Landsat 8 image of the Camp Fire

Satellite Image Guide for Journalists and Media

Using satellite images

Landsat 8 image of the Camp Fire
Landsat 8 image of the Camp Fire – Informative and catchy at the same time

A picture is worth a thousand words. Most journalists will agree that this still holds true, and therefore satellite images can be worth a lot to journalists and media outlets.

So you would like to use a satellite image in your article and you would like to explain it to your viewers? Here is a short guide covering some of the most frequently asked questions and giving some general explanations on satellite images. It by no means covers all aspects, as there are far too many types of satellite images, but should give you a good start to find out more on your own and maybe motivate you to create your own images, which has become quite easy and quick even with no prior knowledge of it.

You might also want to read my guest blog post on the Sentinel Hub blog “Why Newsrooms need People with Expertise in Remote Sensing“.


Satellite images come in different resolutions, depending on the satellite the resolution can be anywhere from a few hundred meters per pixel to around half a meter per pixel for civilian usage. While images from 10-meter resolution onward are often available freely, better resolutions are usually commercial products.

Aside from this spatial resolution there is of course also the temporal resolution. What is the revisitation time (the time it takes for the satellite to look at the same location again) of the satellite or the satellite system? Will you get an image once a day, once a week, or maybe only once a month?

I mention resolution because sometimes journalists ask for “the original picture in full resolution” and are somewhat disappointed at what I could send them.

This close-up of Boston demonstrates very clearly the difference between 10-meter and 0.8-meter resolution. In the SkySat image you can also see the “leaning” effect of the skyscrapers, not imaged at nadir (from directly overhead) but at a slight angle.



The 10-meter resolution of the Sentinel-2A satellite seems not very attractive in this close-up, however, don’t let that fool you. When showing bigger areas, the 10-meter resolution delivers superb images, as can be seen here in this image of the Sermilik Fjord and surrounding glaciers in Greenland.


The Landsat satellites have a similar resolution with 15 meters per pixel and deliver comparable results. Because the Landsat program is much older, the archives of images are especially interesting and important for time-series stretching over a longer period of time to show changes and monitor slow processes. 

Sentinel-2A image of the Helheim glacier.

Now that is looking odd…

Some common surprises when looking at natural color satellite images include colors that look a bit wrong and less saturated than expected and often some kind of haze in the image.

Forests can look surprisingly dark in satellite images
Forests can look surprisingly dark in satellite images

Forests for example can look surprisingly dark and not at all as green as you might expect them to be. Images also often show a somewhat bluish tint, this is caused by atmospheric scattering of the light which is stronger at shorter (blue) wavelengths.

Bluish tint from atmospheric effects
Bluish tint from atmospheric effects

This bluish tint and haze can sometimes be experienced when looking out of the window of a plane, but because of the higher altitude of remote sensing satellites and therefore more atmosphere to penetrate, those effects are usually more pronounced here. More information on this effects you can also find here.

No bluish tint because of atmospheric correction
No bluish tint because of atmospheric correction

Atmospheric correction

Atmospheric correction can be used to reduce the impact of the atmosphere on the image quality. Some data providers deliver images that already are corrected, but sometimes you have to do it on your own.

There are several methods to do so, some very scientific, others more artistic in their approach. Which one you need depends on your application, for usage in media, scientific or artistic doesn’t make much of a difference as long as the result is visually appealing.

Visual, infrared, and more…

Satellites can often not only see the Earth in visual light, delivering natural color images as the above Greenland image, but also have sensors that can detect other wavelengths, most commonly near infrared and short-wave infrared, sometimes also thermal bands to get temperature readings.

Not seeing the Earth in visual light but also other wavelengths bands allows us to see things, that would otherwise be hard to see, or could even be completely invisible. Here is a comparison of an image in visual light and near infrared/short wave infrared combination.


As you can see the burn scar pops out in a very distinct red in the infrared view, while healthy vegetation appears in greenish colors. This kind of view is very common in satellite imagery. The burn scar can be seen in visual light as well, but the infrared makes it so much easier to see the difference here. If you use images using not only visual light you should always state that fact, as otherwise the colors might look odd to the casual reader expecting something completely different.

Blue snow…

Another common source of confusion is snow. In infrared images snow and ice often appear in cyan/bluish colors. Since infrared images are so common, this is something you should know about. Take a look at the snow on top of the mountains of the Novaya Zemlya archipelago. You should also note that water can appear quite dark blue or even almost black in these infrared views.

Sentinel-2A infrared image of the Novaya Zemlya archipelago.

However, with some manipulation it is also possible to generate infrared images in which the snow appears white, possibly making it easier to understand but also making it harder to distinguish snow and ice from clouds. See an example below.

Columbia River Washington, NIR/SWIR view

Different band combinations can also be used to distinguish between different types of rock. In this image the Jabal Arkanu (in the top part of the image) shows a more or less circular granite intrusion (showing up slightly purplish in the NIR/SWIR image), flanked by a sandstone component (greenish in the NIR/SWIR image) in the north-east. The Gabal El Uweinat (bottom part) also has a circular granite intrusion, with sandstone and siltstone components surrounding much of the intrusion, pretty much looking like a big “hand of sandstone” holding a “granite ball“. If you are interested, you can read more on it here.


Another combination that is used quite often produces images like this one. Healthy vegetation appears in bright red, while burn scars appear dark red/brown to black. This image has additionally highlighted hot spots. These red false color images are quite popular, they work reasonably well to show the burn scars and the red color, especially combined with bright hot spots, usually helps to create interest in the image.

Sentinel-2 false color image of the Thomas Fire in California, USA.

Here is an image showing three different way to show the same area. Left in natural colors, the middle a NIR/SWIR image, and on the right a version highlighting the water in the image. Depending on what you are writing about, I would recommend to always also show a natural color image, as most people find those easier to understand/read, image sliders as I have used above are also a nice way to do this.

Landsat 8 images of the Kootenay River, British Columbia, Canada.

Getting and processing your own satellite images

Okay, enough of looking at satellite images, but how do do get them? You are a Journalist or have an otherwise media related job that makes it necessary for you to get pictures that go with your story? You want those images to be satellite images? Well, then thanks to open data policies and services such as the Sentinel Hub EO Browser you are no longer limited to the pool of available and pre-processed satellite images, but you can process images of your area of interest yourself in a matter of seconds.

I will not talk about how to get an image with the Sentinel Hub EO Browser, as it is so dead simple, you should just head over and do it yourself. Many different visualizations are available as presets, making it easy to have a look at your area of interest in different ways. But it doesn’t stop there. Thanks to custom scripting you can get even more functionality out of the EO Browser. An example would be my script for wildfire visualization I wrote about here. But there is also a repository with many other scripts, and you can of course always try your own ideas.

And while there are a lot of different possibilities to get satellite data or processed images, Sentinel Hub in my opinion stands out, as it allows to process satellite data to very usable images with little to no knowledge and background in satellite data processing, making it an ideal tool for journalists.

Finding satellite images of big cities and famous locations is easy - But what if your story takes place in Novy Urengoy, Russia?
Finding satellite images of big cities and famous locations is easy – But what if your story takes place in Novy Urengoy, Russia?

Images that match your story

This will make it possible for you to get images that truly match your story, be it general reference images, for example satellite images of a city or area, or more specific images of ongoing events such as wildfires, volcano eruptions, floods, hurricanes, and other events. Especially for major events you will usually find an ample amount of pre-processed images, but with your own images you can focus on specific aspects and sometimes even be quicker than others, as you don’t have to wait for pre-processed imagery to become available. The advantages for smaller events are even bigger, as pre-processed images of smaller wildfires or other events are usually rare.

Another advantage is the license situation, as media can freely use the images created in EO Browser in accordance with the CC BY 4.0 license.

Know your audience

Satellite images are in some way not different from the stories you are writing. It is important to know your audience. Important because knowing your audience will give you at least an educated guess on what they already know and also what kind of image will grab their attention and the level of detail they might expect.

Scientists or fire-fighters will be perfectly fine with some gray-scale images of a fire perimeter and burn scars if they are easily readable and accurate. The general public, however, is not a big fan of gray-scale images. To grab their attention, a natural-color image or a punchy false-color image will be working much better. So to get attention, at least the lead image of your story should be something that appeals to your audience, you can always add additional imagery within the story or in galleries for readers who would like some more detail and background.

The right tool for the job

Sentinel-2 Thomas Fire image
Example of some post-processing in Photoshop (Click to enlarge)

So, should you have decided to use the Sentinel Hub EO Browser to get images, great choice. However, as much as I’m a fan of it and all its capabilities, it still is far from being able to do everything.

Photoshop annotations simple example
Here an example of super-quick annotations in Photoshop still adding a lot of value

If you want a quick satellite image for a recent news event and there is Sentinel or Landsat imagery available, then the EO Browser will help you in getting a quick and usable result. Sometimes the result right out of the browser will be perfect for you. But sometimes it won’t. So you will need to do a little work yourself.

Using Sentinel imagery as decorative, yet thematically fitting element

GIMP, Photoshop, and more

Because of that I recommend, that you don’t restrict yourself. Using the EO Browser is fine, but don’t shy away from additionally using other tools.

Using GIMP (free) or Photoshop (commercial) to post-process the EO Browser generated images is a great way to enhance image quality or focus on specific things in the image. You can manipulate the image itself or just add some annotations and explanations right in the image as seen in this example here. I added some annotations, an inlay, and used Photoshop to bring the colors to a more natural-like appearance.

But even if Gimp or Photoshop are relatively new to you or you simply just don’t have the time to spend on some fancy annotations, even the most simple annotations, which can be done by beginners in a matter of minutes, can be helpful and add value to an image for your audience. See the second example to the right.

Not only allows a combinations of tools for more useful images, with just minimal knowledge of GIMP or Photoshop you can use Sentinel imagery as decorative yet thematically fitting element of a story, as can be seen in the example image of the fictive “Water in New York City” story. It is not much on its own but can give your story the final touch and looks quite nicely.


A storm over the Gulf of Alaska, Aqua MODIS imagery. QGIS was used to add the scale, borders/cities, and to reproject the image.
A storm over the Gulf of Alaska, Aqua MODIS imagery. QGIS was used to add the scale, borders/cities, and to reproject the image.

With QGIS you can even get a free geographic information system (GIS) which, at least for some basic operations, you will be able to use within 30 to 60 minutes. How far you want to go depends on your interest, time constraints, and needs, but even some basic usage of it can be helpful.

Take a look at the example image with the storm over the Gulf of Alaska. QGIS was used to add the scale, borders/cities, and to reproject the image. It will help your audience to get a feel of the scale and better be able to identify locations and geographical features.

I should also mention that there is a Sentinel Hub plugin for QGIS, more about that you can read here.

The possibilities here are pretty much endless, there are many other tools available and depending on what you want to highlight you should really experiment what is working for you.

Keep it real

Wildfire in the Amur Oblast in Russia, natural colors with NIR/SWIR overlay to show IR emissions

Keep it real with your images. By that I mean that you should communicate to your audience what can be seen. I’m processing a lot of wildfire images, many of which ended up in some media publications. I like to create images combining natural colors with an IR highlights overlay, like this image on the right. This way zones with active fires and lots of residual heat are very visible in the image. The resulting image looks a lot like you could really see the fire, as the zones of high IR emissions line up with the smoke emanating from the fires. Here is where you should keep it real and tell your audience that what can be seen are not actual fires but actually an IR overlay that to some degree lines up with the active fires and hot spots.

I have seen this explanation very rarely, and without it those images are to some degree misleading. But good examples of how to communicate it can be seen here in Discover Magazine Blog by and here in Engadget by .

Explain, explain, explain…

Another thing worth noting is, that you should tell your audience in which way an image might have been altered. Take this image of the Salton Sea for example.

You could say it is a natural color image, but to be fair, in this image the colors have been slightly exaggerated to make the agricultural fields stand out more. Additionally the contrast within the Salton Sea has been enhanced to better show the details now visible.

It is not necessary to explain every single step of processing, however you should speak about the effects your processing had on the image.

Some good examples of satellite image usage

Good is relative and what is good for one person/application might not work for others. But take a look at those images of some very talented people to see some nice usage of satellite imagery. 

An annotated map of the Xe-Namnoi dam breach made by Simon Gascoin. Make sure to read the whole thread for more information. It also shows that simple annotations can be really helpful in conveying the meaning of an image.

Very impressive is this animation of the Pine Island Glacier calving, created by Stef Lhermitte from 108 Sentinel-1 images.

Comparison animation of forests between Moena and Predazzo after heavy rain falls in Italy by Annamaria Luongo.

Timelapse of the Oroville Spillway destruction and reconstruction by Harel Dan.

The Öræfajökull volcano in Iceland by Antti Lipponen.

Thousands of cars filling up Volkswagen’s Diesel graveyard in Victorville, California, by Kamil Onoszko.

Sentinel Hub Education Pages and Future Updates of the EO Browser

I would encourage you to read “Educational Role of EO Browser and New Features” by , giving you a nice overview of some of the recently added features of the EO Browser and giving some more information about the Sentinel Hub Education Pages, which is also a nice resource if you want to learn a bit more about Earth Observation.

The EO Browser is a work in progress. Some of the more exciting updates planned for the future are data combination (for example using Sentinel-2 data and Sentinel-5p data in a single image), the ability to build time lapses from data of multiple satellite missions, additions to the education pages regarding volcanoes and air pollution, and, maybe especially interesting for journalists to visualize things, the ability to create 3D-images of scenes within the EO Browser by combining data from the Sentinel satellites and a digital elevation model.

There are also updates planned for the more advanced users, the new API V2.0 will support POST requests, removing the char number limit in URLs. We will also see multi-part responses, e.g. GeoTiff (for raster data) + JSON (for meta-data or some script debugging information), better support for machine learning, and the possibility to get raw satellite data e.g. unchanged Sentinel-2 reflectance values.

Useful Resources

Some useful resources to get some more information. Interesting Twitter accounts to follow for Sentinel images (and satellite images in general) as well as some articles/blog posts explaining some aspects of image processing in greater detail.

Twitter Accounts

, the official account of the European Union Earth Observation Programme. You’ll get all sorts of information about the Sentinels as well as a nice selection of images. They are also helpful with any questions you might have about the Sentinels or the Programme itself.

, the Copernicus Emergency Management Service providing mapping products based on satellite imagery together with forest fires Flood and drought early warning products.

, more information about the European Earth Observation endeavors as well as Sentinel imagery.

, for news about the EO Browser and the Sentinel Playground as well as a stream interesting satellite images

, if snow and glaciers are your thing, you might want to follow Simon. But even aside from those topics he’ll surprise you with nice usage of Sentinel imagery.

, just as Simon, Stef likes to look at the colder things on Earth. Follow him for some nice timelapse animations and informative use of imagery, especially glaciers.

, you should follow Antti for a beautiful selection of storm images from a variety of different satellites and some excellent data visualizations. Also take a look at his Flickr here.

, bringing you interesting images and especially (timelapse) animations from Sentinel satellites.

, tweeting interesting Landsat and Sentinel images with a focus on forests and areas in Poland.

, who is posting all kinds of different Sentinel images with some very interesting GIFs, showing how to present satellite data in an engaging way.

Further Reading

A lot of these are not meant for the Sentinel Hub EO Browser but rather deal with manual image processing, however, they give you a deeper insight into the processing of good satellite images and the methods behind it and are all well worth reading. I would recommend to especially read the first one, as it will help you to understand the general process of creating a natural color image from the raw data of the satellite.

I would also like to remember you of what I have said earlier. Don’t restrict yourself to one tool. The EO Browser is versatile, quick, and easy to use. But you should see it as a starting point. Sometimes you can use images right out of the browser, sometimes a little tweaking with tools like GIMP or Photoshop can get you far ahead in terms of image quality and usefulness for your audience. So some knowledge of all of this is always a plus.

How To Make a True-Color Landsat 8 Image by
A hands-on guide on how to manually process Landsat 8 images. Very useful to get you started and learn about the basics. And even though he used Landsat 8 data his workflow is pretty much transferable to many other satellites, including the European Sentinel-2 satellites. Even if manually processing images is not your goal, you should read it.

How to Pan-sharpen Landsat Imagery by
A useful extension to the above guide by Robert Simmon. Joshua describes how to manually pan-sharpen Landsat 8 data. Again, the workflow is transferable to other satellites as well. Very useful especially if you are planning on manually processing natural color Landsat 8 images.

A Hands-On Guide to Color Correction by
If your satellite images look like old photographs bleached by too much sunlight or have a strange blue hue then this guide on color correction might be helpful. Get some tips on how to manually mitigate some of the effects our atmosphere has when taking images through hundreds of kilometers of it and create images with more vivid and natural-like colors.

Making Sense of Satellite Data, An Open Source Workflow by
This is a five-part series of articles dealing with Accessing Data (Part 1), Pre-processing Multi-band Data with QGIS (Part 2), Stitching Data with QGIS (Part 3), Color correction with GIMP (Part 4), and Saturation and Sharpness (Part 5). This series of articles guides you step-by-step from getting Sentinel-2 data over combining the data in QGIS to manual correction of colors, saturation, and sharpness to your final image. Especially useful to read if you want to get the basics of how to integrate and work with satellite data in QGIS.

A Gentle Introduction to GDAL by
If you don’t shy away from using some command line tools, then this introduction to GDAL in three parts will get you started. You’ll get introduced to the basics (Part 1), learn about Map Projections & gdalwarp (Part 2), and Geodesy & Local Map Projections (Part 3). GDAL is a very powerful tool and helps you to manipulate satellite data in many different ways. If your goal is to just get some nice looking satellite images you don’t necessarily need GDAL, however, it can be very helpful and can of course be used in batch-processing, so it can speed up your workflow a lot, especially when you are about to process a lot of images.

Color Correction with JavaScript by
In this article Miha is talking about some of the basics of atmospheric correction of satellite images to  get better and more natural colors. He gives a short explanation of his JavaScript meant to be used in the Sentinel Hub EO Browser to automatically color-correct images. If you are using the EO Browser you should give this script a try, it can produce really good results.


Once again thanks to the team at Sentinel Hub, patiently, and quickly, answering all of the annoying questions I might have had and for making the EO Browser available to the general public.

I would also like to thank all of you who gave me some form of feedback when I bugged them with questions. 

Landsat 8 data courtesy of the U.S. Geological Survey, Resourcesat data courtesy of ISRO and the U.S. Geological Survey, Contains modified Copernicus Sentinel data [2016, 2017, 2018], SkySat data by Planet Labs under CC BY-SA 4.0 license

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  1. This article is amazing! I will say it is very informative and educative. This is a great material for enhancing my remote sensing skills .

  2. Hi Pierre,
    I also appreciate your article very much. You are very good at explaining difficult things in a simple language. What a great skill! Thank you.

  3. So much remote sensing knowledge and information concentrated in a few words… Congratulations and thank you so much.

  4. Pingback: 2falsetto

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