Geosynchronous continental land-atmosphere sensing system (g-class): persistent radar imaging for earth science

More frequent imaging of Earth system processes is recognised as one of the emerging needs in Earth observation. Conventional low Earth orbit satellites are limited in their ability to provide this, whereas satellites in geosynchronous orbit can in principle provide continuous imaging. A new mission de- sign has been developed from studies for a previous geosynchronous radar mission concept (GeoSTARe) to improve its technical feasibility and geographical coverage, and to rein- force its science focus. This new mission (Geosynchronous - Continental Land Atmosphere Sensing System (G-CLASS)) is presented. G-CLASS is in fact a family of missions: we present a version focussed on the diurnal water cycle - G-CLASS:H2O - for which geosynchronous radar has great potential. G-CLASS:H2O is being developed as a proposal for ESA’s Earth Explorer programme

Joint multi-baseline SAR interferometry

We propose a technique to provide interferometry by combining multiple images of the same area. This technique differs from the multi-baseline approach in literature as (a) it exploits all the images simultaneously, (b) it performs a spectral shift preprocessing to remove most of the decorrelation, and (c) it exploits distributed targets. The technique is mainly intended for DEM generation at centimetric accuracy, as well as for differential interferometry. The problem is framed in the contest of single-input multiple-output (SIMO) channel estimation via the cross-relations (CR) technique and the resulting algorithm provides significant improvements with respect to conventional approaches based either on independent analysis of single interferograms or multi-baselines phase analysis of single pixels of current literature, for those targets that are correlated in all the images, like for long-term coherent areas, or for acquisitions taken with a short revisit time (as those gathered with future satellite constellations)

Coherent Change Detection for repeated-pass interferometric SAR images: An application to earthquake damage assessment on buildings

During disaster response, the availability of relevant information, delivered in a proper format enabling its use among the different actors involved in response efforts, is key to lessen the impact of the disaster itself. Focusing on the contribution of geospatial information, meaningful advances have been achieved through the adoption of satellite earth observations within emergency management practices. Among these technologies, the Synthetic Aperture Radar (SAR) imaging has been extensively employed for large-scale applications such as flood areas delineation and terrain deformation analysis after earthquakes. However, the emerging availability of higher spatial and temporal resolution data has uncovered the potential contribution of SAR to applications at a finer scale. This paper proposes an approach to enable pixel-wise earthquake damage assessments based on Coherent Change Detection methods applied to a stack of repeated-pass interferometric SAR images. A preliminary performance assessment of the procedure is provided by processing Sentinel-1 data stack related to the 2016 central Italy earthquake for the towns of Amatrice and Accumoli. Damage assessment maps from photo-interpretation of high-resolution airborne imagery, produced in the framework of Copernicus EMS (Emergency Management Service - European Commission) and cross-checked with field survey, is used as ground truth for the performance assessment. Results show the ability of the proposed approach to automatically identify changes at an almost individual building level, thus enabling the possibility to empower traditional damage assessment procedures from optical imagery with the centimetric change detection sensitivity characterizing SAR. The possibility of disseminating outputs in a GIS-like format represents an asset for an effective and cross-cutting information sharing among decision makers and analysts

Excess path delays from sentinel interferometry to improve weather forecasts

A synthetic aperture radar can offer not only an accurate monitoring of the earth surface deformation, but also information on the troposphere, such as the total path delay or the columnar water vapor at high horizontal resolution. This can be achieved by proper interferometric processing and postprocessing of the radar interferograms. The fine and unprecedented horizontal resolution of the tropospheric products can offer otherwise unattainable information to be assimilated into numerical weather prediction models, which are progressively increasing their resolving capabilities. A number of tricks on the most effective processing approaches, as well as a novel method to pass from multipass differential interferometry products to absolute tropospheric columnar quantities are discussed. The proposed products and methods are assessed using real Sentinel-1 data. The experiment aims at evaluating the accuracy of the derived information and its impact on the weather prediction skill for two meteorological events in Italy. The main perspective of the study is linked to the possibility of exploiting interferometric products from a geosynchronous platform, thus complementing the inherent high resolution of SAR sensors with the required frequent revisit needed for meteorological applications

First results of the ALOS PALSAR verification processor

Among the several applications that will take advantage of the newly available data from the ALOS PALSAR instrument, considerable interest is in the peculiar features that derive from the penetration and polarimetric capabilities of the system. These capabilities, new for a single spaceborne sensor, need specific software tools for the processing of the different acquisition modes. This paper presents a verification processor, developed under ESA contract, for the generation of polarimetric, interferometric and polarimetric-interferometric geocoded products derived from ALOS PALSAR data. The processor, developed with a modular approach, contains the following main elements: - Phase-preserving fine resolution processor; - Phase-preserving ScanSAR processor; - Interference removal tools; - Polarimetric calibration tools; - Polarimetric analysis tools; - Fine resolution interferometric processor; - ScanSAR interferometric processor; - Polarimetric-interferometric processor; - Geocoding; - Atmospheric modelling tools. The processor architecture is presented; highlights are given on specific modules and algorithms. Early results are shown, in particular of the processing of polarimetric and polarimetric-interferometric data over different test sites

A Synergistic Use of a High-Resolution Numerical Weather Prediction Model and High-Resolution Earth Observation Products to Improve Precipitation Forecast

open20siThe Mediterranean region is frequently struck by severe rainfall events causing numerous casualties and several million euros of damages every year. Thus, improving the forecast accuracy is a fundamental goal to limit social and economic damages. Numerical Weather Prediction (NWP) models are currently able to produce forecasts at the km scale grid spacing but unreliable surface information and a poor knowledge of the initial state of the atmosphere may produce inaccurate simulations of weather phenomena. The STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims to investigate whether Sentinel satellites constellation weather observation data, in combination with Global Navigation Satellite System (GNSS) observations, can be used to better understand and predict with a higher spatio-temporal resolution the atmospheric phenomena resulting in severe weather events. Two heavy rainfall events that occurred in Italy in the autumn of 2017 are studied—a localized and short-lived event and a long-lived one. By assimilating a wide range of Sentinel and GNSS observations in a state-of-the-art NWP model, it is found that the forecasts benefit the most when the model is provided with information on the wind field and/or the water vapor content.openLagasio, Martina; Parodi, Antonio; Pulvirenti, Luca; Meroni, Agostino N.; Boni, Giorgio; Pierdicca, Nazzareno; Marzano, Frank S.; Luini, Lorenzo; Venuti, Giovanna; Realini, Eugenio; Gatti, Andrea; Tagliaferro, Giulio; Barindelli, Stefano; Monti Guarnieri, Andrea; Goga, Klodiana; Terzo, Olivier; Rucci, Alessio; Passera, Emanuele; Kranzlmueller, Dieter; Rommen, BjornLagasio, Martina; Parodi, Antonio; Pulvirenti, Luca; Meroni, Agostino N.; Boni, Giorgio; Pierdicca, Nazzareno; Marzano, Frank S.; Luini, Lorenzo; Venuti, Giovanna; Realini, Eugenio; Gatti, Andrea; Tagliaferro, Giulio; Barindelli, Stefano; Monti Guarnieri, Andrea; Goga, Klodiana; Terzo, Olivier; Rucci, Alessio; Passera, Emanuele; Kranzlmueller, Dieter; Rommen, Bjor