This study is to present the lesson learned during the activities related to the Italian Space Agency (ASI) funded APPLICAVEMARS project which aims at estimating sea surface wind field from L-, C- and X-band Synthetic Aperture Radar (SAR) imagery. The paper focuses on the X-band results and it describes a new approach to estimate ancillary wind direction info from the SAR image itself using neural networks.

In this study, a multi-frequency analysis on the polarimetric scattering of harsh coastal environments is addressed. The polarimetric properties of different scattering scenarios including grasslands and agricultural crops over land, mudflats and sea water are analyzed using the polarization signatures and the correlation between the co-polarized channels. To this purpose, C- and X-band quad-polarimetric synthetic aperture radar satellite measurements are collected over the Solway Firth, along the western coastal area at the borders between England and Scotland. The Solway Firth coastal area represents a meaningful showcase since it consists of a mixture of harsh environments resulting from different meteo-marine conditions and natural/anthropogenic coastal processes, e.g., erosion, tides, sedimentation, storms, sea currents. Preliminary results obtained at C-band show that polarimetric analysis tools allow improving the understanding of natural and anthropogenic processes in harsh coastal environments that may support the development of advanced and robust scattering-based algorithms for coastal management purposes.

In this paper, preliminary results obtained within the framework of the Italian Space Agency-funded APPLICAVEMARS project are presented. The project aims at estimating sea surface wind field from L-, C- and X-band Synthetic Aperture Radar (SAR) imagery. In particular, the key element is the adaptation/improvement of geophysical model functions to transform the microwave reflectivity map into an added-value product, i.e., a wind map characterized by a spatial resolution much finer than the one obtainable from scatterometer measurements. First experimental results related to C-band Sentinel-1 SAR imagery are presented.

The ability to enhance the spatial resolution of measurements collected by a conical-scanning microwave radiometer (MWR) is discussed in terms of noise amplification and improvement of the spatial resolution. Simulated (and actual) brightness temperature profiles are analyzed at variance of different intrinsic spatial resolutions and adjacent beams overlapping modeling a simplified 1-D measurement configuration (MC). The actual measurements refer to Special Sensor Microwave Imager (SSM/I) data collected using the 19.35 and the 37.00 GHz channels that match the simulated configurations. The reconstruction of the brightness profile at enhanced spatial resolution is performed using an iterative gradient method which allows a fine tuning of the level of regularization. Objective metrics are introduced to quantify the enhancement of the spatial resolution and noise amplification. Numerical experiments, performed using the simplified 1-D MC, show that the regularized deconvolution results in negligible advantages when dealing with low-overlapping/fine-spatial-resolution configurations. Regularization is a mandatory step when addressing the high-overlapping/low-spatial-resolution case and the spatial resolution can be enhanced up to 2.34 with a noise amplification equal to 1.56. A more stringent requirement on the noise amplification (up to 0.6) results in an improvement of the spatial resolution up to 1.64.

An enhanced spatial resolution brightness temperature product is proposed for future conical scan microwave radiometers. The technique is developed for Copernicus Imaging Microwave Radiometer (CIMR) measurements that are simulated using the CIMR antenna pattern at the L-band and the measurement geometry proposed in the Phase A study led by Airbus. An inverse antenna pattern reconstruction method is proposed. Reconstructions are obtained using two CIMR configurations, namely, using measurements collected at L-band by the forward (FWD) scans only, and combining forward and backward (FWD+BWD) scans. Two spatial grids are adopted, namely,$3 \mathrm {\,\,km} \times 3 \mathrm {\,\,km}$and$36 \mathrm {\,\,km} \times 36 \mathrm {\,\,km}$. Simulation results, referred to synthetic and realistic reference brightness fields, demonstrate the soundness of the proposed scheme that provides brightness temperature fields reconstructed at a spatial resolution up to ~1.9 times finer than the measured field when using the FWD+BWD combination.

In this study, a new antenna pattern deconvolution method to enhance the spatial resolution of multi -channel microwave radiometer (MWR) measurements is developed. This technique, based on a conventional gradient-like iterative method, utilizes the information contained in a high-frequency channel to enhance the spatial resolution of the lower-frequency channel in a data-fusion fashion. The physical idea consists of initializing the gradient -like inversion scheme using higher frequency details that are filtered out by the system measurement function. Experiments, performed on a dataset that includes both simulated and actual radiometer measurements, show that the proposed technique allows outperforming the conventional gradient method, while being very robust with respect to artifacts that could be induced by the higher frequency channel.

In this letter, a new approach is proposed to ameliorate the performance of iterative gradient-like regularization schemes aimed at enhancing the spatial resolution of microwave radiometer measurements in the Hilbert space. The approach consists of preconditioning the ill-conditioned discrete problem to let the iterative gradient-based inversion technique be more computer-time effective. Experiments undertaken on the simulated radiometer brightness profiles demonstrate the soundness of the proposed rationale that outperforms conventional gradient-like methods in terms of both computer-time effectiveness and accuracy in reconstructing spot-like discontinuities while resulting in larger fluctuations over the background.

In this study, a method to improve the reconstruction performance of antenna-pattern deconvolution based on the gradient iterative regularization scheme is proposed. The method exploits microwave measurements acquired by a multichannel radiometer to enhance their native spatial resolution. The proposed rationale consists of using the information carried on a high-frequency (finer spatial resolution) channel to ameliorate the spatial resolution of the lowest resolution radiometer channel. Experiments performed using both synthetic and real special sensor microwave/imager (SSM/I) radiometer data demonstrate that an enhanced spatial resolution 19.35-GHz channel can be obtained by ingesting in the algorithm information coming from 37.0-GHz channel. This multichannel spatial resolution method is also shown to outperform the conventional gradient-like regularization scheme in terms of both observation of smaller targets and reduction of ringings and fluctuations.

In this study, an enhanced spatial resolution product is proposed for the forthcoming L-band microwave radiometer operated on board of the FSSCat mission. The product is obtained reconstructing the brightness temperature on a finer spatial resolution grid using an antenna pattern deconvolution method. Simulated FSSCat noisy measurements are used to showcase the soundness of the proposed approach that results in remarkable reconstruction performance.

This study addresses the spatial resolution enhancement of synthetic microwave radiometer observations as obtained by the forthcoming Copernicus Imaging Microwave Radiometer (CIMR). An antenna pattern deconvolution scheme is used to exploit the actual L-band CIMR antenna pattern together with a regularization scheme to reconstruct the brightness field at enhanced spatial resolution. Simulation results, referred to synthetic and realistic reference brightness fields, demonstrate the soundness of the proposed scheme that provides brightness fields reconstructed at a spatial resolution up to 1.5 times finer than the measured field.

In this paper, we introduce a novel approach to enhance the spatial resolution of single-pass microwave data collected by mesoscale sensors. The proposed rationale is based on an Lp-minimization approach with a variable p exponent. The algorithm automatically adapts the p exponent to the region of the image to be reconstructed. This approach allows taking benefit of the advantages of both the regularization in Hilbert (p = 2) and Banach (1 <; p <; 2) spaces. Experiments are undertaken considering the microwave radiometer and refer to both actual and simulated data collected by the special sensor microwave imager (SSM/I). Results demonstrate the benefits of the proposed method in reconstructing abrupt discontinuities and smooth gradients with respect to conventional approaches in Hilbert or Banach spaces.

We present a novel approach to enhance the spatial resolution of microwave data collected by meso-scale sensors. The proposed rationale is based on an LP-penalization approach with a variable exponent p, ranging in the interval [1.5,2]. This allows taking benefits of the advantages of both Hilbert and Banach spaces. Preliminary experiments undertaken using simulated and actual radiometer data confirm the effective improvement in the signal reconstruction by using this approach with respect to conventional Hilbert and Banach-space methods.

Plastic pollution in world oceans is estimated to have reached 270.000 tones, or 5.25 trillion pieces. This plastic is now ubiquitous, however due to ocean circulation patterns, it accumulates in the ocean gyres, creating “garbage patches”. This plastic debris is colonized by microorganisms which can create unique surfactants and bio-film ecosystems. Microbial colonization is the first step towards disintegration and degradation of plastic materials: a process that releases metabolic by-products from energy synthesis. These byproducts include the release of short-chain and more complex carbon molecules in the form of surfactants, which we hypothesize will affect the fluid dynamic properties of waves (change in viscosity and surface tension) and make them detectable by the SAR sensor. In this study we used Sentinel-1A and COSMO-SkyMed SAR images in selected sites of the North Pacific and North Atlantic oceans, close to the ocean gyres and away from the coastal interference. Together with SAR processing we conducted contextual image analysis, using ocean geophysical products of the sea surface temperature, surface wind, chlorophyll, wave heights and wave spectrum of the ocean surface. In addition, we started lab experiments under controlled conditions to test the behaviour of microbes colonizing the two most common marine pollutants, polyethylene (PE) and polyethylene terephthalate (PET) microplastics. The analysis of the SAR images had shown that a combination of surface wind speed and Langmuir cells- ocean circulation pattern is the main controlling factor in creating the distinct appearance of the surfactants, sea-slicks and microbial bio-films. The preliminary conclusion of our study is that SAR remote sensing may be able to detect plastic pollution in the open oceans and this method can be extended to other areas.