The future Copernicus Imaging Microwave Radiometer (CIMR), funded by the European Commission and developed by the European Space Agency (ESA), will include a spaceborne radiometer able to provide the highest ground spatial resolution as of launch date for C- to Ka-bands, due to its large deployable mesh reflector able to produce images over a swath 2000 km wide. Because of side and grating lobes in the patterns, the antenna subsystem introduces distortions in the acquired data that result in a loss of radiometric accuracy caused by unwanted energy collected far from the antenna boresight. A candidate antenna pattern correction (APC) algorithm based on an iterative formulation that takes advantages of measurements belonging to the same swath was already proposed by the authors. As the antenna boresight moves closer to the edges of the swath, the antenna pattern (AP) senses regions not already observed, where the correction needs a guess or auxiliary out-of-swath information. This article evaluates the performance of the candidate APC algorithm according to various possible a priori assumptions. Eventually, an evaluation is performed using a timeseries derived from Special Sensor Microwave/Imager enhanced-resolution data. This was done to get a first assessment of the impact of past ancillary information on the APC process.

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 method for mapping the crop biomass, expressed as Plant Water Content (PWC in kg/m2), at high resolution is proposed. The method is based on SAR data at C and X bands and machine learning algorithms, and it is composed of some steps, including crop classification, soil moisture (SMC) retrieval and finally PWC retrieval. It has been developed and validated in an agricultural area located in Tuscany (Central Italy), for which timeseries of Sentinel-1 and COSMO-SkyMed images were available, along with in situ measurements of the main soil and vegetation parameters.The retrieval, so far limited to the wheat crops, resulted in correlation coefficient R=0.92 and RMSE=0.5 (kg/m2) between estimated and target PWC, by confirming the feasibility of using SAR for monitoring vegetation biomass at high resolution.

The rapid evolution of the effects observed in various areas of our planet related to climate change poses urgent questions about the knowledge of the state of the polar area and requires satellite acquisitions with fine spatial resolution and high accuracy to develop advanced products. The Copernicus Imaging Microwave Radiometer (CIMR) mission, based on a multifrequency microwave radiometer and designed to observe the ocean, sea ice, and Arctic environment, requires brightness temperature measurements with a total absolute uncertainty of 0.5 K and a spatial resolution of 5 km. This constraint demands very large reflectors with a gain value of tens of decibels. Mechanical constraints will be attained by using a mesh reflector, which guarantees the required resolution but with the drawback of a radiation pattern characterized by many grating lobes that contaminate the value of the brightness temperature associated with the boresight position. In this article, an antenna pattern correction (APC) is proposed to correct these effects. The algorithm takes advantage of an iterative formulation based on the Jacobi Method, providing a suitable correction that depends on the chosen spatial resolution. The APC algorithm was tested at both K- and Ka-bands with similar performance. Here, only the results from the latter are shown, as its antenna pattern is the most challenging among CIMR.

This paper deals with the analysis of the backscattering sensitivity at C and X bands to the agricultural crop characteristics and the implementation of a method for estimating crop biomass. The study areas were located in Tuscany (Central Italy) close to Florence. Series of Sentinel-1 and COSMO-SkyMed images have been collected for several years. An accurate crop classification method was first realized in order to separate crops characterized by different scattering behaviors, namely broad- and narrow-leaf crops. The backscattering trends have been simulated by using electromagnetic models based on radiative transfer theory. Algorithms based on Neural Network approaches have been implemented for estimating the crop biomass by using multi-frequency and multi-polarization SAR data at C and Xband.

The research results described in this paper have been obtained in the framework of the 2019–2022 ALGORITMI project between the Italian Space Agency (ASI) and the Institute of Applied Physics of the National Research Council (CNR-IFAC). The focus of the research was the development of innovative algorithms for the estimation of geophysical parameters of soil, snow, and vegetation with the aim of monitoring soil, snow cover and agricultural crop conditions. The estimation of soil moisture, vegetation biomass, snow water equivalent, and crop classification was improved by using retrieval algorithms based on machine- learning approaches and temporal series of SAR images from COSMO-SkyMed (CSK) and Sentinel-1 (S-1) missions, along with optical images from Sentinel-2. This paper provides an overview of the most recent and valuable results obtained during the project. In particular, the validation of soil moisture provided R=0.89 and RMSE=0.025 m3/m3by integrating data from S-1 and CSK and that one of snow water equivalent gave R=0.85 with RMSE=86.24 mm (CSK HIMAGE) and R=0.86 with RMSE=71.59 mm (CSK PP). Early mapping results showed an almost monotonic progression in overall accuracy over time higher than 90% by increasing the available images.

In this paper, an investigation on the agricultural management in semi-arid Mediterranean regions is presented. The selected test areas are located in Tunisia, near the Kairouan town. The agricultural fields are mainly cultivated with olive trees together with cereals, fruit trees and vegetables. The possibility to monitor this area by means of COSMO-SkyMed (CSK) data, thanks to the ASI Open Call initiative, is an added value to retrieve information concerning the temporal evolution of crop conditions and the use of water in semi-arid regions. The CSK images have been acquired in the period 2018–2019 and the spring 2021. The objectives of this research concern the use of CSK data to evaluate the correct growth of agricultural crop. The preliminary analysis shows that X -band backscatter is able to follow the seasonal moisture conditions and to identify different types of crops.

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.

In this paper the integration between the Dense Media Radiative Transfer (DMRT-QMS) model and the SNOWPACK model was investigated in order to simulate snow parameters and the backscattering at X band (9.6 GHz) from nivo-meteorological data. The role of the stickiness parameter ($\tau$) in DMRT-QMS was analyzed by using experimental data of backscattering collected from COSMO-SkyMed (CSK) and snow data generated by SNOWPACK. The relationships between$\tau$and both ice volume fraction ($\phi$) and coordination number ($n_{c}$) were assessed. The DMRT and SNOWPACK simulations were compared with CSK backscattering measurements showing a significant agreement, although for a limited dataset.

In this paper, an algorithm based on Convolutional Neural Networks (CNNs) was developed to correctly classify an agricultural area in central Italy, by using SAR images. This preliminary step is vital for mastering the different influence of crop types in SAR data before the implementation of algorithms devoted to estimate of vegetation biomass. In situ data collected on the test site were used for validating the CNN algorithm-based classification. After the agricultural species recognition, a sensitivity analysis between C-band Sentinel-1 and X-band COSMO-SkyMed backscatter coefficients and crop biomass was carried out, laying the foundation for the implementation of algorithms able to estimate the biomass of different crop types.

Modern agriculture is facing new challenges about food production for a growing population in a sustainable manner. Crop mapping at local and regional scale could provide valuable information in support of agricultural policy. This paper describes a field mapping investigation in a populated area in Tuscany (Italy). Satellite images from Sentinel-1 C-band and COSMO-SkyMed X-band SAR and Sentinel-2 optical sensors are input of classifiers based on deep learning and convolutional neural networks. Results pinpointed that the use of optical images allowed the best overall classification accuracy (99.7%), nevertheless X-band SAR imagery, providing an accuracy of 94.6%, could be a good substitute of optical indices in case of lack of cloud-free multispectral data.

The use of the normalized differential spectral attenuation (NDSA) approach in the microwaves and (later) in the millimeter waves has been recently introduced to retrieve information about integrated water vapor (IWV) content from attenuation measurements along microwave links between two low Earth orbit (LEO) satellites. NDSA was originally proposed for a configuration of two counter-rotating satellites, to achieve vertical profiles of water vapor (WV) from IWV measurements. In this paper, we analyze the exploitability of the NDSA approach in the case of a constellation of corotating LEO satellites in order to estimate the WV content in a 2-D field lying in the orbital plane of the satellites. The WV retrieval is formalized as the solution of an inverse problem. Simulation results are presented in order to demonstrate the feasibility of the proposed approach: a complete acquisition system composed of transmitting and receiving LEO satellites, including tropospheric scintillation effects, has been modeled, and the reconstruction of 2-D maps of the atmospheric WV from multiple IWV measurements is shown.

This paper presents a study for the estimation of 2-D maps of atmospheric water vapour content from integrated water vapour measurements carried out by a constellation of co-rotating low earth orbit satellites. The proposed method uses the normalised differential spectral attenuation (NDSA) approach - able to achieve integrated water vapour content information from attenuation measurements over microwave links among the satellites - and tomographic techniques to solve the inverse problem of atmospheric water vapour field reconstruction. Simulation results demonstrate the feasibility of the proposed approach to retrieve a 2-D map of the atmospheric water vapour content. This study has been developed under the framework of the ANISAP project funded by the European Space Agency.

The normalised differential spectral attenuation (NDSA) approach has been recently introduced to retrieve integrated water vapour (IWV) content information from attenuation measurements over microwave links between two low earth orbit (LEO) satellites. In the original formulation, two counter-rotating satellites were considered in order to achieve vertical profiles of water vapour. In this paper, an extension of the method to the case of a constellation of co-rotating LEO satellites is proposed. Simulation results demonstrate the feasibility of the proposed approach to retrieve 2-D maps of the atmospheric water vapour content from IWV measurements.

In the past few decades, several methods have been developed for despeckling synthetic aperture radar (SAR) images. A considerable number of them have been derived under the assumption of a fully-developed speckle model in which the multiplicative speckle noise is supposed to be a white process. Unfortunately, the transfer function of SAR acquisition systems can introduce a statistical correlation, which decreases the despeckling efficiency of such filters. In this paper, a whitening method is proposed for processing a complex image acquired by a SAR system. We demonstrate that the proposed approach allows the successful application of classical despeckling algorithms. First, we perform an estimation of the SAR system frequency response based on some statistical properties of the acquired image and by using realistic assumptions. Then, a decorrelation process is applied on the acquired image, taking into account the presence of point targets. Finally, the image is despeckled. The experimental results show that the despeckling filters achieve better performance when they are preceded by the proposed whitening method; furthermore, the radiometric characteristics of the image are preserved.

This paper describes the most recent achievements in speckle reduction of COSMO-SkyMed (CSK@) synthetic aperture radar (SAR) data. An advanced multiresolution despeckling filter, based on undecimated wavelet transform (UDWT) and maximum a-posteriori (MAP) estimation has been specialized and optimized to CSKê data, both single- and multi-look. The tradeoff between performances and computational complexity has been investigated: Laplacian-Gaussian and generalized Gaussian (GG) priors for MAP estimation in UDWT domain differ by one order of magnitude in computation cost. Pre-processing of point targets and segmentation of wavelet planes has been exploited to effectively handle the heterogeneity of the data. Besides traditional supervised methods to evaluate the quality of despeckling, a novel procedure, fully automated, based on bivariate analysis of noisy and denoised image has been devised.

The undecimated wavelet transform and the maximum a posteriori probability (MAP) criterion have been applied to the problem of synthetic-aperture-radar image despeckling. The MAP solution is based on the assumption that wavelet coefficients have a known distribution. In previous works, the generalized Gaussian (GG) function has been successfully employed. Furthermore, despeckling methods can be improved by using a classification of wavelet coefficients according to their texture energy. A major drawback of using the GG distribution is the high computational cost since the MAP solution can be found only numerically. In this letter, a new modeling of the statistics of wavelet coefficients is proposed. Observations of the estimated GG shape parameters relative to the reflectivity and to the speckle noise suggest that their distributions can be approximated as a Laplacian and a Gaussian function, respectively. Under these hypotheses, a closed form solution of the MAP estimation problem can be achieved. As for the GG case, classification of wavelet coefficients according to their texture content may be exploited also in the proposed method. Experimental results show that the fast MAP estimator based on the Laplacian-Gaussian assumption and on the classification of coefficients reaches almost the same performances as the GG version in terms of speckle removal, with a gain in computational cost of about one order of magnitude.

The undecimated wavelet transform and the maximum a posteriori (MAP) criterion have been applied to the problem of despeckling SAR images. The solution is based on the assumption that the wavelet coefficients have a known distribution. In previous works, the generalized Gaussian function has been successfully employed. In this case, a major problem is the computational cost, since the solution can be found only numerically. In this work, a different modeling is proposed. The observation of the experimental histograms of the wavelet coefficients related to the reflectivity and to speckle noise demonstrates that their distributions can be approximated as a Laplacian and a Gaussian function, respectively. Under these hypotheses, a closed form solution of the MAP estimation problem can be achieved. In addition, a closed form estimator based on the MMSE criterion also exists. The experimental results show that the fast MAP and MMSE estimators reach almost the same performances of their generalized Gaussian based counterparts in terms of speckle removal, with a computational gain of about one order of magnitude.