Dr. Emanuele Santi Profile

Dr. Emanuele Santi

at Istituto di Fisica Applicata Nello Carrara

SPIE Involvement:

Conference Chair | Conference Program Committee | Editor | Author

Publications (16)

SPIE Conference Volume | January 31, 2018

Active and Passive Microwave Remote Sensing for Environmental Monitoring

PROCEEDINGS ARTICLE | December 9, 2016

Integration of radar and radiometric data from SMAP and Sentinel-1 sensors for soil moisture and vegetation monitoring (Conference Presentation)

Proc. SPIE. 10003, SAR Image Analysis, Modeling, and Techniques XVI

KEYWORDS: Radar, Soil science, Data modeling, Sensors, Synthetic aperture radar, Vegetation, Microwave radiation, Ka band, Data integration, Radar sensor technology

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PROCEEDINGS ARTICLE | December 9, 2016

Soil moisture retrieval at regional scale from AMSR2 data (Conference Presentation)

Proc. SPIE. 10003, SAR Image Analysis, Modeling, and Techniques XVI

KEYWORDS: Soil science, Data modeling, Sensors, Pollution control, Radiometry, Spatial resolution, Algorithm development, Microwave radiation, Process modeling, Resolution enhancement technologies

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The aim of this work is to exploit the potential of AMSR2 for hydrological applications on a regional scale and in heterogeneous environments characterised by different surface covers at subpixel resolution. The soil moisture content (SMC) estimated from Advanced Microwave Scanning Radiometer 2 (AMSR2) through the ANN-based “HydroAlgo” algorithm is firstly compared with the outputs of the Soil Water Balance hydrological model (SWBM). The comparison is performed over Italy, by considering all the available overpasses of AMSR2, since July 2012. The SMC generated by HydroAlgo is then considered as input for generating a rainfall product through the SM2RAIN algorithm. The comparison between observed and estimated rainfall in central Italy provided satisfactory results with a substantial room for improvement. In this work, the ANN “HydroAlgo” algorithm [1], which was originally developed for AMSR-E, was adapted and re-trained for AMSR2, accounting for the two C band channels provided by this new sensor. The disaggregation technique implemented in HydroAlgo [2], devoted to the improvement of ground resolution, made this algorithm particularly suitable for the application to such a heterogeneous environment. The algorithm allows obtaining a SMC product with enhanced spatial resolution (0.1°), which is more suitable for hydrological applications. The AMSR2 derived SMC is compared with simulated data obtained from the application of a well-established soil water balance model [3]. The training and test of the algorithm are carried out on a test area in central Italy, while the entire Italy is considered for the validation. The last step of the activity is the use of the HydroAlgo SMC into the SM2RAIN algorithm [4], in order to exploit the potential contribution of this product at enhanced resolution for rainfall estimation. [1] E. Santi, S. Pettinato, S. Paloscia, P. Pampaloni, G. Macelloni, and M. Brogioni (2012), “An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo”, Hydrology and Earth System Sciences, 16, pp. 3659-3676, doi:10.5194/hess-16-3659-2012. [2] E. Santi (2010), “An application of SFIM technique to enhance the spatial resolution of microwave radiometers”, Intern. J. Remote Sens., vol. 31, 9, pp. 2419-2428. [3] L. Brocca, S. Camici, F. Melone, T. Moramarco, J. Martinez-Fernandez, J.-F. Didon-Lescot, R. Morbidelli (2014), “Improving the representation of soil moisture by using a semi-analytical infiltration model”, Hydrological Processes, 28(4), pp. 2103-2115, doi:10.1002/hyp.9766. [4] Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128-5141, doi:10.1002/2014JD021489.

PROCEEDINGS ARTICLE | October 15, 2015

Cosmo-SkyMed and RADARSAT2 image investigation for the monitoring of agricultural areas

Proc. SPIE. 9642, SAR Image Analysis, Modeling, and Techniques XV

KEYWORDS: Agriculture, Soil science, Data modeling, Polarization, Backscatter, Sensors, Synthetic aperture radar, Vegetation, X band, RGB color model

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PROCEEDINGS ARTICLE | October 15, 2015

Large area robust identification of snow cover from multitemporal COSMO-SkyMed images

Proc. SPIE. 9642, SAR Image Analysis, Modeling, and Techniques XV

KEYWORDS: Landsat, Detection and tracking algorithms, Synthetic aperture radar, Linear filtering, Quantization, Image filtering, Snow cover, Earth observing sensors, Lawrencium, Algorithms

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PROCEEDINGS ARTICLE | October 21, 2014

An overview of neural network applications for soil moisture retrieval from radar satellite sensors

Proc. SPIE. 9243, SAR Image Analysis, Modeling, and Techniques XIV

KEYWORDS: Radar, Soil science, Data modeling, Polarization, Sensors, Satellites, Synthetic aperture radar, Vegetation, Algorithm development, Microwave radiation

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Frequent and spatially distributed measurements of soil moisture (SMC), at different spatial scales, are advisable for all applications related to the environmental disciplines, such as climatology, meteorology, hydrology and agriculture. Satellite sensors operating in the low part of microwave spectrum are very suitable for this purpose, and their signals can be directly related to the moisture content of the observed surfaces, provided that all the contributions from soil and vegetation to the measured signal are properly accounted for. Among the algorithms used for the retrieval of SMC from both active (i.e. Synthetic Aperture Radar, SAR or real aperture radars) and passive (radiometers) microwave sensors, the artificial neural networks (ANN) represent the best compromise between accuracy and computation speed. ANN based algorithms have been developed at IFAC, and adapted to several radar and radiometric satellite sensors, in order to generate SMC products at different spatial resolutions, varying from hundreds of meters to tens of kilometers. These algorithms, which use the ANN techniques for inverting theoretical and semi-empirical models, such as Advanced Integral Equation (AIEM), Oh models, and Radiative transfer Theory (RTT), have been adapted to the C-band acquisitions from SAR (Envisat/ASAR) and real aperture radar (ASCAT) and to the X-band SAR acquisitions of Cosmo-SkyMed and TerraSAR-X. Moreover, a specific ANN algorithm has also been implemented for the L-band active and passive acquisitions of the incoming SMAP mission. The latter satellite will carry onboard simultaneously one radar and one radiometer operating at the same frequency, but with different spatial resolutions (3 and 40 km, respectively). Large datasets of co-located satellite acquisitions and direct SMC measurements on several test sites located worldwide have been used along with simulations derived from forward electromagnetic models for setting up, training and validating these algorithms. An overall quality assessment of the obtained results in terms of accuracy and computational cost was carried out, and the main advantages and limitations for an operational use of these algorithms have been evaluated.

SPIE Journal Paper | August 7, 2014

Snow cover area identification by using a change detection method applied to COSMO-SkyMed images

Pettinato, Simone , Santi, Emanuele , Paloscia, Simonetta , Aiazzi, Bruno , Baronti, Stefano , Garzelli, Andrea

JRS Vol. 8 Issue 01

KEYWORDS: Snow cover, Synthetic aperture radar, Landsat, Meteorology, Satellites, Lawrencium, Speckle, Backscatter, Image processing, Earth observing sensors

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PROCEEDINGS ARTICLE | October 17, 2013

Comparison of three algorithms for the retrieval of soil moisture from ASCAT data in the framework of the round robin exercise

Proc. SPIE. 8891, SAR Image Analysis, Modeling, and Techniques XIII

KEYWORDS: Soil science, Backscatter, Calibration, Remote sensing, Vegetation, Climate change, Neodymium, Algorithm development, Microwave radiation, Climatology

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PROCEEDINGS ARTICLE | October 17, 2013

GNSS-R sensor sensitivity to soil moisture and vegetation biomass and comparison with SAR data performance

Proc. SPIE. 8891, SAR Image Analysis, Modeling, and Techniques XIII

KEYWORDS: Agriculture, Soil science, Reflection, Sensors, Synthetic aperture radar, Surface roughness, L band, Vegetation, Satellite navigation systems, Lawrencium

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In this paper the results obtained from an experiment focused on the capabilities of GNSS-R sensors for land applications are described. This experiment was carried out within the framework of two ESA projects devoted to the investigation of GNSS-R signal over land: LEiMON(Land Monitoring with Navigation Signals) and GRASS (GNSS Reflectometry Analysis for BiomaSS Monitoring). The latter project consisted in the analysis of GNSS-R signal collected by a dual polarization sensor installed on an aircraft which flew over a test area in Italy with agricultural fields and poplar plots. It has been observed that the LR reflection coefficient was sensitive to changes in the surface soil moisture, with a total variation of about 6 dB between the dry and wet seasons, within an interval between -8/-17 dB.Whereas, the RR reflection coefficient was generally very low for all surfaces, in the range -20/-25 dB, with an increasing trend with incidence angle. LR reflection coefficient was directly related to the main parameters of soil and vegetation, namely soil moisture and vegetation biomass and rather good sensitivity to these parameters was observed. The sensitivity to soil moisture was of about 0.25dB/%soil moisture. These results have been compared with those obtained in the LEiMON project showing a good agreement. A clear correlation was also observed between LR reflection coefficient and poplar biomass, especially at steep incidence angles (17-23°). The observed sensitivity was of about 1.0dB/(50-100t/ha of dry biomass). These results have been subsequently compared with those obtained at the same frequency (L-band) with SAR sensors. From the comparison it was observed that the sensitivity of GNSS-R signal is generally lower than that one of SAR, except for the case of forest biomass. The obtained results suggest good prospects of GNSS-R especially for soil moisture and forest biomass monitoring, also in view of the increasing availability of GNSS constellations and the potential synergy with other Earth Observation sensors like SAR’s.

PROCEEDINGS ARTICLE | November 21, 2012

COSMO SkyMed X-band SAR imagery for snowpack characterization in mountain areas

Proc. SPIE. 8536, SAR Image Analysis, Modeling, and Techniques XII

KEYWORDS: Data modeling, Polarization, Backscatter, Satellites, Synthetic aperture radar, Particles, Crystals, X band, Meteorology, Snow cover

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PROCEEDINGS ARTICLE | October 27, 2011

Soil moisture mapping using Sentinel 1 images: the proposed approach and its preliminary validation carried out in view of an operational product

Proc. SPIE. 8179, SAR Image Analysis, Modeling, and Techniques XI

KEYWORDS: Radar, Soil science, Data modeling, Polarization, Backscatter, Synthetic aperture radar, Vegetation, Artificial neural networks, Meteorology, Algorithm development

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PROCEEDINGS ARTICLE | October 25, 2010

Neural network adaptive algorithm applied to high resolution C-band SAR images for soil moisture retrieval in bare and vegetated areas

Proc. SPIE. 7829, SAR Image Analysis, Modeling, and Techniques X

KEYWORDS: Soil science, Data modeling, Polarization, Backscatter, Synthetic aperture radar, Image resolution, Vegetation, Neural networks, Algorithm development, Evolutionary algorithms

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PROCEEDINGS ARTICLE | September 29, 2009

Soil moisture retrieval from SAR images as a calibration tool for soil moisture index derived from thermal inertia with MODIS images

Proc. SPIE. 7477, Image and Signal Processing for Remote Sensing XV

KEYWORDS: Radar, MODIS, Soil science, Sensors, Calibration, Synthetic aperture radar, Image analysis, Vegetation, Image retrieval, Temperature metrology

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PROCEEDINGS ARTICLE | September 29, 2009

Snow cover mapping by using optical and SAR data

Proc. SPIE. 7477, Image and Signal Processing for Remote Sensing XV

KEYWORDS: Radar, MODIS, Data modeling, Sensors, Synthetic aperture radar, Clouds, Landslides, Spatial resolution, Floods, Snow cover

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PROCEEDINGS ARTICLE | September 29, 2009

Spatial and temporal soil moisture monitoring in semi-arid and humid areas with high resolution ASAR images

Proc. SPIE. 7477, Image and Signal Processing for Remote Sensing XV

KEYWORDS: Soil science, Polarization, Backscatter, Sensors, Calibration, Synthetic aperture radar, Vegetation, Data acquisition, Neural networks, Neurons

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PROCEEDINGS ARTICLE | October 9, 2006

An algorithm based on neural networks for generating multi-temporal soil moisture maps from ENVISAT/ASAR images

Proc. SPIE. 6363, SAR Image Analysis, Modeling, and Techniques VIII

KEYWORDS: Agriculture, Soil science, Data modeling, Backscatter, Satellites, Synthetic aperture radar, Artificial neural networks, Neural networks, Evolutionary algorithms, Neurons

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