Freedom of design, mass customisation, waste minimisation and the ability to manufacture complex structures, as well as fast prototyping, are the main benefits of additive manufacturing (AM) or 3D printing. A comprehensive review of the main 3D printing methods, materials and their development in trending applications was carried out. In particular, the revolutionary applications of AM in biomedical, aerospace, buildings and protective structures were discussed. The current state of materials development, including metal alloys, polymer composites, ceramics and concrete, was presented. In addition, this paper discussed the main processing challenges with void formation, anisotropic behaviour, the limitation of computer design and layer-by-layer appearance. Overall, this paper gives an overview of 3D printing, including a survey on its benefits and drawbacks as a benchmark for future research and development.

Additive manufacturing (3D printing): A review of materials, methods, applications and challenges

Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services

Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).

Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

Persistent Scatterer Interferometry (PSI) is a powerful remote sensing technique able to measure and monitor displacements of the Earth’s surface over time. Specifically, PSI is a radar-based technique that belongs to the group of differential interferometric Synthetic Aperture Radar (SAR). This paper provides a review of such PSI technique. It firstly recalls the basic principles of SAR interferometry, differential SAR interferometry and PSI. Then, a review of the main PSI algorithms proposed in the literature is provided, describing the main approaches and the most important works devoted to single aspects of PSI. A central part of this paper is devoted to the discussion of different characteristics and technical aspects of PSI, e.g. SAR data availability, maximum deformation rates, deformation time series, thermal expansion component of PSI observations, etc. The paper then goes through the most important PSI validation activities, which have provided valuable inputs for the PSI development and its acceptability at scientific, technical and commercial level. This is followed by a description of the main PSI applications developed in the last fifteen years. The paper concludes with a discussion of the main open PSI problems and the associated future research lines.

https://zenodo.org/record/45568/files/Persistent_Scatterer_Interferometry.pdf

Persistent Scatterer Interferometry: A review

Obtaining accurate and widespread measurements of the vertical structure of the Earth’s forests has been a long-sought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6° N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 ​m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.

/pdf/the-global-ecosystem-dynamics-investigation-high-resolution-1i9f1upo3p.pdf

The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography

GMES Sentinel-1 mission

As part of the European Copernicus programme, the Sentinel-1 mission, based on a constellation of two SAR satellites, ensures continuity for Europe of C-band SAR observations. Sentinel-1A and Sentinel-1B were respectively launched from Kourou on 3rd April 2014 and 25th April 2016. The paper, as part of the Sentinel-1 invited session, gives an overview of the overall mission status and focuses on the Sentinel-1A routine operations activities that started in June 2015 following the operational qualification phase, in terms of mission achievements, mission observation scenario, ground segment operations, throughput and data access. It also provides few examples of applications based on Sentinel-1 data.

Sentinel-1 Mission Status

This paper provides an overview of the Senitnel-1 Ground Segment and its main operations concepts. It presents the Sentinel-1 Payload data Ground Segment (PDGS), responsible during the Sentinel-1 mission operations for the Sentinel-1 mission planning, X-Band data downlink, data processing, quality performance, data dissemination, archiving activities and user interface, with the main objective to make the core ground segment products available to the Sentinel-1 users within the expected timeliness and quality. The paper describes as well the Sentinel- 1 operational user product family and their characteristics.

Sentinel-1 Ground Segment

C-band high resolution radar (SAR, for Synthetic Aperture Radar) is the only space-borne instrument able to probe at very high resolution and over all ocean basins the sea surface under extreme conditions. As part of the space component of the European Copernicus program dedicated to Earth observation, the two C-band SAR on board the Sentinel-1 constellation routinely provide high resolution acquisitions in wide swath. Sentinel-1 A & B operate according to acquisition plans defined in the HLOP (High Level Operation Plan) and regularly updated. This paper aims to show how plan can be modified on short notice allows to capture Tropical Cyclones (TC) over the oceans before landfall including open ocean and coastal areas. Examples of Sentinel-1 derived TC parameters used by the science community and TC forecaster in WMO operational TC centres are presented. The benefit of using Sentinel-1 acquisitions in combination with other missions is also discussed.

Sentinel-1 Contribution To Tropical Cyclones Observations At High Resolution

CYMS is an ESA-funded project aiming at scaling up an operational service for Tropical Cyclone monitoring, in view of its potential integration as part of a Copernicus Service. In 2020, the demonstration of such a service has been operated and user's requirements refined. More than 90 scenes of TC have been acquired worldwide with Sentinel-1 A, Sentinel-1 B and Radarsat-2 thanks to the late programming acquisitions. These images have been processed into ocean surface wind field and disseminated to the user community. The user feedback on the data confirms that the capabilities of SAR to probe the ocean surface at high resolution is unique and offer potential for science applications related to the analysis of the TC inner core structure, possibly bringing new insight on the processes within the eye. Those observations are also crucial over regions lacking any aircraft observation or ground-based meteorological Radars for monitoring and validating the cyclone forecasts. One of the main requirements is the need for Near Real-Time distributions of CYMS observations to allow their operational use. This is currently stated as a project but one of the objectives is to ensure that one of the Copernicus services hosts a service dedicated to Cyclone Observation to allow acquisitions on Cyclones in a Copernicus framework.

Pierre Potin

Papers

GMES Sentinel-1 mission

Sentinel-1 Mission Status

Sentinel-1 Ground Segment

Sentinel-1 Contribution To Tropical Cyclones Observations At High Resolution

Cyclone Monitoring with Sentinel-1: Service Demonstration