Degradation and desertification represent serious threats, as they present severe environmental and socio-economic consequences, demanding immediate action. Although a recognized methodology for assessing degradation and desertification is missing, remote sensing has been recognized as a powerful support for its accessibility and efficacy. The aim of this study is to examine the application of remote sensing for assessing land and soil degradation and desertification. A total of 278 research papers retrieved from Scopus/Web of Science database and published over the past decade have been analyzed. From the analysis of scientific publications, a rising interest for these topics and a dominance of research from China has been registered. Established satellite data, Landsat, and MODIS, despite limitations in accuracy and resolution, remain popular due to easy access. This restricts research to broader scales and limits practical applications like land management. The prevalent use of vegetation indexes, while convenient, can be misleading due to their indirect connection to soil health. Consequently, vegetation-based models may not fully capture the complexities involved. To improve understanding, the study suggests a shift towards multi-indexes models and a move away from relying solely on readily available data products. Moreover, the application of data fusion methods could provide a more holistic view.

Land degradation is a complex and context-specific phenomenon with significant implications for rural areas, where agricultural and livestock activities intersect with natural ecosystem processes. Despite growing efforts to monitor land degradation, the absence of standardized methodologies limits the comparability of results and the implementation of coherent mitigation strategies. This study introduces RURALIS, a multi-model framework, based on remote sensing, specifically designed to assess land degradation in the rural areas of Italy. Drawing on the structure and outputs of three existing models, RURALIS adopts a model-learning approach. A Random Forest classifier is then employed to compare outputs from all models and identify areas of severe degradation across all models. The analysis reveals that approximately 2.34 million hectares (13.6%) of Italy’s rural lands are severely degraded, with hotspots in northern Puglia, Sicilia, and parts of northern Italy. The model demonstrates strong classification performance and provides a flexible, high-resolution tool that leverages the shared foundation of remote sensing to deliver spatially detailed, decision-ready outputs for rural land management.

Digital twin (DT) technology has expanded far beyond its industrial origins, increasingly finding application across environmental and territorial domains. This review provides a structured mapping of DT deployments at environmental and territorial scales over the period 2020–2025, examining 117 peer-reviewed publications (109 applied studies and 8 review articles) through a structured 16-parameter classification framework. The review traces three major conceptual shifts in the DT paradigm: from industrial assets to living entities, from discrete systems to Earth-scale representations, and from closed deterministic models to ecological and systemic frameworks, as reflected in the emergence of ecological digital twins (EcoDTs), environmental digital twins (EDTs), and territorial digital twin (TDT) definitions. The results reveal a clear growth trajectory in DT applications across themes, with urban systems as the most consolidated application domain, and progressive diversification into marine, coastal, forestry, river/lake, and Earth system applications from 2022 onward. Institutional actors dominate production in this space, aligned with European flagship initiatives such as Destination Earth (DestinE) and the European Digital Twin of the Ocean (EDITO). The findings position and expand the notion of territorial digital twins as an evolving paradigm, underscoring both the momentum generated by EU digital and environmental policy and the need for integrated tools to answer and respond to key environmental challenges.

Coastal zones and their connected freshwater and marine systems are among the environments most acutely exposed to accelerating climate hazards. European policy has responded by placing Digital Twin (DT) infrastructures at the centre of its climate adaptation and ocean governance agenda: Destination Earth for the Earth system, and the European Digital Twin of the Ocean (EDITO) for the marine and coastal domain. This scoping review defines and evaluates Hydro Digital Twins (HDTs), encompassing Digital Twins of the Ocean (DTOs), coastal-zone DTs, and freshwater-system DTs, as instruments for coastal management, water territory governance, and climate adaptation decision support. Conducted in accordance with PRISMA-ScR, the review screened 231 records (January 2020 to September 2025) and retained 21 studies, each coded against a thirteen-parameter framework organised on a governance axis and a data-centric axis. Governance objectives consistently outweigh technical motivations: risk reduction dominates (7/21 studies, 33%), followed by environmental monitoring (5/21 studies, 24%) and coastal management (4/21 studies, 19%). Remote sensing is used in 19/21 studies (90%) as the near-universal data backbone; artificial intelligence, coded as an analytical method rather than a data source, appears in 10/21 studies (48%) and concentrates in the River/Lakes domain (6/7 studies, 86%), which also holds three of the four predictive outputs; citizen science data, though a stated European policy priority, appears operationally in only 2/21 studies (10%). The modal maturity category is TRL 4–5 (6/21 studies, 29%), only one study reaches TRL 6, and 6/21 studies (29%) report no formal validation. The review clarifies the architectural distinction between HDTs and Web GIS and situates these findings within the European coastal governance agendas of Destination Earth and EDITO. All proportions above are properties of the reviewed corpus and are not extrapolated to the HDT field as a whole.

In response to the pressing call for climate action, the International Panel on Climate Change (IPCC) has highlighted the key contribution that carbon dioxide removal will play to counterbalance hard-to-abate residual emissions, alongside efforts to reduce and avoid them.Coastal blue carbon habitats are responsible for supporting up to 71% of carbon storage in oceanic sediments for centuries, outsizing terrestrial ecosystem carbon storage performances. In the Mediterranean Sea, Posidonia oceanica is not only the most widespread but also the most efficient carbon removal.Under the Paris Agreement, marine ecosystems have been formally recognized as carbon removal, supporting Parties’ commitments to mitigation. Blue carbon habitats such as Posidonia can be included in compliance carbon crediting systems developed under Article 6 of the Agreement, and they also hold promise within the Voluntary Carbon Market (VCM), where coastal ecosystem credits may be sold as offsets.Despite this potential, the integration of Posidonia oceanica into carbon markets faces several challenges. For the compliance market under the Paris Agreement, definitions and frameworks are still evolving, while VCM integration is hindered by limited historical data and by the need for affordable, and robust technologies and methodologies to establish a baseline. The definition of a baseline is crucial to set the monitoring period, to measure the activity sustainability and even more to ensure the correct accounting of carbon removal units, by avoiding double counting.This paper presents how Earth Observation (EO) can support blue carbon removal monitoring, reporting and verification (MRV) through the creation of a rating system. Developed in the framework of the ESA-funded Feasibility Study, “SCORE – Scale for Carbon Organic Resilient Ethereal”, this rating system not only is expected to help Marine Protected Areas (MPA) boost coastal conservation and protection but also provides insights into new business models supported by VCM.