August 23, 2016
by chris

Some new satellite image mosaics

Recently i completed a number of new cloud free satellite image mosaics i want to introduce here.

The first one is an image of Cuba based on Landsat imagery. Seen from above Cuba is one of the most photogenic parts of the earth with very diverse landscapes and colorful coastal reefs and wetlands.

Then i have two smaller images of the Canary islands and Madeira.

The Canary islands mosaic is made from Sentinel-2 imagery while the Madeira image is a mixture of Sentinel-2 and Landsat 8. These are still somewhat experimental – you might notice the overall color balance being somewhat different from other mosaics. There are two special problems here – one is these islands feature extremely steep relief so to avoid long shadows preference was given to summer images with a high sun position while normally i target the maximum plant growth. This brings up a second issue: sun glint – which affects appearance of water surfaces. I will write about this in more detail in a separate post.

Finally there is also a new Sentinel-2 based mosaic of the Falkland Islands.

Click on the full image previews above to visit the mosaics’ pages on where you can find more information, previews and can inquire for using them.


August 8, 2016
by chris

Snow in different places

I have two satellite images to show here featuring unusual amounts of snow for this time of year. The first in from already a few weeks back in July showing fresh summer snow in the southwestern Alps:

Depending on the weather snow can fall in the Alps at higher altitude all around the year but it does not last long usually once the weather changes. Here fresh snow reaches down to about 2000m which is already pretty far for this time of year.

The second image is from Lesotho in southern Africa which received large amounts of winter snow this year.

Winter snow is common in Lesotho as well as in South African mountains further west but most of it usually does not last very long. Much of the snow that originally fell has already vanished in this image. See also here for more detailed explanations.

First image is created from Landsat 8 data, second from Copernicus Sentinel 2 data.


August 5, 2016
by chris

ALOS AW3D30 relief data review

When NASA after the SRTM (shuttle radar topography mission) in 2000 released the first SRTM data (the latest version of which i reviewed previously) this was for large parts of the wold the first detailed relief information available at all and for even larger parts the first relief data that was accessible at reasonable costs and with a uniform quality standard. There probably is no other single data set that was of such fundamental influence for today’s geodata landscape as SRTM.

Today, 15 years later, SRTM is still in some part of the world the best freely available relief data source but there is an increasing number of other open elevation data products as well. Most of them locally produced data covering only smaller areas, there are still very few free data sets with a more or less global scope. The newest one is the ALOS AW3D30 Global Digital Surface Model from the Japanese space agency JAXA. I now wrote a detailed review of this data set.


July 19, 2016
by chris

Arctic off-Nadir Landsat 2016

As mentioned previously when writing about images of the Antarctic the USGS announced some time ago they’d be starting to regularly record off-nadir images with Landsat 8 of the polar areas beyond the regular Landsat recording limit. As likewise mentioned these areas are also not covered by Sentinel-2.

A few days ago the first set of such images since the early ones from May 2013 i also wrote about previously was recorded in the Arctic in northern Greenland.

The path chosen is slightly different from the one from May 2013 which is shown here for comparison.

Note both images are in polar stereographic projection so they are not north oriented. Both cover all land areas that are not in the regular Landsat coverage.

The new images are not completely free of clouds but fairly clear overall. Mid July is not yet the snow and ice minimum, especially ice of lakes and at the coast is going to melt further in the coming weeks. But the time chosen is a good compromise between snow and ice on the one hand and sun position on the other. And it is possible that further images are going to be recorded in the coming weeks although likely not as many as in the Antarctic half a year ago since this much more interferes with regular image recording on the northern hemisphere because you cannot instantly switch between normal and off-nadir image recording.

Here a few crops from this new imagery showing the northmost land on earth.

You can also find these on the OSM images for mapping – together with a few ASTER images of northern Ellesmere Island likewise from the past few weeks. Overall with regards to mapping in OSM these areas, which a few years ago were hardly mappable at all, have now a significantly better and more accessible image coverage than many other parts of the planet.

July 16, 2016
by chris

What satellites see

In the last post here i showed an illustration of the spectral sensitivities of current earth observation satellites. I like to comment this a bit more in detail separately here.

The list of satellites is not meant to be complete, in particular not listed are military or otherwise secret systems, older geostationary weather satellites and also various systems with unclear operational status. Except for Landsat 5 (no more) and GCOM-C (not yet) i only included operational systems – Sentinel-3 however is not yet producing data for the public.

Satellites are grouped into three groups:

  • At the bottom the commercial and limited access systems
  • The center group with brighter background are the higher resolution open data systems
  • On top the lower resolution continuously recording systems

I also listed the revisit and global coverage intervals. Revisit is somewhat ill defined for satellites with pointing capability but is generally considered to be 1-6 days for a single satellite with small field of view – depends somewhat on the orbit of course.

Global coverage is only specified for those satellites which regularly record a global or near global coverage. Several commercial operators claim this but there is no reliable data to verify those claims. Landsat 5 and 7 have both in the past been used to produce global image collections over the course of several years. Landsat 8 now usually records all land surfaces within its coverage area in the 16 day revisit cycle with only occasional gaps. The lower resolution polar orbiting systems (MODIS/VIIRS/Sentinel-3) all have a well defined global coverage interval since they are capable of continuously recording. AVHRR is meant – using several satellites – to achieve a 6 hour revisit frequency and of course geostationary systems can record their area of view in much higher frequency. DSCOVR EPIC finally – by not being a real satellite – has its own special recording scenario.

Apart from DSCOVR EPIC and the geostationary satellites nearly all systems shown are in a sun synchronous orbit, they record earth at approximately the same time of the day locally. The only exception are the Planetscope satellites in ISS orbit.


July 15, 2016
by chris

On false colors

On various occasions i have written about the problem of accurate colors in satellite images before. However what often makes satellite imagery such a seemingly complicated and difficult matter are usually not these subtle details of color fidelity and perception. What makes images frequently difficult to understand is that what is widely advertised as satellite imagery is often not images or photos is a strict sense but a broad range of visualizations and artwork (if you want to call it art) based on remote sensing data. Such pictures are generally indiscriminately presented as satellite images, often without explanation and no established boundary exists where the domain of photos ends and abstract visualizations begin.

Compare that with what you see in a newspaper for example – you have photos there, you have occasionally drawings of real world situations like from court room procedures where no cameras are allowed. And you of course have things like diagrams, caricatures and other graphics that are not meant to directly depict a real world view. Having these clearly distinguished types of pictures is in many ways central to the concept and self image of journalism.

Ironically science marketing and science journalism are one of the fields where this distinction tends to get blurred most, especially when it comes to satellite data.

It is difficult to make reliable estimates but i would say at least half of what you usually run across in newspapers, television, on websites etc. as satellite images are not photos in the sense of an attempt to create a reproduction of a certain imaging measurement with even the most rudimentary effort of faithfulness.

About half of these non-photographic satellite images are completely abstract depictions, usually some kind of 2d data set based on remotely sensed data visualized in some map coordinate system that allows you to recognize geographic features. This includes for example shaded depictions of relief data (yes, i have seen those being called ‘satellite image’) and radar data visualizations (which are usually based on run time/phase information and not a 2d imaging process).

The other half is what you usually call false color images and i want to discuss this topic in more detail here.

False color

False color images are a concept that predates digital imagery. It has its origin in classic chemical photography, specifically color infrared films that are sensitive in various parts of the visible and infrared spectrum but develop into visual range colors. This concept was then later reused in digital images.

To show what false color images mean first for comparison a Landsat image in normal visual colors:

This shows parts of the western Caucasus mountains with a large variety of surface features in the image. You can see good contrast between some surface types, like rock/snow and vegetation and even forests and grassland/agricultural areas can be well differentiated. On the other hand contrast between vegetation and water is rather small.

The visual range sensor channels of the satellite are here directly mapped to the red, green and blue channels of the image file and ultimately the display device. No matter how well the sensor sensitivities match the definition of the display color space and the sensitivities of the human eye – this is generally called true color or visual color. This does not necessarily mean the image shows realistic colors of course.

In addition to the blue, green and red color channels the satellites also record a number of other channels across the electromagnetic spectrum, most of them in the infrared (that is longer wavelengths than the visible light):

Just like with color infrared film you can map these to visual range colors to visualize these measurements. A traditional mapping is

NIR → red
red → green
green → blue

which suited early earth observation satellites which often had a green, red and near infrared channel and which is also close to the characteristics of color infrared film. Water surfaces are in much better contrast to vegetation here:

The important thing to realize is that since such a mapping has absolutely no physical basis it is ultimately completely arbitrary – you can map your data to any combination of visual range colors, as i for example showed in context of the ASTER instrument it is sometimes preferred to have the vegetation in green and not red so the same data can also be mapped like this:

And you do not even need to have a one-to-one mapping of the spectral channels to the red, green and blue color components – you can use an arbitrary linear or even non-linear mapping. For example here is a mapping that retains brightness from the visual range but defines the color tone using infrared data:

For false color images to be of any practical use and not just abstract art there need to be conventions on their definition and you need to learn to read them. This is a huge problem because the definitions are often quite hairy and the need to learn how to read these images is widely ignored and underestimated. Showing a false color image is a bit like printing an article in Chinese in a German newspaper – the main difference is that with the text the reader obviously realizes it is not understandable while with the false color image you can easily get a false impression of understanding. But in fact it is even worse – even with a lot of experience and training at using a certain type of false color image you always have a high risk of erroneously believing to correctly interpret certain features while this can easily be an optical illusion or a cognitive fallacy. Since training and experience with false color satellite image is always based exclusively on looking at satellite images and lacks the real life context of normal visual color perception it can never reach the level of reliability and robustness of reading true color images.

So with these immense disadvantages why do people none the less use false color images? This is because

  • there is a lot of information available in the infrared data that is not present in the visual bands,
  • using the full range of a color display and our visual perception can be helpful when visualizing complex multidimensional data and
  • the human mind can be trained to recognize similarities and patterns in color images, even if they are based on artificial colors.

Here is – for the area previously shown – the today most common false color band combination for multispectral satellite images. This is based on the mapping

SWIR → red
NIR → green
red → blue

This band combination is so popular because it manages to compress a real lot of information into a single color image. Since it does not use the blue and green visual range bands there is fairly little atmospheric influence, you can see quite well even through veiling clouds. For basic orientation the following basic properties:

  • Vegetation is generally green – different tones of green primarily indicate different vegetation density and different states in the growth cycle, different types of vegetation are often not so easy to distinguish.
  • Water is generally dark blue, dry snow and ice is bright blue to cyan, wet snow is darker.
  • Bare ground appears in various tones of brown, red and gray. Differences between different geological settings are often better differentiated than in the visible range, for example in the area shown here the limestone mountains south of the main ridge appear significantly brighter than the crystalline/metamorphic rocks further north. You need to be careful however since soil humidity also influences this quite a lot.
  • Clouds are usually fairly colorless bright gray and white although they can sometimes also be somewhat bluish or reddish.

Although this type of band combination is routinely used with data from many different satellites the results vary since where exactly the NIR and SWIR bands are located varies, much more than it does for the visual range bands which are obviously often tied to the human perception. Here is a diagram of current earth observation satellites, an extension of what i showed for the visual range previously.

There are a huge number of other false color band mappings – many of them variants of the SWIR-NIR-visual mapping above with either the other SWIR band or another visual range band substituted – these all look fairly similar at the first glance but vary in the details. There are also a few completely different mappings you occasionally see, for example the pure infrared mapping SWIR2-SWIR1-NIR and a blue-SWIR1-SWIR2 combination:


All of this is only with the most common spectral bands – ultimately the possibilities are endless and it is easy to turn this into a kind of secret code with images only a few initiated experts can read. But the use of such endeavors is highly questionable – as i hope i made clear reliably interpreting even the most common false color image types is full of difficulties. If you think certain things can be reliably determined from the appearance in a false color depiction it is often better to formulate this conclusion in a quantitative way based on the data values. This also has the advantage you can take into account more than the three variables you can independently represent in a color image.


To sum things up the following recommendations

  • Do not use false color satellite images in publications or marketing.
  • If you use false color satellite images publicly include also a true color image for reference and include a clear warning that colors are artificial.
  • Consider using single band images or single value processings like band ratios or difference indices, possibly with a well designed color gradient (no rainbow colors!) instead of a false color image.
  • When interpreting false color satellite images be wary of the possibility of misconceptions and verify your interpretation is consistent with the true color appearance if possible.
  • Be aware that there are serious differences between different satellites with equivalent false color band combinations.

July 7, 2016
by chris

Beyond Mercator

FOSSGIS 2016 in Salzburg is over – many interesting talks you can all watch on the FOSSGIS Youtube channel. You can now also find the slides for my talk on Frab.

Many people showed interest in the Peirce quincuncial projection for which i showed a demo at the end – you can find the proj.4 implementation on github and the demo map on – or included directly below, pressing shift while panning gives you automated north orientation.

I will try to write up a bit more on this in the future – there are a lot of interesting aspects about this projection, both from a technical viewpoint as well as from a user perspective.


July 1, 2016
by chris

Talk Announcement

On Monday the the FOSSGIS conference starts in Salzburg – after a more informal OSM-Sunday. I will give the opening talk there (in German) – with the title Jenseits von Mercator (Beyond Mercator) where i will be talking about the meanwhile omnipresent and seemingly unavoidable mercator map projection. I am going to discuss its virtues and disadvantages and show some alternatives. Should be interesting for anyone involved in use and production of maps somehow.

Those who would like to come by spontaneously can do so – OSM and Open Source community members can visit for free. Those who can’t be there is person will probably be able to watch it live or as recording on the internet – on

Salzburg by the way currently looks like this:


June 28, 2016
by chris

The new Google Landsat mosaic

It has been some time since i wrote the last review of a satellite image mosaic product. In fact the last one i did was for the last Google image mosaic based on Landsat 7 data three years ago. Now they have rolled out a new version of their base mosaic i am going to review here.

But first a general look at the three years in between. Why did i not do any reviews since then? Apparently because there was not much to review. Of course there were a few cases where map services put in some local image updates and improvements in their image layers. For example Bing recently filled some of the most obvious gaps in image coverage. But technologically these three years have been pretty uninteresting, nothing really revolutionary was released in the field of global mosaics, at least not publicly – so nothing to review really.

In terms of data – we remember Landsat 8 began operations a bit more than three years ago so during all this time a veritable heap of free high quality data has been piling up. And Google apparently thought it was time to put this to use in their base image product.

Has something changed?

If you look at the new image compared to the old at a coarse scale (new left):


you can hardly see a difference. Overall apparently not much has changed in their processing system. This especially means they apparently use the same color homogenization and color transfer techniques they have used before which leads to a globally fairly consistent appearance (for which – in light of the many other mosaics with colors grossly off – i commended them in my last review). But this also means locally color nuances get lost and the fairly good color reproduction of Landsat 8 is – i hate to say it – mostly wasted.

Comparison new-old from northern Ellesmere Island

Comparison new-old from Siberia

But before we have a closer look locally some basic facts overall. The image mosaic, just like the old one, is nearly global – as far as the Landsat coverage goes but excluding the Antarctic. In areas where they previously already used other images at the low zooms they continue to do so (Svalbard, South Georgia and a few other places). For the Antarctic they continue using LIMA, in the north beyond the Landsat coverage they now have a fairly poor low resolution image with the source not identifiable.

At the Landsat limit in northern Greenland

My own Greenland Mosaic for comparison

As you can also see in the sample above they still use the same inaccurate coastline mask cutting off their image data in the middle.

What is improved?

Well – since their main source of data now is Landsat 8 data from the last three years one thing that improved for sure is the imagery is more up-to-date. Previously with the Landsat 7 data basis the average image was likely from around 2007 and now it is likely from 2014. Note however that a three years data basis is not much data to deal with (which is why we have not seen any other global Landsat 8 mosaic so far). It seems Google did also continue to use Landsat 7 data to some extent – this is generally well visible in form of the characteristic striping which will not occur with Landsat 8 images.

Landsat 7 striping in the new Google mosaic

Their clouds handling seems to have in parts somewhat improved – possibly because they used the Landsat 8 data in addition to the images used before there

while in other areas it is significantly worse

While the areas shown previously are simply very hard concerning clouds there are also cases where they would have been completely avoidable:

Probably partly due to the relatively small overall image volume areas with strong seasonality like high latitudes and high mountain areas are often quite bad as well.

Google in the Alps

My Alps mosaic for comparison


Overall i am not really sure why Google produced this update. There was no real pressure from the competition making their imagery appear outdated – after all most map services still use the much older Landsat 7 images from before the SLC failure (1999-2003) as base imagery. I suppose they mostly see this as a demonstration of their satellite image processing platform which they also market independently.

In terms of technological development i don’t see much here though. If you know the difference between Landsat 7 and 8 regarding the source data quality, especially noise levels and dynamic range – the new mosaic seems rather disappointing. So the main thing this demonstrates is that their processing framework is sufficiently robust to produce a new mosaic from a changed data basis with comparable results.

And of course they show that they can still with their pinkie do what others have not been able to do for many years – namely produce a globally consistent and visually at least at a coarse look cloud free satellite mosaic in this resolution range.


June 24, 2016
by chris

Panama Canal

These days the new locks of the Panama Canal that will allow passage for significantly larger ships are going to be opened for regular use. As usual most image sources for OpenStreetMap are not quite up-to-date here, they generally show construction work in progress and accordingly mapping of the locks in OSM is fairly incomplete. I now put up an up-to-date image on the OSM images for mapping. This is not high resolution but sufficient to see the overall configuration with the water storage pools and other structures. Of course as usual for Panama there are lots of clouds in the image which also partly obscure view of the Pacific side locks.

Besides you can also find another image from northwest Indonesia where a fairly large island (Pulau Mapur) and various smaller islands are currently missing in OpenStreetMap. Other image sources have data here too but also subject to lots of clouds and with varying quality.


June 21, 2016
by chris

Midsummer Night

On occasion of the summer solstice i put together a midnight sun satellite image mosaic of the Scoresby Sound area in Eastern Greenland from images of the past weeks.

I explained and demonstrated the concept of nighttime satellite images at high latitudes before. As you can see the sun is in the northwest meaning it is late evening local time when the images are taken. The mosaic contains some clouds in the southwest but is otherwise showing very clear weather. Here a few crops

same area in the Landsat mosaic of Greenland.

This shows the end of the Daugaard-Jensen Glacier and the dissolving ice on the inner fjords.

same area in the Landsat mosaic of Greenland.

This shows the Mestersvig station at the frozen King Oscar Fjord.

same area in the Landsat mosaic of Greenland.

Images are produced from USGS Landsat data.


June 20, 2016
by chris

Spring thaw in Northern Greenland

While here summer is approaching (although spring has been very rainy this year) in the high Arctic spring is just starting. Here a satellite image from Northern Greenland of the Petermann Glacier where snow and ice are thawing. The strong blue colored area is sea ice where the winter snow layer on top has mostly melted and the ice is largely covered by either wet snow or meltwater. The sea ice is still solid, it will break up towards the Nares Strait in the southwest in the next weeks although further to the north of the sea will stay mostly ice covered for the whole year and the ice will just be more mobile than in winter.

Image is based on Copernicus Sentinel-2 data.