Trick question: it depends on what you are trying to do.
What does resolution mean?
When it comes to Earth observation, you might hear about spatial resolution, spectral resolution, and temporal resolution. While all three need to be considered when looking for the satellite data, most often, when people ask about resolution, they mean “spatial resolution”.
Spatial resolution is the size of one pixel on the ground. Pixel stands for 'picture element' – the smallest individual 'block' that makes up the image. With a finer spatial resolution, 30 cm for example – where each pixel represents a 30 x 30 cm area, for optical data – you would be able to distinguish details, such as houses or cars. With a coarser resolution, an image of a similar digital size would cover a much larger surface on Earth and smaller features become harder to distinguish.
Note from the SkyWatch SAR expert: The above definition only applies to optical data. Synthetic aperture radar data (SAR) is not acquired at nadir like optical data but rather on a slant. Therefore the data is in slant range and the pixels on the ground are not square.
To better illustrate, here are two satellite pictures of the same location (Burj Khalifa, Dubai) taken with different spatial resolution sensors. On the left, a 30 cm resolution from Triple Sat Constellation, on the right, a 15 m resolution from Landsat-8
Spectral resolution is related to the granularity of the breadth of coverage of the electromagnetic spectrum captured by the satellite sensors. A finer spectral resolution can discriminate between narrower bands of wavelength, differentiating, for example, between red, green, and blue bands and allowing for coloured images.
Satellite sensors are able to capture data that would be invisible to the naked eye and a higher spectral resolution can provide us with a different view of objects and landscapes. For example, the shortwave infrared ranges enable highly effective geological mapping, because rocks and minerals have their own spectral pattern in these bands.
Temporal resolution refers to the time elapsed between viewings of the same area on Earth at the same angle. It can range from continuous coverage for geostationary platforms – such as a weather satellite, set at a fixed point over the Earth’s surface – to several days between revisits for low earth orbiting platforms (LEO). A higher temporal resolution means a shorter revisit time.
What resolutions are available in 2017?
Spatial resolution for Earth observation satellites in the early 1980s was around 80 m – as was available on Landsat-4. Now, you can find remote-sensing data to purchase with spatial resolutions as low as 30 cm. For open data, some of the finer sensor can capture up to 15 m resolution images.
The spectral resolution also improved drastically over the past few decades, as sensors were refined and more bands became available for study. Some of the most recent satellites sensors can now capture information on more than 1,000 different spectral bands.
As for temporal resolution, it is still very much varies based on the satellite. However, if you are interested in data regarding one specific area, the sheer amount of satellites that were launched has increased your chances of obtaining multiple, non cloud-covered, usable pictures.
Why a higher spatial resolution is not always better?
Higher spatial and spectral resolutions can be obtained by using the most recent technology. This usually requires heavy investments and this data can be extremely pricey. Additionally, thanks to the Copernicus program and the Landsat program, large amount of coarser resolution satellite data archives are available for viewing and download. Open satellite data can be obtained for free through the SkyWatch API.
Newer commercial satellites often work on a “tasking” basis, which means clients could request a specific satellite to cover a certain area at a certain time. If a priority account task the satellite you relied on, the data you needed might get delayed or simply not be available. When it comes to larger government programs, like Landsat and Sentinel, images are systematically acquired instead of tasked – these satellites follow consistent paths and rhythms – which means you can expect to always get an image in the same mode without worrying about conflicts.
Covering the same surface area with a 50 cm spatial resolution would render an image with 20 times the amount of pixels than the same surface covered by a 10 m sensor. This means an image of the same area will be a much larger file, and of course, take longer to download. This can be an important consideration factor when building an app.
While some of the most recent satellites can offer imagery up to 25 cm resolution, a large number of satellites currently circling the Earth have sensors that only offer coarser resolutions – in today’s standards. Additionally, data collected by older satellites from the Landsat and Copernicus programs over the past decades have been made freely available. As a result, specifying a coarser spatial resolution is likely to drastically enhance your chances of obtaining more than one image for the same area.
Shorter time period
With the improvements in sensor technology, for the first time in the early 2000, IKONOS made sub-meter resolution images available for purchase. Numerous satellites offering commercially available very high resolution images have since then been successfully launched. However, in studies of longer trends will require to use data that could be 10, 20, or 30 years old and will have a coarser resolution.
Numerous satellite data applications, such as climate studies, look at larger patterns and global trends. In such cases, short revisit rates, and high spectral resolution are key to answering questions and global data from Sentinel-3 and MODIS are more valuable than sub-meter imagery. A coarser spatial resolution would actually be preferred.
How to decide which spatial resolution you need?
Our advice, when doing remote-sensing data analysis or building a space app: think first about what you are trying to achieve and what resolutions you need to solve your business problem.The most detailed spatial resolution may not always be the best.
For example, a consortium, led by the Joint Research Centre (JRC) has successfully combined data from multiple coarser resolution satellites to monitor forest fires, using each satellite to compensate for the deficiencies from the other sensor – cloud perturbations for Sentinel-2 and sensitivity to ground moisture for Sentinel-1.
Continuous improvements in sensors, as well as the higher amount of available satellite data have helped dramatically expand the applications of satellite imagery and the possibilities are now almost limitless.