Exploring The Use Of SAR Remote Sensing To Detect Microplastics Pollution In The Oceans

The increase in plastic pollution is advancing micro level pollution and the total weight of microplastics (8 tons and in the North Atlantic as 10.4 x 108 tons. The plastic in marine environment will eventually degrade and it will be promptly colonized by bacteria releasing surfactants. Such surfactants will have the effect of damping the capillary and small gravitational waves on the ocean surface. Since SAR is sensitive to roughness induced by capillary waves, it may be exploited to detect bacterial activities related to plastic pollution. In this work we used Sentinel-1A and COSMO SkyMed radar images acquired in the Atlantic and Pacific gyres to detect surfactants that may be associated to plastic pollution. We are using SAR, because the damping properties of surfactants produce dark areas in images. Since area of low backscattering in SAR images could also be produced by other oceanographic/meteorological event, we exploited geophysical remote sensing products associated to time and locations synchronised to SAR acquisitions. Among other products we considered sea surface temperature, surface wind, chlorophyll, surface reflectance, turbidity and wave heights. Additionally we made sure that the areas were not within busy shipping routes. The result of the analysis is that, including effects due to colocation errors of SAR and meteorological data, we could identify a large amount of linear slicks in SAR images that were not directly related to apparent meteorological conditions. Such slicks in the gyres have the appearance of oil slicks, however in some areas they are in large amount and they are not connected to large ship traffic. At the moment these slicks seems to only be visible when the wind conditions are moderate (e.g. 6m/s) as it happen for ordinary oil slicks. Besides the work on radar data, we are making controlled experiments with micro-plastic pollution in sea water, to understand the amount and type of surfactants produced by microbes colonising plastics. The conclusion of our study is that radar remote sensing has the potential to detect plastic pollution areas under sorter meteorological conditions

Detecting microplastics pollution in world oceans using SAR remote sensing

Plastic pollution in the world’s 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 create unique 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 by-products 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 SAR sensor. In this study we used Sentinel-1A and COSMO-SkyMed SAR images in selected sites of both the North Pacific and North Atlantic oceans, close to ocean gyres and away from coastal interference. Together with SAR processing we conducted contextual 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 experiments under controlled conditions to test the behaviour of microbes colonizing the two most common pollutants, polyethylene (PE) and polyethylene terephthalate (PET) microplastics. The analysis of SAR images has 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 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

On the trade-off between enhancement of the spatial resolution and noise amplification in conical-scanning microwave radiometers

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.Peer ReviewedPostprint (author's final draft

An enhanced resolution brightness temperature product for future conical scanning microwave radiometers

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 km x 3 km and 36 km x 36 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.The work of Claudio Estatico was supported in part by the Gruppo Nazionale di Calcolo Scientifico–Istituto Nazionale di Alta Matematica (GNCS-INDAM), Italy. This work has been produced for the European Space Agency (ESA) in the frame of the Copernicus Program as a partnership between ESA and the European Commission.Peer ReviewedPostprint (author's final draft

A resolution-enhanced product for the SMAP L-band radiometer

This study addresses the spatial resolution enhancement of synthetic microwave radiometer observations obtained by the Soil Moisture Active Passive (SMAP) L-Band Radiometer. An antenna pattern deconvolution method is used together with an iterative regularization scheme to reconstruct the brightness field at enhanced spatial resolution. Results obtained processing both synthetic and actual SMAP measurements show that sharper edges and coastlines can be reconstructed in a very effective way

Comparison of Accelerated Versions of the Iterative Gradient Method to Ameliorate the Spatial Resolution of Microwave Radiometer Products

In this study, the enhancement of the spatial resolution of microwave radiometer measurements is addressed by contrasting the accuracy of a gradient-like antenna pattern deconvolution method with its accelerated versions. The latter are methods that allow reaching a given accuracy with a reduced number of iterations. The analysis points out that accelerated methods result in improved performance when dealing with spot-like discontinuities; while they perform in a similar way to the canonical gradient method in case of large discontinuities. A key application of such techniques is the research on global warming and climate change, which has recently gained critical importance in many scientific fields, mainly due to the huge societal and economic impact of such topics over the entire planet. In this context, the availability of reliable long time series of remotely sensed Earth data is of paramount importance to identify and study climate trends. Such data can be obtained by large-scale sensors, with the obvious drawback of a poor spatial resolution that strongly limits their applicability in regional studies. Iterative gradient techniques allow obtaining super-resolution gridded passive microwave products that can be used in long time series of consistently calibrated brightness temperature maps in support of climate studies

Comparison of Accelerated Versions of the Iterative Gradient Method to Ameliorate the Spatial Resolution of Microwave Radiometer Products

In this study, the enhancement of the spatial resolution of microwave radiometer measurements is addressed by contrasting the accuracy of a gradient-like antenna pattern deconvolution method with its accelerated versions. The latter are methods that allow reaching a given accuracy with a reduced number of iterations. The analysis points out that accelerated methods result in improved performance when dealing with spot-like discontinuities; while they perform in a similar way to the canonical gradient method in case of large discontinuities. A key application of such techniques is the research on global warming and climate change, which has recently gained critical importance in many scientific fields, mainly due to the huge societal and economic impact of such topics over the entire planet. In this context, the availability of reliable long time series of remotely sensed Earth data is of paramount importance to identify and study climate trends. Such data can be obtained by large-scale sensors, with the obvious drawback of a poor spatial resolution that strongly limits their applicability in regional studies. Iterative gradient techniques allow obtaining super-resolution gridded passive microwave products that can be used in long time series of consistently calibrated brightness temperature maps in support of climate studies

A Multichannel Data Fusion Method to Enhance the Spatial Resolution of Microwave Radiometer Measurements

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

An Adaptive Lp-Penalization Method to Enhance the Spatial Resolution of Microwave Radiometer Measurements

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<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