InGARSS 2021
IEEE India Geoscience and Remote Sensing Symposium 2021
Paolo Gamba is Professor at the University of Pavia, Italy, where he leads the Telecommunications and Remote Sensing Laboratory. He served as Editor-in-Chief of the IEEE Geoscience and Remote Sensing Letters from 2009 to 2013, and as Chair of the Data Fusion Committee of the IEEE Geoscience and Remote Sensing Society (GRSS) from October 2005 to May 2009. He has been elected in the GRSS AdCom since 2014, served as GRSS President from 2019 to 2020, and is currently GRSS Junior Past President. He has been the organizer and Technical Chair of the biennial GRSS/ISPRS Joint Workshops on “Remote Sensing and Data Fusion over Urban Areas” from 2001 to 2015. He also served as Technical Co-Chair of the 2010, 2015 and 2020 IGARSS conferences, in Honolulu (Hawaii), Milan (Italy), and on-line, respectively.
David Marzi is in his last year of a PhD course in Electronic Engineering at the University of Pavia. His work focuses on the analysis of multitemporal SAR data for vegetation mapping purposes within the ESA “Climate Change Initiative (CCI)” project. David also collaborates with an academic Spin-off in Earth Observation as a Remote Sensing Specialist, where he is involved in developing innovative agro-tech solutions.
The morning part will be devoted to introducing the concepts of geospatial data fusion, pixel-based data fusion, feature fusion and decision fusion, with particular focus on applications of geospatial data fusion for urban remote sensing. The tutorial will provide an overview of the different opportunities given by current satellite systems to exploit fusion of data, features and decisions for the characterization of the elements of the urban environment. The afternoon part of the tutorial will be a hands-on training aimed at learning how to implement data fusion approaches for land cover mapping using Google Earth Engine, a planetary-scale cloud platform for Earth science data and analysis. During the tutorial, the fundamental tools for developing a simple, yet powerful and efficient land cover mapping application will be provided and several topics, such as machine learning algorithms, satellite data processing and time series analysis will be addressed.
Notes: Each tutorial attendee should have a personal computer with a reasonably good internet connection. Moreover, to be able to successfully follow the tutorial in real time with the lecturer, the registration to the Google Earth Engine service is mandatory. An account can be created at https://earthengine.google.com/. Further information will be provided in due course.
Adriano Camps joined in 1993 the Electromagnetics and Photonics Engineering Group, Department of Signal Theory and Communications, UPC, as an Assistant Professor, Associate Professor in 1997, and Full Professor since 2007. In 1999, he was on sabbatical leave at the Microwave Remote Sensing Laboratory, of the University of Massachusetts, Amherst. His research interests are focused in microwave remote sensing, with special emphasis in microwave radiometry by aperture synthesis techniques (MIRAS instrument onboard ESA’s SMOS mission), remote sensing using signals of opportunity (GNSS-R), and nanosatellites as a tool to test innovative remote sensors.
Adriano Camps is the Scientific Coordinator of the CommSensLab at the Department of Signal Theory and Communications. Within CommSensLab, he co-led the Remote Sensing Lab (https://prs.upc.edu/), and leads the UPC NanoSat Lab (https://nanosatlab.upc.edu/en). He is the PI of the first four UPC nano-satellites: 1) 3Cat-1, a 1U CubeSat with 7 small technology demonstrators and scientific payloads, 2) 3Cat-2, a 6U CubeSat with the first dual-frequency dual-polarization GNSS-R payload, launched on August 15th 2016 using a Chinese LM-D2 rocket, 3) 3Cat-4, a 1U Cubesat with a software defined radio to implement a microwave radiometer, a GNSS-Reflectomer, and an AIS receiver, and 4) FSSCat, a tandem mission formed by two 6U CubeSats, winner of the ESA Sentinel Small Satellite challenge and overall winner of the Copernicus masters competition 2017 that was launched on September 3rd, 2020. FFSCAT has generated the first sea ice thickness map of Antarctica using Artificial neural Networks, and it is the very first cubesat-based mission contributing to the EU Copernicus program.
He was Chair of uCal 2001, Technical Program Committee Co-chair of IGARSS 2007, co-chair of GNSS-R '10, co-chair of IGARSS 2020, and the Federated and Fractionated Satellite Systems 2021. He was Associate Editor of Radio Science, and the IEEE Goscience and Remote Sensing Letters, and he is Associate Editor of the IEEE Transactions on Geoscience and Remote Sensing, President-Founder of the IEEE Geoscience and Remote Sensing Society (GRSS) Chapter at Spain, and 2017-2018 President of the IEEE Geoscience and Remote Sensing Society.
He has received several awards: 2nd National Award of University Studies (1993); INDRA award of the COIT to the best PhD in Remote Sensing (1997); UPC extraordinary Ph.D. Award (1999); Research Distinction of the Generalitat de Catalunya (2002); the European Young Investigator Award (2004), the ICREA Academia award (2009, 2015), and in 2011 he was elevated to the grade of Fellow of the IEEE. As a member of the Microwave Radiometry Group, he received in 2000, 2001, and 2004: the 1st Duran Farell and the Ciutat de Barcelona awards for Technology Transfer, and the "Salvà i Campillo" Award of the COETC for the most innovative research project for MIRAS/SMOS activities, and in 2010 the 7th Duran Farell award for Technological Research for the work on GNSS-R instrumentation and applications. Also, in 2015 he and Mr. Querol received the ESNC Award-Barcelona Challenge for the FENIX system to detect and mitigate RFI in GNSS receivers (2015), and in 2017 the ESA Sentinel Small Satellite Challenge and the Overall Winner of 2017 Copernicus Masters Competition.
Although originally designed for navigation, signals from the Global Navigation Satellite System (GNSS), i.e. GPS, GLONASS, Galileo and COMPASS, exhibit strong reflections from the Earth and ocean surface. Effects of rough surface scattering modify the properties of reflected signals. Several methods have been developed for inverting these effects to retrieve geophysical data such as ocean surface roughness (winds) and soil moisture. Extensive sets of airborne GNSS-R measurements have been collected over the past 15 years. Flight campaigns have included penetration of hurricanes with winds up to 60 m/s and flights over agricultural fields with calibrated soil moisture measurements. Fixed, tower-based GNSS-R experiments have been conducted to make measurements of sea state, sea level, soil moisture, ice and snow as well as inter-comparisons with microwave radiometry.
GNSS reflectometry (GNSS-R) methods enable the use of small, low power, passive instruments. The power and mass of GNSS-R instruments can be made low enough to enable deployment on small satellites, balloons and UAV’s. Early research sets of satellite-based GNSS-R data were first collected by the UK-DMC satellite (2003), and then by the UK Tech Demo Sat-1 (2014), the 8-satellites forming NASA CYGNSS constellation (2016), the BuFeng-1A/B twin satellites (2019), and the FSSCat 3Cat-5A/B (2020), the 2017 Copernicus Masters overall winner and first CubeSat-based mission contributing to the EU Copernicus system, which combined GNSS-R, L-band microwave radiometry and hyperspectral data for sea ice concentration, extent, thickness, and soil moisture mapping. Recently ESA approved the Scout mission HydroGNSS. Availability of spaceborne GNSS-R data and the development of new applications from these measurements, is expected to increase significantly following launch of these new satellite missions.
Actually, GNSS-R can be understood as a multi-static radar using navigation signals, but recently, some of the methods have been also applied to other satellite transmissions in other frequencies, ranging from P-band (230 MHz) to K-band (18.5 GHz). So-called “Signals of Opportunity” (SoOp) methods enable microwave remote sensing outside of protected bands, using frequencies allocated to satellite communications. Measurements of sea surface height, wind speed, snow water equivalent, and soil moisture have been demonstrated with SoOp. In this tutorial, the basic principles of GNSS-R and using SoOp will be presented, as well as the main applications.
Francesca Bovolo received the Laurea (B.S.) degree, the Laurea Specialistica (M.S.) degree (summa cum laude) in telecommunication engineering, and the Ph.D. degree in communication and information technologies from the University of Trento, Trento, Italy, in 2001, 2003, and 2006, respectively. She is currently the Founder and the Head of Remote Sensing for Digital Earth Unit, Fondazione Bruno Kessler, Trento. Her research interests include remote-sensing image processing, multitemporal remote sensing image analysis, change detection in multispectral, hyperspectral, and synthetic aperture radar images, and very high-resolution images, time series analysis, content-based time series retrieval, domain adaptation, and Light Detection and Ranging (LiDAR) and radar sounders. She conducts research on these topics within the context of several national and international projects.
Dr. Bovolo is a member of the program and scientific committee of several international conferences and workshops. She was a recipient of the First Place in the Student Prize Paper Competition of the 2006 IEEE International Geoscience and Remote Sensing Symposium (Denver, 2006). She served the community as (co-)chair of several international conferences and workshops as well as Associate Editor of the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING since 2011 and the Guest Editor of few Special Issues.
Florence Tupin received the engineering degree and the Ph.D. degree in signal and image processing from Ecole Nationale Superieure des Telecommunications (ENST), Paris, France, in 1994 and 1997, respectively, and the Habilitation a Diriger des Recherches degree from the Universityof Rennes, Rennes, France, in 2007. From 1997 to 1998, she was with SAGEM, Paris, France, where she worked on fingerprint recognition. She is currently a Professor of image and signal processing with LTCI, Telecom Paris, Paris, France. Since 2014, she has been the Head of the Image, Modeling, Analysis, GEometry,and Synthesis Team of LTCI. She has coauthored more than 200 papers. Her research interests include image processing and analysis, especially for remote sensing and synthetic aperture radar imaging applications, and earth observation. Pr. Tupin has been a member of several international and national technical conference committees since 2003. She was the Chair of the Urban Remote Sensing Joint Event held in Paris in 2007. She was an Associate Editor for the IEEE Transactions on Geoscience and Remote Sensing from 2007 to 2016. She was the recipient of several awards, among them the IEEE GRSS Transactions Prize Paper Award in 2016 for work on nonlocal speckle filtering.
Emanuele Dalsasso received the B.Sc. degree in electronics and telecommunications engineering and the M.Sc. degree in information and communication engineering (summa cum laude) from the University of Trento, Trento, Italy, in 2016 and 2018, respectively. He is currently working toward the Ph.D. de-gree in signal, images, automatique et robotique withthe LTCI Lab of Télécom Paris, Paris, France. His research interests are related to remote sensing, image processing and deep learning. He is concentrating on the development of deep learning-based approaches for synthetic aperture radar image interpretation and understanding.
Long time series of remote sensing images, SAR or optical data, are nowadays widely available thanks to sensor constellations spanning over multiple years, showing various combinations of temporal, spatial and acquisition frequency or polarization characteristics. This huge amount of data opens the way to new processing methods and raises new challenges to fully exploit this information. In this tutorial we will present some methods to extract information from these multi-temporal data: image improvement or combination, change detection and change characterization or classification. In a first part we will focus on optic data while the second part will be devoted to SAR images. The session will end with a practical part to illustrate some of the presented methods (jupyter notebook on google colab).
Carlos Lopez-Martinez received the MSc. degree in electrical engineering and the Ph.D. degree from the Universitat Politècnica de Catalunya, Barcelona, Spain, in 1999 and 2003, respectively.
Dr. Lopez-Martinez is Associate Professor in the area of remote sensing and microwave technology in the Universitat Politècnica de Catalunya, Barcelona, Spain. He has a large professional international experience at DLR (Germany), at the University of Rennes 1 (France), and as a group leader of the Remote Sensing and Natural Resources Modelling team in the Luxembourg Institute of Science and Technology (Luxembourg). His research interests include Synthetic Aperture Radar (SAR) theory, statistics and applications, multidimensional SAR, radar polarimetry, physical parameter inversion, advanced digital signal processing, estimation theory, and harmonic analysis.
Dr. López-Martínez has authored more than 200 articles in journals, books, and conference proceedings, and received the EUSAR 2002 Conference Student Prize Paper Award, co-authored the paper awarded with the EUSAR 2012 Conference First Place Student Paper Award, and received the IEEE-GRSS 2013 GOLD Early Career Award. Dr. López-Martínez has broad academic teaching experience from bachelor, master, and PhD levels to advanced technical tutorials presented at international conferences and space and research institutions worldwide. He is an associate editor of the IEEE-JSTARS journal and the MDPI Remote Sensing, acting also as invited guest editor for several special issues. He has collaborated in the Spanish PAZ and the ESA’s SAOCOM-CS missions, in the proposal of the Parsifal mission and he is member of the ESA’s Sentinel ROSE-L Mission Advisory Group. He was appointed vice-president of the IEEE-GRSS Spanish chapter, and in 2016 he became its secretary and treasurer. From 2011 Dr. López-Martínez collaborates with the IEEE-GRSS Globalization initiative in Latin America, contributing to the creation of the IEEE-GRSS Chilean chapter and the organization of the 2020 LAGIRSS conference, being appointed as Latin America liaison in 2019. He is also co-chair of the Tutorial Technical Committee of the Indian 2020 InGARSS conference.
Nowadays, several space borne Polarimetric Synthetic Aperture Radar (PolSAR) systems are in operation as TerraSAR-X (X-Band) launched in June 2007, RADARSAT-2 (C-Band) launched in December 2007, Sentinel-1A&B (C-band) launched in April 2014, ALOS-2 (L-band) launched in May 2014. Also, future missions as BIOMASS (P-band), SAOCOM (L-band), RCM (C-band) and GF3 &10 (C-Band) are designed to have parametric sensitivity.
The availability of spaceborne PolSAR data provides an unprecedented opportunity for applying advanced PolSAR information processing techniques to the important tasks of environmental monitoring and risk management. PolSAR remote sensing offers an efficient and reliable means of collecting information required to extract quantitative geophysical and biophysical parameters from Earth’s surface. This remote sensing technique has found many successful applications in crop monitoring and damage assessment, in forestry clear cut mapping, deforestation and burn mapping, in land surface structure (geology) land cover (biomass) and land use, in hydrology (soil moisture, flood delineation), in sea ice monitoring, in oceans and coastal monitoring (oil spill detection) etc. The scope of different applications is increasing nowadays thanks to the availability of mulitemporal and polarimetric acquisitions.
SAR Polarimetry represents today a very active area of research in Radar Remote Sensing, and for instance operational polarimetric applications start to be operational in the frame of the Sentinel-1. Consequently, it becomes important to train and to prepare the future generation of researchers to this very important topic.
Alexander Jacob coordinates the research group Advanced Computing for Earth Observation, within the Institute for Earth Observation at EURAC Research. He did his undergraduate studies in Geodesy at TU Darmstadt in Germany and continued with specialization with a MSc and PhD studies in geoinformatics from the Royal Institute of Technology in Stockholm, Sweden. He is an expert in earth observation and data science and is currently working on standardizing and improving access to cloud processing facilities and organization of earth observation data into data cubes. He is an active project manager for EURAC Research’s contribution to the ESA openEO Platform project and the PI of the ESA SAR2CUBE project. He has been actively involved in the original openEO H2020 project as well, coordinating and developing the back-end implementation of Eurac Research.
Michele Claus is a junior researcher at EURAC Research, in the group Advanced Computing for Earth Observation. He has experience and interest in data science, computer vision and machine/deep learning. He holds a double degree obtained during his EIT Digital master between TU Delft and UNITN. At EURAC Research he deals with satellite big data with the use of python libraries for earth observation, developing tools to improve the efficiency of the processing pipelines with the help of parallelization. He is actively involved in the openEO Platform and the SAR2Cube project, contributing to the Xarray/Dask based openEO processing engine.
During the workshop we will introduce the openEO API and some examples of its implementation in Europe based on the developments carried out in the ESA SAR2CUBE and openEO platform projects.
OpenEO is a very modern high-level API aiming at defining EO processing workflows independent from the infrastructure in which they are processed. It is based on the concept of virtual data cubes and the chain ability of operations that can be called on those data cubes.
Many data cubes have been developed recently, but very few have tackled the difficulties of working with complex data in the Slant-Range geometry. In the SAR2CUBE project a framework for defining SAR data cubes in their natural format and make them compatible with other geo-projected raster data has been developed.
Both projects are completely based on open-source technology and are continued to be developed as open source. SAR2CUBE makes use of the openEO API to expose the newly defined processes specifically for working with SAR data.
Ronny Hänsch received his graduate degree in computer science and his Ph.D. degree in engineering from the Technische Universität Berlin, Germany, in 2007 and 2014, respectively. He is currently with the SAR Technology department of the German Aerospace Center (DLR) in Oberpfaffenhofen, Germany, and continues to lecture at the Technical University of Berlin. From 2016-17, he was part of the Flying Faculty program at Turkish-German University, Istanbul, Turkey, where he worked as a lecturer in Computer Science. In 2017, he was the recipient of a JSPS PostDoc fellowship and a guest researcher at the Tokyo Institute of Technology, Japan. His current research interests focus on ensemble methods and deep learning for analysis of remote sensing images with a focus on synthetic aperture radar. He is co-chair (2017-2021) and from 2021 on chair of the IEEE GRSS Image Analysis and Data Fusion (IADF) Technical Committee and co-chair of the ISPRS Working Group II/1 (Image Orientation). He serves as reviewer for major international conferences (e.g. CVPR, ICCV), as Guest Editor for IEEE JSTARS, as Associate Editor for IEEE GRSL, and as Editor in Chief for the GRSS e-Newsletter.
Despite the wide and often successful application of machine learning techniques to analyse and interpret remotely sensed data, the complexity, special requirements, as well as selective applicability of these methods often hinders using them to their full potential. The gap between sensor- and application-specific expertise on the one hand, and a deep insight and understanding of existing machine learning methods on the other hand often leads to suboptimal results, unnecessary or even harmful optimizations, and biased evaluations. The aim of this tutorial is threefold: First, to provide insights and a deep understanding of the algorithmic principles behind state-of-the-art machine learning approaches including Random Forests and Convolutional Networks. Second, to illustrate the benefits and limitations of machine learning with practical examples. Third, to inspire new ideas by discussing unusual applications from remote sensing.
Riccardo Lanari received the Laurea degree in electronic engineering (summa cum laude) from the University of Napoli, Federico II, Napoli, in 1989. In the same year he joined IRECE and after that IREA, both Research Institutes of the Italian Council of Research (CNR). Since December 2010 Riccardo Lanari is the Director of IREA-CNR and he has more than 30 years of research experience in the remote sensing field, particularly on space-borne synthetic aperture radar (SAR) and SAR interferometry (InSAR) data processing methods developments, and their applications in the geosciences. On these topics, he is the holder of two patents and he has coauthored the book Synthetic Aperture Radar Processing (CRC Press, 1999) and 135 peer-reviewed publications on ISI journals that have, nowadays, more than 14000 citations (H-index = 56, source: Google Scholar).
He has been a Visiting Scientist in different foreign research institutes, including the German Aerospace Research Establishment (DLR), Germany (1991 and 1994), the Institute of Space and Astronautical Science (ISAS), Japan (1993), and the Jet Propulsion Laboratory (JPL), CA, USA (1997, 2004, and 2008). He has been Adjunct Professor with the University of Sannio (Benevento), Italy, from 2000 to 2003 and, from 2000 to 2008, main Lecturer in the Institute of Geomatics in Barcelona, Spain. Moreover, he has achieved the national scientific habilitation as a Full Professor of telecommunications (December 2013) and as a Full Professor of geophysics (February 2014).
He is (since 2001) a Distinguished Speaker of the Geoscience and Remote Sensing Society of the IEEE and he has lectured in several national and foreign universities and research centers and served as a Chairman/Convener and/or Scientific Program Committee member at many international conferences.
He is member (from 2020 to present) of the Advisory Group of the Sentinel-1 Next Generation mission, (from 2020 to present) of the Advisory Group of the ROSE-L mission and (from 2017 to present) of the National Commission for the Prevision and Prevention of Big Risks (Commissione Nazionale Grandi Rischi). Moreover, he has been member (from 2015 to 2019) of the Advisory Group of the Italian Space Agency (ASI) for the COSMO-SkyMed missions of first and second generation
He received from NASA a recognition (1999) and a group award (2001) for his activities related to the SRTM mission. He received the Dorso prize (2015), for the Special Section “Research,” held under the patronage of the Senate of the Italian Republic. Moreover, he received the Christiaan Huygens Medal (2017) of the European Geosciences Union (EGU) and the Fawwaz Ulaby Distinguished Achievement Award (2020) of the IEEE Geoscience and Remote Sensing Society.
Francesco Casu received the Laurea degree (summa cum laude) and the Ph.D. in electronic engineering from the University of Cagliari, Cagliari, Italy, in 2003 and 2009 respectively. Since 2003, he has been with the IREA-CNR (Italy) where he currently holds a Senior Researcher position.
He was a Visiting Scientist with the University of Texas at Austin (2004), the Jet Propulsion Laboratory, Pasadena (2005), and the Department of Geophysics at the Stanford University (2009). His main research interests are in the DInSAR field, in the multi-pass interferometry (particularly concerning the improvement of the SBAS-DInSAR algorithm) and in the SBAS-DInSAR measurement assessment, with particular emphasis on space-born constellations. More recently, he has been involved in the development of DInSAR algorithms for unsupervised processing of huge SAR data archives by exploiting High Performance Computing platforms, such as the GRID and Cloud Computing ones.
Dr. Casu is the scientific responsible of the IREA-CNR activities as Center of Competence of the Italian Civil Protection Department. Finally, he acts as a reviewer of several peer-reviewed international journals and he has served as a scientific committee member of a number of international conferences.
Claudio De Luca was born in Naples, Italy, in 1987. He received the Laurea degree in Telecommunication Engineering in 2012 at the University of Napoli, Federico II, and the Ph.D degree in Computer Science and Automation Engineering at Department of Electrical Engineering and Information Technology (DIETI) from the University of Napoli, Federico II, Italy, in 2016. From 2013 to present, he works at Institute for Electromagnetic Sensing of the Environment (IREA), Italian National Research Council (CNR), where he currently holds a permanent researcher position. He has been and is currently involved, also as responsible, in several national and international research projects. His research activities and interests include the treatment of satellite Synthetic Aperture Radar (SAR) data for the monitoring of surface deformation due to natural and man-induced phenomena. In particular, he has fully grown a great experience in the development of algorithms and methodologies concern the exploitation of HPC platform (grid and cloud computing) for DInSAR application. He has maturated high skills in the development of innovative DInSAR technique aimed at generation of interferometric analysis at national and continental scale by exploiting SAR data acquired by the new Sentinel-1 constellation.
Ivana Zinno was born in Naples, Italy, on July 13, 1980. She received the Laurea degree (summa cum laude) in Telecommunication Engineering and the Ph.D. degree in Electronic and Telecommunication Engineering both from the University of Naples Federico II, Naples, in 2008 and 2011, respectively. In 2011 she received a grant from the University of Naples to be spent at the Department of Electronic and Telecommunication Engineering for research in the field of remote sensing. Since January 2012, she has been with the IREA-CNR (National Research Council), Naples, where she currently holds a Researcher position.
In 2017 she has been Visiting Scientist with the Jet Propulsion Laboratory (JPL), Pasadena (Ca). Her work is mainly centered on the development of algorithms and techniques for Synthetic Aperture Radar (SAR) data processing and information retrieval. In particular her work is focused on the development of advanced Differential SAR Interferometry (DInSAR) techniques for the generation of time series and velocity maps of surface displacement aimed at analyzing and monitoring Earth surface deformation due to both natural (earthquakes, seismic events, volcanoes, etc.) and anthropic phenomena (mining activities, water withdrawal, gas injection, etc.). In the last years her activity concerned also the exploitation of distributed computing architectures (GRID and Cloud Computing platforms) for the parallel, automatic, large scale processing of Big SAR data. In this frame-work particular emphasis has been given to novel generation sensors, such the Sentinel-1 constellation of the European Copernicus program, as well as to the development of satellite web tools for the automatic generation and the scientific distribution of advanced DInSAR products also exploiting the Copernicus Data Information Access Services (DIAS) platforms.
Ivana Zinno is author of about 200 scientific publications and reviewer for many ISI international journals.
In the context of space-borne geodetic techniques, Differential Synthetic Aperture Radar Interferometry (DInSAR) has demonstrated its high reliability in measuring surface displacements with centimeter (sub-centimeter, in some cases) accuracy in different conditions and scenarios, both natural and anthropic.
In this short course we first provide an overview of the main principles and processing techniques relevant to the space-borne DInSAR technology, highlighting the main strengths and weaknesses. Then, the advanced DInSAR time series methods (with a focus on the Small BAseline Subset - SBAS - technique), allowing us to analyze both the spatial and temporal variability of the detected surface displacements, are discussed.
Real examples related to natural hazards (volcanoes, earthquakes and landslides) and human-induced deformation (subsidence due to aquifer exploitation, mining operations, and building or large infrastructure displacements) are shown.
A focus on the DInSAR processing capabilities offered by the public exploitation platforms is also provided while the final discussion is dedicated to analyze the current availability of public DInSAR data and the present and future DInSAR scenarios.