The new era of satellite data, born when ESA launched the first Sentinel 2 satellite, necessitates new solutions for data distribution and user interfaces. Vastly growing amount of Sentinel satellite data together with NASA's Landsat 8 data have produced a base for large time series of high resolution Earth Observations (EO). The Earth Observation activities of the Finnish Environment Institute (SYKE) concentrate on the Northern Baltic Sea - especially Finnish coastal waters, land areas and lakes. Although this is a spatially limited area the amount of data and possibilities to develop products for users is overwhelming. New solutions are needed not only for data processing systems, but also for providing images and results to end users.

With the high spatial resolution of Sentinel-2A and -2B MSI instrument (10-20m) and Landsat-8 OLI (30m) novel products were included to SYKE’s EO catalogue. SYKE has long experience in estimating chlorophyll a, turbidity and surface algal blooms in the Baltic Sea with medium resolution instruments. In 2016-2017, also algorithms for Secchi disk depth and absorption of CDOM (colored dissolved organic matter) were developed for Finnish coastal regions and thousands of lakes. Together with water quality products also an interest for true color imaginary (RGB) has grown fast.

A Web Map Service (WMS), built on an open source geospatial server, GeoServer, provides the base for data distribution to end users. A web application TARKKA (www.syke.fi/tarkka/en) is based on open source solutions and utilizes open data products provided on SYKE’s EO WMS. Also complementary datasets of SYKE’s GIS open data have been included to the TARKKA interface to help visualization and interpretation of EO products. Depending on the satellite overpasses and cloudiness, the water quality data products provided in the WMS service cover part of Finnish coastal waters and lakes. Cloud cover prevents some of the observations and as a result roughly 5-20 maps of each water quality product per month were available during demonstration period in the summer 2018. With growing amount water quality data the WMS as well as TARKKA service has been further developed to provide data as time dependent datasets.

TARKKA provides currently a large demo set of water quality product maps. SYKE’s processing environment (CalFin, build on Calvalus parallel processing system) enables calculation of water quality products from every overpass, although only demonstration products based on user interest are shown. TARKKA also provides validation information to give confidence on EO product quality for users. The validation information is presented as time series of EO and station observations on reference stations, approximately 200 lake stations and 30 coastal stations. The reference time series provide comparisons of monitoring station observations and EO estimations of turbidity, Secchi disk depth and absorption of CDOM. As the time series of new instruments is getting longer, TARKKA will be further technically developed to use dynamic time series to enable flexible visualization of varying data sources, such as data assimilation model outputs.

True color imaginary has increased citizen and expert interest on EO data and TARKKA service. At first, SYKE produced its own true color maps for TARKKA. As public interest grew fast, a new daily true color image service based on the data interfaces of Sinergise Company was included in TARKKA in the beginning of summer 2018. This complete RGB-image catalogue has proven to be effective for observing changes over water areas, ice cover over lakes and coastal waters but also rapid changes in land cover. During a demonstration year in 2018, a complete time series of RGB images of Sentinel 2A, Sentinel 2B and Landsat 8 have been used for monitoring and visualizing various phenomena, examples include dredging, resuspension events, Cyanobacteria (algal) blooms, aquatic vegetation of lakes, pollen, effects of stormy weather on Finnish coastal waters, different water quality of lakes, seasonal variations of lake’s water quality, riverine impact areas, lake and coastal areas ice, forest fires etc.

The development of TARKKA web map application and the EO data it contains have been funded by a chain of national projects and international co-operation: TARKKA is one of the user interfaces utilized in EU Horizon 2020 EOMORES project. The basics of the data products and technical solutions were formed in national projects, mainly VESISEN, and ENVIBASE. The water quality algorithm development was done in VESISEN project whereas the processing chain facilities and data publishing in GeoServer were part of ENVIBASE project.

In this presentation TARKKA and it’s datasets will be demonstrated.

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Thermal remote sensing and in extension the Land Surface Temperature (LST) is important for environmental studies and enables the monitoring of landscape processes and responses. LST is among the Essential Climatic Variables defined by the World Meteorological Organization. Thus, monitoring the LST from satellites contributes to the characterization of Earth’ s climate. Despite the growing demand for such global studies there is a lack of high spatial LST products derived from satellites like Landsat that can provide valuable information as they are operational for more than 30 years.

A web application for estimating LST for the globe from Landsat thermal infrared sensors has been developed using the Google Earth Engine (GEE) platform. GEE, an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, by taking advantage of the latest cloud computing and storage technologies. All the necessary input products for the LST estimation are available in Google’s cloud database and the calculations are performed in Google’s cloud infrastructure. Therefore, the LST application requires no computational resources or data input from the users, making it a very lightweight and fast. The application provides to users the ability to draw their area of interest on the global map and select their preferred date range, Landsat (Landsat 5,7 or 8) and emissivity source (MODIS (Moderate Resolution Imaging Spectroradiometer), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) or NDVI (Normalized Difference Vegetation Index)) as their input data. Then, a list with LST products which meet the criteria is created from where the user can either display them as a layer over the map for inspection or download them locally for further use.

A single channel LST algorithm is used for consistency among the estimated LST products for the whole Landsat 5, 7 and 8 archive covering the time period from 1984 to present., Additionally, the option to select the emissivity from different sources such as MODIS, ASTER and NDVI depending the area of interest provides flexibility and better accuracy for the algorithm’s implementation.

To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER as higher-level products based on the temperature-emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 °C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover.

The web application is accessible through: http://rslab.gr/downloads_LandsatLST.html

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Hydrosphere stands for an important part of Earth observation. Water covers about 70% of the Earth’s surface, essentially as oceans and seas but water also exists in rivers, lakes, as vapor in the air, as ice on land and in the ground as soil moisture and in aquifer. The CLS Seewater tool addresses hydrosphere data visualization use cases.
If it is currently possible to visualize over time meteorological, oceanographic and continental water data, including satellite and in-situ observations, numerical model outputs, the types of visualization of these multi-dimensional data (3 or 4D) is often limited to coloured images (gridded data values associated with color palette), arrows or streamline for vectors, isolines, positions, profiles and time series.

The CLS Seewater tool proposes new functionalities for the visualization of the oceanographic or meteorological gridded 4D data. Among them,

• Easy change in color palette parametrisation

For gridded data where values are associated with colors, user shall be able to parameterise the color palette (type, range) depending on the area of interest. The frequent modification of color palette implies that images must be generated on-the-fly, by reading the netCDF/CF raw data. A lot of work has been done on standard WMS server to optimize performance displaying the very large amount of data involved.

• A simple and intuitive time coverage selection

Simultaneous multiple layers visualization implies that the user can easily figure out the temporal coverage of each layer. The time coverage selection is possible through a time line, years, months and days (temporal coverage available can exceed 30 years). Each layer with a time dimension has necessarily not exactly the same temporal coverage, nor the same temporal resolution than the others layers. To figure out the temporal overlap of all layers, each of them is associated with a separated color visible in the time line.

• Visualization of in-situ data and time series for adding user’s data of interest.

Thanks to a clustering approach, the CLS Seewater tool provides an overview of all platforms available at the zoom level. The selection of the platform types and parameters is clear and makes it possible to do a selection adapted to the user’s needs (Moring, Argo, Drifter, Depths, Salinity, ...). Visualization of in situ data trajectories with contextual environmental data can allow the user to correlate trajectories and data layers visualized.

The CLS Seewater tool is developed with a user centred approach. It is a distributed and integrated multiplatform system that provides access and visualization to the hydrosphere data. This tool contributes to the needs of marine, hydrological and coastal research in the context of climate change.

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The Open-Loop Tracking Command (OLTC) mode represents a major evolution of the altimeter tracking function that was first developed for Jason-2 and SARAL/Altika missions as probative mode (called Diode/DEM mode). Taking into account this experience, this tracking mode’s efficiency has been proven and it is now stated as operational mode for Jason-3 and Sentinel-3 missions.

On Sentinel-3, areas of interest define where SRAL is operated in OLTC mode. Outside these areas, SRAL altimeters operate in Close-Loop mode (a.k.a. autonomous tracking mode), with no a priori elevation information used to set the echo reception window.
When operated in OLTC mode, the SRAL altimeter computes the distance between its antenna and the on-ground surface by combining the satellite altitude provided by DIODE navigator of DORIS instrument with the on-ground elevation available in the OLTC tables. These tables are uploaded onboard the altimeter memory. It is a static file that can only be updated by telecommand operation.
That is why it is of high importance to define the most accurate and enriched contents of the OLTC onboard tables.

A new website (https://www.altimetry-hydro.eu), developed by Noveltis for ESA, offers the possibility to display OLTC elevation tables onboard ESA Sentinel-3 SRAL altimeters.
An interactive map allows visitors to view elevations defined onboard Sentinel-3 altimeters and navigate over inland water targets worldwide. Several visualization tools have been added to enhance the OLTC website experience: choice of map layout, display of satellite ground tracks and areas of interest.
Users wishing to further contribute to OLTC contents may sign up and have access to additional functionalities: once logged in, they have the possibility to submit modifications of existing elevations as well as requesting targets that may not yet be defined in the onboard OLTC tables. For example, adding a virtual station over a river or a lake, in order to enhance the OLTC contents and make sure that the altimeter will acquire quality echoes over the defined targets. Requests may be submitted directly using the “Contribute” tool or by sending a list of targets to the OLTC database management Committee.

We encourage scientists and Sentinel-3 users to visit the website and contribute to OLTC contents in order to enhance the altimeter’s capability over inland waters.

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The scope of this presentation is to feature the G-POD SARvatore service to users for the exploitation of CryoSat-2 and Sentinel-3 data, which was designed and developed by the Altimetry Team in the R&D division at ESA-ESRIN. The G-POD service coined SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research & Exploitation) is a web platform that allows any scientist to process on-line, on-demand and with user-selectable configuration CryoSat-2 SAR/SARin and Sentinel-3 SAR data, from L1A (FBR) data products up to SAR/SARin Level-2 geophysical data products.

The G-POD graphical interface allows users to select a geographical area of interest within the time-frame related to the Cryosat-2 SAR/SARin FBR and Sentinel-3 L1A data products availability in the service catalogue. The processor prototype is versatile, allowing users to customize and to adapt the processing according to their specific requirements by setting a list of configurable options. Pre-defined processing configurations (Official CryoSat-2, Official Sentinel-3, Open Ocean, Coastal Zone, Inland Water (20Hz & 80Hz), Ice and Sea-Ice) are available. After the task submission, users can follow, in real time, the status of the processing. The output data products are generated in standard NetCDF format, therefore being compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info) and typical tools.

Initially, the processing was designed and optimized uniquely for open ocean studies. It was based on the SAMOSA model developed for the Sentinel-3 Ground Segment. However, since June 2015, the SAMOSA+ retracker is available as a dedicated retracker for coastal zone, inland water and sea-ice/ice-sheet. A new retracker (SAMOSA++) has been recently developed and will be made available in the future. The scope is to maximize the exploitation of CryoSat-2 and Sentinel-3 data over all surfaces providing user with specific processing options not available in the default processing chains.

Recent improvements include: 1) A Join & Share Forum to allow users to post questions and report issues (https://wiki.services.eoportal.org/tiki-custom_home.php); 2) A data repository to better support the growing Altimetry Community avoiding the redundant reprocessing of already processed data (https://wiki.services.eoportal.org/tiki-index.php?page=SARvatore+Data+Repository&highlight=repository).

To respond to the request of hydrologists, and simulate data that a rive gauge would provide, SARvatore will soon include a post-processing service to convert water level estimates in L2 data to virtual station water level values, which are typically required by hydrologists.

The service is open, free of charge (supported by the ESA SEOM Programme Element) for worldwide scientific applications and available at https://gpod.eo.esa.int/services/CRYOSAT_SAR/, https://gpod.eo.esa.int/services/CRYOSAT_SARIN/, https://gpod.eo.esa.int/services/SENTINEL3_SAR/.

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The new technique of Delay Doppler altimetry opens up a new range of exciting options for bespoke processing, selecting different processing options according to the surface or problem being studied.

In the DeDop Project, isardSAT and Brockmann Consult, together with a group of scientists in a larger consortium, have developed an interactive tool to allow the user to select input data, choose and run processing options, and immediately query and view the results.

This introduces new paradigm in altimeter processing, allowing a much more direct and immediate interaction with the processor. Previously, the approach was to provide a single product, produced by a selected developer, with periodic updates implemented over a long-time scale.

The DeDop tool comprises both a command-line interface, the DeDop Shell, and a graphical user interface, the DeDop Studio. The tool’s primary goal is to make it easy to configure and run and to provide a number of analysis functions to inspect and compare the L1B results. The tool’s target users are community scientists wishing to learn, modify or extend the DeDop processor configuration and/or code and then use the tool for comparisons between outputs of varying configurations generated by the DeDop.

The image shows two components: DeDop Studio and DeDop Core. DeDop Core consists of DeDop processor, DeDop Shell, and DeDop webapi. With DeDop Shell, users will be able to do all the operations using a command-line interface. With DeDop Studio, users can also perform the same operations as in DeDop Shell (modifies & writes config, read L1A data, etc.) and in the end it invokes DeDop processor in DeDop Core via the webapi interface.

DeDop invites scientists to use the DeDop tool on a number of different scientific problems, processing over ice, inland waters, icebergs and open sea, testing the impact of different processing approaches in terms of their ability to retrieve the desired parameters.

Because of the quick response and easy configurability of the processor we hope to introduce the fun of altimetry processing to a new group of young scientists and technicians, maybe not experts in programming or altimeter processing, but interested in learning some new skills

And – if you want to try something completely different, we offer DeDop fx

DeDopFX is an experimental (=fun) tool which converts satellite altimeter measurement data into sound. Currently it can transform the L1B data from the SRAL sensor mounted on the ESA Sentinel-3 satellite into audio samples.

DeDopFX can play the L1B NetCDF output files from the DeDop Processor or the SRAL sample files from the Sentinel-3A Altimetry Test Data Set.

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The scope of this work is to showcase the BRAT (Broadview Radar Altimetry Toolbox) and GUT (GOCE User Toolbox) toolboxes.

The Broadview Radar Altimetry Toolbox (BRAT) is a collection of tools designed to facilitate the processing of radar altimetry data from all previous and current altimetry missions, including Sentinel-3A L1 and L2 products. A tutorial is included providing plenty of use cases on Geodesy & Geophysics, Oceanography, Coastal Zone, Atmosphere, Wind & Waves, Hydrology, Land, Ice and Climate, which can also be consulted in http://www.altimetry.info/radar-altimetry-tutorial/.

BRAT's last version (4.2.1) was released in June 2018. Based on the community feedback, the front-end has been further improved and simplified whereas the capability to use BRAT in conjunction with MATLAB/IDL or C/C++/Python/Fortran, allowing users to obtain desired data bypassing the data-formatting hassle, remains unchanged. Several kinds of computations can be done within BRAT involving the combination of data fields, that can be saved for future uses, either by using embedded formulas including those from oceanographic altimetry, or by implementing ad-hoc Python modules created by users to meet their needs. BRAT can also be used to quickly visualise data, or to translate data into other formats, e.g. from NetCDF to raster images.

The GOCE User Toolbox (GUT) is a compilation of tools for the use and the analysis of GOCE gravity field models. It facilitates using, viewing and post-processing GOCE L2 data and allows gravity field data, in conjunction and consistently with any other auxiliary data set, to be pre-processed by beginners in gravity field processing, for oceanographic and hydrologic as well as for solid earth applications at both regional and global scales. Hence, GUT facilitates the extensive use of data acquired during GRACE and GOCE missions.

In the current version (3.2), GUT has been outfitted with a graphical user interface allowing users to visually program data processing workflows. Further enhancements aiming at facilitating the use of gradients, the anisotropic diffusive filtering, and the computation of Bouguer and isostatic gravity anomalies have been introduced. Packaged with GUT is also GUT's Variance/Covariance Matrix (VCM) tool, which enables non-experts to compute and study, with relative ease, the formal errors of quantities – such as geoid height, gravity anomaly/disturbance, radial gravity gradient, vertical deflections – that may be derived from the GOCE gravity models.

BRAT and GUT toolboxes can be freely downloaded, along with ancillary material, at https://earth.esa.int/brat and https://earth.esa.int/gut.

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SARbian OS (https://eo-college.org/sarbian/) is a free and open operating system based on Debian Linux. SARbian OS includes free and open processing tools for the processing of synthetic aperture radar (SAR) data from satellites such as Sentinel 1 or TerraSAR-X.

SARbian OS is designed to lower the threshold to get started with the work in radar remote sensing. It is provided as a bootable .iso file as well as a virtual machine, therefore it canbe used as a linux live system on any other operating system or as a native Linux OS.
The idea behind SARbian roots in the difficulties with installing and compiling of free source SAR processing software that many users experience. Especially if they are new to the world of data processing and use of free software. SARbian is an attempt to make SAR data processing easy.

The following software is included in the recent version of SARbian OS:
General SAR processing
- SNAP
- ASF Map Ready
- pyroSAR

SAR polarimetry
- PolSARpro

SAR interferometry
- DORIS
- SNAPHU
- PyAPS
- PyRAT
GIS
- QGIS
- GDAL
- GRASS GIS
Misc
- R (+R Studio, +R Shiny)
- Python (+Gdal, +Pip2, +Pip3)

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The successful launch of the Copernicus Sentinel-1 mission marked a new era in spaceborne SAR interferometry. The systematic acquisition planning of the mission at global scale together with the free and open data policy increasing the popularization of SAR data and the number of Earth Observation (EO) practitioners, especially for land applications.
To support the increasing trend of practitioners, the European Space Agency (ESA) continued its long tradition of developing scientific toolboxes. The ESA Sentinel Application platform (SNAP) toolbox, which includes the Sentinel-1 toolbox [1], consists of a collection of data product readers and processing tools, and supporting large number of spaceborne Synthetic Aperture Radar (SAR) missions, including Sentinel-1, ESA’s historical missions (ERS-1, ERS-2 and Envisat) as well as Third Parties missions.
Similarly, scientists from different Universities and research institutions freely share their processing code with the scientific community, through initiatives like GitHub, contributing to the openness of science. As such, the Stanford Method of Persistent Scatterer (StaMPS/MTI) software [2] is a freely distributed package for research purposes with large user community, that incorporates Persistent Scatterer Inteferometry (PSI) and Small Baseline methods to measure ground displacements from time series of SAR acquisitions.
Towards the same direction, our initial effort involved the development of a bridge between the SNAP Interferometric SAR (InSAR) functionalities and StaMPS PSI, called “snap2stamps package”, which is a set of python scripts that call routines from the ESA SNAP allowing the automatic generation of differential interferograms and prepare the directory structure with all inputs required by StaMPS PSI.
The first version of the snap2stamps package automatizes the single master DInSAR analysis with a specific workflow for the Sentinel-1 TOPSAR SLC data. The user only needs to set up few parameters in a configuration file and select the bursts to be processed as a pre-processing step of the master image. Then, the code performs the automatic assembling of slaves, when several slices cover the defined area of study, until interferogram computation and finally exports data into compatible format to feed the StaMPS processing.
In the current work, we extend the SNAP-StaMPS PSI processing capabilities [3] available in the previous version of snap2stamps package [4], by adding support to SAR data acquired in stripmap mode. This will serve a large number of SAR missions, including ERS SAR and ENVISAT ASAR as well as other higher resolution SAR missions, such as TerraSAR-X and COSMO-SkyMed. Following previous strategy, this new release shall be also published as open source package via Github and Zenodo.

[1] Desnos, Y. L., Foumelis, M., Engdahl, M., Mathieu, P. P., Palazzo, F., Ramoino, F., & Zmuda, A. (2016, July). “Scientific Exploitation of Sentinel-1 within ESA's SEOM programme element”. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International (pp. 3878-3881). IEEE
[2] Hooper, A.; Bekaert, D.; Spaans, K. and Arikan, M. “Recent advances in SAR interferometry time series analysis for measuring crustal deformation”. Tectonophysics, 514-517, 1-13, 2012.
[3] Foumelis, M.; Delgado Blasco, J. M.; Desnos, Y-L.; Engdahl, M.; Fernandez, D.; Veci, L.; Lu, J. and Wong, C. “ESA SNAP - StaMPS Integrated processing for Sentinel-1 Persistent Scatterer Interferometry”,IEEE Int. Geosci and Remote Sensing Symposium. IGARSS 2018. IEEE.
[4] Delgado Blasco, J.M.; Foumelis, M. “Automated SNAP Sentinel-1 DInSAR processing for StaMPS PSI with open source tools”. 2018. https://zenodo.org/record/1322353

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Hydrological extremes such as floods and droughts have enormous environmental, social and economic consequences and it is expected that climate change effects combined with a growing global population will increase their impacts in the future. The ESA-funded HASARD (2012-2014) project led to the generation and implementation of different innovative Synthetic Aperture Radar-based flood hazard mapping function. The most notable algorithms can be used to systematically, rapidly and sautomatically detect, record and disseminate all observable and relevant changes of water bodies and to exploit large SAR data collections to map flood hazard. Many of these algorithms have been implemented on ESA’s Grid Processing On Demand platform (G-POD) which opens up the possibility to process even high-resolution data sets at a large scale in a fully automatic fashion.

The main objective of this study is to make use of a SAR-based flood mapping algorithm implemented on G-POD. The G-POD applications are operated by ESA’s Research and Service Support service(RSS) [1] which has the mission to provide tools and services to support the EO community in exploiting data, researchers in developing new algorithms and applications, and service providers in generating and delivering value-added information. The flood mapping algorithm proposed in [2] has been implemented on G-POD, and exploits pairs of satellite images in order to generate accurate floodwater maps. Within SAR images, water areas are characterized by low backscatter values. From a statistical point of view, water and non-water regions are generally characterized by bimodal distributions. Since flooded areas often represent a low fraction of SAR scenes, the parametrization of a bimodal distribution from an entire SAR image might be not possible. Therefore the proposed algorithm makes use of a hierarchical tiling methodology that identifies image subsets of variable size enabling a robust parameterization of the water and non-water PDFs. The PDF of open water backscatter is then used to define an optimal set of parameters for the subsequent histogram thresholding, region growing and change detection processes that complete the flood mapping. The algorithm is integrated in an automatic processing chain that provides rapid and direct access to the required input data. The user can select the input data from the entire ESA collection of Sentinel-1 and Envisat data. The images are afterwards automatically pre-processed directly by making use of the G-POD infrastructure and processing and storage resources. Then, follows the step corresponding to the flood delineation algorithm and as soon as this is completed, the generated flood output files, both in raster and vector formats together with a A4 cartographic product, are available for download. The HASARD service is available on G-POD to external users and opens up the possibility to process high-resolution data for obtaining accurate flood extents maps in a timely manner.

[1] P.G. Marchetti, G. Rivolta, S. D’elia, J. Farres, N. Gobron, and G. Mason, “A model for the scientific exploitation of earth observation missions: The esa research and service support,” IEEE Geosci Newsl, vol. 162, pp. 10– 18, 2012.
[2] M. Chini, R. Hostache, L. Giustarini, and P. Matgen, “A hierarchical split-based approach (hsba) for automatically mapping changes using sar images of variable size and resolution: flood inundation as a test case.,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 12, pp. 6975 – 6988, Dec. 2016.

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The ESA Atmospheric Toolbox is one of the ESA Sentinel Toolboxes.
It consists of a set of software components to read, analyze, process and visualize a wide range of atmospheric data products. In addition to the Sentinel-5P mission it supports a wide range of other atmospheric data products, including those of previous ESA missions, ESA Third Party missions, Copernicus Atmosphere Monitoring Service (CAMS), ground based data, etc.

The toolbox consists of four main components that are called CODA, HARP, VISAN, and QDOAS

CODA provides interfaces for direct reading of data from earth observation data files. These interfaces consist of command line applications, libraries, direct interfaces to scientific applications (IDL and MATLAB), and direct interfaces to programming languages (C, Fortran, Python, and Java).
CODA provides a single interface to access data in a wide variety of data formats, including ASCII, binary, XML, netCDF, HDF4, HDF5, CDF, GRIB, RINEX, and SP3.

HARP is a toolkit for reading, processing and inter-comparing satellite remote sensing data, model data, in-situ data, and ground based remote sensing data.
The main goal of HARP is to provide easy access to data and to assist in the inter-comparison of datasets. By appropriately chaining calls to HARP command line tools one can pre-process datasets such that two datasets that need to be compared end up having the same temporal/spatial grid, same data format/structure, and same physical unit.
The toolkit comes with its own data format conventions, the HARP format, which is based on netCDF/HDF. Ingestion routines (based on CODA) allow conversion from a wide variety of atmospheric data products to this common format. In addition, the toolbox provides a wide range of operations to perform conversions on the data such as unit conversions, quantity conversions (e.g. number density to volume mixing ratios), regridding (including L2 to L3 gridding), vertical smoothing using averaging kernels, collocation of two datasets, etc.

VISAN is a cross-platform visualization and analysis application for atmospheric data and can be used to visualize and analyze the data that you retrieve using the CODA and HARP interfaces. The application uses the Python language as the means through which you provide commands to the application. The Python interfaces for CODA and HARP are included so you can directly ingest product data from within VISAN. Powerful visualization functionality for 2D plots and geographical plots in VISAN will allow you to directly visualize the ingested data.

QDOAS is a cross-platform application which performs DOAS retrievals of trace gases from spectral measurements (satellite, ground-based, mobile or aircraft-based instruments). This application already existed for many years but has recently been added to the toolbox as one of its components.
All components from the ESA Atmospheric Toolbox are Open Source and freely available.

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The Sentinel-2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel-2A and Sentinel-2B are equipped with an optical imaging sensor MSI (Multi-Spectral Instrument) which acquires high spatial resolution optical data products. The Sentinel-2 mission is dedicated to land monitoring, emergency management and security. It serves for monitoring of land-cover change and biophysical variables related to agriculture and forestry, monitors coastal and inland waters and is useful for risk and disaster mapping.

Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high quality applications. Therefore, the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map including cloud / cloud shadow classes for further processing.

Sen2Cor processor can be downloaded from ESA website as a stand-alone tool for individual Level-2A processing by the users. It can be run either from command line or as a plug-in of the Sentinel-2 Toolbox (SNAP-S2TBX).

In parallel, ESA started in June 2017 to use Sen2Cor for systematic Level-2A processing of Sentinel-2 acquisitions over Europe. Since March 2018, Level-2A products are generated by the official Sentinel-2 ground segment (PDGS) and are available on the Copernicus Open Access Hub within 12 hours of sensing time.

The objective of this paper is to provide users with an overview of the Level-2A product contents and up-to-date information about the data quality of the Level-2A products generated by Sentinel-2 PDGS since March 2018, in terms of Cloud Screening and Atmospheric Correction.

In addition, the paper will give an outlook on the upcoming improvements available in the Sen2Cor development branch: preliminary results obtained for cloud screening over bright surfaces, cirrus detection in mountainous areas are presented. Concerning atmospheric correction, the novel method using meteorological information from the Copernicus Atmosphere Monitoring Service will be introduced.

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"sen2r" is an R package which allows to simplify and automate searching, downloading and preprocessing time series of Sentinel-2 optical data over user-selected areas of interest. The package is currently available on GitHub (https://github.com/ranghetti/sen2r), and is released under the GNU GPL-3 license. Its main purpose is to facilitate and speed up (and eventually automate) several steps which Sentinel-2 end users commonly perform by hand, including not only finding and downloading products matching certain spatial-temporal conditions, but also transforming them to an easier to use format and extracting / computing specific products (i.e. multiband Surface Reflectance images or Classification maps; spectral indices; RGB images). In particular, "sen2r" allows to: i) retrieve the list of available products on a selected area (which can be provided by specifying a bounding box, by loading a vector file or by drawing it on a map) in a given time window; ii) download the required SAFE products, being both Level-1C (Top-Of-Atmosphere reflectances) or Level-2A (Bottom-Of-Atmosphere reflectances); iii) automatically perform atmospheric correction using sen2cor (if Level-2A data is required and only Level-1C data is available); iv) clip the required output products (TOA/BOA reflectances, Surface Classification maps) on the specified area (adjacent tiles belonging to the same frame are merged), v) perform the required geometric manipulations (e.g. reprojection or rescaling); vi) mask cloudy pixels; vii) compute spectral indices; viii) create colour RGB images from surface reflectances (using any combination of spectral bands) and JPEG thumbnails of all the products. The package was developed in R, one of the most widespread programming languages among the scientific community. This allows data scientists to take advantage of powerful ready-to-use functions focused on Sentinel-2 data download and processing, giving them the instruments to easily build customised processing scripts. Nevertheless, usage of the package by people with limited R programming skills is considerably facilitated thanks to the "sen2r" Graphical User Interface (based on R Shiny). The GUI easily allows to define all processing parameters and to immediately launch the processing, or to store them in an external file for later use. The possibility to launch the processing with a set of saved parameters makes easy building scripts devoted to automatically update an archive of Sentinel-2 products over a specified area through scheduled execution of "sen2r". The aforementioned characteristics make "sen2r" useful for different types of end users, from researchers (aiming at easily creating Sentinel-2 time series using a simple interface), to data scientists (which can use the "sen2r" R functions to build custom processing chains) and software engineers (which can schedule a processing chain to be used as the operational back-end of a service-oriented architecture). In the framework of the open data policy adopted for the Copernicus programme, "sen2r" open source package can therefore contribute to broaden the diffusion and usage of Sentinel-2 imagery.

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In preparation for future instrument Calibration and Validation activities of the EarthCARE mission, there has been a growing interest in understanding the possibilities of combining data from different types of products, acquired over the same geographical area within a given period of time between observations, as well as assessing the overpass opportunities over calibration ground-sites.

The combined use of the tools distributed by ESA EOP System Support Division made feasible the preliminary analysis to obtain the revisit time between observations of the same area by different satellite instruments within a limited time interval and their geographical distribution. Knowledge of the instrument overlap patterns and its repetition frequency would help teams involved in instrument calibration and product validation activities to select the most beneficial mission data products. The Instrument Collocation tool [REF 1] provides a mechanism to identify instrument collocation opportunities for different types of instruments (Optical, Radar, Altimeters) available on different satellites, observing the same area within a limited time interval. With ESOV [REF 2] the obtained collocation opportunities can be displayed as function of time difference between observations. The visualisation capabilities of SAMI [REF 3] facilitated the demonstration of the multi-satellite instrument overlap dynamics, illustrating in a very intuitive way the resulting collocation patterns between EarthCARE and instruments from the various relevant EO satellites part of the case study.

The coverage of calibration ground site (e.g. radar) locations and pre-defined areas of interest for ground campaigns could also be determined, leading to the study of potential measures to maximise the number of observable sites (e.g. tuning the orbit longitude phasing at ascending node). With the Ground Site Network Overpass tool [REF 4] it was possible to identify the observation opportunities to plan for future on-ground campaigns and assess the level of coverage of a given network of calibration ground sites. Several parameters have been defined to quantify the impact of changing orbit characteristics (e.g. reference ground-track) on the network coverage.

The set of software tools used in the analysis, including pre-defined configurations of ESA Earth Observation missions, is freely available to the CAL/VAL users part of the ESA Earth Observation Earth Explorer and Copernicus satellites community.

In conclusion, the study presents results involving observation planning aspects as part of Instrument Calibration and Validation activities applied to EarthCARE mission, although the considerations can be extended to EO satellites in general.

REFERENCES

[REF 1] INSTRUMENT_COLLOCATION website: http://eop-cfi.esa.int/index.php/applications/tools
[REF 2] ESOV website: http://eop-cfi.esa.int/index.php/applications/esov
[REF 3] SAMI website: http://eop-cfi.esa.int/index.php/applications/sami
[REF 4] GROUND SITE NETWORK OVERPASS website: http://eop-cfi.esa.int/index.php/applications/tools

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