Scenario: Requesting and Using Copernicus Satellite Data

Snap4City provides a generic tool to access and exploit Copernicus Satellite data from IOT App and on Dashboards, see for example:

This tools allows to process Sentinel's satellite from European Union's Earth Observation Programme Copernicus and to create corresponding heatmaps as shown in the example above, simplifying all the process of search and analysis of complex satellite data.

Different data from Copernicus Sentinel satellites can be exploited by using Snap4City. Some of them can be processed authomaticcaly by using the interface as described below. Others are available under request.

  • SENTINEL-1 is an imaging radar mission providing continuous all-weather, day-and-night imagery at C-band. The SENTINEL-1 constellation provides high reliability, improved revisit time, geographical coverage and rapid data dissemination to support operational applications in the priority areas of marine monitoring, land monitoring and emergency services.
  • SENTINEL-2 is a wide-swath, high-resolution, multi-spectral imaging mission, supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas.
  • The main objective of the SENTINEL-3 mission is to measure sea surface topography, sea and land surface temperature, and ocean and land surface colour with high accuracy and reliability to support ocean forecasting systems, environmental monitoring and climate monitoring. The main instruments of the Sentinel-3 mission are: Ocean and Land Colour Instrument (OLCI), Sea and Land Surface Temperature Radiometer (SLSTR), SAR Radar Altimeter (SRAL), MicroWave Radiometer (MWR) and Precise Orbit Determination (POD) instruments.
  • SENTINEL-5P TROPOMI covers three environmental themes related to Air Quality: Stratospheric Ozone Layer and Climate Change Monitoring and Forecasting.

Snap4City exploits Copernicus Data provided by SENTINEL-3 and SENTINEL-5P satellites. The following table summarizes the list of data that can be exploited in Snap4City. Data in bold green are operative and can be processed automatically as described below. The others can be processed under request.






atmospheric_temperature_profile K Air temperature profile Sentinel-3 OLCI
humidity % Relative humidity Sentinel-3 OLCI
OGVI   OLCI Global Vegetation Index Sentinel-3 OLCI
temperature_tx K 2m air temperature Sentinel-3 SLSTR
nitrogendioxide_tropospheric_column mol m-2 Tropospheric vertical column of nitrogen dioxide Sentinel-5P TROPOMI
ozone_total_vertical_column mol m-2 total ozone column Sentinel-5P TROPOMI
carbonmonoxide_total_column mol m-2 Vertically integrated CO column Sentinel-5P TROPOMI
sulfurdioxide_total_vertical_column mol m-2 total vertical column of sulfur dioxide for the polluted scenario derived from the total slant column Sentinel-5P TROPOMI
cloud_fraction   effective radiometric cloud fraction Sentinel-5P TROPOMI
horizontal_wind m.s-1 Horizontal wind vector at 10m altitude Sentinel-3 OLCI
reference_pressure_level hPa Reference pressure level Sentinel-3 OLCI
sea_level_pressure hPa Mean sea level pressure Sentinel-3 OLCI
total_columnar_water_vapour kg.m-2 Total column water vapour Sentinel-3 OLCI
total_ozone kg.m-2 Total columnar ozone Sentinel-3 OLCI
IWV kg.m-2 Integrated water vapour column above the current pixel Sentinel-3 OLCI
OTCI   OLCI Terrestrial Chlorophyll Index Sentinel-3 OLCI
dew_point_tx K 2m dew point Sentinel-3 SLSTR
east_west_stress_tx N m-2 s East-west integrated surface wind stress Sentinel-3 SLSTR
latent_heat_tx W m-2 s Integrated surface latent heat flux Sentinel-3 SLSTR
north_south_stress_tx N m-2 s North-south integrated surface wind stress Sentinel-3 SLSTR
sea_ice_fraction_tx   Sea ice fraction Sentinel-3 SLSTR
sea_surface_temperature_tx K Sea surface temperature Sentinel-3 SLSTR
sensible_heat_tx W m-2 s Integrated surface sensible heat flux Sentinel-3 SLSTR
skin_temperature_tx K Skin temperature Sentinel-3 SLSTR
snow_albedo_tx   Snow albedo Sentinel-3 SLSTR
snow_depth_tx metre Snow liquid water equivalent depth Sentinel-3 SLSTR
soil_wetness_tx m Volumetric Soil Water Layer 1 Sentinel-3 SLSTR
solar_radiation_tx W m-2 s Integrated surface solar radiation Sentinel-3 SLSTR
surface_pressure_tx hPa Surface pressure Sentinel-3 SLSTR
temperature_profile_tx K Atmospheric temperature profile Sentinel-3 SLSTR
thermal_radiation_tx W m-2 s Integrated surface solar radiation Sentinel-3 SLSTR
total_column_ozone_tx kg m-2 Total column ozone Sentinel-3 SLSTR
total_column_water_vapour_tx kg m-2 Total column water vapour Sentinel-3 SLSTR
aerosol_index_354_388 1 Aerosol index from 388 and 354 nm Sentinel-5P TROPOMI
aerosol_index_340_380 1 Aerosol index from 380 and 340 nm Sentinel-5P TROPOMI
formaldehyde_tropospheric_vertical_column mol m-2 vertical column of formaldehyde Sentinel-5P TROPOMI
aerosol_mid_pressure Pa air_pressure_at_center_of_aerosol_layer Sentinel-5P TROPOMI
aerosol_mid_height m Height at center of aerosol layer relative to geoid Sentinel-5P TROPOMI
cloud_top_pressure Pa cloud optical centroid top pressure Sentinel-5P TROPOMI
cloud_base_pressure Pa cloud base pressure assumed in ROCINN retrieval Sentinel-5P TROPOMI
cloud_top_height m cloud top height Sentinel-5P TROPOMI
cloud_base_height m cloud base height assumed in ROCINN retrieval Sentinel-5P TROPOMI


How to exploit satellite operative data in Snap4city

The access to this tool is available only for an Area Manager user.

To exploit the tool it is necessary to request the authorization to access the tool by sending an email to


From top to bottom the following info have to be inserted:

  • Map name: a name to be assigned to the heatmap. It is possible to specify yet existing heatmaps to be updated;
  • Metric name: select the interested metric from the list. the following metrics are available to be exploited authomatically from satellite data: Air Temperature, Humidity, Altitude, Vegetation Index (OGVI), Cloud Fraction, SO2, O3, NO2, CO
  • Description: a generic description;
  • Location: select at which level the heatmap has to be created. It is possible to specify one of the following: City, Country, State and Postal Code;
  • Location Name: specify here the location. It can be, for example, the name of a City or "Città Metropolitana di Firenze", or "Toscana" as State or "Italy" as Country, etc.;
  • Color Map: from the drop down list, select the corresponding color map to be used for the heatmap visualization;
  • Org: from the drop down list, select the organization in Snap4City to be assigned for the heatmap;
  • From Date - To Date: use these forms to specify the time period of the data to be downloaded. Please note that typically satellite data are updated 1 time per day. If a longer period is specified, all data included in the period will be downloaded and, according to the available data, more heatmaps will be generated covering the specified time period;
  • Length: specify here the dimension in meters of the geographical cluster, for example 700;
  • Write: select to write (1) or not (0) to database the data crawled/processed from Sci-Hub. Value (1) is useful to add the possibility to click in any point in the map and see the corresponding value.

Click Submit button to start the process. In the "Result:" the created token will be shown allowing you to check the process status.

To check the process status go to the following page:

Copy the token in the "Token" form, copy the password and click Submit to check the status. The following results can be shown:

  • completed: 0 = not completed), 1 = completed, -3 = no data found
  • indexed: 1 = indexed, 0  = not indexed

The access to this service is also possible by using microservices via an IOTApp.

To this aim, you have to import the microservices in your IOTApp flow:

1. open an IOTApp and on the top right menu icon, select Import

2. Click on the "S4C Nodes" tab and select the Sci-Hub microservices to be imported. Click the Import button to confim. You have to import one microservice node at a time;

3. You will find the new Sci-Hub microservices in the node list on the left;

4. Now you can use Sci-Hub microservices to build your flow and to create your heatmap, as shown in the following example: