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.

This article presents an approach to identify Green Infrastructure (GI), its benefits and condition. This information enables environmental agencies to prioritise conservation, management and restoration strategies accordingly. The study focuses on riparian areas due to their potential to supply Ecosystem Services (ES), such as water quality, biodiversity, soil protection and flood or drought risk reduction. Natural Water Retention Measures (NWRM) related to agriculture and forestry are the type of GI considered specifically within these riparian areas. The approach is based on ES condition indicators, defined by the European Environment Agency (EEA) to support the policy targets of the 2020 Biodiversity Strategy. Indicators that can be assessed through remote sensing techniques are used, namely: capacity to provide ecosystem services, proximity to protected areas, greening response and water stress. Specifically, the approach uses and evaluates the potential of freely available products from the Copernicus Land Monitoring Service (CLMS) to monitor GI. Moreover, vegetation and water indices are calculated using data from the Sentinel-2 MSI Level-2A scenes and integrated in the analysis. The approach has been tested in the Italian Po river basin in 2018. Firstly, agriculture and forest NWRM were identified in the riparian areas of the river network. Secondly, the Riparian Zones products from the CLMS local component and the satellite-based indices were linked to the aforementioned ES condition indicators. This led to the development of a pixel-based model that evaluates the identified GI according to: (i) its disposition to provide riparian regulative ES and (ii) its condition in the analysed year. Finally, the model was used to prioritise GI for conservation or restoration initiatives, based on its potential to deliver ES and current condition.

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<p>Earth Observation (EO) environments have been increasing exponentially in the last decades. New generation of satellites are designed for monitoring climate related hazards, providing higher spatial and temporal resolution images. Hazards processes are triggered by anomalies in precipitation. The service will be able to provide information on the extent of the flood footprint. The test area is located south of the city of Milan, where the urban area of Pavia is located. There was an unexpected high runoff of the Ticino river that produced high water in the flood-plain surface, affecting the local population for three consecutive days and with a total damage estimate of 250,699 euro.</p><p>The identification of datasets counts on a broad availability of EO data processed, such as C-band Synthetic Aperture Radar (SAR) data from the Sentinel 1 satellite constellation together with X-band SAR data provided by the TerraSAR-X.  Methods include in-SAR coherence, by cross-multiplying the two SAR images or techniques like threshold with a final pixel size of Sentinel 1 of 8.9 m and 1.8 m of TerraSAR-X. Imagery from the 25<sup>th</sup> of November (Sentinel 1) with a VV (vertical transmit, vertical receive) polarization and from the 27<sup>th</sup> of November (TerraSAR-X) with a HH (for horizontal transmit and horizontal receive) polarization were selected. Different bands have different characteristics, for instance in penetration and spatial resolution.</p><p>Obtained products include urban footprint and flood detection maps. Results could provide an important decision support tool for a wide range of actors, including public authorities to support the preparedness, mitigation and response phases of the emergency management cycle. In addition, adaptation measurements, intervention and urban planning, as well as flood mitigation activities are additional benefits. Future analysis will include impact estimates and vulnerability analysis on the urban footprint area.</p><p> </p>