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The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is in preparation to carry a unique visible to shortwave infrared spectrometer. CHIME will globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. The mission shall provide Level 1B, 1C and 2A products, as well a set of downstream products related to the different environmental applications, such as the quantification of vegetation traits. In this context, this work presents the first hybrid retrieval models for the operational delivery of vegetation properties products. Within ESA’s CHIME end-to-end (E2E) simulator study, 13 leaf and canopy trait models were developed as part of the L2B vegetation (L2BV) module. The E2E framework functions as a simulated reality that enabled to test and improve the algorithms. The models were further tuned and validated against campaign data using active learning methods. As a proof of concept, the prototype retrieval models were applied to both hyperspectral airborne (HyPlant) and spaceborne (PRISMA) imagery that were first resampled to CHIME band settings. Among the provided vegetation products, it led to a first space-based canopy nitrogen content map over a heterogeneous landscape. The obtained CHIME-like L2BV traits maps demonstrate the feasibility to routinely deliver a collection of next-generation vegetation products across the globe.

Hyperspectral remote sensing can support exploration activities helping in the recognition of lithologycal outcrops over extended areas. This is mainly due to the nature of the mineral reflectance spectra which feature very narrow absorption peaks, therefore requiring hyperspectral sensors to be assessed. In this work, the capabilities for geolithology recognition of aerial hyperspectral data have been tested to map outcrops in an Alpine carbonates test basin, located in the North-Eastern part of Italy. Two hyperspectral datasets, from MIVIS and SASI sensors, have been classified to retrieve the geolithological map of the test basin. The classification procedure was based on a decision tree approach, in order to combine different spectral and morphological features, in a flexible, robust and scalable solution. Despite the geological complexity of the study area (Alpine environment, spread vegetation cover, strong presence of shadow areas and presence of transitional geologic formations) the results, as compared to reference geological maps, prove satisfactory mapping capabilities, particularly for carbonatic rocks. In fact, the use of aerial hyperspectral data not only allows their accurate detection, but also the distinction between dolostones and limestones. The main classification issues are related to the outcrops' extent which, in a such lithology-fragmented area, avoids the detection of many small outcrops.

This paper describes the use of airborne hyperspectral remote sensing for mapping asbestos roofs in an orographic complex area in Northern Italy, the Aosta Valley. Using training samples collected during field surveys, thematic classification was able to detect the majority of asbestos surfaces. Considering the total amount of asbestos areas validation showed a correct detection of about 80%, while considering the number of asbestos roofs correctly detected this value decreased to 43%. This difference pointed out a clear relationship between data spatial resolution and asbestos roofs area. The study served as a first approach to an extensive use of the remote sensing technology for asbestos mapping over large areas and the encouraging results will support Public Administrations for decision making strategies and policies for their removal.

An optimal interpolation technique is formalized and applied to the output of an air quality deterministic model in order to improve the description of the evolution of pollutant (namely particulate matter, PM10) in atmosphere. The paper presents an application of the methodology to the Northern Italy region, often affected by high concentration of PM10. The validation of the methodology performed for winter 2004 shows that the re-analysis highly improves the description of the phenomena both in terms of mean error and correlation coefficient.