This paper addresses the challenges of an early flood warning caused by complex convective systems (CSs), by using Low-Earth Orbit and Geostationary satellite data. We focus on a sequence of extreme events that took place in central Vietnam during October 2020, with a specific emphasis on the events leading up to the floods, i.e., those occurring before October 10th, 2020. In this critical phase, several hydrometeorological indicators could be identified thanks to an increasingly advanced and dense observation network composed of Earth Observation satellites, in particular those enabling the characterization and monitoring of a CS, in terms of low-temperature clouds and heavy rainfall. Himawari-8 images, both individually and in time-series, allow identifying and tracking convective clouds. This is complemented by the observation of heavy/violent rainfall through GPM IMERG data, as well as the detection of strong winds using radiometers and scatterometers. Collectively, these datasets, along with the estimated intensity and duration of the event from each source, form a comprehensive dataset detailing the intricate behaviors of CSs. All of these factors are significant contributors to the magnitude of flooding and the short-term dynamics anticipated in the studied region.

Soil moisture (SM) datasets at high spatial resolutions are beneficial for a wide range of applications, such as monitoring and prediction of hydrological extremes, numerical weather prediction, and precision agriculture. For large scale applications in particular, remotely sensed SM has advantages over in situ data because it provides gridded estimates and because it is less labour-intensive. However, until present, active microwave SM data have not been presented at their native spatial resolution, since the quality of these data is limited by speckle. We explored the potential and limits of high spatial resolution of active microwave SM observations. We used a Sentinel-1 C-band SAR SM dataset at six spatial resolutions ranging from 20 x 20 to 120 x 120 m2. This was compared to a closely spaced (20 m) in situ dataset collected on a non-irrigated agricultural field (+/- 2.5 ha) in the Southeast of Luxembourg. A comparison of the field and satellite datasets demonstrated how Sentinel-1 data with a high spatial resolution can be used to quantify temporal within-field SM variability. SM was accurately estimated at spatial resolutions of 60 x 60 m2 and coarser, where the temporal correlation was found to be 0.67 and sub-field variations in SM were still detected. Spatial correlation was limited by the absence of SM variability within the field. These results indicate that high spatial resolution SM estimates from Sentinel-1 data can be valuable for monitoring temporal SM variations within agricultural fields.

Change detection has been widely used in many flood-mapping algorithms using pairs of Synthetic Aperture Radar (SAR) intensity images as floodwater often leads to a substantial decrease of backscatter. However, limitations still exist in many areas, such as shadow, layover, urban areas and densely vegetated areas, where the SAR backscatter is not sufficiently impacted by floodwater-related surface changes. This study focuses on these so-called exclusion areas, i.e. areas where SAR does not allow detecting water based on change detection. Our approach considers both pixel-based time series analyses and object-based spatial analyses using 20m Sentinel-1 Interferometric Wide Swath data, including 922 Sentinel-1 tiles covering the River Severn basin (UK) and the Lake Maggiore area (Italy). The results show that our exclusion map presents a good agreement (∼63%) with reference data derived from different data sources and indicate that it may complement SAR-derived flood extent maps. Allowing to accurately identify potential misclassifications in flood extent mapping, our exclusion map provides valuable information for flood management and, in particular, flood forecasting and prediction.

Synthetic Aperture Radar (SAR) images acquired by Earth observation satellites often constitute the only source of information for monitoring the progression of flood events over larger regions. Particularly attractive are the SAR data acquired by the Copernicus Sentinel-1 satellites because they are free and open, and combine a short revisit time with a good spatial and radiometric resolution. In this contribution, we discuss how a Sentinel-1 data processing system should be designed to optimally benefit from the dense Sentinel-1 time series and advanced algorithms such as change detection or machine learning methods. This was one of the questions addressed by an expert group tasked by the Joint Research Centre of the European Commission to investigate the feasibility of an automated, global, satellite-based flood monitoring product for the Copernicus Emergency Management Service. Drawing from the expert group report, we distinguish three broad categories of data processing architectures, namely single-image, dual-image, and data cube processing architectures. While the latter architecture is the most demanding in terms of large storage and compute capacities, it is also the most promising to derive high-quality Sentinel-1 flood maps comprised not just of the flood mask but also of data fields describing the retrieval uncertainty and masks showing where Sentinel-1 cannot detect floods due to physical reasons. Therefore, we recommend to use data cube processing architectures and showcase the use of the Austrian Data Cube for monitoring a small-scale flood event that occurred in Austria in November 2019.

Climate change will likely lead to more regular and more severe drought events in the near future, with large impacts on agriculture, especially during long-lasting precipitation deficits or heat w...

The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

Due to the improved spatial resolution, Earth observation (EO) data, either from Synthetic Aperture Radar (SAR) or optical sensor, provide the opportunity to assess earthquake damage of individual buildings. However, the operational use of EO data for earthquake damage mapping is basically limited to the visual inspection of Very High Resolution (VHR) optical imagery. In this work we investigate the feasibility of a damage assessment product at single building scale from a pair of VHR SAR images acquired before and after a seismic event. We perform the change analysis using the Kullbach-Leibler divergence and the intensity ratio and then we associate detected changes to a building map provided as GIS layer. Finally the expected SAR signature of a collapsed building is considered to identify severely damaged buildings. In order to test the proposed methodology we use Spotlight COSMO-SkyMed SAR imagery of L'Aquila (Italy) collected before and after the earthquake occurred on April 6, 2009. A macroseismic survey on the whole central area of L'Aquila city based on the European Macroseismic Scale 1998 is used to assess the capability of VHR SAR images to map damage.

Volcanic eruptions affect at different levels the population and economy of interested areas. Moreover, volcanic ash detection represents a key issue for aviation safety due to the harming effects on aircraft. For these reasons, an accurate and fast analysis of the data is needed to monitor the phenomena's evolution and to manage the risk mitigation phase. In this scenario, the introduction of an inversion approach based on Neural Networks (NNs) has significant interest to reduce the need of human interpretation of the ash detection maps as those generated by the application of brightness temperature difference approach. In this work we show that NNs algorithms are suitable for an accurate mapping of ash cloud on Moderate Resolution Imaging Spectroradiometer (MODIS) images in a very cloudy scenario as the ones of 2010 Eyjafjallajökull and 2011 Grimsvötn eruptions.

Operational flood mitigation and flood modeling activities benefit from a rapid and automated flood mapping procedure. A valuable information source for such a flood mapping procedure can be remote sensing synthetic aperture radar (SAR) data. In order to be reliable, an objective characterization of the uncertainty associated with the flood maps is required.This work focuses on speckle uncertainty associated with the SAR data and introduces the use of a non-parametric bootstrap method to take into account this uncertainty on the resulting flood maps. From several synthetic images, constructed through bootstrapping the original image, flood maps are delineated. The accuracy of these flood maps is also evaluated w.r.t. an independent validation data set, obtaining, in the two test cases analyzed in this paper, F-values (i.e. values of the Jaccard coefficient) comprised between 0.50 and 0.65. This method is further compared to an image segmentation method for speckle analysis, with which similar results are obtained. The uncertainty analysis of the ensemble of bootstrapped synthetic images was found to be representative of image speckle, with the advantage that no segmentation and speckle estimations are required.Furthermore, this work assesses to what extent the bootstrap ensemble size can be reduced while remaining representative of the original ensemble, as operational applications would clearly benefit from such reduced ensemble sizes.

Nowadays, space-borne Synthetic Aperture Radar (SAR) sensors, can achieve spatial resolutions in the order of 1 m. However, the exploitation of SAR at very high resolution (VHR) for detecting sparse and isolated damages in urban areas, caused by earthquakes, is still a challenging task. Within urban settlements, the scattering mechanisms are extremely complex and simple change detection analyses or classification procedures can hardly be performed. In this work the 2009, L’Aquila (Italy), earthquake has been considered as case study. Despite about 300 people were killed by the earthquake, few buildings were completely collapsed, and many others were heavily/partially damaged, resulting in a quite sparse damage distribution. We have visually analyzed pairs of VHR SAR data acquired by COSMO-SkyMed satellites, in SPOTLIGHT mode, before and after the earthquake. Such analyses were performed to understand the SAR response of damaged structures surrounded by unaffected buildings, with the aim to identify possible strategies to map the damaged buildings by using an automatic classification procedure. The preliminary analyses based on RGB images, generated by combining pre- and post-event backscattering images, allowed us to figure out how the completely collapsed and the partially damaged buildings are characterized in the SAR response. These outcomes have been taken into account to set up a decision tree algorithm (DTA). Decision rules and related thresholds were identified by statistically analyzing the values of backscattering and derived features. This study point out that many pieces of information and discrimination rules must be exploited to obtain reliable results when dealing with non-extensive and sparse damage within a dense urban settlement.

The present work reports the analysis of a possible relationship due to stress transfer between the two earthquakes that hit the province of Van, Eastern Turkey, on October 23, 2011 (Mw = 7.2) and on November 9, 2011 (Mw = 5.6). The surface displacement field of the mainshock has been obtained through a combined data set made up of differential interferograms from COSMO-SkyMed and ENVISAT satellites, integrated with continuous GPS recordings from the Turkish TUSAGA-AKTIF network. This allowed us to retrieve the geometry and the slip distribution of the seismic source and to compute the Coulomb Failure Function (CFF) variation on the aftershock plane, in order to assess a possible causal relationship between the two events. Our results show that the November 9 earthquake could have been triggered by the October 23 shock, with transferred stress values largely exceeding 1 bar.

The proposed manuscript presents an approach for the retrieval of volcanic ash parameters from remote sensing data by using a classical NN approach. The paper is confuse in some points and difficult to read. However, my main criticism concerns the novelty of the proposed approach. In particular, the use of a NN approach for the retrieval of biophysical parameters from remote sensing data is not new and already exploited in [1] by the same authors for the retrieval of volcanic ash from MODIS images. Aside from the novelty issue, there are many other problems that need to be addressed:

In this work neural networks (NNs) have been used for the retrieval of volcanic ash and sulfur dioxide (SO2) parameters based on Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral measurements. Different neural networks were built in order for each parameter to be retrieved, for experimenting with different topologies and evaluating their performances. The neural networks' capabilities to process a large amount of new data in a very fast way have been exploited to propose a novel applicative scheme aimed at providing a complete characterization of eruptive products. As a test case, the May 2010 Eyjafjallajókull eruption has been considered. A set of seven MODIS images have been used for the training and validation phases. In order to estimate the parameters associated to the volcanic eruption, such as ash mass, effective radius, aerosol optical depth and SO2 columnar abundance, the neural networks have been trained using the retrievals from well-known algorithms. These are based on simulated radiances at the top of the atmosphere and are estimated by radiative transfer models. Three neural network topologies with a different number of inputs have been compared: (a) three thermal infrared MODIS channels, (b) all multispectral MODIS channels and (c) the channels selected by a pruning procedure applied to all MODIS channels. Results show that the neural network approach is able to estimate the volcanic eruption parameters very well, showing a root mean square error (RMSE) below the target data standard deviation (SD). The network built considering all the MODIS channels gives a better performance in terms of specialization, mainly on images close in time to the training ones, while the networks with less inputs reveal a better generalization performance when applied to independent data sets. In order to increase the network's generalization capability and to select the most significant MODIS channels, a pruning algorithm has been implemented. The pruning outcomes revealed that channel sensitive to ash parameters correspond to the thermal infrared, visible and mid-infrared spectral ranges. The neural network approach has been proven to be effective when addressing the inversion problem for the estimation of volcanic ash and SO2 cloud parameters, providing fast and reliable retrievals, important requirements during volcanic crises.

The coseismic slip distribution of the M-w 9.0 2011 Tohoku-Oki earthquake has been estimated by inverting near-field onshore and offshore geodetic data, using Green's function calculated with a 3-D finite element (FE) model. The FE model simulates several geophysical features of the subduction zone that hosted the rupture surface of the event. These features include a 3-D geometric configuration and distribution of material properties of the tectonic system, a precise geometric configuration of the irregular rupture surface, and an irregular free surface according to the topography and bathymetry. A model that simulates rupture along the interface between the relatively weak overriding Okhotsk plate and stiff subducting slab of the Pacific Plate requires less slip to produce the observed surface deformation, compared to a model having uniform material properties across the rupture interface. Furthermore, the estimated slip of the heterogeneous model is more widely distributed over the shallow portion of the plate boundary, whereas the estimated slip of the homogeneous model is more focused updip of the epicenter. This demonstrates the sensitivity of inverse analyses of geodetic data for the 2011 Tohoku-Oki earthquake to the simulated domain geometry and configuration of material properties.

We have investigated the recent deformation affecting the Messina Strait area in South Italy, across the Calabria and the Sicilia Regions. Messina Strait is an extensional tectonic feature that separates peninsular Italy from Sicily, and it is also one of the most seismically active and deforming area of the Mediterranean. It is located between the Neogene Tyrrhenian backarc basin, developed in the hanging wall of the Apennines, and the Mesozoic-Paleogene Ionian basin, which is the foreland and the area of propagation of the Neogene to present Apennines accretionary prism. We have exploited Synthetic Aperture Radar (SAR) data along the time period between 2002 and 2010 by applying the advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) technique referred to as Small BAseline Subset (SBAS) algorithm. GPS data relative to permanent stations within the study area have been also used.

The COSMO-SkyMed mission offers a unique opportunity to obtain all weather radar images characterized by short revisit time, thus being useful for flood evolution mapping. The COSMO-SkyMed system has been activated several times in the last few years in occasion of flood events all over the world in order to provide very high resolution X-band SAR images useful for flood detection purposes. This paper discusses the major outcomes of the experience gained, within the framework of the OPERA Pilot Project funded by the Italian Space Agency, from using COSMO-SkyMed data for the purpose of near real time generation of flood maps. A review of the mechanisms which determine the imprints of the inundation on the radar images and of the fundamental simulation tools able to predict these imprints and help image interpretation is provided. The approach developed to process the data and to generate the flood maps is also summarized. Then, the paper illustrates the experience gained with COSMO-SkyMed by describing and discussing a number of significant examples. These examples demonstrate the potential of the COSMO-SkyMed system and the suitability of the approach developed for generating the final products, but they also highlight some critical aspects that require further investigations to improve the reliability of the flood maps.

SUMMARY In this study, we investigate the rupture history of the 2009 April 6 (Mw 6.1) L’Aquila normal faulting earthquake by using a non-linear inversion of strong motion, GPS and DInSAR data. Both the separate and joint inversion solutions reveal a complex rupture process and a heterogeneous slip distribution. Slip is concentrated in two main asperities: a smaller shallow patch of slip located updip from the hypocentre and a second deeper and larger asperity located southeastwards along-strike direction. The key feature of the source process emerging from our inverted models concerns the rupture history, which is characterized by two distinct stages. The first stage begins with rupture nucleation and with updip propagation at relatively high (∼4.0 km s−1), but still subshear, rupture velocity. The second stage starts nearly 2.0–2.5 s after nucleation and it is characterized by the along-strike rupture propagation. The largest and deeper asperity fails during this stage of the rupture process. The rupture velocity is larger in the updip than in the along-strike direction. The updip and along-strike rupture propagation are separated in time and associated with a Mode II and a Mode III crack, respectively. The comparison between the source models inferred in this study with the Poisson ratio anomalies in the crustal volume containing the fault plane allows the interpretation of the delay in along-strike rupture propagation in terms of a structural control of the rupture history. Our results show that the L’Aquila earthquake featured a very complex rupture, with strong spatial and temporal heterogeneities suggesting a strong frictional and/or structural control of the rupture process.

This paper presents an investigation on the rainfall signature for two COSMO-SkyMed (CSK) satellite case studies. Both of them are relative to a severe precipitation weather event, occurred in northwestern Italy (close to Liguria region) on November 3-8, 2011. This event was monitored by using a number of CSK images provided by the Italian Space Agency (ASI). In this case CSK X-SAR data have been compared with the weather radar (WR) Italian Radar National Mosaic. A third case study is relative to Hurricane “Irene” event, occurred in Eastern United States (close to Delaware) on late August 2011. CSK X-SAR images are compared with respect to concurrent ground-based S-band NEXRAD weather radar reflectivities. The correlation of the precipitating cloud fields between CSK X-SAR and WR images is significant in all case studies. An application of a refined XSAR-based precipitation retrieval method is presented. The X-SAR surface response is estimated using ancillary data, such as land cover maps and a digital elevation model (DEM).

We have analysed the seismic source of the active fault generated Van Mw=7.1 earthquake occurred in Eastern Turkey the 23rd October 2011. To this aim the surface displacement field has been measured applying SAR Interferometry (InSAR) technique to the available dataset of coseismic COSMO-SkyMed image pairs. The seismic source model has been obtained by the use of a data inversion procedure based on the concurrent application of InSAR techniques and Neural Networks. The proposed approach elaborates the information on the coseismic deformation pattern stemming from available differential interferograms. The interferogram is the expression of the active fault at depth, thus its shape, size and its features somehow refer to the geometry and slip of the fault generating the seism. A Neural Network has been trained to recognize some fault parameters (Length, Width, Strike, Dip, Depth) from the unwrapped interferogram. The retrieval exercise consists in estimating these parameters from the coseismic interferogram exploiting Neural Networks.