retrieval of aerosol optical thickness and surface properties from the CISAR algorithm applied both to geostationary and polar-orbiting satellite observations. The theoretical concepts of the CISAR algorithm have been described in Govaerts and Luffarelli (2018). CISAR has been applied to SEVIRI and PROBA-V observations acquired over 20 AERONET stations during the year 2015. The CISAR retrieval from the two sets of observations is evaluated against independent data sets such as the MODIS land product and AERONET data. The performance differences resulting from the two types of orbit are discussed, and the information content of SEVIRI and PROBA-V observations is analysed and compared.