Simone Pettinato

Ground-Based Microwave Investigations of Forest Plots in Italy

In this paper, the result obtained on two forest stands of poplar (Populus alba) and pine (Pinus italica) in Italy, by using multi-frequency microwave radiometers, are described. Measurements were performed at L, C, X, Ku and Ka bands at different incidence angles, both in H and V polarizations, by using microwave radiometers mounted on an hydraulic boom. The sensitivity of L-band emission to woody volume was confirmed, although the effect of soil moisture is significant, especially at low values of forest biomass. Measurements carried out in upward direction gave the possibility of separating the contributions of crowns, trunks and soil and, by using a simplified model based on the radiative transfer theory, measuring consequently the forest transmissivity at different frequencies.

Modeling the Multifrequency Emission of Broadleaf Forests and Their Components

This paper shows a model study about the emissivity of forests. Model outputs are compared with multifrequency airborne measurements carried out over five broadleaf forests in Italy. Two flights took place, in summer 1999 and winter 2002. Available ground truth included important variables, such as biomass, tree density, and average trunk diameter. This data set, in conjunction with allometric equations and information taken from the literature, is used to give inputs to the model. A general agreement between simulated and measured data is observed at L-, C-, and X-bands. The same model is used to investigate the sensitivity of forest emissivity to soil moisture, woody volume, and average diameter. As expected, a moderate effect of soil moisture is observed only at L-band and for forests with a lower woody volume. At L-band, the model predicts a general increase of emissivity with woody volume but indicates that also the trunk diameter exerts an important influence, since it is a variable which controls several geometrical properties. These results allow us to single out the influence of soil moisture, woody volume, and geometrical properties at L-band. The increase of emissivity with frequency, observed in experimental data, is interpreted by means of electromagnetic considerations about branch scattering.

Ground-Based L-Band Emission Measurements at Dome-C Antarctica: The DOMEX-2 Experiment

In recent years, there has been growing interest on the part of the remote sensing community in using the Antarctic area for calibrating and validating data of low-frequency satellite-borne microwave radiometers. In particular, the East Antarctic Plateau appears to be suited for this purpose. The reasons for this interest are the size, structure, spatial homogeneity, and thermal stability of this area. This is particularly interesting for low-frequency microwave radiometers since, due to the low extinction of dry snow, the upper ice-sheet layer is almost transparent and the brightness temperature variability is therefore extremely small. In the context of calibration and validation activities of the European Space Agencys Soil Moisture and Ocean Salinity (SMOS) satellite, an experiment called DOMEX-2, which included radiometric L-band measurements, was carried out at the Italian-French base of Concordia located at Dome C in the East Antarctic Plateau from December 2008 to December 2010. Ground measurements (i.e., snow temperature at different depths, snow structure, meteorological data, etc.) were also collected during the experiment. This paper presents information on the experimental campaign, the characteristics of the radiometric measurements, and the main results. A comparison with SMOS data is also presented.

Sensitivity of bistatic scattering to soil moisture and surface roughness of bare soils

The sensitivity of bistatic scattering coefficient σ° to soil moisture content (SMC) and surface roughness was investigated by means of model simulations of the incoherent scattered fields performed with the advanced integral equation model (AIEM) and the second order small perturbation model (SPM). The study was performed by simulating scattering on the whole upper half space, for different values of incident angles. The achieved results, represented as maps of σ° as a function of azimuth and zenith angles, were evaluated by means of a quality index which takes into consideration the effect of roughness on SMC measurement. The sensitivity analysis has pointed out that for measuring SMC a bistatic observation, by itself or combined with the monostatic one, can make appreciable improvements with respect to classical monostatic radar. Appendix A contains the AIEM formulas corrected for several typographical errors present in the specific literature.

The Intercomparison of X-Band SAR Images from COSMO‑SkyMed and TerraSAR-X Satellites: Case Studies

The analysis of experimental data collected by X-band SAR of COSMO-SkyMed (CSK®) and TerraSAR-X (TSX) images on the same surface types has shown significant differences in the signal level of the two sensors. In order to investigate the possibility of combining data from the two instruments, a study was carried out by comparing images collected with similar orbital and sensor parameters (e.g., incidence angle, polarization, look angle) at approximately the same date on two Italian agricultural test sites. Several homogenous agricultural fields within the observed area common to the two sensors were selected. Some forest plots have also been considered and used as a reference target). Direct comparisons were then performed between CSK and TSX images in different acquisition modes. The analysis carried out on the agricultural fields showed that, in general, the backscattering coefficient is higher in TSX Stripmap images with respect to CSK-Himage (about 3 dB), while CSK-Ping Pong data showed values lower than TSX of about 4.8 dB. Finally, a difference in backscattering of about 2.5 dB was pointed out between CSK-Himage and Ping-Pong images on agricultural fields. These results, achieved on bare soils, have also been compared with simulations performed by using the Advanced Integral Equation Model (AIEM).

The Potential of COSMO-SkyMed SAR Images in Monitoring Snow Cover Characteristics

Monitoring of snow cover is crucial to the study of global climate changes for water resource management, as well as for flood and avalanche risk prevention. The sensitivity to snow characteristics of X-band backscattering of COSMO-SkyMed mission has been analyzed in the framework of experimental and model activities. X-band data have been found to contribute to the retrieval of the snow water equivalent (SWE), provided that the snow cover is characterized by a snow depth (SD) of roughly 60-70 cm (SWE >; 100-150 mm) and with relatively large crystal dimensions. Subsequently, an algorithm for retrieving SD or SWE has been developed and tested with experimental data collected on several ground stations.

Monitoring Snow Characteristics With Ground-Based Multifrequency Microwave Radiometry

Long-term microwave and infrared radiometric measurements of snowpack were carried out with ground-based sensors in winter 2006-2007 and 2007-2008, together with conventional measurements of snow-cover profiles. The first experiment focused on the behavior of snow emission during the destructive and constructive metamorphisms. The second involved a correlation analysis of the small fluctuations related to diurnal solar cycle in order to obtain the time delay of microwave brightness temperatures Tb with respect to the snow surface temperature. From this analysis, it was possible to estimate an effective (weighed average) temperature and the thickness of the layer that mostly contributed to microwave emission at 19 and 37 GHz. The ratio of the brightness temperature to the effective temperature can be assumed to be an equivalent emissivity of the snowpack. Data collected in both years have been compared with simulations carried out using the advanced Institute of Applied Physics (IFAC) Radiative Advanced Dry Snow Emission (IRIDE) model driven by data collected on ground. The model is based on the advanced integral equation method to represent soil, coupled to a layer of dry snow whose electromagnetic properties are described by the dense medium radiative transfer theory with quasi-crystalline approximation applied to a medium (air) filled with sticky particles. Simulations performed by using ground data as inputs to the model have been found to be well in agreement with experimental data. Moreover, the comparison of model simulations with experimental data allowed one to understand some peculiar characteristics of microwave emission from the snowpack related to its physical conditions.

Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site

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

Generation of soil moisture maps from ENVISAT/ASAR images in mountainous areas: a case study

A method for producing soil moisture maps in mountainous areas by using Environment Satellite Advanced Synthetic Aperture Radar (ENVISAT/ASAR) images at C-band is described in this paper. For this purpose, experimental campaigns were carried out in 2004 in the Cordevole watershed in Italy during ENVISAT passes. Ground truth measurements of soil and vegetation parameters were obtained simultaneously using satellite surveys. A preliminary classification of the area was carried out to mask those zones in which soil moisture measurement was unobtainable. The performance of an inversion algorithm, based on artificial neural networks (ANNs), in retrieving soil moisture content (SMC) from the collected images was then tested and compared with ground measurements. The results obtained on a restricted portion of the watershed show reasonable agreement of backscattering (σ0) with ground truth data and meteorological conditions, thus making it possible to extend the algorithm to the entire test area. The contribution of vegetation cover was then simulated by using a discrete elements model based on radiative transfer theory. Three pixel-by-pixel soil moisture maps of the test site, with four levels of soil moisture, were generated from the available images by using a new ANN that took into account the effects of vegetation.

Monitoring sea-ice and dry snow with GNSS reflections

GPS reflected signals have become a source of opportunity for remote sensing of the Earths suface. In this work, we present several capabilities of this technique in two different polar environments: Greenland and Antarctica. The first part is dedicated to the retrieval of sea-ice properties, giving emphasis to the study of the coherent phase for altimetric and roughness estimations, and polarimetric measurements for the determination of the ice salinity variation. The results show good agreement with a tide model and daily ice charts. On the second part, some preliminary results and analysis strategies to retrieve dry snow signatures are presented.