Agricultural drought is one of the most critical effects of climate change. This work proposes a machine learning based approach for agricultural drought monitoring that integrates seven standard remote sensing indices computed from Sentinel-2 multispectral imagery, agricultural drought damage percentage assessed in situ and six meteo-climatic variables, including Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index. We applied the approach to 117 agricultural fields in Italy, using a multinomial logistic regression model to classify the fields into zero-risk, medium-risk, and high-risk drought damage classes. The overall performances of the proposed classification model, summarized by an F1 score equal to 0.61, are not particularly encouraging as the model struggles to distinguish between medium-risk damage and high-risk damage classes. Nonetheless, the model shows promising results in identifying fields with zero drought damage and could be applied to reduce the time and cost of in situ measurements by excluding fields with no damage from the ground data collection.

This study focuses on the capability of extracting the waterline in different cases using quad-polarimetric (QP) X-band Synthetic Aperture Radar (SAR) measurements. A baseline unsupervised waterline extraction scheme is applied which is feed with single-, dual- and quad-polarimetric features to assess the role of polarimetric information. Two different scenarios are accounted for: i) a sandy and gently coastal profile; ii) an inland steep water bodies. The study areas are identified in the Ionian coast of Basilicata region, south of Italy, where QP SAR images collected by the Cosmo-SkyMed Second Generation (CSG) Italian satellite constellation are collected over the Metaponto Plain and the San Giuliano dammed artifical lake, respectively. Preliminary results obtained from the inland water body suggest that full-polarimetric CSG SAR data have potential to be used to perform unsupervised waterline extraction, even though the specific water conditions and coastal morphology must be carefully accounted for.

The GEORES project is funded by the Italian Space Agency (ASI) and aims to develop a geospatial application meant to improve environmental sustainability and resilience to climate changes in urban areas, based on the synergistic use of the most advanced Earth Observation (EO) technologies, Artificial Intelligence (AI) and eXplainable AI (XAI). GEORES is organized into four main modules to support management of the main risks associated with land degradation: (1) Sediment Connectivity; (2) Land Displacement; (3) Urban Floods; (4) Urban Wildfires. For each module, EO data, calculation models and algorithms are integrated to identify "hot-spots" of urban and peri-urban territory at high risk from the point of view of land degradation caused by phenomena of hydrogeological instability, sediment flow or vegetation fires. The extracted information is expressed with specific indicators ("geo-analytics") calculated dynamically and automatically. The demonstration is undertaken in the Metropolitan City of Bari and Gargano Promontory, Apulia Region, southern Italy, and foresees the engagement of final users (i.e. Regional Civil Protection and Municipality of Bari).

In this study a novel architecture of Pulse Coupled Neural Network based on a multilevel topology with interconnected layers is presented. The model is applied to the solution of a segmentation problem of SAR images for the identification of man-made structures on urban landscapes. Thanks to the multilayer architecture, the unsupervised model can deal with dual polarization SAR data, as well as with combination of ascending and descending acquisitions. Such approach mitigates the issues in detecting artificial targets when their orientation with respect the satellite line of sight reduces the object backscattering with respect to the one of background. An example of application to two COSMO-SkyMed STRIPMAP data, acquired by ascending and descending orbits respectively is provided.The proposed approach, owing to their ability of efficiently processing voluminous datasets, can be effectively coupled with machine learning and deep learning to refine or validate their results.

A big flood affected the Emilia-Romagna region (northern Italy) starting from 16 May 2023. During this event, more than 20 rivers burst their banks and about 15 people were killed. A persistent satellite-based mapping was requested by the Italian Department of Civil Protection (DCP) to monitor the time evolution of the Emilia Romagna flood. For this purpose, the X-band SAR data provided by the COSMO-SkyMed constellations (first and second generation) were exploited. This paper proposes a new procedure to map floods using SAR data acquired with different viewing geometries in order to fully exploit the on-demand capability of satellites like COSMO-SkyMed. The procedure includes the tasking of the satellites, the collection of a set of pre-flood images, the selection for each flood image of the most suitable pre-flood ones, and the generation of the flood maps using an automatic algorithm based on change detection. These activities were performed during the May 2023 Emilia-Romagna flood and allowed us to daily provide DCP with flood maps useful to support decision making and assess the damages.

Bayesian estimation of posterior probabilities for the presence of floodwaters, coupled with accurate time series regression methods, show good performance in the monitoring of inundations at high temporal and spatial resolution from long stacks of synthetic aperture radar (SAR) data. We report results on the integration of SAR intensity and cascaded InSAR coherence time series in different polarization channels within a Bayesian framework. The method is being tested over sites in both northern and southern Italy, with X- and C-band SAR data. The results indicate some advantage in using more than one independent channel in the Bayesian inference for some types of land cover, in terms of area under the curve (AUC) when compared to independent flood maps acquired over known events. Stacks of surface water confidence levels computed over both test sites show promising characteristics, both on agricultural and coastal areas.

This paper provides an in-depth exploration of the Crop Module within the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project, specifically addressing challenges in precision agriculture. The study unfolds in the "Fortore" irrigation district (Southern Italy), focusing in particular on the 6/B district. The Crop Module, rooted in AquaCrop crop model architecture, emerges as a pivotal component in simulating and predicting crop growth, development, and water dynamics. It operates across leaf development, crop growth and productivity, and water balance levels, ensuring adaptability to daily temperature variations for real-time simulations. In interaction with the Soil Water Balance Module (SWB) and leveraging insights from satellite imagery, the Crop Module undergoes meticulous calibration and validation. The expected outcomes encompass increased precision in irrigation scheduling, early anticipation of water demand, and improved seasonal forecasting. This comprehensive approach positions stakeholders for informed decision-making, fostering sustainability and efficiency in agricultural practices.

This paper reports on a Spatial Decision Support System (SDSS) for the early, medium, and short-term forecast of irrigation needs in a semi-arid Mediterranean environment. The SDSS is developed in the context of the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project supported by the Italian Space Agency (ASI). THETIS integrates hydrologic and crop growth models with advanced Earth Observation (EO) products, Artificial Intelligence (AI) and a WEBGIS interface to provide basin-scale information for efficient planning of irrigation resources. The study describes initial results concerning the irrigated area of the Apulian Tavoliere (AT) served by the Reclamation Consortium of the Capitanata, Foggia, Italy.

We present in this work the results of an extensive, full resolution Parallel SBAS (P-SBAS) processing activity carried out on large multi-temporal SAR data archives of COSMO-SkyMed first (CSK) and second (CSG) generation SLC images, relevant to some of the main cities of the Italian territory, acquired from ascending and descending orbits since 2009. In particular, we first summarize the key points of the exploited, full resolution P-SBAS processing chain. Subsequently, we also describe the main algorithmic developments aimed to exploit large temporal sequences of CSK and CSG SLC data relevant to wide areas. Finally, we present several results highlighting, through some selected examples, that the use of high resolution SAR images, such as those of the CSK-CSG constellation, processed with techniques like the full resolution P-SBAS technique approach, may provide valued-added information that, properly integrated with in-situ measurements, can be very relevant for the assessment of the built-up environment health conditions, at the national scale.

This study is to analyze the multi-polarization backscattering from intertidal areas using polarimetric synthetic aperture radar imagery collected by the X-band Cosmo-SkyMed Second Generation satellite mission. In this study, we focus on the Solway Firth (Scotland) area that includes different habitats as wetland, salt marshes, sand dunes, and mudflats. In particular, mudflats presents a large variability of normalized radar cross section that may limit significantly the classification performance of intensity-based approaches. Within this context, for the first time, we exploit full-polarimetric Cosmo-SkyMed Second Generation imagery collected in StripMap mode.

It is more than a decade that the Italian Space Agency (ASI) and the Japan Aerospace Exploration Agency (JAXA) are cooperating on the topic of disaster management, by sharing their expertise and satellite assets for the operational use of X-band and L-band SAR data. Over the years, mutual COSMO-SkyMed and ALOS-2 archives were created over Italy and Japan, ad hoc acquisitions were made in response to emergency requests, and data were exchanged to support joint SAR research activities related to Disaster Risk Management. The present paper provides the current status of the cooperation in light of the most recent emergencies, as well as the near future perspectives.

The "Multi-mission and multi-frequency SAR" program (2021-2023) of the Italian Space Agency supported the national community to develop algorithms combining SAR data collected in C, X and L-bands. Main achievements in terms of SAR data exploitation, integration and data fusion, and new products prototyping are presented with regard to agriculture, natural hazards, urban areas, cryosphere, sea and coast. Perspectives are finally outlined with regard to future SAR missions and towards downstream applications.

In this paper the role of the COSMO-SkyMed constellation data in the framework of the Italian Space Agency (ASI)’s "Multimission and Multifrequency SAR" program has been analyzed. The key aspects regarding the provenance of the Principal Investigators, the application fields and information about the exploitation of the COSMO-SkyMed data, for both the first- and second-generation satellites, are highlighted in the study.

The recent launches of Earth Observation (EO) satellites have made numerous SAR images available to dynamically monitor the ocean with improved spatial resolution at shorter revisit time. Focusing on automatic target detection of ships with the specific aim of improving our Maritime Domain Awareness (MDA), this work assesses the capabilities of multi-frequency/multi-mission spaceborne Synthetic Aperture Radar (SAR) data. Specifically, Sentinel-1 (C-band), COSMO-SkyMed (X-band), and SAOCOM (L-band) missions have been considered in this analysis. The aim of the paper is to present an efficient approach for interpreting images acquired within small time gaps, ensuring fisheries and pollution control, anti-piracy actions, and surveillance over coastal/protected regions.

In continuity with the investments made on SAR technologies in the last two decades by the Italian Space Agency (ASI), the “Multi-mission and multi-frequency SAR” program currently supports ten R&D projects aiming to design, develop and test innovative algorithms for exploitation of multi-mission/multi-frequency SAR data. Perspectives of engineering and pre-operational development are demonstrated with regard to various application domains (i.e. agriculture, urban areas, natural hazards, cryosphere, sea and coast). Access to COSMO-SkyMed and SAOCOM images is facilitated by ASI to allow the research consortia to achieve an effective multi-frequency SAR data integration. The program represents a foundational step in the ASI's roadmap towards “scientific downstream applications”, i.e. applications enabled by the exploitation of mature and validated algorithms that have been originally developed to answer scientific questions and/or retrieve geophysical parameters, and have been brought to the stage that they can generate products that address specific user needs beyond scientific and academic purposes only.

The use of COSMO-SkyMed constellation has been tested for post-fire monitoring activities. A typical Mediterranean ecosystem, seriously damaged by a wildfire, has been selected as study area. The multitemporal and multipolarization capabilities of COSMO-SkyMed have been exploited for automatic burnt area detection and monitoring of vegetation regrowth. Different imaging configurations have been tested to define the proper monitoring strategy in Mediterranean areas. Results showed X-band suitability for Mediterranean maquis post-fire monitoring.