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

The Sentinel-1 C-band SAR instrument implements an internal calibration system with a minimum of dedicated calibration elements. The transmit power times receiver gain product, PG, used for correction of the image data, is obtained from five types of calibrations measurements. Leakage signals from TX to RX are the main error sources on the calibration signals. Their impact is minimised by the PCC2 (pulse coded calibration) technique. An RF Characterization mode is implemented to monitor individual elements of the antenna. This mode is based on the PCC512 pulse coded calibration technique. The expected 3sigma accuracy for internal calibration of PG is about 0.3 dB/ 3deg. The expected 3sigma accuracy of RF characterization mode is about 0.4 dB / 5deg.

The Sentinel-1 C-SAR Internal Calibration

The Copernicus Sentinel-1 Earth Radar Observatory, a mission funded by the European Union and developed by ESA, is a constellation of two C-band radar satellites. The satellites have been conceived to be a continuous and reliable source of C-band SAR imagery for operational applications such as mapping of global landmasses, coastal zones and monitoring of shipping routes. The Sentinel-1 satellites are built by an industrial consortium led by Thales Alenia Space Italia as Prime Contractor and with AIRBUS Defence and Space as SAR Instrument Contractor. The paper describes the general satellite architecture, AIT flow and the satellite key performances. It provides also an overview on the CSAR Instrument, its development status and prelaunch SAR performance prediction.

Copernicus Sentinel-1 Satellite and C-SAR instrument

The paper describes the C-SAR instrument for the GMES Sentinel-1 mission. After a brief introduction of the Sentinel-1 instrument requirements a design description of the overall C-SAR instrument as well as of its major subsystems (i.e. the SAR Antenna Subsystem (SAS) and the SES Electronic Subsystem (SES)), is given. The paper concludes with an overview on the predicted instrument performance.

The C-SAR instrument for the GMES sentinel-1 mission

The paper describes the C-SAR instrument for the GMES Sentinel-1 mission. After a brief introduction a description of the overall C-SAR instrument as well as of the design and development status of its major subsystems (i.e. the SES Electronic Subsystem (SES) and the SAR Antenna Subsystem (SAS)) is given. The main focus on this paper is on the SAS, since there is a dedicated paper on the SES within the same session.

Friedhelm Rostan

Papers

GMES Sentinel-1 mission

The Sentinel-1 C-SAR Internal Calibration

Copernicus Sentinel-1 Satellite and C-SAR instrument

The C-SAR instrument for the GMES sentinel-1 mission

The Sentinel-1 C-SAR instrument