This study aims at improving the accuracy of the Strong Fluctuation Theory (SFT) in simulating the backscattering from Alpine snowpacks, by simulating the roughness effect of the snow-soil interface through suitable models, as the Oh model and the Advanced Integral Equation Model (AIEM). As conceived in the original form indeed, SFT considers the air-snow and snow-soil interfaces as flat surfaces: such approximation can lead to inaccurate results under some observed conditions. The reappraised SFT was validated against Dense Media Radiative Transfer (DMRT) model simulations and experimental data available from Sentinel-1 (S-1) C-band and COSMO-SkyMed (CSK) X-band SAR in two alpine test sites located in the Northern Italy. The inclusion of rough soil contribution was found effective in improving significantly the SFT simulation in dry and wet snow conditions, with a significant improvement of correlation with SAR data: as an example, R2increased from 0.05 to 0.57 in the comparison with CSK. The comparison with DMRT pointed out a very good agreement between the two models, (R2=0.88 at C-band and 0.91 at X-band) with the not negligible advantage of an extremely reduced computational cost of the reappraised SFT with respect to DMRT.

In this study, a disaggregation technique based on machine learning is proposed. The technique combines Sentinel 1 and AMSR2 data with the aim of enhancing the spatial resolution of the vegetation biomass, expressed herein as Plant Water Content (PWC), and Soil Moisture (SM) products generated from AMSR2 by the HydroAlgo algorithm developed at IFAC. Validation is still in progress; however, the results obtained so far demonstrated the effectiveness of the proposed disaggregation in mapping both PWC and SM at 100m resolution, thus overcoming the problem of coarse spatial resolution that hampers the potential of satellite microwave radiometers as the AMSR2 for operational applications in small scale basins.

This study aims at relating the stickiness parameter (τ) of the Dense Media Radiative Transfer theory integrated with Sticky Hard Sphere (SHS) model (DMRT-QMS), to the physical parameters of the layered snowpack. A relationship has been derived to express τ, which modulates the attractive contact force between ice spheres, as a function of ice volume fraction (ϕ) and coordination number (nc). Since τ is not a measurable parameter, this is a step forward with respect to what is commonly made in literature, where τ is assumed as an arbitrary parameter, generally ranging between 0.1 and 0.3, to fit simulated backscattering data with those measured. As a first validation, DMRT-QMS was integrated with SNOWPACK model to simulate backscattering at X band (9.6 GHz) driven by nivo-meteorological data acquired on a test area located in Monti Alti di Ornella, Italy. The simulations were compared with Synthetic Aperture Radar COSMO-SkyMed (CSK) satellite observations. The results show a significant agreement (R2=0.68), although for a limited dataset of eight points in a unique winter season.

Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient$R \simeq 0.77$between estimated and target SD, a root-mean-square error (RMSE)$\simeq 13$cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with$R =0.74$, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount ofin situdata required for training the algorithm and of extending the algorithm exportability to other test sites.

The study addresses the problem of characterizing different seasonal conditions of snow cover by using ground-based and satellite radiometry. Experimental data collected with ground based radiometers to correlate microwave emission from snow to its physical conditions, and in particular to liquid water content were carried out by our group since '90s. Now we have reconsidered a long series of data collected during two winter-spring seasons on the Italian Alps with the aim of further studying the temporal evolution of single melting refreezing cycles. Moreover, looking at operational aspects, the potential of AMSR-E/2 radiometers in detecting the beginning of snow melting has been evaluated using data collected in both ascending and descending orbits. This investigation takes advantage of the two passes (in the morning and the afternoon), which correspond approximately to a minimum and maximum of surface temperature and therefore to the expected snow freezing and melting situations, respectively. In particular, a detailed study is in progress on the Mendoza River Basin in Argentina. Indeed, the melting of snow accumulated in the upper Mendoza river basin, during winter is the main water supply for agriculture, industry and human consumption in the area.

In this paper, we study the effect of layered snow in alpine regions on microwave emission at Ku and Ka bands, using both experimental data and model simulations. A recent implementation of the multi-layer dense-medium radiative transfer model (DMRT) under the quasi-crystalline approximation (ML-QCA) was used to account for the effects of snow layers on the emission from dry snow covers. Model simulation have been compared with radiometric measurements, collected with ground based instruments during several long-term experiment carried out over three winter seasons between 2007 and 2011 in the Eastern part of Italian Alps. This comparison has the twofold purpose of validating the model and interpreting some particular aspects of snow microwave emission. The measured brightness temperatures at Ku and Ka bands were compared with those simulated through the ML-QCA model, by using the observed snow parameters as inputs. A direct comparison of measured and simulated data showed that the slope of correlation ranged between 0.7 and 1.0, with determination coefficients between 0.51 and 0.75 and Root Mean Square Error (RMSE) between 11 K and 15 K.

This paper will investigate the effect of layered snow in alpine regions on microwave emission at Ku and Ka bands, using both experimental data and model simulations. A multilayer dense-media radiative transfer model (DMRT), was implemented under the quasi-crystalline approximation (ML-QCA), to account for the effects of snow layers on the emission from dry snow covers. The model then evaluated the sensitivity of two microwave indices, based on frequency and polarization combinations, to snow parameters. Model simulations were compared to radiometric dual frequency/polarization measurements of snow covers, collected during long-term experiments carried out over three winter seasons between 2007 and 2011 in the Eastern Italian Alps. This comparison has the twofold purpose of validating the model with experimental data and verifying the influence of snow layering on microwave emission and related frequency and polarization indices. The wide variations in snow characteristics over several winter seasons allowed for an extended validation of the model, which was demonstrated to account for the complex stratigraphy (up to 15 layers) of snow. The measured brightness temperatures at Ku and Ka bands were compared to those simulated through the multi (ML-QCA) and single-layer (SL-QCA) models, by using the observed snow parameters as inputs. In the case of SL, we used the average value of all layers weighted for the layer thickness. The results showed that the ML-QCA model was better correlated to the radiometric measurements than the SL-QCA. A direct comparison of measured and simulated data showed that the slope of correlation for the single-layer ranged between 0.4 and 0.5, with determination coefficient lower than 0.3; whereas the slope in the multi-layer approach ranged between 0.7 and 1.0, with determination coefficients between 0.51 and 0.75 and Root Mean Square Error (RMSE) between 11K and 15K.

Vegetation features have been assessed by using microwave radiometric data (AMSR-E/2, SMAP) in order to retrieve vegetation biomass maps on a global scale. The tau-omega model has been used for estimating the vegetation optical depth (tau, τ) from microwave data at different frequencies. An algorithm based on Artificial Neural Networks (ANN) and able to ingest data from different frequency channels has been implemented for the inversion of the model and the retrieval of vegetation biomass. The algorithm validation, carried out on the available experimental data, confirmed that microwave emission, and in particular the use of the two polarizations, H and V, can be legitimately used to produce vegetation maps on a global and local scale by separating several levels of biomass, without any need of further information from other sensors.

Monitoring of terrestrial snow on both local and global scales is a topic of ever-increasing interest due to the crucial role that the snow plays in the climate dynamics, water resource management, energy production, and risk prevention. In this framework, microwave remote sensing is an important tool for observing snow cover and retrieving snow depth (SD) and water equivalent (SWE). This paper summarizes the most important aspects of the microwave remote sensing of snow by highlighting major results and problems.

The polarization capabilities in estimating vegetation biomass on both global and local scales by using passive and active microwave satellite data (AMSR-E/2, ENVISAT and COSMO-SkyMed) were investigated. Two algorithms that are based on Artificial Neural Networks (ANN) and are able to ingest data from different frequency channels have been implemented. The algorithm validation, carried out on the available experimental data, confirmed that the two polarizations and related indices can be legitimately used to produce vegetation maps on a global and local scale by separating at least 3-4 levels of biomass, without any need of further information from other sensors.

In this paper, the effects of multilayer structure of snowpack and its temporal evolution on backscattering are investigated by model simulations. The study is focused on layering structures of dry snow that may represent a risk of avalanches in Alpine regions. The implemented model has been validated using X-band Cosmo SkyMed (CSK ®) acquisitions collected in the winters between 2009 and 2013 on a test area located in the Eastern part of the Italian Alps, and corresponding direct measurements of the main snow parameters. After the validation, the models are applied to simulate the backscattering from snow profiles typical of snow covers characterized by a high risk of avalanches.

In this paper, a multilayer version of the Dense Medium Radiative Transfer (DMRT) model has been implemented for the active remote sensing. The effects of multilayer structure of snowpack and its temporal evolution on backscattering have been investigated. The study has been focused on the effect of layering structure of snowpack typical of the Alpine regions. The ground measurements used as model inputs have been collected on the Italian Alps during the 2009–2010 winter season.

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.

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.

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.

Monitoring of snow cover is crucial to the study of global climate changes, for water resource management, as well flood and avalanche risk prevention. The sensitivity of X band backscattering of Cosmo-Skymed mission has been first exploited by using model simulation and experimental data. An algorithm for retrieving snow depth or snow water equivalent has been then developed and test with experimental data.

The results of an experiment carried out in Italy for exploiting the capabilities of X-band SAR in the monitoring of soil and vegetation characteristics are summarized in this paper. Data from X-band Cosmo-Skymed mission have been collected in two agricultural areas and compared with C-band data of ENVISAT/ASAR and with ground truth measurements. In general, a certain sensitivity to vegetation biomass and to moisture of bare soils has been found.

The 2009 IEEE Geoscience and Remote Sensing Society's (GRS-S) Awards were presented at IGARSS 2009. These awards included Fellow Recognitions, Major Awards, Publication Awards, and Certificates of Recognition. Those presented with awards are listed here.

This work deals with mapping snow water equivalent as well as soil moisture at low resolution from multifrequency microwave radiometers. The algorithm developed and implemented in this work produces the spatial distribution at regional scale of snow depth (SD) and of soil moisture (SMC) of snow free areas by using the brightness temperatures of the Advanced Multifrequency Scanning Radiometer (AMSR-E).

The possibility of a global monitoring of hydrological parameters in Africa was endeavored by using both active and passive microwave sensors. Two ALOS/PALSAR images of Ethiopia were compared with optical data and ground information collected on site by the Istituto Agronomico per l'Oltremare, in Florence. Moreover, AMSR-E data were used as a reference for investigating soil moisture and vegetation conditions. The brightness temperature and the backscattering coefficient values have been related to land features, obtained from ground data and cartographic and meteorological information.

An operational algorithm to produce snow cover maps from remote sensing data in the Italian Alps has been implemented in the framework of the Italian national project PROSA to contribute timely information to civil protection from floods and landslides. The algorithm can generate maps in presence of cloud cover by combining optical data from MODIS and SAR data from ENVISAT/ASAR. It has been validated on a wide area in North Italy by comparing the algorithm output with ground measurements.

Experimental campaigns with airborne and satellite microwave sensors have been carried out on an agricultural area in Northern Italy with the main purpose of gathering a suitable set of data to validate two operational algorithms developed to retrieve soil moisture from passive and active microwave sensors at different spatial scales. The algorithms will be used in a pilot project based on the use of Earth observation data in forecasting and monitoring the risk of floods and landslides. Radiometric data have been collected with the airborne IFAC instruments and the AMSR-E, while ENVISAT/ASAR images have been acquired for high resolution estimate of soil moisture at field scale.

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.

The 14 papers in this special issue are organized into topical areas and applications, which are in the general order of the MicroRad technical sessions: Radiometer Techniques (4), Vegetation (2), Ocean (2), Atmosphere and Precipitation (4), and Snow and Sea Ice (2).

In this paper, we report the results of an experimental study aimed toward investigating microwave emission from forests. The experiment was carried out in 2006 on two forest stands of poplar (Populus alba)and pine(Pinus italica), using ground-based microwave radiometers at the L-, C-, X-, Ku-, and Ka-bands, in H and V polarizations. Measurements on poplar were performed on different dates and at different incidence and azimuth angles, looking downward (from the top of trees and from below the crown) and upward (from the soil level). Only one downward-looking measurement was carried out over a pine plot with dry soil in April. All the remote sensing measurements were complemented with ¿ground-truth¿ data. The collected experimental data made it possible to quantify the spectral signatures of poplar, as well as the variation of angular trends of brightness temperature in different seasons of the year. The sensitivity of L-band emission to soil properties and leaf biomass was also investigated. Moreover, the measurements on poplar, combined with a simple radiative transfer model (the so-called omega-tau equation), allowed estimating the transmissivity of the canopy with and without leaves. The analysis of data has shown that for the observed forest type, the sensitivity to soil moisture under defoliated trees can be noted at both the L- and C-bands.

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.

The Antarctic plateau is a part of Antarctic extending for a few 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 this paper temporal and spatial variability of multi-frequency microwave emission from the East Antarctic plateau by using AMSR-E data collected from 2005 to 2007 is analyzed.

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.

In this paper, we present an intercomparison of algorithms for retrieving soil moisture content (SMC) from ENVIronmental SATtellite (ENVISAT)/Advanced Synthetic Aperture Radar images. The algorithms taken into consideration were a feedforward artificial neural network (ANN) with two hidden layers, a statistical approach based on Bayes' theorem, and an iterative algorithm based on the nelder-mead direct-search method. The comparison was carried out by using both simulated and experimental data. Simulated data were obtained by means of the integral equation model (IEM). Experimental data were collected in an agricultural area in Northern Italy during 2003-2005; they included backscattering coefficient at HH and HV polarizations and at an incidence angle of thetas = 23deg, as well as detailed ground truth measurements of SMC, surface roughness, and vegetation parameters. HH-polarized data were related to SMC, whereas the information of the cross-polarized channel was used to correct the backscatter for the effects of surface roughness. A comparison of the algorithms with experimental data showed that all the tested approaches produced SMC values that are very close to the measured ones. However, the predictions of the ANN were slightly more suitable than the other methods for generating maps in reasonable time. The production of moisture maps carried out at different dates using this algorithm pointed out the feasibility of separating up to six levels of spatial/temporal variations of SMC in the range of 10%-35%.

The sensitivity of bistatic scattering coefficient sigmadeg to soil moisture (smc) is investigated on the whole upper half space by means of model simulations of the incoherent scattered fields. The achieved results, represented as maps of sigmadeg as a function of azimuth and zenith angles, are evaluated by means of a quality index which takes into consideration the effect of roughness on smc measurement.

This paper describes the experimental activities carried out during the international experiment CAPMOS’05, which was carried out on an off-shore platform in Katsiveli, Ukraine, in May-June 2005. During the experiment, the sea surface was continuously observed by synchronous active and passive microwave instruments, combined with contact and optical observations, in order to retrieve the wave parameters and to characterize the spectral properties of the waved surface. An additional airborne campaign was carried out on the North Sea to investigate the Radio Frequency Interferences (RFI) effects which seriously may hamper the L-band measurements. Predictions of a two scale emissivity model of waved sea surface resulted in agreement with the radiometric data at S and Ka bands. A method for retrieving the wave spectrum parameters from angular radiometric measurements was developed, and compared with three different spectrum models. An inversion algorithm for retrieving the horizontal wind speed component from radiometric data at S and Ka band was implemented and validated with the experimental acquisitions.

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.

The Antarctic plateau that extends for several hundred kilometers with an average altitude of close to 3000 m a.s.l. is the highest part of the east Antarctic ice cap. This area provides unique opportunities for various scientific disciplines, including glaciology and 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, thanks to the size, structure, and spatial homogeneity of this area, and the thermal stability of deeper snow layers. In this paper, we analyze the temporal and spatial variabilities of multifrequency microwave emission from the area surrounding the Dome-C scientific station using Advanced Microwave Scanning Radiometer data collected throughout 2005. Moreover, a multilayer coherent electromagnetic model is used for estimating the contribution of snow layers to emission at various frequencies. The results are consistent with the physical structure of the ice sheet and with its seasonal and spatial variations.

The importance of microwave sensors in monitoring snow parameters is well recognized. However, several problems are still open regarding the reliability of remote sensing for operational use. In 2002-2005 a series of ERS SAR and ENVISAT ASAR images were collected on the Italian Alps to monitor the temporal evolution of snow cover. In the same time a long sequence of multi-frequency radiometric data was collected with ground based sensors. The measurements confirmed the potential of microwave active and passive sensors in monitoring the extent of wet snow cover and in estimating the liquid water content of wet snow and the snow water equivalent of refrozen snow.

In this paper, the brightness temperatures, and other microwave parameters, measured by using AMSR-E data over two yearly cycles on some test sites selected in different climatic regions of the world, have been related to surface features obtained from ground information and cartography. The areas are characterized by various meteorological conditions and vegetation covers and spread from deserts to dense forests. The analysis was carried out on the data collected with AMSR-E in 2002, 2003 and 2004. The capabilities of the satellite multi- frequency microwave radiometers in the retrieval of land surface parameters were evaluated, focusing the attention to the parameters related to the hydrological cycle, and forest areas. In general, the analysis carried out by using AMSR-E data confirmed the capability of the multi-temporal and multi- frequency analysis in a global scale monitoring of soil moisture as well as snow and vegetation covers, in the limits of the spatial resolution offered by this sensor. An algorithm based on a neural network has been implemented, tested with experimental data collected during SMEX02, and used to generate global maps of soil moisture.

Satellite data are the most suitable tools for monitoring time and spatial variations of snow covered areas and for studying snow characteristics on a global scale. Current knowledge of the microwave emission from the deep ice sheet in Antarctica is limited by the lack of low-frequency satellite sensors and by their inadequate knowledge of the physical effects governing microwave emission at wavelengths exceeding 5 cm. On the other hand, in addition to the interest related to climatic changes and to glaciological and hydrological applications, there is growing interest, on the part of the remote sensing community, in using the Antarctic and, in particular, the Dome-C plateau where the Concordia station is located, for calibrating and validating data of satellite-borne microwave and optical radiometers. This is because of the size, structure, spatial homogeneity, and thermal stability of this area. With a view to the future launches of two new low-frequency spaceborne sensors Soil Moisture and Ocean Salinity mission and Aquarius, an experiment was carried out at Dome-C, thanks to financial support from European Space Agency, aimed at evaluating the stability and the absolute value of the L- and C-band brightness temperature Tb. This paper presents a report on the experimental campaign, the characteristics of the radiometric measurements, and on the main results. The C-band Tb data indicated a diurnal cycle amplitude of a few kelvin. It was confirmed that this takes place as a consequence of observed variability in the physical temperature of the top 4 m of the snowpack around the mean surface value of -24degC. In contrast, the L-band data indicated extremely stable Tb values of 192.32 K (1sigma=0.18 K) and 190.77 K (1sigma=0.57 K) at thetas=45deg and thetas=56deg, respectively

Satellite data are the most suitable tools for monitoring time and spatial variations of snow covered areas and for studying snow characteristics on a global scale. Current knowledge of the microwave emission from the deep ice sheet in Antarctica is limited by the lack of low-frequency satellite sensors and by their inadequate knowledge of the physical effects governing microwave emission at wavelengths exceeding 5 cm. On the other hand, in addition to the interest related to climatic changes and to glaciological and hydrological applications, there is growing interest, on the part of the remote sensing community, in using the Antarctic and, in particular, the Dome-C plateau where the Concordia station is located, for calibrating and validating data of satellite-borne microwave and optical radiometers. This is because of the size, structure, spatial homogeneity, and thermal stability of this area. With a view to the future launches of two new low-frequency spaceborne sensors Soil Moisture and Ocean Salinity mission and Aquarius, an experiment was carried out at Dome-C, thanks to financial support from European Space Agency, aimed at evaluating the stability and the absolute value of the L- and C-band brightness temperature Tb. This paper presents a report on the experimental campaign, the characteristics of the radiometric measurements, and on the main results. The C-band Tb data indicated a diurnal cycle amplitude of a few kelvin. It was confirmed that this takes place as a consequence of observed variability in the physical temperature of the top 4 m of the snowpack around the mean surface value of -24°C. In contrast, the L-band data indicated extremely stable Tb values of 192.32 K (1σ = 0.18 K) and 190.77 K (1σ = 0.57 K) at 0 = 45° and θ = 56°, respectively.

A study of the melting cycle of snow was carried out by using ground-based microwave radiometers, which operated continuously 24 h/day from late March to mid-May in 2002 and from mid-February to early May in 2003. The experiment took place on the eastern Italian Alps and included micrometeorological and conventional snow measurements as well. The measurements confirmed the high sensitivity of microwave emission at 19 and 37 GHz to the melting-refreezing cycles of snow. Moreover, micrometeorological data made it possible to simulate snow density, temperature, and liquid water content through a hydrological snowpack model and provided additional insight into these processes. Simulations obtained with a two-layer electromagnetic model based on the strong fluctuation theory and driven by the output of the hydrological snowpack model were consistent with experimental data and allowed interpretation of both variation in microwave emission during the melting and refreezing phases and in discerning the contributions of the upper and lower layers of snow as well as of the underlying ground surface.

The 8th Specialist Meeting on Microwave Radiometry and \nRemote Sensing Applications (MicroRad04) was held on \nFebruary 24-27, 2004 in Rome, Italy. The coorganizers of the \nConference were Prof. N. Pierdicca of The University of Rome \n“La Sapienza” and Prof. F. S. Marzano of CETEMPS, University \nof L’Aquila. The meeting was an overwhelming success and \ncan be summarized by 162 submitted abstracts, 152 participants, \n85 oral presentations, and 42 interactive posters during four full \ndays of sessions. \nMicroRad04 was held at the Engineering College of the University \nof Rome “La Sapienza” situated in one of the most beautiful \nsites of the Eternal City, between the Colosseum, the archaeological \narea, and the early Christian Basilicas. It was the \nlatest of a series focusing on Microwave Radiometry and Remote \nSensing of the Environment. The very first one dates back \nto March 1983, when itwas organized and supported by the University \n“La Sapienza” of Rome, Italy, as a result of the initiative \nof Prof. G. d’Auria. The satisfactory outcome of the first \nmeeting stimulated an agreement among the participants to ensure \nthe continuity in the form of a periodic meeting, the second \nof which, supported by IROE-CNR in Florence, Italy, occurred \nin 1988. Since then, more regular meetings, every 30 months \napproximately, were scheduled and held in the U.S. (Boulder, \nCO, 1992; Boston, MA, 1996) and in Italy (Rome “Tor Vergata,” \n1994; Florence, 1999), alternately. In 2001, the meeting \nwas hosted by NOAA in Boulder, CO. \nThe MicroRad04 Meeting was organized by the Department \nof Electronic Engineering of the University “La Sapienza” of \nRome and was created as an open invitation to convene again \nin Rome, 20 years after the first meeting. The objective of MicroRad04 \nwas to set up a common forum to report and discuss \nrecent advances in the specific field of microwave radiometry, \nthus to gather all parties belonging to the research and industrial \ncommunity, active in projects and studies in microwave radiometry \nof atmosphere, ocean, and land. \nContributions on topics of primary interest were received, and \nthe papers were separated into both oral and interactive sessions. \nThe 15 sessions of the meeting were focused on classical \nand new advanced topics of environmental remote sensing \nby microwave radiometry, emphasizing the methodological, instrumental, \nand application point of views. Interdisciplinary and \nsensor synergy issues were also stimulated.

In the context of the project HYDRO-POL, a study was carried out to test the efficiency of two different approaches: the use of L band active and passive data or the use of L-C-X bands passive data to retrieve soil moisture of bare soils. Simulated data are generated implementing classical superficial scattering models: IEM model for active L-band and L-C-band passive data, GO model for X-band passive data. Data are simulated considering different roughness conditions and moisture content. As the inversion problem is very complex, artificial feedforward backpropagation neural networks (NN) were employed. The best performing NNs are chosen to simulate a retrieval with a dataset artificially added with noise. In each case, the best retrieved parameter is the real part of the dielectric constant, while roughness parameters, especially autocorrelation length, is not very well retrieved. In many cases, retrieved values are out of range, so that the simulated values and targets appear unrelated. Applying a very generic filter that eliminates values very far from the proper range, correlation coefficients grow up. This filter cleans up the resulting data removing a small part of them. After this filtration, correlation coefficients relative to the real part of the dielectric constant surpass 0.82. In spite of the filtering process, roughness parameters retrieval is of inferior quality. On smooth soil, the three considered configurations work in an equivalent way, excellently retrieving the real part of the dielectric constant, without a need for filtration. On medium and rough soil, inversion results generally more difficult, so that performance gets worse. Active-passive approach results more efficient than the L-C-X one

An experimental campaign will be carried out in Antarctica at Dome-C in 2005 to investigate low frequency microwave emission of snow pack and to evaluate the potential of Antarctic plateau to act as a calibrator for L-band satellite sensors such as SMOS and HYDROS. Simulations carried out by using a multi-layer electromagnetic model coupled with a glaciological model have shown very small variations (< 0.1K) of L-band emission during the yearly cycle, and confirmed that the low frequency microwave emission mostly comes from the deeper layers of ice.

A study of the melting cycle of snow was carried out by combining microwave active and passive measurements with meteorological data and snow modelling. The experiment took place in the eastern Italian Alps from mid February to late May 2003. Brightness temperature at C-, Ku- and Ka- bands (vertical and horizontal polarizations) and backscattering coefficient at Ku-band (VV), were continuously measured (24 h/day) with ground-based sensors. Remote sensing observations were supported by meteorological data, and snow measurements. A continuous simulation of the snow temperature, depth, and liquid water content was performed for the entire monitoring period by means of a physically based distributed snowmelt model. Both hydrological and remote sensing approaches gave useful and coherent results in describing the snow melting and refreezing cycles. Microwave active and passive data were consistent each other. During the melting cycle, the presence of liquid water caused an increase of absorption with a consequent increase of the brightness temperature and a decrease of the backscattering coefficient

The sensitivity of radar backscattering to the principal hydrological parameters, such as vegetation biomass, soil moisture, and surface roughness, is discussed. Results obtained by using multifrequency synthetic aperture radar (SAR) data measured by the Jet Propulsion Laboratory Airborne Synthetic Aperture Radar, Spaceborne Imaging Radar-C, and European Remote Sensing 1/2 sensors are summarized. The sensitivity of L- and C-bands to spatial variations of plant and soil parameters is masked by the presence of surface roughness, which in turn affects the radar signal. However, from the observation of data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture and vegetation biomass is found to be significant, since the effects of spatial variations are smoothed. On the other hand, the sensitivity to surface roughness becomes appreciable when multitemporal data are averaged in time, thus reducing the effects of temporal moisture variations.

Airborne microwave radiometric measurements were carried out in June 1999 and January 2002 on three forest stands in Tuscany (Italy), in order to investigate the the potential contribution of microwave radiometry in analysing the differences between summer and winter features of trees. The analysis of the collected data pointed out that the use of microwave emission at the highest frequencies makes it possible to separate between several forest types, whereas L-band emission is more related to tree biomass. At low biomass and low frequency, emission is more affected by the seasonal differences, since contributions from soil and from soil-trunk interaction are important. For higher values of biomass and/or frequency emission is mostly dominated by branches and less sensitive to season.

In this paper, an experiment aimed at investigating the water balance after soil thawing and the radiation budget is described. The experiment was carried out by combining microwave remote sensing measurements with ground and meteorological data. The higher frequencies were found to be very sensitive to the characteristics of snow, whereas the lower ones were much more influenced by the soil conditions. The sensitivity to soil moisture was well demonstrated at L-band, although the soil was covered by a thick layer of grass, and, to a minor extent, also at C-band.

A study of the melting cycle of snow was carried out by combining microwave radiometric measurements with conventional micrometeorological data and snow modelling. The experiment took place in the eastern Italian Alps. The high sensitivity of microwave emission at 19 and 37 GHz to the melting refreezing-cycles of snow was confirmed. Moreover, micro-meteorological data provided additional insight on the processes. Simulations obtained with electromagnetic and hydrological models were consistent with experimental data.

Dry snow temperature, snow water equivalent (SWE) and snow depth have been retrieved by using the 19 and 37 GHz SSM/I brightness temperatures and artificial neural networks (ANNs). The results obtained have been compared with those obtained using other approaches such as the spectral polarization difference, the HUT model-based iterative inversion, the Chang algorithm and linear regressions. In general, it has been noted that the ANN based technique gives better results than the other approaches, which tend to underestimate the unknown parameters.

An experiment aiming at studying the scattering properties of a collection of dielectric cylinders placed on a reflecting plane has been performed by using the EMSL of the JRC of Ispra Italy. Experimental data have been compared with simulations performed with a coherent electromagnetic model.

Microwave sensors are of primary importance in mapping surface states and measuring some significant quantities which affect the hydrological cycle. A space mission aiming at monitoring soil moisture and surface salinity at a global scale is suggested. The mission is based on a combination of polarimetric active and passive microwave sensors.

The backscattering coefficient and the position of interferometric phase center of wheat and sunflowers during the growing cycle have been computed by using a coherent electromagnetic model. In the model, the scattered fields are added coherently and the attenuation in the canopy is computed by means of Foldy's approximation. The comparison between model simulations and experimental data has shown that model results match reasonably well with the measured backscattering. As the plant grows, the backscattering of wheat ("narrow leaf" crop) decreases, whereas that of sunflowers ("broad leaf") increases. An analysis of the various terms that contribute to backscattering has indicated that the most significant contribution is given by the double scattering soil-stalk and that the position of the interferometric phase center is close to the soil. When the contribution of leaves is more significant, as in the case of sunflowers, the interferometric phase center goes up to about one quarter of the full plant height. This result demonstrates the potential of the interferometric observation in providing significant new information on crop classification algorithms based on scattering mechanisms.

The influence of the shape and dimensions of plant constituents on the backscattering of agricultural vegetation is investigated. Multifrequency multitemporal polarimetric data, collected at C- and L-bands by means of airborne and satellite synthetic aperture radar (SAR), showed that the relations between the backscattering of crops and the vegetation biomass depend on plant type, and that there are different trends for "narrow" and "broad" leaf crops. In the latter crops, backscattering increases with an increase in the biomass, especially at L-band. This behavior is typical of media in which scattering is dominant, whereas on "narrow leaf" plants, the trend is flat or decreasing, denoting a major contribution of absorption. Theoretical simulations obtained with a discrete element radiative transfer model have confirmed that a different backscattering of crops with the same biomass may be due to plant geometry.

Microwave radiometric measurements using airborne instruments in a frequency range from L- to Ka-band were carried out over six broad-leaved and one coniferous forest stands in Tuscany, Italy. Ground measurements of the main tree parameters were performed on the same stands. The analysis of the collected data indicated that the use of microwave emission at the highest frequencies makes it possible to identify some forest types, whereas L-band emission is more closely related to tree biomass. The relationships between emission and some significant tree parameters such as leaf area index, basal area, woody volume, and crown transparency are presented and discussed. The significant relationship between L-band emission and woody volume is further analyzed by means of a first-order radiative transfer model.

Field measurements of microwave emission from snow-covered soil were carried out in 1996, 1997, and 1999 on the Italian Alps using a three-frequency dual polarized microwave system. At the same time, nivological time measurements were carried out using standard methods and an electromagnetic contact probe. Collected data confirmed the possibility of separating wet from dry snow and of estimating the water equivalent of dry snow. Simulations performed by means of a model based on the dense medium radiative theory (DMRT) were able to reproduce experimental data very well.

Multifrequency polarimetric scattering and emissivity measurements have been carried out on three experimental dielectric models, characterized by random surfaces with different statistics. The results of the measurements have been compared with simulations obtained through physical models based on the classical approximations of physical optics (PO), geometrical optics (GO), small perturbation (SP), and integral equation model (IEM). The comparison of experimental data with theory has shown that, even when the parameters of the observed surface are well determined and known, some discrepancy may exist between models and measurements. Except for a few cases, this discrepancy is quite small and may be insignificant for many practical applications. The IEM has been proven to have a wider range of applicability with respect to other tested approximations.

The sensitivity of backscattering coefficient, measured by ERS-1 and JERS-1 radars, to vegetation biomass is discussed and compared with the best results achieved using multifrequency polarimetric JPL-AIRSAR data. Experimental results show that measurements with JERS-1/L-band and ERS-1/C-band SAR provide the means for detecting vegetation growth. In particular, the C-band signal of ERS radar was found to be very well correlated to forest woody volume.

Microwave emission from crops characterized by long vertical structures has been investigated by using experimental data at 10 and 37 GHz and a model based on radiative transfer theory. The effects of single-plant constituents on the total emission from crops are evaluated by means of measurements carried out on plants in natural conditions and after sequential cuts of fruits, leaves, and stems. The experimental results are discussed in terms of emission and scattering properties of the crop. The dependence of optical depth and single-scattering albedo on plant water content has been estimated.

Polarimetric radar data collected by AIRSAR and SIR-C over agricultural fields, forests, and olive groves of the Italian Montespertoli site are analyzed. The objective is to investigate the radar capability in discriminating among various vegetation species and its sensitivity to agricultural and arboreous biomass. Results indicate that a combined use of P(0.45 GHz) and L- (1.2 GHz) bands allows one to discriminate between agricultural fields and other targets, while a combined use of L- and C- (5.3 GHz) bands allows the authors to discriminate within agricultural areas. To monitor biomass, P-band gives the best results for forests and olive groves, L-band appears to be good for crops with low plant density (m/sup -2/), while for crops with high plant density, both L- and C-bands are useful. The availability of crosspolarized data is important for both classification and biomass retrieval.

The effects of spatial distribution of the scattering elements on microwave emission from random media have been investigated using an experimental model composed of long, thin vertical dielectric cylinders on a reflecting screen. The measurements have been carried out at 10 and 37 GHz, H, V polarizations and different nadir and azimuth angles. Random "uniform" and "cluster" distributions as well as periodic "row" configurations with different volume fractions have been studied. When the cylinders are distributed in nearly circular clusters or in parallel rows significant emission enhancement with respect to uniform distribution is detected at 10 GHz and incidence angle lower than 50/spl deg/, whereas, at 37 GHz the emission is lower for clusters and strongly dependent on azimuth angle for the "row" configuration.

Polarization characteristics of centimetric microwave emission from canopy-covered fields are investigated. Experimental data are compared against theoretical predictions obtained by two different models. In a simple and direct approach, vegetation is considered as a uniform absorbing and scattering slab with plane parallel boundaries, while in a more realistic description, plants are modeled as an ensemble of lossy dielectric disks and thin cylinders (needles). A parametric analysis, carried out to assess the sensitivity of the polarization index (PI) to the most significant parameters of vegetation shows that, although the PI is mainly influenced by global parameters such as leaf area index and plant water content, morphological parameters such as disk or needle dimensions and orientation distribution may play a relevant role. In particular, a model composed of a mixture of disks and needles is able to represent the negative value of PI sometimes measured over fully grown vegetation.>

A comparative evaluation of the potential of active and passive microwave sensors in estimating vegetation biomass and soil moisture content is carried out. For this purpose, experimental data collected on an agricultural area by airborne scatterometers and radiometers during the AGRISCATT and AGRIRAD 1988 campaigns have been used. The results show that both microwave backscattering and emission are sensitive to vegetation biomass over a wide frequency range. Multifrequency observations seem to offer good probabilities for separating wide leaf from small leaf herbaceous crops, and for detecting different growth stages. Low frequency data (L band) at a steep incidence angle (10 degrees ) confirm that both the backscattering coefficient and the normalized temperature are correlated and sensitive to soil moisture content.>