This PDF file contains the front matter associated with SPIE Proceedings Volume 11861, including the Title Page, Copyright information, and Table of Contents.

Welcome and Introduction to Conference 11861

HydroGNSS has been selected as the second ESA Scout Earth Observation mission to demonstrate the ability of small satellites to deliver science. This paper summarises the case for HydroGNSS, as developed during its System Consolidation study. HydroGNSS is a high value dual small satellite mission, which will prove new concepts and offer timely climate observations that supplement and complement existing observations and are high in ESA’s Earth Observation scientific priorities. The mission delivers observations of four hydrological Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS) using the new technique of GNSS Reflectometry. These will cover the world’s land mass to 25 km resolution, with a 15 day revisit. The variables are soil moisture, inundation or wetlands, freeze / thaw state and above ground biomass.

Sentinel-3 is a multi-instrument mission designed to measure sea-surface topography, sea- and land-surface temperature, ocean color and land color with high resolution and accuracy. The S3 mission is based on a constellation of two (3A and 3B) polar-orbiting satellites and it is designed and operated in the framework of the Copernicus programme, with planned 3C and 3D to ensure continuity. The mission builds up the legacy of ERS-1, ERS-2, ENVISAT and particularly CryoSat for the altimeter. Seninel-3A was launched in February 2016 and Seninel-3B in April 2018. They are equipped with a dual-frequency (Ku- and C-band) altimeter and can work both in low resolution (LRM) and SAR mode, the latter being designed to achieve high along-track discrimination. The low-resolution mode exploits conventional pulse-limited altimeter operation at C band. To approximate LRM operation at Ku band, a pseudo low-resolution mode is achieved by properly processing SAR acquisitions. Recently, a new research project funded by the European Space Agency, i.e., ALtimetry for BIOMass (ALBIOM), has been initiated to study the possibility of deriving forest biomass using Sentinel-3 altimetry data. ALBIOM aims at improving biomass global dataset, which is defined and classified as an Essential Climate Variable. In the last two decades, the exploitation of radar altimetry for studying land parameters has received renewed interest, including processing for the characterization of vegetation features and soil moisture. The vegetation cover has two main effects on the nadir backscatter measured by the altimeter. It attenuates the coherent reflection of the soil and add an incoherent volume scattering contribution. The relative weight of the two contributions depends of course form the frequency. To assess in what extent radar altimetry data are sensitive to the presence of vegetation forest, a study of the dynamic of the Sentinel-3 power waveforms with respect to the above ground biomass is needed. More importantly, the way radar waveforms are affected by disturbing land parameters, such as soil moisture, topography and surface roughness, has to be understood. In this work, an analysis considering both high- and low-resolution data made available by the Copernicus hub service is carried out. The sensitivity study of Sentinel-3 altimetry data to forest biomass over Africa is based on calibrated Sentinel-3 waveforms combined in space and time with forest biomass maps and ancillary information on the soil topography derived from a Digital Elevation Model. Comparison among Ku- and C-band waveforms are discussed, highlighting the critical aspect of the correct positioning of the time-tracking window over land, which often appears partly or completely misplaced, determining waveforms either truncated or containing noise only. The detrimental effect of the waveform truncation for the estimation of biomass and the possible mitigation approach has been considered. The study revealed that both waveforms and NRCSs can be sensitive to the presence of biomass in the order of 100-400 tons/ha, even if they can be strongly influenced by the presence of irregular topography within the system footprint. Different sensitivities with respect to the three channels (i.e., bandwidths and resolution modes) have been observed. A study about the use of differential NRCSs, defined as the difference between two different bandwidths, proposed by previous studies, is under investigation. Further research activities also connected to a modelling approach are in progress and will be discussed at the conference.

In this paper, the sensitivity to soil moisture variations over an agricultural area characterized by different vegetation covers has been investigated. The objectives of the study were identifying the correlation of one or more polarimetric parameters with soil moisture changes over time. In this respect, we assessed the possibility to separate three individual scattering mechanisms (i.e., surface, double-bounce, volume) offered by radar polarimetry. We apply a model-based polarimetric decomposition to a time-series of high-resolution SAR data collected at L-band by the NASA/JPL UAVSAR airborne radar over the Yucatan Lake region in Louisiana, USA. Thirteen flights were considered and five regions of interest characterized by different surface properties and vegetation covers were selected. The temporal evolution of different polarimetric parameters, obtained by applying the Freeman-Durden decomposition, is reported and discussed. The polarimetric features were compared not only to the NDVI variations derived from Sentinel-2 satellite, but also to precipitation data recorded by a nearby precipitation station as well as to the Soil Water Index derived from the ASCAT sensor onboard Metop satellites. The improved sensitivity of the polarimetric features with respect to the single backscattering coefficients at different polarizations was also assessed.

In recent years, the possibility of estimating Soil Moisture (SM) at different resolution scales improved greatly with the launch of the latest satellite microwave sensors and in particular of the Soil Moisture Active and Passive (SMAP) radar + radiometer and Sentinel-1 (S-1) Synthetic Aperture Radar (SAR). However, the tradeoff between temporal and spatial resolution offered by each of these two sensors is still unable to meet the requirements of many users. The SM SMAP products are available through the NSIDC data portal at resolution varying between 1 and 36 Km [1, 2]. This study exploited the possibility of merging SMAP, S-1 and COSMO-SkyMed X-band SAR (CSK) data through Artificial Neural Networks (ANN) for obtaining SM products at high spatial resolution, with the aim of evaluating the benefits of assimilating higher resolution SM into hydrological models. The algorithm has been implemented and validated on a test area in Tuscany (Val d’Elsa, center coordinates of Ponte a Elsa: 43°41′20.37″N 10°53′42.38″E), in central Italy. The area is characterized by a partially hilly landscape, including agricultural and urban areas areas and forests, with heterogeneities that set important constraints to the potential of SMAP observations for SM monitoring. The SMAP, S-1 and CSK acquisitions available between 2019 and 2020 on the area have been considered for the algorithm development. The reference SM values for validation purposes have been derived from in-situ observations carried out in the framework of the ASI ‘Algoritmi’ project [3], which also provided the CSK images considered in this study. The improvement of spatial resolution of the output SMC product is still under investigation; however, the preliminary results seem showing that the method is able to map SM from the SMAP, S-1 and CSK synergy at a resolution better than 100m, with correlation coefficient R≃0.89 and RMSE≃0.025 m3/m3.

Glacier melt is an important fresh water source. Seasonal changes can have impacting consequences on downstream water resources management. Today’s glacier monitoring lacks an observation-based tool for regional, sub-seasonal observation of glacier mass balance and a quantification of associated meltwater release at high temporal resolution. The snowline on a glacier marks the transition between the ice and snow surface, and is, at the end of the summer, a proxy for the annual glacier mass balance. It was shown that glacier mass balance model simulations closely tied to sub-seasonal snowline observations on optical satellite sensors are robust for the observation date. Recent advances in remote sensing permit efficient and extensive snowline mapping. Different methods automatically discriminate snow over ice on high- to medium-resolution optical satellite images. Other studies rely on lower ground resolution optical imagery to retrieve snow cover fraction at pixel level and produce regional maps of snow cover extent. However, state-of-the-art methods using optical sensors still have important shortcomings, such as cloud-cover related issues. Images acquired by Synthetic Aperture Radar (SAR), which are almost insensitive to cloud coverage, have proofed suitable for transient snowline delineation. The combination of SAR and optical data in a complementary way carries a unique potential for a better monitoring of snow depletion on high temporal and spatial resolution. The aim of this work is to map snow cover over glaciers by combining Sentinel-1 SAR, Sentinel-2 multispectral and lower resolution MODIS images. Consecutively, we developed an approach that can automatically handle classification of multi-source and multi-resolution satellite image stacks. This provides a unique solution for continuous snowline mapping since the beginning of the century. With the provided close-to-daily transient snow cover fractions on glacier level, we provide the basis for a new strategy to directly integrate multi-source satellite image classification into glacier mass balance monitoring.

The Ground Motion Service Germany (BBD) is an operational use of Persistent Scatterer Interferometry (PSI) for the monitoring of ground motions in Germany. The current dataset includes millions of measurement points each with an associated deformation time series covering a period of more than four years (2014/15-2019). The systematic analysis and interpretation of this data is still at the beginning. In this work, we are using an unsupervised learning workflow to analyse a subset of the BBD dataset covering two study areas in North Western Germany. Our approach includes a dimensionality reduction step using PCA and an autoencoder followed by a K-Means clustering. We analyse the results taking into consideration the local geology and land use and test the generalization capabilities of our approach.

Estimating unknown absolute phase from a wrapped observation is a challenging and ill-posed problem that possibly leads to misinterpretation of interferometric SAR (InSAR) deformation results. In this study, we introduce a quality index to cluster post-phase unwrapping multi-master InSAR timeseries outputs based on the estimated phase residuals and redundancy of network of interferograms. The index is supposed to indicate the reliability of a timeseries, including the identification of persistent scatterers (PSs) possibly affected by phase unwrapping jumps. The algorithm was tested on two Sentinel-1 interferometric datasets with 622,991 and 95,398 PSs, generated from the PSI processing chain PSIG of the geomatics division of CTTC. Promising result have been achieved-especially in identifying erroneous PSs with phase unwrapping jumps. Along with existing temporal phase consistency checking algorithms, the approach could provide rich information toward a better interpretation of the deformation timeseries results.

Passive Corner Reflectors (PCR) are often used in spaceborne SAR interferometry as benchmarks. The main goal of the use of PCRs in DInSAR deformation monitoring is to provide pixels with a high and stable response to be used as reference to estimate the deformation when natural persistent scatters are not available. The use of PCRs at C band is not always suitable, especially in areas as glaciers, snow covered regions, and mountain slopes, where accessibility and PCRs’ installation can be very time and cost consuming, or where harsh weather conditions can jeopardize their performance. An alternative to PCRs is Active Reflectors (AR), more compact and lighter apparatus, which need a power source, and are often susceptible to the natural air temperature variations which can affect the stability of their response. The study presented here reports on the use of an AR designed to operate with Sentinel-1 SAR data, installed with some PCRs aimed at comparing the performance of the two approaches. The AR was designed and implemented to provide a fair performance/cost benefit to make feasible the setup of a dense network. Images covering almost one year have been processed to compare the performance of a prototype installed close to our center. A real campaign was also carried out installing an AR together with a network of PCRs in a site, located in a mountain area of Andorra, where a landslide occurred in 2018, and where a monitoring based on DInSAR is ongoing.

The Differential Radar Interferometry (DInSAR) technique provides fast and accurate means for detecting small displacements of the Earth’s surface having a magnitude in centimeters range. Applying this method monitoring of ground movements of natural or anthropogenic origin are reliably registered. The information is produced from the interferograms resulting from purposely processing the phase signal present in two SAR images from different dates over the one and the same area. The motivation behind this research was to study the crustal deformations that pose treat to the archaeological site “Solnitsa-Provadia” located in the area Mirovo salt deposit near the town Provadia, NE Bulgaria. It needs to be mentioned that the said monument is dated back to VI-V millennium BC and includes the remnants of an ancient city near Provadia. The registered deformations in the region are due to natural and anthropogenic factors. The mentioned factors have undisputable negative impact on the preservation of this historical site and justify the necessity of regular monitoring of the ongoing geodynamic processes. In this research the authors provide results based on multitemporal processing of freely accessible SAR data from Sentinel-1 mission by ESA. The information concerning the detected surface deformations was obtained by the DInSAR method. The multitemporal processing included creation of set of interferometric images from several periods with time span of four months. This interval was selected since it was needed to decrease the decorrelation of the phase signal caused by the vegetation and noise introduced by the atmosphere. In order to increase the reliability of the output information SAR data from ascending and descending orbits were processed which provided two different stereoscopic-like views to the investigated area. The results also have been compared with the trends of ground motions using data from repeated multi-year results geodetic measurements made at Mirovo geodynamic network.

Differential SAR interferometry (DInSAR) is an effective remote sensing technique for determining land surface deformation and displacement. The earlier limitations of this technique were the loss of coherence and the presence of atmospheric noise. For this reason, advanced DInSAR techniques, such as Persistent scatterer InSAR (PS-InSAR) and Short Baseline InSAR (SB-InSAR), have been proposed to overcome these limitations by exploiting multiple interferograms. The region studied in this work is the city of Al Hoceima located in Morocco. This region is characterized by dynamic seismic activity that induced several earthquakes between May 27, 2015, and April 27, 2016, where several magnitudes ranging from 3.9 to 6.3 Mw (on 01/25/2016) were measured. 19 Sentinel-1 Single Look Complex (SLC) images of this region of Al Hoceima were exploited in this work, and a three-pass DInSAR approach in SB-InSAR was used to correct topographic errors when generating velocity maps. The analysis of United States Geological Survey (USGS) seismic data showed an increase of 1 magnitude (Mw)/day at a constant rate over time in the region. The effects of the disaster on the region were shown by a rapid rate of ground subsidence velocity at a rate of more than -23 cm/year in the year of observation.

It is shown theoretically and experimentally that the value of integrated water vapor content in the atmosphere measured by ground-based radiometric facilities in real time can be used, along with the temperature profile, as a predictor of aircraft icing in clouds. The quantitative criterion of occurrence of the conditions leading to aircraft icing is found, and the method for determining these conditions by ground-based remote microwave sensing of the atmosphere in real time is formulated.

In Rome, the expansion of urbanization, the increase of population density, and the subsequent escalation of traffic are common factors in road infrastructure vulnerability, especially when these aspects coexist with the presence of ancient subterranean environments, such as ancient tuff quarries. These wide networks of subterranean structures are often in endangered preservation conditions, also because their position and extension are only partially known. Furthermore, the aerial bombing attacks that the city of Rome experienced during the II World War are considered here as another critical factor favouring ground instability processes. In the present research, the joint exploitation of SAR dataset, "historical photograms", and vectorization of historical records have been applied on circumstanced test areas to estimate the quarries' dimension and typology and to evaluate their conservation state related to these anthropogenic aspects. The aims were addressed mainly with the twofold use of the SAR Cosmo-SkyMed dataset, from the processing of both intensity and phase information contents. The intensity has been used to distinguish low and high backscattering anomalies attributed to the presence of open cast and subterranean structures. The phase information was processed from SAR long time-series, through the PSInSAR method, to test its performance in monitoring cavity stability state. The extraction of Permanent Scatterers was carried out to evaluate its suitability to detect entities of displacement through a wide time span, especially using interpolation maps, to identifying patterns related to ancient hypogea. This stratification of information has been analyzed around endangered areas. Using this method to analyze the features mentioned, a relationship between these anthropic factors and sinkholes was revealed.

Multi-temporal SAR interferometry (MTInSAR) allows analysing wide areas, identifying critical ground instabilities, and studying the phenomenon evolution in a long time-scale. Nowadays satellite SAR constellations provide datasets covering time periods of several years with short revisit times, which allow investigating ground displacements showing non-linear kinematics. These are particularly interesting since they include warning signals related to pre-failure of natural and artificial structures. Recently, approaches have been proposed for recognising and analysing nonlinear displacements, which use different strategies. The authors have introduced two innovative indexes for characterising MTInSAR time series: one relies on the fuzzy entropy and measures the disorder in a time series, the other performs a statistical test based on the Fisher distribution for selecting the polynomial model that more reliably approximate the displacement trend. This work reviews the theoretical formulation of these indexes and evaluate their performances by simulating time series with different characteristics in terms of kinematic, level of noise, signal length and temporal sampling. Finally, the proposed procedures are used for analysing displacement time series derived by processing real datasets acquired by both Sentinel-1 and COSMO-SkyMed constellations. In particular the hilly villages of Pomarico and Montescaglioso have been investigated, which are located in Southern Italian Apennine (Basilicata region), in an area where several landslides occurred in the recent past, causing damages to houses, commercial buildings, and infrastructures. The MTInSAR displacement time series have been analysed by using the proposed methods, searching for nonlinear trends that are possibly related to relevant ground instabilities and, in particular, to potential early warning signals for the landslide events affecting Mtescaglioso in 2013 and Pomarico in 2019. Acknowledgments - This work was supported in part by the Italian Ministry of Education, University and Research, D.D. 2261 del 6.9.2018, Programma Operativo Nazionale Ricerca e Innovazione (PON R&I) 2014–2020 under Project OT4CLIMA.

Landslide due to heavy rains and earthquakes is major hazards to human life and property. Applications for rapid detection and mapping of the damage situation and extent using earth observation satellite imageries are expected. Especially, Synthetic Aperture Radar (SAR) imagery is effective due to the capabilities of cloud penetration and is independent of solar illumination. It was, however, difficult to extract landslide areas in SAR images accurately using the traditional methods. Therefore, we tried to extract landslide areas using Convolutional Neural Networks (CNNs), which are being used for computer vision. We adopted U-Net, one of the CNNs, for Landslide extraction. The U-Net enables accurate segmentation from a small amount of training data. We verified the landslide extraction with U-Net, using the collapsed areas caused by the 2018 Hokkaido Eastern Iburi Earthquake that occurred on September 6, 2018. Landslide extraction was performed using pre- and post-event X-band COSMO-SkyMed imageries. For pre-processing, we performed multi-looking, radiometric calibration, and ortho-rectification using 10 m DEM data. The U-Net was trained for 100 epochs with a mini- batch size of 24, 32, and 40. Two types of dataset were prepared for the model input, that is, (1) pre- and post-event COSMO-SkyMed amplitude and the ratio of pre- and post-event COSMO-SkyMed amplitude, (2) pre- and post-event COSMO-SkyMed amplitude and slope. As a result, the optimal value of the F-measure (70.9%) was obtained with the dataset (1) using 128 × 128 strides and batch size of 32. Topographic factor (slope) did not improve landslide extraction in this study.