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The papers in this special section examine the orbit performance pf the Cyclone Global Navigation Satellite System (CYGNSS), a constellation of eight satellites that were successfully launched on December 15, 2016 into low earth orbit. Each satellite carries a four-channel bistatic radar receiver that measures GPS signals scattered by the earth surface. Global Navigation Satellite System Reflectometry (GNSS-R) techniques are used to retrieve near surface wind speed over ocean and soil moisture over land. The measurements are unique in several respects, most notably in their ability to penetrate through high levels of precipitation, made possible by the low frequency at which GPS operates, and in the frequent revisit time and complete sampling of the diurnal cycle, made possible by the large number of satellites in a low inclination orbit. Engineering commissioning of the constellation was successfully completed in March 2017. Since then, CYGNSS science data products have been continuously produced.

Frequent and spatially distributed measurements of soil moisture (SMC), at different spatial scales, are advisable for all applications related to the environmental disciplines, such as climatology, meteorology, hydrology and agriculture. Satellite sensors operating in the low part of microwave spectrum represent an important tool for this purpose, and their signals can be directly related to the moisture content of the observed surfaces, provided that all the contributions from soil and vegetation to the measured signal are properly accounted for. Among the algorithms used for the retrieval of SMC from microwave sensors (both active, such as Synthetic Aperture Radar-SAR, and passive, radiometers), the artificial neural networks (ANN) represent the best compromise between accuracy and computation speed. ANN based algorithms have been developed at IFAC, and adapted to several radar and radiometric satellite sensors, in order to generate SMC products at a resolution varying from hundreds of meters to tens of kilometers (according to the spatial scale of each sensor ). These algorithms, which are based on the ANN techniques for inverting theoretical and semi-empirical models, such as Advanced Integral Equation (AIEM) and Oh models and on Radiative transfer Theory (RTT, in the simplified form of the 'Water-Cloud' model), have been adapted to the C- to Ka- band acquisitions from spaceborne radiometers (AMSR-E/AMSR2), SAR (Envisat/ASAR, Cosmo-SkyMed) and real aperture radar (MetOP ASCAT). Large datasets of co-located satellite acquisitions and direct SMC measurements on several test sites worldwide have been used along with simulations derived from forward electromagnetic models for setting up, training and validating these algorithms. An overall quality assessment of the obtained results in terms of accuracy and computational cost was carried out, and the main advantages and li

Haphazard urban development may have far reaching impacts not only around the urban vicinity but also across regional and perhaps global scales. To investigate urban change and impacts, the PO PLain EXperiment (POPLEX) was conducted in May 2014. The focus of POPLEX was to conduct the most effective study, by closely coordinating the field campaign with the science team in different zones of the Po Plain in northern Italy, and also with an extension to Florence in the Tuscany region. Northern Italy is one of the most populated areas in Europe and most of its cities registered an urban sprawl pattern in the 2000s. In this view, the POPLEX domain represents a pertinent "pilot area" to identify environmental impacts due to urban sprawl. This is to identify and understand the influence of urban characteristics and urban change on important environmental topics such as: (a) groundwater resource quality assessment and management, (b) air quality assessment, and (c) temperature assessment. POPLEX involved 25 participants from 15 institutions from 5 countries using data from 12 satellites and extensive field networks including approximately 220 meteorological stations, 170 air monitoring stations, hundreds of groundwater monitoring wells, and 20 river gauges together with demographic census data and detailed survey maps of land cover and land use. Innovative processing, with the Dense Sampling Method and Rosette Transform applied to satellite scatterometer data, has allowed a successful development of a spatially and temporally consistent dataset delineating urban extension and thus to monitor the annual rate of changes, in each pixel of a 1-km grid, throughout the decade of 2000s across the landscape without spatial gaps. Such dataset enables the introduction of the time dimension into dynamic analyses combining both anthropogenic and natural factors including atmospheric, geophysical, and hydrogeological characteristics of each study area. Initial results have been obtained and will be presented on: (i) accelerated urbanization gradient in Milan transition areas, (ii) air pollution over urban areas and its constrain in the Po Plain surrounded by mountains, (iii) water contaminations due to urbanization in Lombardy, and (iv) hot-spot urbanization pattern of new built-up pockets in the Florence-Prato plain.

Within the framework of the MAP and RAPHAEL projects, airborne experimental campaigns were carried out by the IFAC group in 1999 and 2000, using a multifrequency microwave radiometer at L, C and X bands (1.4, 6.8 and 10 GHz). The aim of the experiments was to collect soil moisture and vegetation biomass information on agricultural areas to give reliable inputs to the hydrological models. It is well known that microwave emission from soil, mainly at L-band (1.4 GHz), is very well correlated to its moisture content. Two experimental areas in Italy were selected for this project: one was the Toce Valley, Domodossola, in 1999, and the other, the agricultural area of Cerbaia, close to Florence, where flights were performed in 2000. Measurements were carried out on bare soils, corn and wheat fields in different growth stages and on meadows. Ground data of soil moisture (SMC) were collected by other research teams involved in the experiments. From the analysis of the data sets, it has been confirmed that L-band is well related to the SMC of a rather deep soil layer, whereas C-band is sensitive to the surface SMC and is more affected by the presence of surface roughness and vegetation, especially at high incidence angles. An algorithm for the retrieval of soil moisture, based on the sensitivity to moisture of the brightness temperature at C-band, has been tested using the collected data set. The results of the algorithm, which is able to correct for the effect of vegetation by means of the polarisation index at X-band, have been compared with soil moisture data measured on the ground. Finally, the sensitivity of emission at different frequencies to the soil moisture profile was investigated. Experimental data sets were interpreted by using the Integral Equation Model (IEM) and the outputs of the model were used to train an artificial neural network to reproduce the soil moisture content at different depths. Keywords: microwave radiometry, soil moisture mapping, river basins, vegetative biomass, neural networks

An empirical approach for estimating leaf area index from multifrequency SAR data is presented. The analysis of multifrequency-multipolarization SAR data, collected over agricultural fields revealed a correlation between the L -band backscattering coefficient and the leaf area index of certain types of crops. Since the C - and P -bands are also sensitive to vegetation biomass, the backscattering coefficient at P -, L - and C -bands has been related to a volumetric leaf area index (i.e., the leaf area index multiplied by the average leaf thickness) normalized to the wave number. This relationship has been used to estimate the leaf area index from multifrequency SAR data.

This chapter aims to provide an overview of the most recent techniques for estimating soil moisture (SMC) using microwave radiometric techniques, with a particular focus on those most suitable for applications and described in the context of some case studies Many problems can arise in the retrieval of soil moisture from satellite, since its estimate requires low-frequency observations and relatively high ground resolutions, to be representative of the moisture spatial variations. Nonetheless, the possibility of obtaining surface feature information, particularly in areas of the world where very few data are available (e.g. large boreal and equatorial forests, deserts, poles, etc.), is appealing and therefore prompted this research. A long series of satellites with on-board multi-frequency, multi-polarization microwave radiometers (i.e. SMMR, SSM/I, AMSR-E, SMOS, and more recently AMSR2, onboard G-COMW1) have been able to retrieve information from the Earth's surface. An impressive amount of information on various surfaces has been compiled over many years, which have been very useful for interpreting and modelling satellite data. Many models and algorithms for the estimating soil moisture on a large scale have proven fundamental along with hydrological models in retrieving moisture at a soil depth which benefits many applications. Models for simulating emission from a surface are usually based on the radiative transfer theory, expressed in the simplified form of the 'tau-omega' model. The additional step which involves inverting the model for estimating the soil moisture parameters is accomplished in several ways, through use of semi-empirical or statistical inversion algorithms. In this paper an overview of the most suitable and recent methods for retrieving soil moisture content will be provided, with particular focus on those most applicative and described in the context of some case studies. In particular, an algorithm for hydrological purposes (called hereinafter HydroAlgo), able to generate maps of soil moisture content (SMC) from AMSR-E/AMSR-2 data, will be described. It was developed and implemented within the framework of the JAXA ADEOS-II/AMSR-E and GCOM/AMSR-2 programs, as well as within a project headed by the Italian Space Agency which was devoted to civil protection from floods and landslides. Keywords: soil moisture, microwave radiometry, soil emissivity, radiative transfer theory, inversion algorithms

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 Terra-SARX satellites. In this paper the potentials of both emissivity and backscatter at X-band for the monitoring of plant parameters are investigated and the interrelations between these two quantities are discussed. Remote sensing data collected in agricultural surfaces and for different crop types in Italy have been analyzed and compared with vegetation parameters (mainly plant water content and leaf area index) measured on ground. A discrete element radiative transfer model tuned for both active and passive cases was used to perform a sensitivity analysis. A direct comparison of measure emissivity and backscattering is carried out.

An analysis on the capabilities of microwave radiometers in estimating soil moisture, snow cover and vegetation biomass was carried out on a global scale by using AMSR-E (Advanced Microwave Scanning Radiometer for EOS) data. The temporal trends of brightness temperature together with some microwave indexes, namely combinations of polarizations and frequencies, were taken into account over some test areas. In case of the estimate of soil moisture, the use of these indexes makes it possible eliminating deserts, dense vegetation and snow areas, as well as correcting for the effect of light vegetation. Afterwards, the inversion to retrieve soil moisture is performed by means of an Artificial Neural Network (ANN). Lastly, a technique based on a multi-sensor image fusion technique for enhancing the C-band spatial resolution is described here.

Detailed and geo-referenced maps identifying the locations of saturated and dry levees can be produced using microwave radiometric measurements from a light aircraft or helicopter, and integrated with GPS for positioning and orientation. The development of synergetic remote sensing technology for raised groundwater and seepage detection by the joint use of microwave and optical data along with GIS databases is an effective and most contemporary way of supporting risk assessment and facilitating disaster prevention and management. In this paper we present a remote sensing microwave technology for monitoring and detection of areas of water seepage through irrigation constructions, levees and dykes as well as for revealing areas with dangerously high groundwater level. The possibility for emergency response mapping, integrated with GPS and GIS data, facilitates the risk assessment and management services. The passive microwave radiometry (PMR) is based on spectral measurements in the millimeter to decimetre range of wavelengths. Compared to other remote sensing techniques, such as colour and infrared photography, thermal images and lidar, PMR is the only technology taking measurements under the earth's surface and therefore is very well suited for water seepage and underground water monitoring in a fast and reliable way.

A multi-temporal analysis of AMSR-E data has shown the potential of this sensor in monitoring land surface features by using appropriate combinations of frequency and polarization channels. 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

In this paper an empirical approach for retrieving leaf area index from multifrequency SAR data is presented. From previous results obtained through multifrequency-multipolarization SAR data analysis collected on agricultural fields over the past years, a clear relationship between L-band data and the leaf area index of certain crop types has been observed, mainly at HV polarization. Since other bands, such as C- and P-bands, have been found sensitive to vegetation biomass as well, although to a lesser extent, a multifrequency approach has been tried in order to extend this relation to other crops. In doing so the backscattering coefficent at P-, L- and C-bands has been related to a volumetric leaf area index (i.e., multiplied by the average leaf thickness) normalized to the wave number. The obtained correlation has been used to retrieve leaf area index from multifrequency SAR data.