- What is CREODIAS?
- Computing & Cloud
- Data & Processing
- Pricing Plans
- Fight with COVID-19
- Examples of usage
- Utility of CREODIAS data
- CREODIAS for your application
- Solutions for agriculture with CREODIAS
- Earth Observation data for Emergency response
- Security Applications with Satellite Data
- Climate Monitoring with Satellite Data
- Water Analysis on CREODIAS
- CREODIAS for land and agriculture monitoring
- Solutions for atmospheric analysis
- Example of tool usage
- Processing EO Data and Serving www services
- Processing and Storing EO
- Embedding OGC WMS Services into Your website
- GPU Use Case
- Using the EO Browser
- EO Data Finder API Manual
- Use of SNAP and QGIS on a CREODIAS Virtual Machine
- Use of WMS Configurator
- DNS as a Service - user documentation
- Use of Sinergise Sentinel Hub on the CREODIAS EO Data Hub
- Load Balancer as a Service
- Jupyter Hub
- Use of CREODIAS Finder for ordering data
- ESRI ArcGIS on CREODIAS
- Use of CEMS data through CREODIAS
- Searching, processing and analysis of Sentinel-5P data on CREODIAS
- ASAR data available on CREODIAS
- Public Reporting Dashboards
- Sentinel Hub Documentation
- Integration Guides
- OGC API
- Custom Processing Scripts
- Legal Matters
- Partner Services
- About Us
Example of usage
Utility of CREODIAS data
A wide range of satellite data available on CREODIAS platform can be successfully applied in many areas. Its utility depends only on the user’s knowledge and creativity. Nowadays more and more useful applications are created for analysing agriculture, marine, weather, geology, smart-cities, industry, architecture, mining, epidemiology and many other.
All of that is possible thanks to smart gaining from various spectral, time and spatial resolution combinations. In this article we present a short overview of the most important sensor parameters used in research and available in CREODIAS.
Satellites dedicated to various purposes are designed in different spectrum, from visible VIS, through infrared (near NIR, medium MIR and thermal TIR) to microwaves. The data available on CREODIAS platform is free of charge and open for any use from 10 m field accuracy with 5 day revisit to dozens of kilometres with 1 day repetition for a given area.
WIDE RANGE OF OPEN DATA
The CREODIAS platform offers an access to a wide range of satellite data, including Sentinel series funded by European Union under Earth Observation Program – Copernicus. Table below shows major characteristics. Applications depend mainly on spectral, spatial and time resolution of the sensors deployed on the satellite.
Thanks to the Copernicus Program, it is now much easier to implement all the above mentioned possibilities and applications than a few years ago. This common effort aims at providing multitasking, complementary and free data for the benefit of all European Citizens.
The above table shows the spectral coverage of our data.
VIS means visible for human range of spectrum from 400 to 700 nm.
NIR - near infrared from 700 to 1500 nm.
MIR - middle infrared above 1500 nm.
Thermal infrared TIR means that scanner register emitted radiation from the surface in 8-15 μm which is related to the object’s temperature.
Microwaves of 1 mm-1 m are used in radar and altimetry active techniques meaning that they register wavelengths come back, which they first send to the Earth.
POTENTIAL OF VEGETATION INDICES
The satellites dedicated to the terrestrial research, such as Sentinel-2 or Landsat, record radiation in the visible light, near and mid-infrared range with a field accuracy of 10 or 30 meters. This enables the observation of biophysical parameters of vegetation or other topographic objects.
Combining the information from various ranges – Vegetation Indices is a popular method for boosting the information value of remote sensing data. CREODIAS Browser gives the possibility of quick preview of a few of them:
NDVI (red, NIR)
Moisture index (MIR, NIR)
SWIR (MIR, NIR, red)
NDWI (green, NIR)
NDSI (green, MIR)
We need to know which band is used by which satellite in order to know how to use a give index. Vegetation health and condition is reflected in a significant difference between red – green or red – NIR response. Good moisture condition can be observed with the relation of visible or near infrared and middle infrared.
APPLYING CREODIAS DATA
Agriculture lands, orchards, forests, pastures and wetlands can all be monitored using CREODIAS data. Depending on the goal, different imagery should be applied. Open and free Sentinel-1 and -2 series gives possibility to detect crop types, yield, canopy, nitrate needs or water stress.
VIS are great for general view of terrain, object recognition and changes in time. Photosynthesis, fluorescence processes, thermal stress, water stress, heat stress can be observed even just using multispectral imagery taken in visible range of spectrum.
Widening that range to NIR and MIR gives more possibilities and ability to observe different properties of surface for instance: proper need for fertilization, water dosage, crop diseases, losses caused by heavy rains, hail, frost or illegal grasses burning, and many others.
The next important factor determining the usefulness of the Earth Observation data is the field resolution. The smaller the size of a pixel in the field, the greater the range of a single image, and analyses on the scale of seas, geographical regions or entire countries are possible. Thus less effort is needed to create a land use map for Europe with Envisat MERIS than with Sentinel-2. Both products can be very useful but for different purposes.
Land cover from Sentinel-2 allows to track detailed changes for a single agricultural holder.
Land cover made with Envisat MERIS gives a quick and general overview of major changes and trends crucial for environmental and political agencies.
With Sentinel-1 you can register ice cover changes and parameters on every single mountain in Alps region, what will be impossible with SMOS MIRAS (it will give general information of ice presence in particular region).
From the other hand for Arctic regions it is better to use data with 50 km pixel size available on SMOS or at least 300 m on Sentinel-3. (see the images below)
Respectively, satellites dedicated for atmospheric research have to be less detailed than those dedicated for land analysis. Nevertheless, the revision time is shortest for meteorological data such as Sentinel-5P. Multiple imaging of the atmosphere parameters over a given area allows not only to develop weather maps but also pollution or air composition.
The images of Garmisch-Partenkirchen from two different satellites: the upper image from Sentinel-2 allows you to see if the snow is present and ready for skiing. The other one covers the same area (red square) showing data from Sentinel-3 with the general information about the winter condition in the region.
Author: Sylwia Nasiłowska PhD, Project Manager in CloudFerro