The PRIMARY (PRIsma for Monitoring AiR quality) project objective is to address air quality monitoring, especially in urban areas, exploiting the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission. Utilizing PRISMA's hyperspectral data, the project aims to gain insights into atmospheric aerosol content and composition, crucial for understanding environmental and health impacts, especially in urban areas. Overcoming spatial resolution limitations and the inverse problem's complexity in satellite-based characterization, PRISMA's decametric spatial resolution and artificial intelligence play crucial roles. A synthetic PRISMA-like dataset, relying on data provided by the Copernicus Atmosphere Monitoring service (CAMS), was generated for training neural networks for estimating aerosol characteristic exploiting PRISMA data. Preliminary results are encouraging. Properly field campaigns were performed in Rome (autumn 2022) and Milan (winter to summer 2023) to support the validation of the PRIMARY project's outcomes. In addition, drone-based campaigns are currently ongoing.

This paper introduces a hybrid approach to dimension reduction of PRISMA hyperspectral data, employing both linear and non-linear techniques: Principal Component Analysis (PCA) and autoencoders. The study aims to validate the efficacy of autoencoders by comparing results with the well-established PCA method. Our primary objective is to harness the complementary strengths of both methods in a hybrid framework, wherein certain bands may exhibit superior performance with autoencoders, while others fare better with PCA in dimension reduction. This strategic amalgamation not only accelerates data transfers and lowers computational costs for real-time applications but also leverages the specific advantages offered by each technique. The paper underscores the potential of this hybrid approach for optimizing hyper-spectral data for enhanced feature extraction in various neural network applications.

This work addresses the Bidirectional Reflectance Distribution Function (BRDF) characterization by means of MAIA multispectral camera onboard an Unmanned Aerial System (UAS). The proposed procedure relies on the design and execution of UAS flight plan for multi-angular acquisitions, which can be automatically repeated over different land cover types. Then, the inversion of the RossThick-LiSparse (Ross-Li) BRDF model is pursued in order to retrieve the fundamental parameters, allowing the complete characterization of the considered surface in terms of reflectance distribution for each band. A key point of this work is the challenge we face related to the development of an optimum strategy for the collection of ground-based dataset for BRDF model inversion.

Air pollution and its effects on human health pose a significant challenge in modern society. The PRIMARY (PRIsma for Monitoring AiR quality) research project aims to utilize the capabilities of the Italian Space Agency's (ASI) PRISMA (PRecursor HyperSpectral Application Mission) to enhance air quality monitoring, particularly in urban areas. In particular, the project focuses on the exploitation of the hyperspectral PRISMA data to obtain detailed qualitative and quantitative data on atmospheric aerosol load and composition in urban environments. Current satellite-based characterization of particulate matter is limited due to spatial resolution constraints and to the complexities of the underlying inverse problem involving multiple variables. The PRIMARY project addresses the first issue through the decametric spatial resolution of PRISMA images, while the second issue is tackled by leveraging artificial intelligence approaches.

In this paper we describe the experimental set-up of a study aiming at testing the capability of UAV (Unmanned Aerial Vehicle) multispectral imagery for the calibration of the electromagnetic quantities measured by the medium resolution satellite Sentinel-2, launched by European Space Agency. This is made feasible by mounting on the UAV a camera characterized by acquisition bands which are designed in order to mimic those on the satellite. Preliminary analysis over a vegetated area shows encouraging results because the spectral signatures of the two instruments appear quite consistent. Theoretical modelling of BRDF (bidirectional reflectance distribution function) is also considered in order to guide the acquisition plan of the UAV measurements

Spaceborne measurements may significantly support monitoring the concentration of atmospheric constituents affecting air quality, such as ozone. However, retrieving tropospheric ozone concentration information from nadir satellite data is an arduous task, given the weak sensitivity of the earth's radiance to ozone variations in the lower part of the atmosphere. We propose a new methodology, based on neural networks (NN), for retrieving the tropospheric ozone column from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) nadir UV/VIS measurements. The design of the NN algorithm is based on an analysis of the information content of measurements in both UV and VIS bands, carried out by a combined radiative transfer model and NN extended pruning procedure. The NN was trained and tested with simulated data and with matching World Ozone and Ultraviolet radiation Data Centre ozonesonde data sets and validated by independent data taken over two test sites. A significant improvement of the retrieval capabilities is observed when VIS wavelengths are included into the input vector. Finally, an example of tropospheric ozone map generated automatically by the methodology at a continental scale is provided and critically discussed.

In this paper we report on the design of a Neural Networks algorithm to retrieve tropospheric ozone information from satellite data. Following a combined radiative transfer model-extended pruning sensitivity analysis for input wavelengths selection, we first made an inversion exercise based on a syn thetically produced radiance-tropospheric ozone concentrations database. Starting from the encouraging obtained results, we tested the Net on ESA-ENVISAT SCIAMACHY Level lb data. A time series of Tropospheric Ozone Columns on some midlatitude sites has been retrieved from the satellite measurements and then compared with collocated and simultaneous ozonesondes reference columns. The inversion results are presented and critically discussed.

The thermal structure of trace gases, their distribution in the atmosphere, and their circulation mechanisms result from a complex interplay between radiative, physical, and dynamical processes. Neural-network algorithms can be a useful tool to face such complexities in retrieval operations. In this paper, their potentialities have been exploited to design real-time procedures for the estimation of vertical profiles of ozone concentration from spectral radiances measured by GOME, the first instrument of the European Space Agency capable of monitoring global distribution of ozone and other trace gases.