We propose a tool that integrates spaceborne radar data from Sentinel-1 and multispectral data from Sentinel-2 satellites to detect fresh debris accumulation or rock exposure on alpine glaciers. The tool leverages the VH cross-polarized backscatter difference between Sentinel-1 radar images to pinpoint clusters of pixels exhibiting prominent changes over time. It then integrates the NDSI information from Sentinel-2 multispectral images to discriminate between potential causes of the observed backscatter variations, such as snow cover changes, rock exposure or debris accumulation. The correlation between time series of VH values further allows to distinguish between localized phenomena like landslides and extensive ones like glacial surface changes due to snow metamorphism or accumulation. The tool is implemented in Google Earth Engine (GEE) and leverages open-source libraries and datasets, making it readily adaptable to other glacial environments.
In this work the potentiality of multi-source and multifrequency Synthetic Aperture Radar (SAR) images to measure surface deformation of the Lazaun alpine active rock glacier were studied. CSG, CSK, TerraSAR-X, Sentinel-1 and SAOCOM SAR data characterized by different wavelength and spatial and temporal resolutions have been tested over the period 2016 to 2022. Intensity tracking and DInSAR complementary SAR techniques have been used to estimate deformation at different temporal scales. We found for Intensity tracking, interannual displacement reaching a Pearson correlation coefficient of about 0.89 and an RMSE of 0.34mm/day.
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.
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.
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.
KEYWORDS: Soil science, Synthetic aperture radar, MODIS, General packet radio service, Data processing, Data acquisition, Sensors, Composites, Control systems, Data modeling
In this work we address the synergy of optical, SAR (Synthetic Aperture Radar) and topographic data in soil moisture retrieval over an Alpine area. As estimation technique, we consider Gaussian Process Regression (GPR). The test area is located in South Tyrol, Italy where the main land types are meadows and pastures. Time series of ASAR Wide Swath - SAR, optical, topographic and ancillary data (meteorological information and snow cover maps) acquired repetitively in 2010 were examined. Regarding optical data, we used both, daily MODIS reflectances, and daily NDVI, interpolated from the 16-day MODIS composite. Slope, elevation and aspect were extracted from a 2.5 m DEM (Digital Elevation Model) and resampled to 10 m. Daily soil moisture measurements were collected in the three fixed stations (two located in meadows and one located in pasture). The snow maps were used to mask the points covered by snow. The best performance was obtained by adding MODIS band 6 at
1640 nm to SAR and DEM features. The corresponding coefficient of determination, R2, was equal to 0.848, and the root mean square error, RMSE, to 5.4 % Vol. Compared to the case when no optical data were considered, there was an increase of ca. 0.05 in R2 and a decrease in RMSE of ca. 0.7 % Vol. This work showed that the joint use of NDVI or water absorption reflectance with SAR and topographic data can improve the estimation of soil moisture in specific Alpine area and that GPR is an effective method for estimation.
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