In the last few decades, Earth observation data became more and more important. This is also thanks to the paradigm change from very expensive satellite images to an open and free data access policy that we can all benefit from. For example, the European Union's Earth observation programme, coordinated and managed by the European Commission in partnership with the European Space Agency (ESA) and others, is a service for Europeans to monitor the Earth’s processes with a fully open and free data policy that includes the possibility of commercial use of the data.

Due to the vast amount of data that is available, choosing the right satellite and type of data can be challenging. Many different instruments installed on satellites observe different aspects of the Earth. Satellites can, for instance, explore the Earth’s gravity or magnetic field, global wind fields or the changing sea level next to ‘normal’ color images (figure with the globes). When facing a new project or question, the following questions are reflected on:

• What process do I want to observe?
• What detail do I want to see?
• When and how often do I want to observe it?

The answers, in fact, will tell you which sensor is best to use depending on the spatial, spectral, and temporal resolution. The following example explains the process of choosing the right satellite and type of data.

We are already practicing remote sensing – i.e., sensing without touching - when we simply see with our eyes. Our eyes then work as a sensor to catch the light in the so-called visible spectrum from 400 – 700 nm. The camera in our cell phone works in exactly the same manner when capturing red, green and blue light and it stitches these channels together into a colored image. In addition, sensors on satellites measure the sunlight in other parts of the electromagnetic spectrum. Every satellite has sensors to capture light at different parts of the spectrum depending on its observation target. For example, to observe whether vegetation is healthy, researchers also need information on the near-infrared, as healthy plants reflect (send the light back into space) these wavelengths to protect the leaf structure from overheating. Therefore, in images showing the infrared light in red, forests and trees appear red. Using this information, we can create products that detect healthy vegetation.

The spatial resolution that we want to choose depends on the process and the details that we want to see. The spatial resolution is defined by the area that one pixel of a satellite image covers on the Earth’s surface, measured in meters. A spatial resolution of 1 km might be enough to study weather phenomena or global change. However, if we want to look at the urban expansion of a city, we will need a higher spatial resolution, ideally about the size of a building – around 10 meters or even less.

Finally, we need to define how often an area needs to be observed. We may want to observe a phenomenon every hour such as the clouds for weather forecasts, or we may want to check how a specific region changed during a season. In the first case, we would need a satellite to measure the target area every hour. In the second case it might be enough for a satellite with a revisit time of 5 days. We can then observe dynamic processes with these time series such as growing urban areas or changing vegetation.

For further information please visit NPOC – a public service that provides scientific advice to anyone in Switzerland who wishes to use satellite data.