Papers by Simone Pettinato
2011 XXXth URSI General Assembly and Scientific Symposium, 2011
A long term analysis of vegetation emission and scattering characteristics was carried out by usi... more A long term analysis of vegetation emission and scattering characteristics was carried out by using microwave airborne and ground-based radiometric systems by the Microwave Remote Sensing Group of IFAC-CNR. A comparison with active sensors at the same frequency became promising after the launch of Cosmo-Skymed and TerraSARX satellites. In this paper the potentials of both emissivity and backscatter at X-band
Model analysis and experimental investigations of X-band backscattering sensitivity to snowpack characteristics
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
ABSTRACT Monitoring of snow cover is crucial in water resource management and hydrological risk p... more ABSTRACT Monitoring of snow cover is crucial in water resource management and hydrological risk prevention. Experiments have shown the ability of C-band SAR in mapping the extent of wet snow. But, detection of dry snow at this frequency is difficult due to the high transmissivity of the snowpack. A model sensitivity study, corroborated by experimental data, has demonstrated that COSMO-Skymed X-band data can give significant information for generating maps of SWE for snow depth higher than about 50-60 cm.
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
RETRIEVAL OF SOIL MOISTURE WITH AIRBORNE AND SATELLITE MICROWAVE SENSORS E.Santi, S.Paloscia, P.P... more RETRIEVAL OF SOIL MOISTURE WITH AIRBORNE AND SATELLITE MICROWAVE SENSORS E.Santi, S.Paloscia, P.Pampaloni, S.Pettinato, M.Brogioni CNR-IFAC, Firenze (Italy) [email protected] ... IGARSS 2009 Page 2. (PROSA) funded by the Italian Space Agency. ...
The retrieval and monitoring of vegetation parameters from COSMO-SkyMed images
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
ABSTRACT The capability of COSMO-SkyMed in estimating vegetation biomass has been investigated in... more ABSTRACT The capability of COSMO-SkyMed in estimating vegetation biomass has been investigated in this paper. SAR data from COSMO-SkyMed were collected on two agricultural areas in Italy in 2010 at different dates during the vegetation cycle. The performances of X-band data have been compared with accurate ground truth measurements of soil and vegetation carried out simultaneously to satellite passes. Experimental data have been compared with model simulations obtained with a discrete element radiative transfer model. Moreover, an inversion algorithm, based on an Artificial Neural Network and trained by using AIEM and the radiative transfer model, has been applied to retrieve the plant water content of wheat and sunflower crops and to generate the corresponding plant water content maps.
RESUME It has been established that optical and near-infrared sensors can monitor the seasonal va... more RESUME It has been established that optical and near-infrared sensors can monitor the seasonal variations of snow cover in alpine areas in cloud free conditions. However, only microwave sensors are able to acquire data independently of day light and in adverse weather conditions. The effects of dry snow on currently available C-band SAR data are rather small and difficult to detect. On the contrary, several experiments have documented their ability in mapping the extent of wet snow. In 2002-2004 a series of ERS SAR and ENVISAT ASAR images were collected on the Italian Alps to further evaluate the potential of C-band SAR data in remote sensing of snow and, in particular, in detecting temporal evolution of snow cover. A sequence of SAR images was analysed by comparing the backscattering of specific sub-areas of interest with in-situ snow and meteo data, and through simulations performed with a model based on the Strong Fluctuation Theory. A multi-temporal analysis of C-band SAR images...
The aim of this paper was to verify the capability of ENVISAT/ASAR images in providing soil moist... more The aim of this paper was to verify the capability of ENVISAT/ASAR images in providing soil moisture maps of agricultural areas. SAR images were collected in two sites in Italy (Alessandria and Montespertoli) in 2003 and 2004, along with ground measurements of soil moisture and roughness. The performances of some inversion statistical algorithms (Regression, Bayes, Neural Network, Nelder-Mead) in retrieving several levels of soil moisture were tested and compared to each other. Up to six levels of soil moisture were correctly retrieved by using these techniques.
Remote Sensing, 2013
In this paper, the results of a comparison between the soil moisture content (SMC) estimated from... more In this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test site located in North-west Italy, in the Scrivia river basin. The hydrological model used for the simulations consists of a one-layer soil water balance model, which was found to be able to partially reproduce the soil moisture variability, retaining at the same time simplicity and effectiveness in describing the topsoil. SMC estimates were derived from the application of a retrieval algorithm, based on an Artificial Neural Network approach, to a time series of ENVISAT/ASAR images acquired over the Scrivia test site. The core of the algorithm was represented by a set of ANNs able to deal with the different SAR configurations in terms of polarizations and available ancillary data. In case of crop covered soils, the effect of vegetation was accounted for using NDVI information, or, if available, for the cross-polarized channel. The algorithm results showed some ability in retrieving SMC with RMSE generally <0.04 m 3 /m 3 and very low bias (i.e., <0.01 m 3 /m 3 ), except for the case of VV polarized SAR images: in this case, the obtained RMSE was somewhat higher than 0.04 m 3 /m 3 (≤0.058 m 3 /m 3 ). The algorithm was implemented within the framework of an ESA project concerning the development of an operative algorithm OPEN ACCESS Remote Sens. 2013, 5 4962 for the SMC retrieval from Sentinel-1 data. The algorithm should take into account the GMES requirements of SMC accuracy (≤5% in volume), spatial resolution (≤1 km) and timeliness (3 h from observation). The SMC estimated by the SAR algorithm, the SMC estimated by the hydrological model, and the SMC measured on ground were found to be in good agreement. The hydrological model simulations were performed at two soil depths: 30 and 5 cm and showed that the 30 cm simulations indicated, as expected, SMC values higher than the satellites estimates, with RMSE higher than 0.08 m 3 /m 3 . In contrast, in the 5-cm simulations, the agreement between hydrological simulations, satellite estimates and ground measurements could be considered satisfactory, at least in this preliminary comparison, showing a RMSE ranging from 0.054 m 3 /m 3 to 0.051 m 3 /m 3 for comparison with ground measurements and SAR estimates, respectively.
<title>An algorithm based on neural networks for generating multi-temporal soil moisture maps from ENVISAT/ASAR images</title>
SAR Image Analysis, Modeling, and Techniques VIII, 2006
In this paper the actual capabilities of ENVISAT/ASAR images in providing soil moisture maps have... more In this paper the actual capabilities of ENVISAT/ASAR images in providing soil moisture maps have been tested. Several SAR images were collected on two test areas: a flat agricultural region in the Alessandria area, in Italy, and the natural area of Kemijoki river system, in Finland. An inversion algorithm based on Artificial Neural Networks (ANN) for the retrieval 4-5 levels of soil moisture from backscattering data was tested and successfully compared to ground measurements.
The Sensitivity of Cosmo-SkyMed Backscatter to Agricultural Crop Type and Vegetation Parameters
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
ABSTRACT The capability of COSMO-SkyMed (CSK) in monitoring vegetation has been investigated in t... more ABSTRACT The capability of COSMO-SkyMed (CSK) in monitoring vegetation has been investigated in this paper. SAR data from CSK were collected on two agricultural areas in Italy from 2010 to 2012, at different dates during the vegetation cycle. X-band data have been compared to accurate ground truth measurements of soil and vegetation parameters carried out simultaneously to satellite passes. Significant sensitivity of backscatter to vegetation water content of agricultural crops was observed. However, the backscattering showed an opposite trend as a function of biomass of wheat and sunflower, which belong to two very different vegetation types, namely narrow-leaf and broad-leaf crops. Similar trends emerged at lower frequencies (i.e., C and L bands) for the same crop types. In order to investigate the role of different parameters of soil and vegetation (e.g., surface roughness and moisture, plant density and height, dimensions of leaves, stem diameter, and water content) on the backscatter behavior for these two different crop types, model simulations were performed using a discrete element radiative transfer model for vegetation, whereas soil was modeled using the advanced integral equation model (AIEM). A sensitivity analysis of the model was carried out by varying the dimensions of vegetation components within the range of parameters directly measured on ground during the experimental campaigns. The model simulations were successively compared with experimental data of backscattering. The good agreement found between experimental and simulated data encouraged the follow-up of the research toward the implementation of inversion algorithms, which can be able to retrieve vegetation biomass from SAR data and from an operative point of view.
An operational algorithm for snow cover mapping in hydrological applications
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
... Simone Pettinato, Emanuele Santi, Marco Brogioni, Simonetta Paloscia, Paolo Pampaloni ... The... more ... Simone Pettinato, Emanuele Santi, Marco Brogioni, Simonetta Paloscia, Paolo Pampaloni ... The Italian National Project: PROSA (PRodotti di Osservazione Satellitari per l'Allerta Meteorologica) is a pilot project funded by the Italian Space Agency, which aims at contributing to ...
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008
The satellite mission COld REgions Hydrology High-resolution Observatory (CoReH2O) is one of the ... more The satellite mission COld REgions Hydrology High-resolution Observatory (CoReH2O) is one of the six missions which has been selected for scientific and technical feasibility studies within the Earth Explorer Programme of the European Space Agency. The mission aims at closing major gaps in present snow and ice observations. The focus of the mission is on spatially detailed repeat measurements of snow and ice properties in order to advance the understanding of the role of the cryosphere in the climate system and to improve the knowledge and prediction of water cycle variability and changes. CoReH2O will provide spatially detailed observations of extent, water equivalent and melting state of the seasonal snow cover, of snow accumulation and diagenetic facies on glaciers, of permafrost features, and of sea ice properties with emphasis on new ice formation and snow burden. A dual frequency SAR is proposed, operating at X-band (9.6 GHz) and Ku-band (17.2 GHz), VV and VH polarizations, with a swath width of about 100 km. Ku-band is more sensitive to shallow snow, whereas X-band provides greater penetration for sensing deeper snow.
An accurate sensitivity analysis of microwave emission and scattering to snow water equivalent (S... more An accurate sensitivity analysis of microwave emission and scattering to snow water equivalent (SWE) is performed by using a two layer Dense Medium Radiative Transfer Model implemented for both active and passive case. In order to evaluate the potential of the recent Cosmo-Skymed and TerrasarX missions , the study is mainly focussed on X band sensors . Model simulations have shown that an appreciable sensitivity of X-band backscattering and emission to dry snow can be found for Snow Water Equivalent higher than 70-100 mm and relatively high values of snow density and crystal dimensions. These results have been confirmed by experimental data taken from Cosmo Skymed mission and ground based multifrequency radiometers on a test site in the Italian Alps.
The Sensitivity of Cosmo-SkyMed Backscatter to Agricultural Crop Type and Vegetation Parameters
ABSTRACT The capability of COSMO-SkyMed (CSK) in monitoring vegetation has been investigated in t... more ABSTRACT The capability of COSMO-SkyMed (CSK) in monitoring vegetation has been investigated in this paper. SAR data from CSK were collected on two agricultural areas in Italy from 2010 to 2012, at different dates during the vegetation cycle. X-band data have been compared to accurate ground truth measurements of soil and vegetation parameters carried out simultaneously to satellite passes. Significant sensitivity of backscatter to vegetation water content of agricultural crops was observed. However, the backscattering showed an opposite trend as a function of biomass of wheat and sunflower, which belong to two very different vegetation types, namely narrow-leaf and broad-leaf crops. Similar trends emerged at lower frequencies (i.e., C and L bands) for the same crop types. In order to investigate the role of different parameters of soil and vegetation (e.g., surface roughness and moisture, plant density and height, dimensions of leaves, stem diameter, and water content) on the backscatter behavior for these two different crop types, model simulations were performed using a discrete element radiative transfer model for vegetation, whereas soil was modeled using the advanced integral equation model (AIEM). A sensitivity analysis of the model was carried out by varying the dimensions of vegetation components within the range of parameters directly measured on ground during the experimental campaigns. The model simulations were successively compared with experimental data of backscattering. The good agreement found between experimental and simulated data encouraged the follow-up of the research toward the implementation of inversion algorithms, which can be able to retrieve vegetation biomass from SAR data and from an operative point of view.
An algorithm for soil moisture mapping in view of coming Sentinel-1 satellite
ABSTRACT The main objective of this paper is to assess the capability of a soil moisture (SMC) al... more ABSTRACT The main objective of this paper is to assess the capability of a soil moisture (SMC) algorithm adapted to the GMES Sentinel-1 characteristics, developed within the framework of an ESA project (SMAD-1). The SMC product shall be generated from Sentinel-1 data in near-real-time and delivered to the GMES services within 3 hours from observations. Two different complementary approaches were proposed: the first approach was based on Artificial Neural Networks (ANN), which represented the best compromise between retrieval accuracy and processing time, thus being compliant with the timeliness requirements. The second approach was based on a Bayesian Multi-temporal method, allowing an increase of the retrieval accuracy, especially in case of few ancillary data available, at the cost of computational efficiency, taking advantage of the frequent revisit time achieved by Sentinel-1. The algorithm was validated in several test areas in Italy, US and Australia, and finally in Spain by performing a &#39;blind&#39; validation.
Soil moisture mapping using Sentinel 1 images: The proposed approach and its preliminary validation carried out in view of an operational product
ABSTRACT The main objective of this research is to develop, test and validate a soil moisture (SM... more ABSTRACT The main objective of this research is to develop, test and validate a soil moisture (SMC)) algorithm for the GMES Sentinel-1 characteristics, within the framework of an ESA project. The SMC product, to be generated from Sentinel-1 data, requires an algorithm able to process operationally in near-real-time and deliver the product to the GMES services within 3 hours from observations. Two different complementary approaches have been proposed: an Artificial Neural Network (ANN), which represented the best compromise between retrieval accuracy and processing time, thus allowing compliance with the timeliness requirements and a Bayesian Multi-temporal approach, allowing an increase of the retrieval accuracy, especially in case where little ancillary data are available, at the cost of computational efficiency, taking advantage of the frequent revisit time achieved by Sentinel-1. The algorithm was validated in several test areas in Italy, US and Australia, and finally in Spain with a &#39;blind&#39; validation. The Multi-temporal Bayesian algorithm was validated in Central Italy. The validation results are in all cases very much in line with the requirements. However, the blind validation results were penalized by the availability of only VV polarization SAR images and MODIS lowresolution NDVI, although the RMS is slightly &gt; 4%.
Comparison of COSMO-SkyMed and TerraSAR-X data for the retrieval of land hydrological parameters
ABSTRACT The backscattering coefficient variations of Cosmo-SkyMed and TerraSAR-X SAR sensors hav... more ABSTRACT The backscattering coefficient variations of Cosmo-SkyMed and TerraSAR-X SAR sensors have been investigated. When possible, the data of the two sensors have been compared and a quantitative analysis was carried out. The comparison of SAR data has been also performed taking into account the temporal variations, in order to quantify potential changes of surface parameters. A series of both Cosmo-SkyMed (CSK) and TerraSAR-X (TSX) images were collected on both mountain and agricultural areas. The potentials of X-band backscattering in estimating hydrological parameters of the surface were investigated.
Monitoring the spatial and temporal homogeinty of microwave emission on the East-Antarctic plateau
2010 11th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, 2010
ABSTRACT The East Antarctic plateau is a part of Antarctica extending several hundred kilometers ... more ABSTRACT The East Antarctic plateau is a part of Antarctica extending several hundred kilometers around the South Pole with an average elevation close to 3000 m a.s.l. This area provides unique opportunities for various scientific disciplines including Glaciology, Atmospheric and Earth Sciences. In addition, there is growing interest, in using the Antarctic plateau, for calibrating and validating data of satellite-borne microwave radiometers. This is because the size, structure, spatial homogeneity and thermal stability of this area. In this paper we analyse temporal and spatial variability of multi-frequency microwave emission from an area around the Dome-C scientific station by using AMSR-E data collected along the years 2003-2008. From the analysis two candidates areas were founded.
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008
Estimating Snow Characteristics with Multifrequency Microwave Radiometry
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008
Microwave radiometric measurements of snow pack were carried out with ground based sensors in win... more Microwave radiometric measurements of snow pack were carried out with ground based sensors in winter 2007-2008. Data collected on dry snow, showed small fluctuations related to diurnal solar cycle and presented a time delay of microwave brightness temperatures with respect to the snow surface temperature. The measurement of these delays, together with a correlation analysis of the brightness and physical temperature of snow, made it possible estimating the thickness of layers that mostly contributed to microwave emission at 19 and 37 GHz. Simulations performed with IRIDE model were consistent with experimental data.
Microwave Emission from Forested Areas by Using Microwave AMSR-E Data
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008
In this paper an overview of the main microwave characteristics observed on three forest areas se... more In this paper an overview of the main microwave characteristics observed on three forest areas selected in different areas worldwide is given. Microwave parameters were analyzed for a yearly cycle (2007-2008), paying particular attention to the seasonal variations forest leaf biomass, expressed as leaf area index (LAI), and climatic conditions. Some microwave indexes of polarization and frequencies are in good agreement with the seasonal variations of vegetation. The emission at C-band was found to be related to the moisture conditions of the area.
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Papers by Simone Pettinato