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Novel estimation of shallow water bathymetry using ICESat-2 laser altimetry, signal processing and machine learning and Sentinel-2 optical data in a highly automated approach

The project “Novel estimation of shallow water bathymetry using ICESat-2 laser altimetry, signal processing and machine learning and Sentinel-2 optical data in a highly automated approach” was funded under the European Space Agencies’ Open Space Innovation Platform (OSIP). The overall objective was to build a self-calibrating and efficient bathymetry mapping system by leveraging accurate and consistent ICESat-2 laser altimeter data as a calibration source for high-resolution Copernicus Sentinel-2 based water depth retrievals.

Denmark

ETD 2020-02-c

DHI Gras
TISPLALI

There is growing global concern over the chemical, biological and ecological impact of marine plastic pollution. Every year many millions of tons of plastic enter the global oceans but where it goes remains largely unknown. Remote sensing has the potential to provide long-term, global monitoring but for plastics in the ocean it is still in early stages.

UK

20-D-T-TEC-01-g

North Highland College
Ge on Si Feasibility

The focus of study is to build near-IR capability onto Si CCDs for Time-Delay-Integration (TDI) applications, with the main requirement being charge domain summation, which allows the lowest noise performance on these types of devices. Currently there is no product that exists to do this. Si CCDs are a well-established technology which, whilst being an “old” technology, are still world leading in certain applications up to a wavelength of ~1μm.

UK

S 2020-07

Teledyne e2v
Mapping Windrows as Proxy for Marine Litter Monitoring from Space Remote sensing for marine litter - WASP

WASP is a data processor, developed in the frame of the ESA Discovery Campaign, exploiting Copernicus Sentinel-2 L1C images to detect and catalogue the presence of filaments of floating marine debris with high probability of containing anthropogenic litter.

UK

20-D-S-TEC-01-b

ARGANS LIMITED
AIR-SOS: AIRBORNE & SATELLITE OBSERVATION STRATEGIES FOR MARINE LITTER MONITORING

Marine Litter is a global issue and can be found in all the seas from the equator to the poles, and in freshwater systems, such as rivers and lakes. Most of the marine litter is plastic and, as plastic production continues to increase, greater impacts are expected. Plastic marine litter dramatically affects marine life and ecosystems and has a great economic impact

The emerging field of satellite remote sensing for plastic detection is promising, but reliable in situ validation data are required to improve algorithms and approaches.

Luxembourg

20-D-T-TEC-01-f

SkyfloX
Detection of Ocean Litter Plastics with Hyper-to-multispectral Infrared Neural Networks (DOLPHINN)

The “Detection of Ocean Litter Plastics with Hyper-to-multispectral Infrared Neural Networks” (DOLPHINN) study explored a novel image processing technique to improve the use of space assets to map the distribution of plastic across the globe. The DOLPHINN project objective was to determine the feasibility of using a hyperspectral-to-multispectral association machine learning approach to improve the capability of space-based short wave infrared (SWIR) multispectral assets to detect, quantify, and track plastic litter in waters and on shorelines.

Canada

20-D-S-TEC-01-d

MDA systems
Remote sensing for marine litter – Early Technology Development Scheme (REACT)

The main objectives of the project are to:
▪ Assess how plastic litter can be detected and quantified with current and future remote sensing tools;
▪ Develop adaptive indices insensible to biases induced by sunglint on satellite radiometric products and indicate the constraints of current satellite missions under various atmospheric and illumination conditions;

Italy

20-D-T-TEC-01-j

Planetek
Using Deep Learning Methods for Plastic Litter Detection from Satellite Remote Sensor

For decades humans have been producing plastic materials and this production has never ceased growing to a point that, due to waste mismanagement, a global environmental issue has been generated. One of the most mediatic manifestations of such a pollution is the Great Pacific Garbage Patch (GPGP), an area in the North Pacific ocean with higher density of macro and microplastic representing between three and six times the size of France .

France

20-D-T-TEC-01-g

Capgemini
Investigating spectroscopy and forward model improvements for atmospheric retrieval of methane in the thermal infrared

The aim of this study was to explore and consolidate the possible sources of methane biases in retrievals from thermal infrared sensors, to establish necessary actions and perform the related work for mitigating biases. The study distinguishes three types of error sources, which shall be covered: spectroscopic knowledge and input data to radiative transfer models, forward model errors, and instrument biases.

France

15/029

SPASCIA
Investigating spectroscopy and forward model improvements for atmospheric retrieval of methane in the thermal infrared

The aim of this study was to explore and consolidate the possible sources of methane (CH4) biases in retrievals from thermal infrared (TIR) sensors, to establish necessary actions and perform the related work for mitigating biases. The study distinguisged three types of error sources: spectroscopic knowledge and input data to radiative transfer models (RTM), forward model errors, and instrument biases.

For achieving this goal, the study addresses three main objectives, corresponding to three identified tasks:

France

15/029

SPASCIA

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