ALGORITMI

Development of algorithms for estimation and monitoring of hydrological parameters by using satellite data and drones

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The analysis of the hydrological cycle using remote sensing data is a research topic that presents interesting technical and scientific challenges to obtain a precise evaluation of the natural and anthropogenic contributions to the water balance. Significative examples are the estimates of the use of water resources, the extent and thickness of the snowpack, and agricultural-forest biomass. The monitoring of these individual contributions is fundamental for both the management of water resources and for the prediction of risk events (e.g. floods, avalanches, landslides).

The ALGORITMI project aims at developing innovative algorithms and techniques based on machine learning methods for the estimation of biophysical parameters like soil moisture, agricultural-forest biomass and snow related to characterizing the different components of the hydrological cycle. These algorithms, mainly based on the use of SAR satellite data, will be implemented, calibrated and validated by Eurac at regional scale (South Tyrol) and, in more detail, on the Mazia Valley for the study of agricultural-forestry coverage and on Val Senales for snow, with the support of ancillary data and in-situ measurements coming from field campaigns organized concurrently to satellite passes.

Contact person: Claudia Notarnicola claudia.notarnicola@eurac.edu and Giovanni Cuozzo giovanni.cuozzo@eurac.edu

Publications
Novel Approaches for the Estimation of Hydrological Parameters using Long Time Series of COSMO-SkyMed and Sentinel-1 SAR Data at Different Polarizations, Airborne Radiometers and In-situ Measurements
Tapete D, Cigna F, Paloscia S, Santi E, Pettinato S, Fontanelli G, Lapini A, Chiarito E, Notarnicola C, Cuozzo G, Jacob A, De Gregorio L, Rossi M (2021)
Presentation/Speech

Conference: Fringe 2021 | Online | 31.5.2021 - 4.6.2021

More information: https://fringe.esa.int/

https://hdl.handle.net/10863/18541

Snow water equivalent retrieval from COSMO-SkyMed observations through machine learning algorithms and model simulations
Santi E, Paloscia S, Pettinato S, Notarnicola C, Cuozzo G, De Gregorio L, Cigna F, Tapete D
(2021)
Presentation/Speech

Conference: IGARSS 2021 - Virtual Conference | Bruxelles | 12.7.2021 - 16.7.2021

More information: https://igarss2021.com/view_paper.php?PaperNum=3254

https://hdl.handle.net/10863/18617

Snow water equivalent retrieval from COSMO-SkyMed observations through machine learning algorithms and model simulations
Santi E, Paloscia S, Pettinato S, Notarnicola C, Cuozzo G, De Gregorio L, Cigna F, Tapete D
(2021)
Conference proceedings article

Conference: IGARSS 2021 - Virtual Conference | Bruxelles | 12.7.2021 - 16.7.2021

More information: https://ieeexplore.ieee.org/abstract/document/9553985

https://doi.org/10.1109/IGARSS47720.2021.9553985

https://hdl.handle.net/10863/19665

Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data.
Chiarito E, Cigna F, Cuozzo G, Fontanelli G, Mejia Aguilar A, Paloscia S, Rossi M, Santi E, Tapete D, Notarnicola C (2021)
Journal article
European Journal of Remote Sensing

More information: https://www.tandfonline.com/doi/full/10.1080/22797254.2021.1 ...

https://doi.org/10.1080/22797254.2021.1901063

https://hdl.handle.net/10863/18197

SAR multi-frequency observations of vegetation in agricultural and mountain areas
Paloscia S, Fontanelli G, Lapini A, Santi E, Pettinato S, Notarnicola C, Chiarito E, Cuozzo G, Tapete D, Cigna F (2020)
Conference proceedings article

Conference: Union Radio-Scientifique Internationale - General Assembly and Scientific Symposium (URSI GASS) 2020 | Rome | 29.8.2020 - 5.9.2020

More information: https://www.ursi.org/proceedings/procGA20/papers/URSI2020Veg ...

https://doi.org/10.23919/URSIGASS49373.2020.9232372

https://hdl.handle.net/10863/19099

SWE retrieval in Alpine areas with high-resolution COSMO-SkyMed X-band SAR data using
Artificial Neural Networks and Support Vector Regression techniques
Santi E, Paloscia S, Pettinato S, De Gregorio L, Cuozzo G, Jacob A, Notarnicola C, Cigna F, Tapete D (2020)
Conference proceedings article

Conference: Union Radio-Scientifique Internationale - General Assembly and Scientific Symposium (URSI GASS) 2020 | Rome | 29.8.2020 - 5.9.2020

More information: https://www.ursi.org/proceedings/procGA20/papers/ursi2020sno ...

https://doi.org/10.23919/URSIGASS49373.2020.9232247

https://hdl.handle.net/10863/19472

SWE retrieval in Alpine areas with high-resolution COSMO-SkyMed X-band SAR data using Artificial Neural Networks and Support Vector Regression techniques
Santi E, Paloscia S, Pettinato S, De Gregorio L, Cuozzo G, Jacob A, Notarnicola C, Cigna F, Tapete D (2020)
Presentation/Speech

Conference: Union Radio-Scientifique Internationale - General Assembly and Scientific Symposium (URSI GASS) 2020 | Rome | 29.8.2020 - 5.9.2020

More information: http://www.ursi.org/proceedings/procGA20/presentations/Palos ...

https://hdl.handle.net/10863/18656

SAR multi-frequency observations of vegetation in agricultural and mountain areas
Paloscia S, Fontanelli G, Lapini A, Santi E, Pettinato S, Notarnicola C, Chiarito E, Cuozzo G, Tapete D, Cigna F (2020)
Presentation/Speech

Conference: Union Radio-Scientifique Internationale - General Assembly and Scientific Symposium (URSI GASS) 2020 | Rome | 29.8.2020 - 5.9.2020

More information: http://www.ursi.org/proceedings/procGA20/presentations/Palos ...

https://hdl.handle.net/10863/18654

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