- What is CREODIAS?
- Computing & Cloud
- Data & Processing
- Pricing Plans
- Fight with COVID-19
- Examples of usage
- AgroTech project as an example of how CREODIAS can be used for food and environmental research
- Monitoring Air Quality of Germany in Pre vs During COVID Lockdown Period
- Common Agricultural Policy monitoring with Earth Observation
- Meteorological data usage on the CREODIAS platform
- Building added value under Horizon Europe with CREODIAS
- CREODIAS: Introduction to SAR Sentinel-1 data
- Land subsidence and landslides monitoring based on satellite data
- Satellite imagery in support of the Common Agriculture Policy (CAP) and crop statistics
- Useful tools for data processing, available on CREODIAS platform
- CREODIAS for hydrological drought modelling
- CREODIAS for managing Urban Heat Islands
- CREODIAS for Digitising Green Spaces
- CREODIAS for Air Quality
- Advanced data processors on CREODIAS
- 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
Monitoring water resources from space
Water is a crucial component of Earth for human life. That is why it needs proper recognition, monitoring and care treatments. The management of water resources with remote sensing techniques may prove highly beneficial.
Satellite data has many attributes, both spectral and spatial ones, providing adequate information for oceans, seas, coastal areas, mangroves and any other water bodies. CREODIAS data catalogue provides resources for water analysis in all dimensions, from local to global and from visible to radar techniques. Copernicus Marine Environment Monitoring Service (CMEMS) products available via CREODIAS platform are particularly dedicated to water application. The article below examines the variety of these products and presents the opportunities they offer.
WATER SURFACE FROM SPACE
Satellite orbit perspective presents an overall picture of surfaces covered by water, impossible to obtain by other means. Registering spectrum which is invisible for a human eye, broadens the perception of various parameters. The expert knowledge about processes taking place under and next to water surfaces, combined with in-situ measurements emphasize the potential of satellite data.
- PHY: physical parameters like temperature, salinity, velocity, ice area fraction or thickness,
- BIO: ocean biochemistry with data according to net primary production of biomass, chlorophyll, phytoplankton, dissolved molecular oxygen, nitrate, phosphate, silicate, dissolved iron and carbon dioxide in sea water,
- WAV: waves analysis with precise information about its height, density, drift, mean period or direction.
INSIGHT TO QUALITY OF SURFACEWATERS
Multispectral and thermal imagery can be beneficial for water quality assessment. Pollutions change spectral characteristic and as a result they alter brightness of the registered image. Clear water seems to be dark on satellite images. Depending on the mineral and chemical composition, it can look dark blue, dark green or dark brown. On infrared images should be black when it is clear.
The image below presents an algal bloom on Yellow Sea occurred between May and July 2008. Algae look like bright green spots on a dark blue sea. At this time, China had prepared for the Olympic games planned to be held on that seas in August 2008. China engaged thousands of people in order to make the water useful for competitions. The imagery of the Yellow See was captured in July 2008 by ENVISAT MERIS. The presented image is a combination of three bands: 2, 10 and 5- blue, NIR (Near Infrared), and green respectively.
- Band 2 (442 nm) the chlorophyll absorption maximum.
- Band 5 (560 nm) the chlorophyll absorption minimum.
- Band 10 (753 nm) water sediments minimum.
Due to cloud cover, a water composition analysis with optical data is not always possible. 3-day repetition for ENVISAT satellite with MERIS scanner onboard gives a higher chance of acquiring imagery without cloud cover. Landsat with 16-day repetition makes it more challenging, resulting in a less amount of good quality Thematic Mapper imagery of that algal bloom in Yellow Sea. ENVISAT has been operating since 2012, that is why, a Sentinel-3 two-day coverage is so essential for assuring the continuation of good quality imagery for Earth water surfaces.
The image below presents Sentinel-3 OLCI RGB composition of bands 3, 12 and 6 respectively for 442.5, 753.75 and 560 nm of algal bloom, which occurred in June and July 2019 in Yellow Sea.
WATER DIVERSITY WITH MEDIUM RESOLUTION SATELLITE DATA
Landsat and Sentinel-2 are similar satellite systems for the continuous monitoring of the Earth’s surface. On CREODIAS, Landsat imagery are available from 1984, and Sentinel-2, from 2015. The satellite data from these missions is mainly applied in: monitoring sea surface monitoring, shipping conditions, oil spills detection, river plumes or water biology and chemistry. Information about water quality and constituents can be derived from optical visible and near infrared data. Thermal ranges inform about water temperature, which can be used for understanding factors of generation and inhibition phenomenon and processes in the water.
The example below was acquired by Sentinel-2, 4 days after an oil spill in Balikpapan Bay port at the west coast of Borneo Island in Indonesia on March 31st 2018. This is a True Colour band combination. The greenish and brownish water colour is natural for this region (caused by sediments from agriculture and urban areas). The black spots and streamers present the remains after the oil spill.
SENTINEL-1, with its C-SAR instrument, offers a reliable and repeated monitoring of water. An image taken on 1st April, just one day after the accident, shows a huge dark pollution stream flowing out of the port of the Balikpapan Bay. It is visible thanks to a radar technique, which is sensitive to water roughness and independent of the cloud cover. Sentinel-1 intensity VV imagery highlights relatively plane areas covered by oil.
VERY HIGH RESOLUTION SUPPORT FOR SURFACEWATERS
Very high resolution imagery satellite technique is complementary to freely available data. CREODIAS platform offers access to commercial satellite imagery, such as Jilin, KOMPSAT, and KazEOSat with accuracy above one meter (panchromatic, 3-4 m in multispectral) allowing to focus on smaller regions and perform analysis on local rivers, ponds or wetlands. The aforementioned items are important elements of smart cities management or assuring a small retention for single agricultural holdings. Flooding after downpours, inundation or snow melting pace and regions are other examples of farming applications.
GAINS OF PROCESSING ON CREODIAS
Water is crucial for life on Earth, therefore, monitoring should be adequate, constant and precise. CMEMS is an effect of a common European effort to provide all stakeholders with that kind of data free of charge. CREODIAS complements those services with a cloud computing infrastructure for a wide range of marine applications from small lakes to global oceans analysis. The CREODIAS platform allows combining all data types in one place and generating new products of any subject or region.
Author: Sylwia Nasiłowska PhD, Project Manager in CloudFerro