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.

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.

In recent years, the focus on sustainability has grown by everyone, including policymakers, companies, and consumers. In this perspective, recycling plays an important role because it allows to reduce the amount of waste to be disposed of, at the same time reducing the need for raw materials. This paper presents ScanBage, a web application designed and developed to support users in separating waste collection. It exploits two machine learning algorithms to automatically classify garbage categories and it employs Gamification elements with the aim of increasing user involvement.

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.

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.

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.

This paper presents a new algorithm for the joint retrieval of surface reflectance and aerosol properties with continuous variations of the state variables in the solution space. This algorithm, named CISAR (Combined Inversion of Surface and AeRosol), relies on a simple atmospheric vertical structure composed of two layers and an underlying surface. Surface anisotropic reflectance effects are taken into account and radiatively coupled with atmospheric scattering. For this purpose, a fast radiative transfer model has been explicitly developed, which includes acceleration techniques to solve the radiative transfer equation and to calculate the Jacobians. The inversion is performed within an optimal estimation framework including prior information on the state variable magnitude and regularisation constraints on their spectral and temporal variability. In each processed wavelength, the algorithm retrieves the parameters of the surface reflectance model, the aerosol total column optical thickness and single-scattering properties. The CISAR algorithm functioning is illustrated with a series of simple experiments.

My issue is that I see no evidence that this algorithm currently produces acceptable results.Fig. 14 is not good.It's not bad enough to imply your technique is without merit,

I think Part II does a rather nice job in introducing

This paper outlines an algorithm to retrieve the optical properties of atmospheric aerosol and the surface from visible and infrared satellite imagery. The vast majority of equivalent algorithms (including previous iterations of this technique) assume the optical properties of the aerosol particles observed (known as the aerosol type). The paper proposes considering multiple types simultaneously, such that the retrieval can freely explore a continuous space in single scattering albedo and asymmetry factor. A theoretical demonstration of the algorithm is presented using idealised data.

Sentinel-4 will offer unprecedented possibilities to monitor the daily cycle of trace gases over Europe provided the surface reflectance field is well characterised. This paper discusses possible ways to define a surface LER when the AF BRF is known.