This study addresses the challenge of subpixel occurrence in identifying Lithium Cesium Tantalum (LCT) pegmatites, crucial sources of lithium for electric batteries. Previous spectral unmixing methods have been applied in brownfield sites with pre-existing large mines, facilitating pegmatite endmember acquisition. However, this work focuses on a method for greenfield exploration, presenting a knowledge transfer approach to transfer pre-extracted pegmatite endmembers to new areas using a spectral unmixing based method. Two study areas in Minas Gerais, Brazil, were chosen: Area 1 (A1) as a brownfield site for deriving a pegmatite endmember, and Area 2 (A2) simulates a greenfield area to test the efficacy of the pre-extracted endmember. The Mixture Tuned Matched Filtering (MTMF) classification method was employed, showcasing potential value in scenarios where no pegmatite occurrences are known.

Several methodologies can be employed in the prospection of Lithium (Li) in hard-rock (pegmatites). Spectrometry analysis, a Remote Sensing (RS) technique, can be applied to understand the surface spectral response of a sample, both to identify the rock-forming or alteration minerals in its composition and to validate the data collected in situ (by sensors onboard satellites/drones, for example). This paper aims to make available for public use the information acquired on the spectral composition of rock samples from the Barroso pegmatite field in Portugal, within the scope of the INOVMINERAL4.0 project. As a result, a spectral library was created with 47 spectra, collected from 11 different samples. All data is made available in a universal format, thus contributing to open science, corroborating the validation of local spectral data, and stimulating the creation of other databases, in other locations.

Remote Sensing has been successfully applied in the identification of pegmatitic targets. However, the spatial resolution of open data satellites, which is often much larger than the outcrop size of the target mineral or rock, has been a recurrent challenge in this scientific field. This restricts remote sensing methods that are dependent on large outcrop sizes for successful identification. This work applied spectral unmixing approach on WorldView-3 satellite imagery, to evaluate the potential for high spatial resolution imagery on the Tysfjord Niobium-Yttrium-Fluorine (NYF) pegmatite field (Norway). The preliminary results of this research are encouraging and make a strong contribution to the scientific field of mineral exploration.

PRISMA data is still underexploited in what concerns mineral exploration, while the high demand for battery components such as lithium (Li) instigates exploration of other low-grade deposits such as St. Austell (Cornwall, UK). This study assesses the potential of PRISMA data to target such Li deposits through the detection of topaz as a proxy to the mineralization using band math and partial unmixing techniques. The topaz distribution maps obtained are coherent between each other and with the known geology of the area, highlighting the PRISMA potential, although there could be some shortcomings related to its spatial resolution. Comparison with Sentinel-2 or Worldview-3 data shows the limitations of multispectral products, despite some potential to use Worldview-3 that need to be further investigated. In the future, new approaches to directly detect Li-micas and field validation of the results must be conducted.

Several algorithms were developed to map lithium (Li)-pegmatites in recent years. This preliminary study attempts to validate the same algorithms in the Trancoso region (Portugal), an area with unknown economic potential. The application of RGB combinations, Band Ratios (BR), and Selective Principal Component Analysis (PCA) to Sentinel-2 and Landsat-8 images allowed to identify four target areas for Li-exploration. Some targets correspond to an inert exploitation of the former mine's waste, confirming the potential of this methodology. Moreover, outcropping metric Li-dykes were missed with this approach, highlighting the need to refine the existing algorithms. Reflectance spectroscopy studies of Li-dykes and host rocks support these conclusions and will be crucial to improve the methodology. The collected spectra increased the knowledge on this greenfield area and its economic potential and provided information on new mineralization proxies to be used in future satellite detection studies.

We introduce the OCARIoT Data Acquisition App, a mHealth open source solution that synchronizes and manages data in Health IoT pilots. It was developed during the H2020 project entitled OCARIoT (Smart childhood Obesity CARing solution using IoT potential) and is innovative in the sense that it allows efficient IoT data collection, integration of different commercial devices, improves the user's privacy, data's security and the management of data controllers respecting legal and ethics policies. The application acts as a token manager for accessing external services. Besides the main features of the application, we report the integration of Fitbit services, and the deployment in four Health IoT pilots in Brazil, Greece and Spain. A survey was conducted with users in these three pilots and collected information about their user experience. The results presented excellent feedbacks from the users, with initial needs of synchronizing different children's accounts and privacy, which justified the development of the tool. Finally, we got feedback of improvements in ethics, technological and reuse in Health IoT pilots.