Researchers from NASA’ Jet Propulsion Laboratory (JPL) and the Ohio State University (OSU) used satellite altimeters to observe “merging tsunamis”. The image comes from a data-based computer model that shows Tohoku-oki tsunami waves propagation. Waves peaks are depicted in red-brown, while depressions in sea surface appear in blue-green. Grayscale outlines show the location of mid-ocean ridges, peaks, and islands. Image: NASA.


A tsunami is a series of travelling waves of extremely long length and period, generated when a large volume of ocean water is rapidly displaced by a sudden displacement of the seabed. These series of waves are generated by a displacement of massive amounts of water through underwater earthquakes, volcanic eruptions or landslides. Tsunami waves travel at very high speed across the ocean but as they begin to reach shallow water they slow down and the wave grows steeper (IRDR Glossary).

The majority of tsunami are generated by shallow large earthquakes in subduction zones. Tsunami is also known as seismic sea waves because it is most often generated by earthquakes (UNESCO).

Facts and figures

The word tsunami is derived from the Japanese word “tsu” and “nami”, meaning “Harbor” and “Wave” respectively.

The speed of tsunami waves depends on ocean depth rather than the distance from the source of the wave. Scientists can predict when a tsunami will arrive at various places by knowing the source characteristics of the earthquake that generated the tsunami and the characteristics of the seafloor along the paths to those places. When the ocean is over 19,685 feet (6,000 m) deep, unnoticed tsunami waves can travel over 500 mph (804.67 kmh). One coastal community may see no damaging tsunami wave activity while in another nearby community destructive waves can be large and violent. Reefs, bays, entrances to rivers, undersea features and the slope of the beach help to modify the tsunami as it approaches the coastline (NOAA).

Dependent on the distance of the tsunami from its source, it may be classified as a:

  • Local/near field tsunami A tsunami from a nearby source for which its destructive effects are confined to coasts less than 1 hour tsunami travel time or typically within about 100 km from its source.
  • Regional tsunami A tsunami that is capable of destruction in a particular geographic region.
  • Destructive tsunami Happens when tsunami waves become extremely large in height, they savagely attack coastlines, causing devastating property damage and loss of life. A small wave only 30 cm high in the deep ocean may grow into a much larger wave 30 m high as it sweeps over the shore.
  • Non-Destructive Tsunami Mostly happens as a result of minor earthquakes and/or other events. It can be due to the source being far away from land or the earthquake being too small to have any effect when approaching the shore. When a small tsunami comes to the shoreline it is often seen as a strong and fast-moving tide (Caribbean Tsunami Information Center).

Related content on the Knowledge Portal

SAM Satellite

Landsat 3 was launched on March 5, 1978, three years after Landsat 2.
The Landsat program’s technical and scientific success together with political and economic pressures lead to the decision to commercialize an operational Landsat. To this end, responsibility was slated to shift from NASA (a research and development agency) to the National Oceanic and Atmospheric Administration (NOAA), the agency charged with operating the weather satellites. This was done via Presidential Directive/NSC-54 signed on Nov. 16, 1979 which assigned NOAA “management responsibility for civil operational land remote sensing activites.” (However, operational management was not transfered from NASA to NOAA until 1983).
Landsat 3 carried the same sensors as its predecessor: the Return Beam Vidicon (RBV) and the Multispectral Scanner (MSS). The RBV instrument on-board Landsat 3 had an improved 38 m ground... read more

Launch date:

Landsat 2 was launched into space onboard a Delta 2910 rocket from Vandenberg Air Force Base, California on January 22, 1975, two and a half years after Landsat 1. Originally named ERTS-B (Earth Resource Technology Satellite B), the spacecraft was renamed Landsat 2 prior to launch. The second Landsat was still considered an experimental project and was operated by NASA.
Landsat 2 carried the same sensors as its predecessor: the Return Beam Vidicon (RBV) and the Multispectral Scanner System (MSS).
On February 25, 1982 after seven years of service, Landsat 2 was removed from operations due to yaw control problems; it was offically decommissioned on July 27, 1983.

Return Beam Vidicon (RBV)
Multispectral Scanner (MSS)

Launch date:

Landsat 1 was launched on July 23, 1972; at that time the satellite was known as the Earth Resources Technology Satellite (ERTS). It was the first Earth-observing satellite to be launched with the express intent to study and monitor our planet’s landmasses. To perform the monitoring, Landsat 1 carried two instruments: a camera system built by the Radio Corporation of America (RCA) called the Return Beam Vidicon (RBV), and the Multispectral Scanner (MSS) built by the Hughes Aircraft Company. The RBV was supposed to be the prime instrument, but the MSS data were found to be superior. In addition, the RBV instrument was the source of an electrical transient that caused the satellite to briefly lose altitude control, according to the Landsat 1 Program Manager, Stan Weiland.
To help understand the data and to explore the potential applications of this new technology, NASA oversaw 300 private research investigators. Nearly one third of these were international scientists. These... read more

Launch date:

Data Source

Copernicus Open Access Hub. Image Credit: ESA.
Publishing institution: European Space Agency (ESA)
The Copernicus Open Access Hub provides complete, free and open access to Sentinel missions data.


Coastal area captured by Sentinel-2. Image: ESA (CC BY-SA 3.0 IGO).

Recent advances in satellite technology in terms of higher spatial resolution, multi-spectral bands and open data access, have enhanced the ability for the monitoring and management of coastal areas. Satellite images are to be one of the most potential alternatives to water depth estimation due to the wide area coverage, repeatability, and low cost.

Depth retrieval can be achieved using either Analytical or Empirical Bathymetry methods. Empirical methods need additionally in situ measurements and can follow two approaches: Either the one of Lyzenga et al. (1978, 2006), proposing log-linear correlation between multiband and water depth values, and focusing mainly on removing all other reflected parameters attenuating water bottom signals, or the approach of Stumpf et al. (2003), using a ratio of bands and the difference in attenuation of different bands in water.
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The role of the Earth Observation industry in emergency situations and crisis response has significantly grown in recent years. Climate change brought forest fires, floods and other emergency states that have put Earth Observation and its data to the forefront. Data provided by the Earth Observation industry provides insights that allow emergency services to put their action plan in place appropriately and act timely. During this panel we will talk about Earth Observation data's role in emergency prevention, response and recovery mapping in the context of Copernicus Emergency Management Services and other emergency services.


  • Jens Danzeglocke, Space Administration & Earth Observation, German Aerospace Center (DLR)
  • Franck Ranera, Strategic Partner Development Manager, Serco
  • Philippe Campenon, Government Sales, Planet Labs
  • Moderator: Annett Wania, Innovation Project Manager, Planet Labs

The event will take... read more


Having reliable and timely population distribution data can make a life or death difference for individuals facing crises or living in conflict-ridden regions. These data are also essential for development decision-making and planning and for monitoring progress towards the UN Sustainable Development Goals (SDGs) established by the international community. We need to know where people are located, what conditions they are facing, what infrastructure is available, and what basic services they can access. We also need to ensure that no one is left off the map in pursuit of meeting the SDGs. 

Gridded population data, which often use remote sensing inputs to improve the spatial allocation of population within a country, are vital for all these purposes. Together with the  growing variety of applications that require spatial population data, there is now a bewildering array of population grids, and users need to know which ones are most suitable for their applications.

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Sign warning of volcanic activity in Ecuador. Image: DLR (CC-BY 3.0).

The use of multi-risk information systems is crucial in confronting the increasing risks posed by natural hazards. In some cases, risk is increasing due to inadequate land-use norms or regulations that allow for the construction of infrastructure in areas exposed to such natural hazards. In other cases, vulnerability increases due to lack of awareness or extreme poverty.  The need to address risks from the point of view of multiple hazards is necessary to contribute to sustainable development and has been incorporated as an essential element of the Sendai Framework for Disaster Risk Reduction 2015-2030.  For this purpose, the RIESGOS 2.0 project was launched in March 2021. Under the coordination of the German Aerospace Center (DLR), the project builds on the accomplishments of its predecessor - RIESGOS - as a multi-risk information system that models and simulates natural hazards to support disaster... read more

Publishing date: 17/03/2021


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