An unprecedented amount of data generated in space missions triggers lots of practical challenges and concerns with its transfer, storage, and analysis. As Lunar and deep space missions emerge, we need to also face the challenges of distributed computing and big data analytics. In this paper, we outline these issues and discuss how to design and analyze Lunar data centers, being space data centers designed for distributed computing, and data analysis for (not only) Lunar missions. We investigate the opportunities and chances of such space architectures to lay the foundations for practical space data centers and real-life use cases.

Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers. Semantic segmentation plays a key role in mapping the raw hyper-spectral data coming from the satellites into a human understandable form assigning class labels to each pixel. Traditionally, water index based methods have been used for detecting water pixels. More recently, deep learning techniques such as U-Net started to gain attention offering significantly higher accuracy. However, the latter are hard to interpret by humans and use dozens of millions of abstract parameters that are not directly related to the physical nature of the problem being modelled. They are also labelled data and computational power hungry. At the same time, data transmission capability on small nanosatellites is limited in terms of power and bandwidth yet constellations of such small, nanosatellites are preferable, because they reduce the revisit time in disaster areas from days to hours. Therefore, being able to achieve as highly accurate models as deep learning (e.g. U-Net) or even more, to surpass them in terms of accuracy, but without the need to rely on huge amounts of labelled training data, computational power, abstract coefficients offers potentially game-changing capabilities for EO (Earth observation) and flood detection, in particular. In this paper, we introduce a prototype-based interpretable deep semantic segmentation (IDSS) method, which is highly accurate as well as interpretable. Its parameters are in orders of magnitude less than the number of parameters used by deep networks such as U-Net and are clearly interpretable by humans. The proposed here IDSS offers a transparent structure that allows users to inspect and audit the algorithm’s decision. Results have demonstrated that IDSS could surpass other algorithms, including U-Net, in terms of IoU (Intersection over Union) total water and Recall total water. We used WorldFloods data set for our experiments and plan to use the semantic segmentation results combined with masks for permanent water to detect flood events.

Cloud removal is a relevant topic in Remote Sensing, fostering medium- and high-resolution optical image usability for Earth monitoring and study. Recent applications of deep generative models and sequence-to-sequence-based models have proved their capability to advance the field significantly. Nevertheless, there are still some gaps: the amount of cloud coverage, the landscape temporal changes, and the density and thickness of clouds need further investigation. We fill some of these gaps in this work by introducing an innovative deep model. The proposed model is multi-modal, relying on both spatial and temporal sources of information to restore the whole optical scene of interest. We use the outcomes of both temporal-sequence blending and direct translation from Synthetic Aperture Radar (SAR) to optical images to obtain a pixel-wise restoration of the whole scene. The reconstructed images preserve scene details without resorting to a considerable portion of a clean image. Our approach’s advantage is demonstrated across various atmospheric conditions tested on different datasets. Quantitative and qualitative results prove that the proposed method obtains cloud-free images coping with landscape changes.

Crop type mapping represents one of the most challenging problems in remote sensing. Spatial, spectral, and temporal information are required in order to obtain a unambiguous distinction among the types of crop. This paper presents a multi-sensor approach, where labelled high-resolution images from drones, limited to small areas, are used to enhance the classification ability of machine learning models based on Sentinel 2 time series. The project described in this paper is organized into three major activities. The first part focused on the exploitation of RGB drone images by using transfer learning and convolutional networks, and it has already been described in a previous work by the team. The second part deals with preliminary analysis of multi-spectral Sentinel 2 time-series using the labelled data from the drones campaign and trees-based machine learning algorithms. Finally, the third ongoing part deals with the combination of drones and satellite data in order to show how drones data can help the Sentinel 2 classification by reducing the effort needed to collect reference crop type information.

Over the last few years, crop type mapping has gained importance in remote sensing as it represents one of the most challenging problems in this field. Precise and continuous spectral signatures can significantly help to obtain a unambiguous distinction among the types of crop. This paper presents a discussion about the application of different types of hyperspectral imagery to crop-type mapping. This project is part of a collaboration among the Italian Space Agency (ASI), the Φ-lab in the ESRIN centre of the European Space Agency (ESA), and the Italian National Research Council (CNR). This works is mainly focused on the analysis of the PRISMA hyperspectral images, comparing them to airborn imagery from the Compact Airborne Spectrographic Imager (CASI) and short-wave infrared (SWIR) Airborne Spectrographic Imager (SASI). The continuous spectral signature over the SWIR and the visible and near-infrared (VNIR) channels will be used to perform a binary classification by means of a one-dimensional convolutional neural network. The test case with 3 tomato fields and 4 corn fields is sited near Gros-seto, in Tuscany, Italy. Results will show the potentialities offered by the PRISMA mission for remote sensing applications.

In this work, we present a new architecture for the analysis multitemporal SAR data combining classic synthetic aperture radar processing and geographical object-based image analysis. The architecture exploits the characteristics of the recently introduced RGB products of the Level-1α and Level-1β families, employing self-organizing map clustering and object-based image analysis aiming at the definition of opportune layers measuring scattering and geometric properties of candidate objects to classify. The obtained results have been compared with those given by literature and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semi-arid environment.

This communication describes the optical processing chain to use Sentinel-3 OLCI and MODIS data as part of the ESA funded Synergy project of the Scientific Exploitation of Sentinel Missions (SEOM) component of the EO Envelope programme. One of the goals of the project is to use Data Assimilation techniques to produce land surface products combining the data from Sentinels-2 and 3. Some of the derived products are the OLCI atmospherically corrected data that can be used to generate a spectral BRDF product from OLCI and MODIS, broadband albedo and different vegetation parameters. The project also implements a series of efficiency improvements to the algorithms to speed up the processing. The demonstrator product uses one year of OLCI and MODIS data (2017).

The Sentinel-1 Mission is the European Imaging Radar Observatory for the Copernicus joint initiative of the European Commission (EC) and the European Space Agency (ESA). The objective of the current paper is to provide a brief overview of the latest ESA activities, in the frame of the Scientific Exploitation of Operational Missions (SEOM) programme, aimed to facilitate the scientific exploitation of Sentinel-1 mission as well as discuss future opportunities for research.

ESA's Scientific Exploitation of Operational Missions (SEOM) programme represents a pathfinder for science and innovation addressing the needs and requirements of the Earth system science community in terms of providing novel observations, new algorithms and products that will be a driver for new and innovative scientific discoveries. The current paper aims to provide a brief overview of the various SEOM activities relevant to the first Copernicus Sentinel 1 mission and to present the main achievements and discuss future opportunities for research.

The Jakarta province proposed the Jakarta Giant Sea Wall as the waterfront city for the new urban settlement zone and the deep seaport for the new economic zone along the coastal areas at northern Jakarta. This letter investigated land deformation at 11 watersheds of the West Java Mega Urban Region using the persistent scatterer interferometry technique of the Advanced Land Observing Satellite phased-array-type L-band synthetic aperture radar data. The result shows that land deformation at the study area, particularly the Bandung city area gives a significant impact to sedimentation velocity along the eastern Jakarta strait, particularly the deep seaport for 43 years later. This letter recommends to evaluate land conservation at upland watersheds and the well management of artificial canals to reduce the impact of sedimentation at the Jakarta strait, particularly the new depth seaport.

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal synthetic aperture radar (SAR) data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1β products are particularly oriented toward nonexpert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of this paper is the design of a suitable RGB representation of data aiming to enhance the information content of the time-series. The physical rationale of the products is presented through examples, in which we show their robustness with respect to sensor, acquisition mode, and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new European Space Agency sensor. Finally, we propose two applications based on the use of Kohonen's self-organizing maps dealing with classification problems.

LeanEO! is a 2-year Earth Observation education project funded by the European Space Agency (ESA) and developed by different European Institutions. Its main aim is to increase the understanding and knowledge of satellite data obtained from ESA missions and demonstrate how these can be used when faced with environmental problems in the real world. The project has developed hands-on training resources for use primarily (but not exclusively) by teachers and students at upper high school to university level. Each lesson comes complete with data, analysis tools and exhaustive background information necessary for the completion of the suggested activities and provides answers to the various study questions. Model answers are supplied for users working on their own or with limited specialist support. In this paper the aims and the opportunities provided by the project will be described in detail.

The study has developed an interactive mission benefit analysis (MBA) tool that allows instantaneous evaluation of a range of potential mission designs. The designs are evaluated in terms of their constraint on carbon and water fluxes through calibration of a terrestrial bisphere model. The constraint is quantified by methematically rigorous uncertainty propagation in CCDAS. Applying the MBA tool, the study showed that the benefit of FAPAR data is most pronounced for hydrological quantities and moderate for quantities related to carbon fluxes from ecosystems. In semi-arid regions, where vegetation is strongly water limited, the constraint delivered by FAPAR for hydrological quantities was especially large, as documented by the results for Africa and Australia. Sensor resolution is less critical for successful data assimilation, and with even relatively short time series of only a few years, significant uncertainty reduction can be achieved.

ESA's strong involvement in Education, Training and Capacity Building for Remote Sensing from Space, includes a plethora of activities, ranging from training courses, workshops and other events addressed to schools, universities and professionals, to special publications (e.g. atlases and teacher's packs), on-line material and educational software development. These activities are realized within ESA, but also through the cooperation with ESA Member States and other space agencies, as well as international cooperation, such as that with UNESCO, UNOOSA and CEOS. This article first gives an overview of general ESA activities in Education for Earth Observation (EO) for schools. Subsequently, it describes the training activities of the next generation Principal Investigators and scientists from Europe and China, as well as capacity building in Africa and worldwide. Finally, it concludes with the challenges in the ESA strategy for education and training in the next years, including the adoption of e-education.

In the present paper, we propose the application of a differential synthetic aperture radar interferometry (DInSAR) technique to retrieve the volume changes of the damaged area and the volcanic sediment on the sloped surface of Merapi volcano, Indonesia, which erupted on October 26 and November 4, 2010. This technique was used to investigate the thickness of volcanic ash and the volume change of post-ejected wet lava (sand and rock) surrounding Merapi volcano by assessing L-band ALOS PALSAR data. The results reveal the volume of the damaged area, or deformation, and the volume of sedimentation in the study area are 1.4 million and 2.2 million m3, respectively, and indicate that the radius of the dangerous area is 15.6 km.

In this paper, the long-term consecutive differential interferometric synthetic aperture radar (SAR) technique is used to measure the volume change during land deformation. This technique was used to investigate the subsidence of Bandung city, Indonesia, by assessing the data from two Japanese L-band spaceborne SARs (Japanese Earth Resources Satellite 1 SAR and Advanced Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar) during the periods of 1993–1997 and 2007–2010. The results are confirmed using GPS observation data, ground survey data, local statistics, ground water level trend data, and the geological formation of the study area. The obtained results reveal a close correlation between the subsidence measurements and changes in the ground water level due to water pumping, population growth, industry growth, and urbanization of the study area.