Nature-based solutions are increasingly relevant tools for spatial and environmental planning, climate change adaptation (CCA), and disaster risk reduction (DRR). For this reason, a wide range of institutions, governments, and financial bodies are currently promoting the use of green infrastructure (GI) as an alternative or a complement to traditional grey infrastructure. A considerable amount of research already certifies the benefits and multi-functionality of GI: natural water retention measures (NWRMs), as GIs related specifically to the water sector are also known, are, for instance, a key instrument for the prevention and mitigation of extreme phenomena, such as floods and droughts. However, there are persisting difficulties in locating and identifying GI and one of the most promising solutions to this issue, the use of satellite-based data products, is hampered by a lack of well-grounded knowledge, experiences, and tools. To bridge this gap, we performed a review of the Copernicus Global Land Service (CGLS) products, which consist of freely-available bio-geophysical indices covering the globe at mid-to-low spatial resolutions. Specifically, we focused on vegetation and energy indices, examining previous research works that made use of them and evaluating their current quality, aiming to define their potential for studying GI and especially NWRMs related to agriculture, forest, and hydro-morphology. NWRM benefits are also considered in the analysis, namely: (i) NWRM biophysical impacts (BPs), (ii) ecosystem services delivered by NWRMs (ESs), and (iii) policy objectives (POs) expressed by European Directives that NWRMs can help to achieve. The results of this study are meant to assist GI users in employing CGLS products and ease their decision-making process. Based on previous research experiences and the quality of the currently available versions, this analysis provides useful tools to identify which indices can be used to study several types of NWRMs, assess their benefits, and prioritize the most suitable ones.

No abstract

The XGIS (X and Gamma Imaging Spectrometer) is one of the three instruments onboard the THESEUS mission (ESA M5, currently in Phase-A). Thanks to its wide field of view and good imaging capabilities, it will efficiently detect and localize gamma-ray bursts and other transients in the 2-150 keV sky, and also provide spectroscopy up to 10 MeV. Its current design has been optimized by means of scientific simulations based on a Monte Carlo model of the instrument coupled to a state-of-the-art description of the populations of long and short GRBs extending to high redshifts. We describe the optimization process that led to the current design of the XGIS, based on two identical units with partially overlapping fields of view, and discuss the expected performance of the instrument.

No abstract

<p>Geochemical investigations of agricultural soils are fundamental to characterize pedosphere dynamics that sustain ecosystem services linked with agriculture. Parameters like soil moisture, soil organic matter (SOM), and soil organic carbon (SOC) are strong instruments to evaluate carbon sink potential.</p><p>Satellite Earth Observation is a significant source of free data that can be linked to soil characteristics and dynamics and employed to produce temporal series. Access to these data is nowadays facilitated by platforms such as ADAM (https://adamplatform.eu), which allow users to quickly search for, visualize and subset data products, greatly reducing the volume of data that end users must handle.</p><p>In this work we demonstrate the usefulness of such systems by carrying out a geochemical investigation of 100 superficial (0-15 cm) soil samples collected in the province of Ferrara (North-Eastern Italy) and using the ADAM platform to associate to each a time series of Sentinel 2 data. The samples were collected in October 2021 in fields that were ploughed or mono-cultivated at maize, soybean, rice, and winter vegetables. To obtain the average soil properties over a spatial scale larger than the satellite sensor resolution, we adopted a composite sampling strategy, merging 5 sub-samples collected at the vertexes and at the center of a 30x30 m<sup>2</sup> area. Soil granulometry was recognized from clay to medium sand, with exception of peat deposits. Soil moisture, and SOM, contents were estimated by loss on ignition (LOI), respectively at 105°C (values from 0.3 to 7.4 wt%), and 550°C (values from 2.1 to 21.0 wt%). SOC contents (values from 0.7 to 9.3 wt%) were determined through DIN19539 analysis performed with an Elementar soliTOC Cube. Using the ADAM platform, we associated a temporal series from 2016 to 2021 of the Sentinel 2 NDVI data product to each sampling location, using a cloud coverage mask to eliminate values taken on cloudy days. Localized phenological cycles for each year are recognizable in the remotely-sensed data. Hence, our database describes for each parcel, geochemical parameters and vegetative temporal series.</p><p>In a separate study, we also attempted to train a neural network to predict geochemical properties from the soil spectrum measured by the hyperspectral satellite PRISMA. We used the geochemical properties of our 100 samples as training data, associated with the PRISMA spectra of the sampling locations measured on April 7 2020, when, according to our NDVI data, none was covered in vegetation.</p>