The NDSA technique, proposed for estimating the IWV (Integrated Water Vapor) through a tropospheric radio link, operates by transmitting two sinusoidal signals at K band and gauging their reception power. This enables the estimation of IWV based on the different signals’attenuation. In 2018, an instrument prototype was finalized for the first measurement campaign, showcasing the NDSA method. In this paper we describe the improved version of the instrument, used for a second campaign in 2022, the procedures for its characterization and generation of data, and some of the campaign outcomes.

The Normalized Differential Spectral Attenuation technique (NDSA) has been proposed as a method for measuring Integrated Water Vapor (IWV) by means of attenuation measurements in the Ku-/K-band along tropospheric microwave links. After a first measurement campaign aimed at demonstrating the NDSA method, a second field experiment has been carried out from August 1 to November 30, 2022. The transmitter was placed on the top of Monte Gomito (44.1277° lat, 10.6434° lon, 1892 meters a.s.l.) and the receiver on the roof of the Department of Information Engineering of the University of Florence (43.7985° lat, 11.2528° lon, 50 meters a.s.l.). The resulting radio link length was 61.15 km. Four ground weather stations of the regional weather service were selected among those available. The purpose is comparing the IWV estimates provided by NDSA measurements with the ground point data of air temperature, air humidity, barometric pressure, and rainfall.

Since 2004 ESA has supported the Domex experiment to monitor the emission of the East Antarctic Plateau at Dome C to verify and assess the calibration and stability of SMOS L1 products. The region has been monitored from monthly to yearly scale. This work focuses on the absolute calibration methodology developed for the Domex experiment and its improvements along the experiment's life. The calibrated dataset consisting of almost nine years of brightness temperatures is discussed here. Results were compared with the other L-band datasets over Dome-C, such as SMOS, SMAP and DOMECair 2013.

Active microwave remote sensing data at different frequencies can provide crucial information on crop morphology and conditions, thus effectively supporting agronomic management at different scales. Despite the ever-increasing availability of spaceborne platforms and the extensive research developed throughout more than two decades, some knowledge gaps still await to be filled toward operational use, dealing with SAR backscatter response to crop-specific features and seasonal dynamics, including the effects of agronomic practices. In this work, we used variance-based global sensitivity analysis (GSA) as a quantitative framework for investigating the sensitivity of X-band backscattering to agronomic and morphological features typical of two different crops maize and rice. To this end, we jointly exploited empirical data on crop status and growth, high-resolution TerraSAR-X (TSX) data, and microwave radiative transfer model (RTM) simulations. Phenology-informed simulations allowed us to quantify the contributions of different scattering mechanisms for the two crops under varying observation setups, to assess the sensitivity of X-band backscattering to morphostructural crop biophysical parameters (BPs) (and their interactions), and to evaluate the effects of crop biomass on backscatter across growth stages. In particular, multidimensional GSA outputs accounting for model input correlations through Shapley effects provided a comprehensive suite of information on the relative proportion of total backscatter variance explained by a range of parameters and a quantitative description of the different behavior in vertical and horizontal polarization (changing throughout plant growth) in paddy rice, and the mixed contribution of canopy density and leaf angle distribution (depending on the incident angle) in maize.

Measurement of water vapor (WV) in the lower troposphere on a continuous temporal basis would improve our knowledge of the atmospheric dynamics and the performance of numerical weather prediction models. In recent studies, a new measurement concept, the normalized differential spectral attenuation (NDSA) approach, was proposed. It is based on measurements of differential attenuation at 18.8 and 19.2 GHz performed along a tropospheric radio link. While NDSA measurement at a fixed elevation angle provides information on integrated WV (IWV), measurements at different elevation angles allow to retrieve the vertical WV content profile. A prototype NDSA demonstrator, which consists of two units, a synthesized transmitter and a software-defined radio receiver, has been designed and implemented. The system was accurately characterized through several laboratory tests, and then a first experimental campaign was conducted at fixed elevation angle along a ground-to-ground radio link. Obtained results confirm the sensitivity of the NDSA measurements to the IWV along such link with a good agreement with the existing ground-based and satellite data products.

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.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission [1] has been in orbit for over 11 years, and its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) in two dimensions keeps being fully operational. This II-year long lifetime of SMOS, so far, has enabled the calibration and Level-1 processor team to improve the calibration procedures and the image reconstruction resulting in a new version of the Level-1 data processor, v724. To present the main performance features of this new version and the improvement in the calibration procedures constitute the main objective and content of this presentation.

In recent years, special attention has been paid, in microwave remote sensing from space, to extend observations to low frequencies (i.e lower than L band). Active mission at P-band (i.e. the ESA's Biomass mission based on SAR) will be launched in 2022 and missions based on low frequency microwave radiometer are proposed to both ESA and NASA. Because of the high penetration depth in ice, these sensors are particularly suitable for investigating parameters of the cryosphere. Nevertheless, in order to properly derive information on such geophysical parameters, there is the need to improve our knowledge of the interaction of electromagnetic waves and the different media. In this paper we present and discuss a first series of results obtained during a propagation measurement campaign carried out at the French–Italian Concordia research station during the summer campaign 2019-2020 in Antarctica in order to investigate on the electromagnetic properties of the Antarctic's firn in the frequency range 0.4-2 GHz. Specific instrumentation has been designed and assembled, essentially consisting of a transmitting unit and a receiving one, to be let down into two boreholes in the firn. Preliminary measurement results are presented and discussed.

The knowledge of the water vapor (WV) distribution in the Earth's atmosphere is of great importance for weather prediction. Meteorological models, in particular, the so-called limited area models, can assimilate humidity measurements, increasing the reliability of the simulated atmospheric dynamics. An important improvement can be achieved, for instance, if we are able to provide the total column with a sufficient precision and accuracy. In this paper, the novel normalized differential spectral attenuation (NDSA) approach is applied to retrieve the vertical profile of WV-and thus the total column-from measurements of differential attenuation signals at microwaves. A forward model (FM) has been used to simulate the ray-tracing of a microwave signal from a transmitter to a receiver in the atmosphere by using the 3-D atmospheric parameters as provided by a numerical weather prediction (NWP) model. From the NDSA measurement, the integrated WV (IWV) content can be directly derived. A further retrieval code is able to invert the measurements of IWV along the path length, providing the vertical humidity profile, which is directly related to the total vertical column assimilated by weather prediction models. In this paper, we show that the values of the total column can be retrieved with a precision and accuracy up to about 0.6% and 2.1%, respectively, which could have a positive impact on NWP models at short time scale.

Earth's cold regions are key elements of the planet's climate system: they have strong feedbacks with global change and they have a direct impact on human activities. Despite their importance, at present they are not adequately monitored by state-of-the-art instruments. In order to fill this gap, a dedicated spaceborne mission called Cryorad has been proposed in the framework of the ESA Earth Explorer 10 call. The mission would comprise a 0.4-2 GHz nadir-looking radiometer installed on a polar-orbit satellite. Scientific and technical studies are underway, as well as experimental campaigns in Greenland and Antarctica.

A new space-borne mission based on a multi-channel microwave radiometer in the frequency range 0.5-2 GHz as been recently approved by the Italian Space Agency for a preliminary concept study. The innovative instrument will be devoted to the study of the Cryosphere and in particular on sea ice volume, ice sheet temperature and soil status. Scientific objectives and first mission concept are presented here and in depth discussed at the conference.

The internal ice sheet temperature is a key parameter for the understanding of the ice sheet dynamics which, at present, is available only from glaciological models or in the few boreholes where temperature has been measured. From the analysis of space-borne L-band data from SMOS, collected over Antarctica, it was proved that they are sensitive to the ice sheet temperature profile. In this paper it is demonstrated that starting from satellite data and using a combination of glaciological and microwave emission model it is possible to retrieve this important geophysical parameter at continental scale.

Antarctica is a very unique continent, completely covered by thousands of meter of ice and firn. Therefore its microwave signature is different from any other place on Earth. Although the emission at low microwave frequency of the East Antarctic plateau is temporally stable for long periods, especially at V polarization and for incidence angles close to the Brewster one, a detailed analysis of satellite and ground based L-band measurements revealed that changes in the characteristics of snow surface can have an appreciable effect on the emission at H polarization. This work is focused on analyzing the temporal variations of the brightness temperature and on linking it to their primary causes. Ground measurements and model simulations indicate that, when strong wind event take place, they could swipe away the first weak layers of snow, exposing the more dense ones. This superficial change can cause a variation of the overall snowpack emission which causes the brightness temperature at H pol to decrease appreciably. On the contrary, the accumulation and densification processes make the Tb to increase.

Different researches were addressed to the assessment of the boreal forest environment using active microwave remote sensing. Some of these activities were also devoted to estimate the ground parameters under the forest (i.e. soil moisture, snow mass) and, in order to understand the complex mechanisms which govern the radar backscattering, different electromagnetic models were developed for simulating the boreal scenario. An improvement of these models, for better characterizing the sparse forests, also considered the effect of shadow induced by the trees. Besides the computation of the attenuation caused by shadow on ground backscattering, the first needed parameter was the quantification of the percentage of the pixel affected by shadow. The estimation of this parameter can be obtained from high resolution optical images which are not always available at global scale. An alternative semi- empirical method based on 3D CAD modelling and ancillary information, which allow to quantify the amount of the shaded area is presented in the paper. Different forest profiles, height and densities and different geometry of observation were considered, and semi-empirical relationships between shadow area extension and these parameters were founded. The effect of electromagnetic shadowing was also quantified by performing model simulations. Finally a validation of the method was achieved by using high-resolution data collected from optical sensors in a forested area of Finland.

Recent studies, which were carried out within the framework of ESA's CoReH2O Phase-A mission, demonstrated that multi-frequency SAR data are able to quantify the amount of snow mass on land or glaciers (SWE). On the other hand the presence of forest has a significant impact on the propagation of the radar signal, depending on its structure, biomass, water content and cover fraction. In particular for dense forest, scattering of vegetation strongly hides the signal from snow and, consequently, compromises the sensitivity to snow parameters. A method for the compensation of the vegetation effect for the SWE retrieval in boreal forests is presented in this research. The procedure is based on the possibility to separate the scattering contribution from soil and vegetation from the data itself using ancillary information. The compensation follow through the estimation of the direct scattering from vegetation by using a minimization technique. Moreover, using a time series of backscatter images, this method is able to monitor the evolution of soil and vegetation status changes along the season, and improve the quality of the SWE retrieval algorithm.

Satellite-based snow-cover monitoring is performed using optical, synthetic aperture radar (SAR), and passivemicrowave sensors. Effects of forest canopy on the observed signal need to be considered with all of these sensor types. Various models describing the interaction of electromagnetic radiation with forest canopy have been developed, but many of these are overly complex with high computational and ancillary data requirements. However, for retrieval purposes, simple models are preferred. This work aims at increasing the understanding of the effect of forest canopy on remote sensing observations of snow-covered terrain for both microwave and optical regimes and at quantifying the capability of simple zeroth-order models in simulating these effects. To achieve these goals, a spatial analysis of optical, SAR, and passive-microwave remote sensing data in the northern boreal forest region was performed. Model parameters for vegetation transmissivity as well as the properties of the underlying surface were optimized by utilizing lidar-ranging- and Landsat-based simplified proxy parameters describing forest canopy closure and stem volume. The results demonstrated that despite using these relatively simple proxies, a zeroth-order model can accurately estimate the extinction of electromagnetic signals in a forest, particularly for passive microwave and optical data. The SAR model successfully estimated the median of the observations, but larger scatter of the observations was reflected by a higher root mean square error and lower correlation between models and observations. Due to both good estimation accuracy and simplicity, the presented models can be considered to be applicable in existing snow retrieval algorithms.

In recent decades, with the development of low-frequency missions such as SMOS and Aquarius, which have a large antenna, the need has arisen to find stable areas for the external calibration of L-band radiometers. “Cold sky” and “calm ocean” are routinely used as low reference temperatures, and Antarctica, in particular the East Antarctic Plateau, has been investigated in recent years as a potential candidate for higher reference temperatures. The reason for this interest lies in its geographical location (it can be seen several times a day by polar-orbiting satellites), as well as in the size, structure, spatial homogeneity, and thermal stability of this area. In particular the area of Dome-C, where the Italian-French base is located, was monitored in the past years using ground based radiometer and satellite data. Data acquired in new experiment, started in 2012, are described in the present study together to an analysis of SMOS data collected in the same area. The results pointed out that the brightness temperature over that region is very stable both in space and time and have individuated a large area able to contain several footprints of space-borne radiometers and thus is suitable for cross calibration between the sensors.

In preparation for the CoReH2O satellite mission, one of the three missions selected for scientific and technical feasibility studies within the Earth Explorer Programme of the ESA, experimental and theoretical studies have been under way in order to improve methods for the retrieval of snow physical properties from SAR data. The aim of this paper is to investigate the impact of vegetation in the retrieval of snow parameters from microwave backscattering measurements. A RTT model capable of simulating scattering from a snow-covered vegetated terrain was developed and implemented. A sensitivity analysis to snow and vegetation parameters was carried out thus a comparison with real SAR data is presented in the paper.

Electromagnetic models, able to simulate scattering from vegetation, are widely used in the remote sensing community in order to interpret space-borne or air-borne SAR data and to develop algorithms capable to derive from them bio-physical quantities (i.e. biomass, tree height, etc.). Most of the models consider the vegetation as an ensemble of simple elements (cylinders, discs) for representing the different parts which compose the plant (e.g. trunk, branches, leaves, etc.). Specific analytical models able to describe the scattering form this elements were developed in the past but theirs validity ranges in terms of frequency or elements dimensions were not yet fully investigated. A comparison between analytical models and a numerical full wave model for cylinders, elliptical and spherical discs at different frequencies in the 1.5 - 20 GHz range is presented here.

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.

Antarctica is an important part of the Earth ecosystem. Due to its temperature, mass of stored water (and related thermal inertia) and position, it influences the atmosphere and marine circulations. Despite of its importance, Antarctica is the most unexplored region of Earth. The knowledge about the Antarctic environment is very limited due to its impervious conditions which hamper the human exploration. In this context remote sensing techniques helps in filling the gap. As pointed out in some preliminary works [1][2], microwave brightness temperature data can be useful for estimating snow sub-surface and air temperature. The aim of this paper is to extend previous studies by using AWS and AMSR-E data collected daily for 6 years for estimating snow and air temperature over the East Antarctic plateau. We assessed that the snow sub-surface temperature can be estimated by using linear regressions with a R between satellite and ground data greater than 0.9 (the retrieval RMSE obtained is around IK), while the determination coefficient of the relationships between the air and brightness temperatures was found to be around 0.7 (the associated retrieval RMSE varies between 2K and 7K).

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.

In preparation of the satellite mission CoReH2O, one of the three missions selected for scientific and technical feasibility studies within the Earth Explorer Programme of the European Space Agency, experimental and theoretical studies started in order to investigate backscatter properties of snow covered terrain and improve the methods for retrieval of snow physical properties from SAR data. The aim of this paper is to investigate the impact of vegetation in the retrieval of snow parameters from backscattering measurements. First a radiative transfer model, able to simulating scattering from a vegetated snow-covered terrain was developed and implemented. Lastly, a sensitivity analysis on snow and vegetation parameters was conducted for coniferous forest. Results confirm that with increasing biomass the sensitivity to SWE strongly decreases. Moreover when biomass is in the 0-150 m3/ha range a procedure to correct the vegetation effect in the SWE retrieval algorithm is suggested.

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).