Rock glaciers represent a typical landform characterizing high mountain periglacial terrains. The... more Rock glaciers represent a typical landform characterizing high mountain periglacial terrains. They are composed by a mixture of frozen debris and interstitial ice and when are affected by downslope displacement related to the permafrost deformation or melting, they are called active rock glaciers. The observation of their changes over time is a critical indicator of the state of water resources and permafrost distribution and is therefore of great importance for risk scenario definition and related natural hazards management, as well as can be related to the climate change. Synthetic Aperture Radar (SAR) data have proven to be a useful tool to estimate surface displacement phenomena in mountainous areas that can be difficult to access. In particular, Differential Interferometry can measure displacement in the line of site direction with high accuracy. However, sometimes the characteristics of the areas in terms of displacement rate and number of available images lead to some limita...
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
This research aimed at exploiting the joint use of machine learning and polarimetry for improving... more This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture (SMC) from SAR acquisitions at C- and X-band.The study was conducted on an alpine test area in Italy and two agricultural areas in Canada, for which series of Radarsat-2 (RS2) and COSMO-SkyMed (CSK) images were available along with direct measurements of SMC from in-situ stations. The analysis confirmed the sensitivity of SAR backscattering (σ°) from both sensors to the SMC variations, with similar correlations (R ≃0.5). The comparison of SMC with the Compact Polarimetric (CP) parameters, computed from the RS2 acquisitions by Radarsat Constellation Mission (RCM) data simulator pointed out that the right and left polarized signals and the Shannon entropy intensity also have some sensitivity to SMC variations, with R ≃0.4 for all the three parameters.Based on these results, two different machine learning (ML) algorithms, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been implemented and tested on the available data. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 4% and 6% of SMC and R between 0.78 and 0.88, depending on the combination of inputs. The ANN algorithm based on CP data was tested on the Canada areas, being able to estimate SMC with a RMSE between 2% and 5% of SMC and R between 0.85 and 0.96.
<p>In the face of climate change and socio-economic developments, water sca... more <p>In the face of climate change and socio-economic developments, water scarcity is a tremendous challenge. In particular, a significant portion of the world’s population rely on water from cryospheric sources such as snow and/or glacier fed mountain rivers. However, the data coverage in mountain regions is often sparse, which substantially hampers the assessment of climate impacts on hydrological systems. Furthermore, the large impact of climate change on snow and glacier hydrology require physically sound hydrological models.</p><p>The gap between the growing need for sustainable water resources management, low data availability and uncertain hydrological projections calls for new approaches. To close this gap, a modular modelling framework was developed to foster the use of complementary data sets in hydrological models. The framework enables a flexible combination of remote sensing and in situ data for model calibration and validation providing a multi-model and multi-input ensemble. The additional consideration of data regarding snow covered area, snow water equivalent and soil moisture allows for physically meaningful representations of key hydrological processes, even in the absence of a dense network of meteorological stations and river discharge gauges.</p><p>Case studies in the European Alps (Inn and Adige/Etsch) and in Central Asia (Ala Archa and Karadarya) illustrate the high value of this approach for physically meaningful representations of the hydrological processes. Furthermore, a high impact of glacier retreat on future water availability was found for the highly glacierised basins of the Fagge river in the upper part of the Inn basin and the Ala Archa river.</p>
<p>The seasonal snow is one of the largest water reservoirs in nature, stor... more <p>The seasonal snow is one of the largest water reservoirs in nature, storing water during winter, and gradually releasing it in spring during the melt. This guarantees freshwater supply for the lowlands even in the long term, making the mountains the “water towers” of the downstream regions. In fact, the delayed water release from the head watersheds to the forelands is essential for a large number of human activities such as irrigation, drinking water supply and hydropower production. On the other hand, snowmelt may cause natural disasters such as wet-snow avalanches, gliding or release of highly enriched accumulated contaminants able to cause severe impact on water quality.</p><p>In recent years, Synthetic Aperture Radar (SAR) has demonstrated capable to provide information about the melting process. In particular, with the launch of the European Commission (EC) Copernicus Programme Sentinel-1 mission, C-band SAR images are regularly acquired every 6 days and delivered free of charge. This opened the possibility to observe a phenomenological relationship between the snow melting process of high altitude snowpacks and the multi-temporal radar backscattering acquired by Sentinel-1. The identification of the temporal signature for each pixel of a Sentinel-1 time series allowed us to detect the onset of the three phases that made up the snowmelt i.e., moistening, ripening and runoff, with a good reliability. However, the mechanisms that drive the snowpack response at microwaves depend on frequency; therefore, different snowpack signatures are expected if using different frequency bands, as the X band available onboard the Italian Space Agency (ASI)’s COSMO-SkyMed (CSK) constellation.</p><p>In this work, we analyze a dense X-band time series acquired by the CSK over the Schnalstal catchment in Italy during the snowmelt season. This allows us to point out the similarities and the differences between the electromagnetic interactions using C- and X-band SAR during the snowmelt. Depending on the shorter wavelength, the X-band is more sensitive than C-band to small quantities of liquid water inside the snowpack. Therefore, X band shows an earlier response than C band to the moistening of the surface snow layer (especially for steep local incidence angles), and a more pronounced loss of interaction with deeper layers. X-band is also more sensitive to the increase in the superficial roughness with the consequence of possibly anticipating the runoff onset. However, by comparing the runoff time in the Schnalstal catchment during the melting season 2020-2021, a general agreement between C- and X-band is found even though the characteristic shape of the signature exhibits more variations at X-band than C-band.</p><p>This research is part of the 2019-2022 project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n. 2018-37-HH.0.</p>
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS... more This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on some test areas in Italy, in order to estimate the main geophysical parameters of soil and vegetation, such as soil moisture and vegetation biomass. A check of the sensitivity of SAR signal to the soil parameters was first carried out on both test sites. Over the South-Tyrol area a retrieval approach based on the Support Vector Regression methodology, which was already tested in this area using C-band data from ENVISAT/ASAR data, was carried out. From these preliminary results it can be concluded that X-band images combined with C-band images could provide valuable information for the retrieval of SMC, even though further investigations should be carried out on a larger time-series and larger set of samples.
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS... more This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on two Italian test areas, located in South-Tyrol and in Tuscany, close to Florence, to estimate soil moisture (SMC, in %) and vegetation biomass (PWC, in kg/m2). Two retrieval approaches based on Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been applied to these areas. Looking at the preliminary results, it has been noted that the integration of X and C band images could provide valuable information for the retrieval of SMC, even though further investigations should be carried out on a larger time-series and set of samples. On the South Tyrol test area, SVR methods provided an accuracy in the estimate of SMC with determination coefficient, R2> 0.85 and root mean square error, RMSE <4%. By using X band alone, the result obtained is worse: ANN algorithm resulted in R2 from 0.35 to 0.8 and RMSE= from 6 to 2 (% SMC), depending on the polarization combinations considered as input. X band allowed instead retrieving the PWC of cereal fields with a satisfactory accuracy (R2=0.94 and RMSE=0.35 Kg/m2).
The Corvara landslide is an active, large-scale, deep-seated and slow moving earthslide of about ... more The Corvara landslide is an active, large-scale, deep-seated and slow moving earthslide of about 30 Mm3 located in the Dolomites (Italy). It is frequently damaging a national road and, occasionally, isolated buildings and recreational ski facilities. Since the mid \u201890s it has been mapped, dated and monitored thanks to field surveys, boreholes, radiocarbon dating, inclinometers, piezometers and periodic D-GPS measurements, carried out by the Geology and the Forestry Planning offices of the Autonomous Province of Bolzano, the Municipality of Corvara in Badia, the University of Modena and Reggio Emilia, the IRPI-CNR of Padua. In 2013, a new phase of characterization and monitoring has started which also involves the EURAC\u2019s Institute for Applied Remote Sensing, the geodesy group of University La Sapienza, the CNR-IREA of Naples and the Leica Geosystems office in Italy. This new phase of characterization and monitoring is meant to investigate the opportunities of innovative SAR interferometry, D-GPS and in-place inclinometers techniques to provide for a high frequency monitoring of the study site in support to the analysis of the investigation of forcing factors leading unsteady, nonuniform landslide motion through different seasons of the year. Monitoring results are also expected to provide a validation of innovative interferometric techniques so to fully evaluate their conformity to be used as a long-term monitoring system in land-use planning and risk management procedures. The monitoring infrastructure now integrates: 16 Corner Reflector for satellite X-Band SAR interferometric products, 13 benchmarks for D-GPS periodic surveys, three on-site GPS receivers for continuous positioning and remote ftp data pushing, two in-place inclinometers and a pressure transducer to record pore-pressure variations. The coupling of SAR-based products with GPS records is achieved using especially designed Corner Reflectors having an appendix dedicated to hold Dual-Frequency GPS antennas. COSMO-SkyMed X-Band SAR acquisitions started on October 2013 and are ongoing with a temporal resolution of 16 days using STRIPMAP (HIMAGE) measuring mode. Discontinuous D-GPS Fast-Static surveys are scheduled with a triple frequency: annual for 24 points outside recent activation areas, monthly for 13 points in the active zone and a bi-weekly for 6 points located in the most active zone. Displacement high-frequency data are acquired thank to the installation of 3 Dual-Frequency GPS in permanent acquisition that have been located in the accumulation, track and source zone of the active portion of the landslide. High frequency data are also obtained by the two inclinometers operating in continuous acquisition located across the main slide surface at 48 m depth into a 90 m borehole drilled in the accumulation zone. A piezometer installed in the source zone and the meteorological station of Piz La Ila (3 km far away) of the Autonomous Province of Bolzano complete the system
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
The monitoring of snow conditions in Alpine areas to support water management and avalanche warni... more The monitoring of snow conditions in Alpine areas to support water management and avalanche warning applications would require the estimate of snow parameters, such as the snow water equivalent (SWE). In this research, COSMO-SkyMed (CSK) X-band SAR data were exploited to estimate the SWE. In-situ snow measurements (depth, density, snow grain radius, temperature) collected in South Tyrol (Italy), were used to simulate the X-band backscatter with the Dense Medium Radiative Transfer (DMRT) electromagnetic model. Two SWE retrieval algorithms based on machine learning approach were implemented. The algorithms are based on Artificial Neural Networks (ANN) and Support Vector Regression (SVR) and have been trained with both experimental data and DMRT model simulations. These algorithms were applied to a selection of CSK StripMap HIMAGE HH-polarized scenes collected over the test area. The obtained results are promising and they confirm the potential of SAR data at X-band to retrieve snow parameters, although the algorithm validation should be improved in the future, with more consistent measurement dataset.
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
This research aims at exploiting the integration of C- and X-band SAR data for the monitoring of ... more This research aims at exploiting the integration of C- and X-band SAR data for the monitoring of Soil Moisture Content (SMC). Time series of Radarsat2 (RS2) and COSMO-SkyMed (CSK) images are collected on two test areas, located in Italy and in Canada. The backscattering sensitivity to SMC measured by in-situ stations is investigated considering the available sensor frequencies and polarizations. In addition, for exploiting the potential of fully polarimetric acquisitions of RS2, simulated Compact Polarimetric (CP) data are computed by using a Radarsat Constellation Mission (RCM) data simulator, and their sensitivity to the target SMC is examined. Based on the experimental findings, two machine learning (ML) approaches to the SMC retrieval, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) are implemented and tested on the two areas. Looking at the preliminary results, the integration of X- and C-band images does provide valuable information for the retrieval of SMC, while the simulated CP parameters exhibit a certain sensitivity to SMC. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 2% and 4% of SMC and correlation coefficient R between 0.85 and 0.97, depending on the combination of inputs. The application of the ML algorithms to the other available images on the Mazia test area and the implementation of the ML retrieval algorithm using CP data are still under investigation.
Active and Passive Microwave Remote Sensing for Environmental Monitoring III, 2019
The main objective of this work is to estimate Snow Water Equivalent (SWE) by jointly exploiting ... more The main objective of this work is to estimate Snow Water Equivalent (SWE) by jointly exploiting the information derived from X-band Synthetic Aperture Radar (SAR) imagery acquired by the Italian Space Agency COSMO-SkyMed satellite constellation in StripMap HIMAGE mode and manual SWE ground measurements. The idea is to verify the sensitivity of the backscattering coefficient at X-band to the SWE and, by means of a Support Vector Regression (SVR) algorithm, to estimate the SWE for the South Tyrol region, north-eastern Italy. The regressor is trained by exploiting about 1,000 simulated backscattering coefficients corresponding to different snowpack conditions, obtained with a theoretical model based on the Dense Media Radiative Transfer theory - Quasi-crystalline approximation Mie scattering of Sticky spheres (DMRT-QMS). Then, the performance is evaluated on the backscattering values derived from COSMO-SkyMed satellite images and using the corresponding ground measurements of SWE as references. The results show a correlation coefficient equal to 0.6, a bias of 10.5 mm and a RMSE of 51.8 mm between estimated SWE values and ground measurements. The limited performance could be related to the DMRT-QMS theoretical model used for the simulations that results to be very sensitive to snow grain size and may have generated a training dataset only partially representative of satellite derived backscattering coefficients used for testing the algorithm.
The fourth industrial revolution is paving the way for Industrial Internet of Things applications... more The fourth industrial revolution is paving the way for Industrial Internet of Things applications where large number of wireless nodes, equipped with sensors and actuators, monitor the production cycle of industrial goods. This paper proposes and analyses LoRaIN, a network architecture and MAC-layer protocol thought for on-demand monitoring of industrial machines. Our proprietary system is an energy-efficient, reliable and scalable solution, where the protocol is built on top of LoRa at 2.4 GHz. Indeed, the low-power characteristics of LoRa allow to reduce energy consumption, while Wireless Power Transfer is used to recharge batteries, avoiding periodic battery replacement. High reliability is obtained through the joint use of Frequency and Time Division Multiple Access. A dynamic LoRaIN scheduler manages the communication and recharging phases depending on the tasks assigned to the nodes, as well as the number of monitoring devices. Performance is measured in terms of network throughput, energy consumption and latency. Results demonstrate that the proposed solution is suitable for monitoring applications of industry machines.
This research aims at investigating the backscatter sensitivity at C and X band to the characteri... more This research aims at investigating the backscatter sensitivity at C and X band to the characteristics of agricultural surfaces and analyzing the integration of these data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on tree agricultural test areas in Italy (San Pietro Capofiume, in Emilia Romagna, Sesto Fiorentino, in Tuscany, and Mazia Valley, in South Tyrol). A preliminary test of the sensitivity of SAR signal to the soil and vegetation characteristics was first carried out by also comparing data from previous experiments. From these results, it can be concluded that X-band data are mainly sensitive to vegetation structure and biomass, and to soil moisture of bare or slightly vegetate soils, whereas C-band images could provide valuable information for the retrieval of soil moisture, even in vegetation covered soils. Two retrieval algorithms were implemented for estimating the main geophysical parameters, namely soil moisture content (SMC) and vegetation biomass (PWC) from these sensors. Over Sesto Fiorentino area, an algorithm based on Artificial Neural Network (ANN) technique was implemented for estimating both SMC of bare or scarcely vegetated soil and vegetation biomass of wheat crops at X band. On the South-Tyrol area, a SMC retrieval approach based on the Support Vector Regression methodology, which was already tested in this area using C-band data from ENVISAT/ASAR data, was adopted. This algorithm integrated data at both X and C bands showing encouraging results, even though further investigations shall be carried out on a larger time-series and larger set of samples.
2014 IEEE Geoscience and Remote Sensing Symposium, 2014
This paper presents the results concerning the application of COSMO-SkyMed SAR images to mountain... more This paper presents the results concerning the application of COSMO-SkyMed SAR images to mountain areas for the challenging retrieval of some key parameters of the hydrological cycle: soil moisture content (SMC), snow depth (SD), and snow water equivalent (SWE). The results obtained so far are encouraging. Regarding SMC, the results indicate that, in spite of the low penetration capability of X-band wavelength, the SAR signal is well related to soil moisture variations, even in presence of vegetation. As far as snow cover is concerned, from the analysis of data it has been observed that the backscattering coefficient remains almost constant until the SD of dry snow accumulated on soil is higher than 50-60 cm and increases rapidly as SD rises up to 150 cm. The use of an inversion algorithm for the retrieval of SWE, based on Artificial Neural Networks, showed a determination coefficient higher than 0.8, with an associated probability value of 95%.
2014 IEEE Geoscience and Remote Sensing Symposium, 2014
In mountain areas, soil moisture is a key parameter for both agricultural management and natural ... more In mountain areas, soil moisture is a key parameter for both agricultural management and natural hazard support. This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a specific focus on mountain areas. The experimental analysis was carried out on images acquired over the Südtirol/Alto Adige Province (Italy) during 2010-2011 from the RADARSAT2 in quad-pol mode and Envisat ASAR in Wide Swath mode in VV polarization. The methodology for soil moisture retrieval is based on the Support Vector Regression (SVR) method specifically trained to be able to consider topographic effects of the mountain areas. The comparison with ground measurements collected during field campaigns indicates an RMSE value of around 5% of SMC% while the comparison with fixed ground stations reports an error of around 9% of SMC%. Comparing RADARSAT2 and ASAR SMC, both datasets reveal very similar distributions of SMC values. The cumulative histogram curve for the two datasets shows a slight underestimation of SMC in the ASAR product. This could be ascribed to the reduced resolution of ASAR WS and the use of VV polarization.
The goal of this study was to assess the applicability of medium resolution SAR time-series, in c... more The goal of this study was to assess the applicability of medium resolution SAR time-series, in combination with in-situ point measurements and machine learning, for the estimation of soil moisture content (SMC). One of the main challenges was the combination of SMC point measurements and satellite data. Due to the high spatial variability of soil moisture a direct linkage can be inappropriate. Data used in this study were a combination of in-situ data, satellite data and modelled SMC from the hydrological model GEOtop. To relate the point measurements with the satellite pixel footprint resolution, a spatial upscaling method was developed. It was found that both temporal and spatial SMC patterns obtained from various data sources (ASAR WS, GEOtop and meteorological stations) show similar behaviors. Furthermore, it was possible to increase the absolute accuracy of the estimated SMC through spatial upscaling of the obtained in-situ data. Introducing information on the temporal behavior of the SAR signal proves to be a promising method to increase the confidence and accuracy of SMC estimations. Following steps were identified as critical for the retrieval process: the topographic correction and geocoding of SAR data, the calibration of the meteorological stations and the spatial upscaling.
Rock glaciers represent a typical landform characterizing high mountain periglacial terrains. The... more Rock glaciers represent a typical landform characterizing high mountain periglacial terrains. They are composed by a mixture of frozen debris and interstitial ice and when are affected by downslope displacement related to the permafrost deformation or melting, they are called active rock glaciers. The observation of their changes over time is a critical indicator of the state of water resources and permafrost distribution and is therefore of great importance for risk scenario definition and related natural hazards management, as well as can be related to the climate change. Synthetic Aperture Radar (SAR) data have proven to be a useful tool to estimate surface displacement phenomena in mountainous areas that can be difficult to access. In particular, Differential Interferometry can measure displacement in the line of site direction with high accuracy. However, sometimes the characteristics of the areas in terms of displacement rate and number of available images lead to some limita...
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
This research aimed at exploiting the joint use of machine learning and polarimetry for improving... more This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture (SMC) from SAR acquisitions at C- and X-band.The study was conducted on an alpine test area in Italy and two agricultural areas in Canada, for which series of Radarsat-2 (RS2) and COSMO-SkyMed (CSK) images were available along with direct measurements of SMC from in-situ stations. The analysis confirmed the sensitivity of SAR backscattering (σ°) from both sensors to the SMC variations, with similar correlations (R ≃0.5). The comparison of SMC with the Compact Polarimetric (CP) parameters, computed from the RS2 acquisitions by Radarsat Constellation Mission (RCM) data simulator pointed out that the right and left polarized signals and the Shannon entropy intensity also have some sensitivity to SMC variations, with R ≃0.4 for all the three parameters.Based on these results, two different machine learning (ML) algorithms, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been implemented and tested on the available data. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 4% and 6% of SMC and R between 0.78 and 0.88, depending on the combination of inputs. The ANN algorithm based on CP data was tested on the Canada areas, being able to estimate SMC with a RMSE between 2% and 5% of SMC and R between 0.85 and 0.96.
&amp;lt;p&amp;gt;In the face of climate change and socio-economic developments, water sca... more &amp;lt;p&amp;gt;In the face of climate change and socio-economic developments, water scarcity is a tremendous challenge. In particular, a significant portion of the world&amp;amp;#8217;s population rely on water from cryospheric sources such as snow and/or glacier fed mountain rivers. However, the data coverage in mountain regions is often sparse, which substantially hampers the assessment of climate impacts on hydrological systems. Furthermore, the large impact of climate change on snow and glacier hydrology require physically sound hydrological models.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The gap between the growing need for sustainable water resources management, low data availability and uncertain hydrological projections calls for new approaches. To close this gap, a modular modelling framework was developed to foster the use of complementary data sets in hydrological models. The framework enables a flexible combination of remote sensing and in situ data for model calibration and validation providing a multi-model and multi-input ensemble. The additional consideration of data regarding snow covered area, snow water equivalent and soil moisture allows for physically meaningful representations of key hydrological processes, even in the absence of a dense network of meteorological stations and river discharge gauges.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Case studies in the European Alps (Inn and Adige/Etsch) and in Central Asia (Ala Archa and Karadarya) illustrate the high value of this approach for physically meaningful representations of the hydrological processes. Furthermore, a high impact of glacier retreat on future water availability was found for the highly glacierised basins of the Fagge river in the upper part of the Inn basin and the Ala Archa river.&amp;lt;/p&amp;gt;
&amp;lt;p&amp;gt;The seasonal snow is one of the largest water reservoirs in nature, stor... more &amp;lt;p&amp;gt;The seasonal snow is one of the largest water reservoirs in nature, storing water during winter, and gradually releasing it in spring during the melt. This guarantees freshwater supply for the lowlands even in the long term, making the mountains the &amp;amp;#8220;water towers&amp;amp;#8221; of the downstream regions. In fact, the delayed water release from the head watersheds to the forelands is essential for a large number of human activities such as irrigation, drinking water supply and hydropower production. On the other hand, snowmelt may cause natural disasters such as wet-snow avalanches, gliding or release of highly enriched accumulated contaminants able to cause severe impact on water quality.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In recent years, Synthetic Aperture Radar (SAR) has demonstrated capable to provide information about the melting process. In particular, with the launch of the European Commission (EC) Copernicus Programme Sentinel-1 mission, C-band SAR images are regularly acquired every 6 days and delivered free of charge. This opened the possibility to observe a phenomenological relationship between the snow melting process of high altitude snowpacks and the multi-temporal radar backscattering acquired by Sentinel-1. The identification of the temporal signature for each pixel of a Sentinel-1 time series allowed us to detect the onset of the three phases that made up the snowmelt i.e., moistening, ripening and runoff, with a good reliability. However, the mechanisms that drive the snowpack response at microwaves depend on frequency; therefore, different snowpack signatures are expected if using different frequency bands, as the X band available onboard the Italian Space Agency (ASI)&amp;amp;#8217;s COSMO-SkyMed (CSK) constellation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this work, we analyze a dense X-band time series acquired by the CSK over the Schnalstal catchment in Italy during the snowmelt season. This allows us to point out the similarities and the differences between the electromagnetic interactions using C- and X-band SAR during the snowmelt. Depending on the shorter wavelength, the X-band is more sensitive than C-band to small quantities of liquid water inside the snowpack. Therefore, X band shows an earlier response than C band to the moistening of the surface snow layer (especially for steep local incidence angles), and a more pronounced loss of interaction with deeper layers. X-band is also more sensitive to the increase in the superficial roughness with the consequence of possibly anticipating the runoff onset. However, by comparing the runoff time in the Schnalstal catchment during the melting season 2020-2021, a general agreement between C- and X-band is found even though the characteristic shape of the signature exhibits more variations at X-band than C-band.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This research is part of the 2019-2022 project &amp;amp;#8216;Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone&amp;amp;#8217;, funded by ASI under grant agreement n. 2018-37-HH.0.&amp;lt;/p&amp;gt;
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS... more This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on some test areas in Italy, in order to estimate the main geophysical parameters of soil and vegetation, such as soil moisture and vegetation biomass. A check of the sensitivity of SAR signal to the soil parameters was first carried out on both test sites. Over the South-Tyrol area a retrieval approach based on the Support Vector Regression methodology, which was already tested in this area using C-band data from ENVISAT/ASAR data, was carried out. From these preliminary results it can be concluded that X-band images combined with C-band images could provide valuable information for the retrieval of SMC, even though further investigations should be carried out on a larger time-series and larger set of samples.
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS... more This research aims at analyzing the integration of C and X band data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on two Italian test areas, located in South-Tyrol and in Tuscany, close to Florence, to estimate soil moisture (SMC, in %) and vegetation biomass (PWC, in kg/m2). Two retrieval approaches based on Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been applied to these areas. Looking at the preliminary results, it has been noted that the integration of X and C band images could provide valuable information for the retrieval of SMC, even though further investigations should be carried out on a larger time-series and set of samples. On the South Tyrol test area, SVR methods provided an accuracy in the estimate of SMC with determination coefficient, R2> 0.85 and root mean square error, RMSE <4%. By using X band alone, the result obtained is worse: ANN algorithm resulted in R2 from 0.35 to 0.8 and RMSE= from 6 to 2 (% SMC), depending on the polarization combinations considered as input. X band allowed instead retrieving the PWC of cereal fields with a satisfactory accuracy (R2=0.94 and RMSE=0.35 Kg/m2).
The Corvara landslide is an active, large-scale, deep-seated and slow moving earthslide of about ... more The Corvara landslide is an active, large-scale, deep-seated and slow moving earthslide of about 30 Mm3 located in the Dolomites (Italy). It is frequently damaging a national road and, occasionally, isolated buildings and recreational ski facilities. Since the mid \u201890s it has been mapped, dated and monitored thanks to field surveys, boreholes, radiocarbon dating, inclinometers, piezometers and periodic D-GPS measurements, carried out by the Geology and the Forestry Planning offices of the Autonomous Province of Bolzano, the Municipality of Corvara in Badia, the University of Modena and Reggio Emilia, the IRPI-CNR of Padua. In 2013, a new phase of characterization and monitoring has started which also involves the EURAC\u2019s Institute for Applied Remote Sensing, the geodesy group of University La Sapienza, the CNR-IREA of Naples and the Leica Geosystems office in Italy. This new phase of characterization and monitoring is meant to investigate the opportunities of innovative SAR interferometry, D-GPS and in-place inclinometers techniques to provide for a high frequency monitoring of the study site in support to the analysis of the investigation of forcing factors leading unsteady, nonuniform landslide motion through different seasons of the year. Monitoring results are also expected to provide a validation of innovative interferometric techniques so to fully evaluate their conformity to be used as a long-term monitoring system in land-use planning and risk management procedures. The monitoring infrastructure now integrates: 16 Corner Reflector for satellite X-Band SAR interferometric products, 13 benchmarks for D-GPS periodic surveys, three on-site GPS receivers for continuous positioning and remote ftp data pushing, two in-place inclinometers and a pressure transducer to record pore-pressure variations. The coupling of SAR-based products with GPS records is achieved using especially designed Corner Reflectors having an appendix dedicated to hold Dual-Frequency GPS antennas. COSMO-SkyMed X-Band SAR acquisitions started on October 2013 and are ongoing with a temporal resolution of 16 days using STRIPMAP (HIMAGE) measuring mode. Discontinuous D-GPS Fast-Static surveys are scheduled with a triple frequency: annual for 24 points outside recent activation areas, monthly for 13 points in the active zone and a bi-weekly for 6 points located in the most active zone. Displacement high-frequency data are acquired thank to the installation of 3 Dual-Frequency GPS in permanent acquisition that have been located in the accumulation, track and source zone of the active portion of the landslide. High frequency data are also obtained by the two inclinometers operating in continuous acquisition located across the main slide surface at 48 m depth into a 90 m borehole drilled in the accumulation zone. A piezometer installed in the source zone and the meteorological station of Piz La Ila (3 km far away) of the Autonomous Province of Bolzano complete the system
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
The monitoring of snow conditions in Alpine areas to support water management and avalanche warni... more The monitoring of snow conditions in Alpine areas to support water management and avalanche warning applications would require the estimate of snow parameters, such as the snow water equivalent (SWE). In this research, COSMO-SkyMed (CSK) X-band SAR data were exploited to estimate the SWE. In-situ snow measurements (depth, density, snow grain radius, temperature) collected in South Tyrol (Italy), were used to simulate the X-band backscatter with the Dense Medium Radiative Transfer (DMRT) electromagnetic model. Two SWE retrieval algorithms based on machine learning approach were implemented. The algorithms are based on Artificial Neural Networks (ANN) and Support Vector Regression (SVR) and have been trained with both experimental data and DMRT model simulations. These algorithms were applied to a selection of CSK StripMap HIMAGE HH-polarized scenes collected over the test area. The obtained results are promising and they confirm the potential of SAR data at X-band to retrieve snow parameters, although the algorithm validation should be improved in the future, with more consistent measurement dataset.
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
This research aims at exploiting the integration of C- and X-band SAR data for the monitoring of ... more This research aims at exploiting the integration of C- and X-band SAR data for the monitoring of Soil Moisture Content (SMC). Time series of Radarsat2 (RS2) and COSMO-SkyMed (CSK) images are collected on two test areas, located in Italy and in Canada. The backscattering sensitivity to SMC measured by in-situ stations is investigated considering the available sensor frequencies and polarizations. In addition, for exploiting the potential of fully polarimetric acquisitions of RS2, simulated Compact Polarimetric (CP) data are computed by using a Radarsat Constellation Mission (RCM) data simulator, and their sensitivity to the target SMC is examined. Based on the experimental findings, two machine learning (ML) approaches to the SMC retrieval, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) are implemented and tested on the two areas. Looking at the preliminary results, the integration of X- and C-band images does provide valuable information for the retrieval of SMC, while the simulated CP parameters exhibit a certain sensitivity to SMC. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 2% and 4% of SMC and correlation coefficient R between 0.85 and 0.97, depending on the combination of inputs. The application of the ML algorithms to the other available images on the Mazia test area and the implementation of the ML retrieval algorithm using CP data are still under investigation.
Active and Passive Microwave Remote Sensing for Environmental Monitoring III, 2019
The main objective of this work is to estimate Snow Water Equivalent (SWE) by jointly exploiting ... more The main objective of this work is to estimate Snow Water Equivalent (SWE) by jointly exploiting the information derived from X-band Synthetic Aperture Radar (SAR) imagery acquired by the Italian Space Agency COSMO-SkyMed satellite constellation in StripMap HIMAGE mode and manual SWE ground measurements. The idea is to verify the sensitivity of the backscattering coefficient at X-band to the SWE and, by means of a Support Vector Regression (SVR) algorithm, to estimate the SWE for the South Tyrol region, north-eastern Italy. The regressor is trained by exploiting about 1,000 simulated backscattering coefficients corresponding to different snowpack conditions, obtained with a theoretical model based on the Dense Media Radiative Transfer theory - Quasi-crystalline approximation Mie scattering of Sticky spheres (DMRT-QMS). Then, the performance is evaluated on the backscattering values derived from COSMO-SkyMed satellite images and using the corresponding ground measurements of SWE as references. The results show a correlation coefficient equal to 0.6, a bias of 10.5 mm and a RMSE of 51.8 mm between estimated SWE values and ground measurements. The limited performance could be related to the DMRT-QMS theoretical model used for the simulations that results to be very sensitive to snow grain size and may have generated a training dataset only partially representative of satellite derived backscattering coefficients used for testing the algorithm.
The fourth industrial revolution is paving the way for Industrial Internet of Things applications... more The fourth industrial revolution is paving the way for Industrial Internet of Things applications where large number of wireless nodes, equipped with sensors and actuators, monitor the production cycle of industrial goods. This paper proposes and analyses LoRaIN, a network architecture and MAC-layer protocol thought for on-demand monitoring of industrial machines. Our proprietary system is an energy-efficient, reliable and scalable solution, where the protocol is built on top of LoRa at 2.4 GHz. Indeed, the low-power characteristics of LoRa allow to reduce energy consumption, while Wireless Power Transfer is used to recharge batteries, avoiding periodic battery replacement. High reliability is obtained through the joint use of Frequency and Time Division Multiple Access. A dynamic LoRaIN scheduler manages the communication and recharging phases depending on the tasks assigned to the nodes, as well as the number of monitoring devices. Performance is measured in terms of network throughput, energy consumption and latency. Results demonstrate that the proposed solution is suitable for monitoring applications of industry machines.
This research aims at investigating the backscatter sensitivity at C and X band to the characteri... more This research aims at investigating the backscatter sensitivity at C and X band to the characteristics of agricultural surfaces and analyzing the integration of these data collected from Radarsat2 (RS2) and COSMO-SkyMed (CSK) systems on tree agricultural test areas in Italy (San Pietro Capofiume, in Emilia Romagna, Sesto Fiorentino, in Tuscany, and Mazia Valley, in South Tyrol). A preliminary test of the sensitivity of SAR signal to the soil and vegetation characteristics was first carried out by also comparing data from previous experiments. From these results, it can be concluded that X-band data are mainly sensitive to vegetation structure and biomass, and to soil moisture of bare or slightly vegetate soils, whereas C-band images could provide valuable information for the retrieval of soil moisture, even in vegetation covered soils. Two retrieval algorithms were implemented for estimating the main geophysical parameters, namely soil moisture content (SMC) and vegetation biomass (PWC) from these sensors. Over Sesto Fiorentino area, an algorithm based on Artificial Neural Network (ANN) technique was implemented for estimating both SMC of bare or scarcely vegetated soil and vegetation biomass of wheat crops at X band. On the South-Tyrol area, a SMC retrieval approach based on the Support Vector Regression methodology, which was already tested in this area using C-band data from ENVISAT/ASAR data, was adopted. This algorithm integrated data at both X and C bands showing encouraging results, even though further investigations shall be carried out on a larger time-series and larger set of samples.
2014 IEEE Geoscience and Remote Sensing Symposium, 2014
This paper presents the results concerning the application of COSMO-SkyMed SAR images to mountain... more This paper presents the results concerning the application of COSMO-SkyMed SAR images to mountain areas for the challenging retrieval of some key parameters of the hydrological cycle: soil moisture content (SMC), snow depth (SD), and snow water equivalent (SWE). The results obtained so far are encouraging. Regarding SMC, the results indicate that, in spite of the low penetration capability of X-band wavelength, the SAR signal is well related to soil moisture variations, even in presence of vegetation. As far as snow cover is concerned, from the analysis of data it has been observed that the backscattering coefficient remains almost constant until the SD of dry snow accumulated on soil is higher than 50-60 cm and increases rapidly as SD rises up to 150 cm. The use of an inversion algorithm for the retrieval of SWE, based on Artificial Neural Networks, showed a determination coefficient higher than 0.8, with an associated probability value of 95%.
2014 IEEE Geoscience and Remote Sensing Symposium, 2014
In mountain areas, soil moisture is a key parameter for both agricultural management and natural ... more In mountain areas, soil moisture is a key parameter for both agricultural management and natural hazard support. This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a specific focus on mountain areas. The experimental analysis was carried out on images acquired over the Südtirol/Alto Adige Province (Italy) during 2010-2011 from the RADARSAT2 in quad-pol mode and Envisat ASAR in Wide Swath mode in VV polarization. The methodology for soil moisture retrieval is based on the Support Vector Regression (SVR) method specifically trained to be able to consider topographic effects of the mountain areas. The comparison with ground measurements collected during field campaigns indicates an RMSE value of around 5% of SMC% while the comparison with fixed ground stations reports an error of around 9% of SMC%. Comparing RADARSAT2 and ASAR SMC, both datasets reveal very similar distributions of SMC values. The cumulative histogram curve for the two datasets shows a slight underestimation of SMC in the ASAR product. This could be ascribed to the reduced resolution of ASAR WS and the use of VV polarization.
The goal of this study was to assess the applicability of medium resolution SAR time-series, in c... more The goal of this study was to assess the applicability of medium resolution SAR time-series, in combination with in-situ point measurements and machine learning, for the estimation of soil moisture content (SMC). One of the main challenges was the combination of SMC point measurements and satellite data. Due to the high spatial variability of soil moisture a direct linkage can be inappropriate. Data used in this study were a combination of in-situ data, satellite data and modelled SMC from the hydrological model GEOtop. To relate the point measurements with the satellite pixel footprint resolution, a spatial upscaling method was developed. It was found that both temporal and spatial SMC patterns obtained from various data sources (ASAR WS, GEOtop and meteorological stations) show similar behaviors. Furthermore, it was possible to increase the absolute accuracy of the estimated SMC through spatial upscaling of the obtained in-situ data. Introducing information on the temporal behavior of the SAR signal proves to be a promising method to increase the confidence and accuracy of SMC estimations. Following steps were identified as critical for the retrieval process: the topographic correction and geocoding of SAR data, the calibration of the meteorological stations and the spatial upscaling.
Uploads
Papers by Gianni Cuozzo