On the Disappearance of Lake Mille Lacs

Two weeks ago, one of Minnesota’s 10,000 lakes disappeared, leaving them with only 9,999. And, it wasn’t a small one, either. It was the state’s second largest inland lake. But, this is not like Goose Lake, which actually did dry up. The lake in question simply became temporarily invisible. So, no need to panic, fishing and boating enthusiasts. But, as you’ll see, the term “invisible” can be just as ambiguous as the term “lake”.

Let’s start with the fact that Minnesota doesn’t have 10,000 lakes. Their slogan is a lie! Depending on how you define a lake, Minnesota has 21,871, or 15,291, or 11,842. But, Wisconsin might have more (or less) and likes to argue with Minnesota about that fact. Michigan might have way more (62,798) or way less (6,537). And, they all pale in comparison to the number of lakes in Alaska. Here is an article that explains the situation nicely.

With that out of the way, today’s story comes from “current GOES” and what one colleague noticed during a cursory examination of GOES Imager images. Here’s the GOES-13 visible image from 19:30 UTC 27 January 2017:

GOES-13 visible image, taken 19:30 UTC 27 January 2017

GOES-13 visible image, taken 19:30 UTC 27 January 2017

Compare that with the visible image from 19:15 UTC 2 February 2017:

GOES-13 visible image, taken 19:15 UTC 2 February 2017

GOES-13 visible image, taken 19:15 UTC 2 February 2017

Notice anything different between the two images over Minnesota? No? Then let’s flip back-and-forth between the two, with a giant, red arrow pointing to the area in question:

Animation of the above images

Animation of the above images. The red arrow points to Lake Mille Lacs.

The red arrow is pointing to the location of Lake Mille Lacs. You might know it as Mille Lacs Lake. (Either way, it’s name is redundant; “Mille Lacs” is French for “Thousand Lakes,” making it Thousand Lakes Lake.) As the above images show, on 27 January 2017 Lake Mille Lacs was not visible in the GOES image. On 2 February 2017, it was. They both look like clear days, so what happened? Why did Lake Mille Lacs disappear?

As I said before, the lake didn’t dry up. It simply became temporarily invisible. But, this requires a discussion about what it means to be “visible”. Lake Mille Lacs shows up in the image from 2 February 2017 because it appears brighter than the surrounding land. That’s because the lake is covered with snow. Aren’t the surrounding land areas also covered with snow? Yes. However, the surrounding lands also have trees which obscure the snow and shade the background surface, which is why forested areas appear darker even when there is snow.

That leads to this question: why does the lake appear darker on 27 January? Because it rained the week before. Want proof? Look at the almanac for Brainerd (NW of Lake Mille Lacs) for the period of 18-22 January 2017. Every day made it above freezing along with several days of rain. Much of the snow melted (including the snow on the lake). Want more proof? Here’s a video taken on the lake from 20 January 2017. See how Minnesotans drive around on frozen lakes – even in the rain? And, see how wet and slushy the surface of the ice is? This makes it appear darker than when there is fresh snow on top. If you’ve ever seen a pile of slush, you know it’s not bright white, but a dull gray color. The less reflective slush on the lake reduced the apparent brightness down to the level of the surrounding woodlands. That’s why the lake appeared to disappear.

Now, this is “current GOES” imagery. We can do better with VIIRS, since we have more channels to play with. And, as we all know, GOES-R successfully launched back in November 2016 and is now in orbit as GOES-16. This satellite has the first Advanced Baseline Imager (ABI) in space. The ABI has many of the same channels as VIIRS, so the following discussion applies to both instruments. “New” GOES will have up to 500 m resolution in the visible, which is much closer to VIIRS (375 m) than “current” GOES (1 km). That’s another thing to think about when we talk about what is visible and what isn’t.

Here are the VIIRS high-resolution visible (I-1) images that correspond to the GOES images above:

VIIRS high-resolution visible (I-1) image, taken 19:35 UTC 27 January 2017

VIIRS high-resolution visible (I-1) image, taken 19:35 UTC 27 January 2017

VIIRS high-resolution visible (I-1) image, taken 19:22 UTC 2 February 2017

VIIRS high-resolution visible (I-1) image, taken 19:22 UTC 2 February 2017

Although, we should probably focus on Minnesota. Here are the cropped images side-by-side:

Comparison between VIIRS high-resolution visible (I-1) images

Comparison between VIIRS high-resolution visible (I-1) images

Remember: you can click on any image to bring up the full resolution version.

Although Lake Mille Lacs is just barely visible in the image from 27 January, it’s much easier to see on 2 February. So, we get the same story from VIIRS that we got with GOES, which is good. That means we don’t have a major fault of a multi-million dollar satellite. It’s a “fault” of the radiative properties of slush, combined with the low resolution of the GOES images above.

Keep your eyes also on the largest inland lake in Minnesota: Red Lake. The Siamese twins of Upper and Lower Red Lake didn’t get as much rain as Lake Mille Lacs and its snow never fully melted, so its appearance doesn’t change much between the two images.

The GOES Imager also has a longwave infrared (IR) channel, and a mid-wave IR channel similar to VIIRS. Since the goal of this is not to compare GOES to VIIRS, but to show how these lakes appear at different wavelengths, we’ll stick to the VIIRS images. Here are the high-resolution VIIRS longwave IR images from the same times:

Comparison of VIIRS high-resolution longwave IR (I-5) images

Comparison of VIIRS high-resolution longwave IR (I-5) images

In both images, the lakes are nearly invisible! This is because the longwave IR is primarily sensitive to temperature changes, and the slush is nearly the same temperature as the background land surface. With no temperature contrast to key on, the lake looks just like the surrounding land. Although, if you zoom in and squint, you might say that Lake Mille Lacs is actually more visible in the image from 27 January. 27 January was a warmer day (click back on that Brainerd almanac), and the surrounding land warmed up more than the slushy ice on the lake. 2 February was much colder on the lake and the land. But, let this be a lesson that, just because the lake doesn’t show up, it doesn’t mean the lake doesn’t exist!

Something interesting happens when you look at the mid-wave IR. All the lakes are visible, and take on a similar brightness, no matter how slushy they are:

Comparison of VIIRS high-resolution mid-wave IR (I-4) images

Comparison of VIIRS high-resolution mid-wave IR (I-4) images

In this wavelength range, both reflection of solar energy and thermal emission are important. Snow, ice and slush are not reflective and they are cold, making the lakes appear darker than the surrounding land. The fact that the land surrounding Lake Mille Lacs and Red Lake is darker on 2 February than it is on 27 January is further proof that it was a colder day with more snow on the ground.

Here’s where we get to the advantage of VIIRS (and, soon, GOES-16): it has more channels in the shortwave and near-IR. The 1.6 µm “snow and ice” band has a lot of uses, and I expect it will be a popular channel on the ABI. Here’s what the high-resolution channel looks like from VIIRS:

Comparison of VIIRS high-resolution near-IR (I-3) images

Comparison of VIIRS high-resolution near-IR (I-3) images

Compare these with the visible images above. Now, the reverse is true: Lake Mille Lacs is easier to see in the first image than the second! You can’t call it invisible at all on 27 January! The presence of liquid water makes the slush very absorbing – more than even ice and snow – so it appears nearly black. In fact, it’s hard to tell the difference between the slushy ice-covered Lake Mille Lacs, and the open waters of Lake Superior, which has no ice or slush on it. On 2 February, we see the fresh layer of snow on Lake Mille Lacs has increased the lake’s reflectivity, but it’s still slightly darker than the surrounding snow covered land. This is for two reasons: snow and ice are absorbing at 1.6 µm and the surrounding woodlands are more reflective.

Here’s a better comparison between the “visible” and the “snow and ice” bands:

Comparison of VIIRS I-1 and I-3 images (animation)

Comparison of VIIRS I-1 and I-3 images (animation)

You’ll have to click on the image to see it animate between the two.

Here’s an animation showing all five high-resolution bands on VIIRS for the two days:

Comparison of VIIRS high-resolution imagery channels (animation)

Comparison of VIIRS high-resolution imagery channels (animation)

Again, you have to click on it to see it animate.

Now, we can combine channels into RGB composites that highlight the snow and ice. We’ve discussed several RGB composites for snow detection before. And, we have been looking at the Natural Color RGB for a long time. This composite combines the high-resolution bands I-1 (0.64 µm), I-2 (0.86 µm) and I-3 (1.6 µm) as the blue, green and red components of the image, respectively. Here’s what it looks like for these two days:

Comparison of VIIRS Natural Color RGB composites

Comparison of VIIRS Natural Color RGB composites using high-resolution imagery bands

Lake Mille Lacs is visible on both days – first because it’s darker than the surroundings, then because it’s brighter. This composite demonstrates how vegetation can obscure the surface snow – it appears more brown in deciduous forests (and bare fields with no snow) and green in coniferous areas. But, the important point is that the wetter the snow and slush, the darker it appears. The fresher the snow, the brighter cyan color it has.

This is exaggerated in the “Snow RGB” that combines moderate resolution bands M-11 (2.25 µm), M-10 (1.6 µm) and M-7 (0.86 µm):

Comparison of VIIRS "Snow RGB" composites of channels M-11, M-10 and M-7

Comparison of VIIRS “Snow RGB” composites of channels M-11, M-10 and M-7

M-11 (2.25 µm) is sold as a “cloud particle size” band, but it also helps with snow and ice detection (and fires). The presence of water in melting snow enhances the darkening at 2.25 µm. In this RGB, that means melting snow appears more red, while fresh snow appears more pink. The slush on Lake Mille Lacs appears very dark – almost as dark as Lake Superior – so a Minnesotan might be forgiven if they see the image from 27 January and decide not to drive out on the lake to go ice fishing because they think the ice isn’t there.

Of course, VIIRS also gives us the True Color RGB – the most intuitive RGB composite – that combines the blue-, green- and red-wavelength visible bands: M-3 (0.48 µm), M-4 (0.55 µm) and M-5 (0.67 µm). If you’re curious, here’s what that looks like:

Comparison of VIIRS True Color RGB composite images

Comparison of VIIRS True Color RGB composite images

The slush on Lake Mille Lacs looks just like dirty slush and the fresh snow looks just like snow. (As it should!)

So, the second biggest lake in Minnesota never disappeared – it just changed its surface properties. And, it will always be “visible” to VIIRS in one channel or another – unless it’s cloudy (or it completely dries up).

December Fluff

By now, you probably know the drill: a little bit of discussion about a particular subject, throw in a few pop culture references, maybe a video or two, then get to the good stuff – high quality VIIRS imagery. Then, maybe add some follow-up discussion to emphasize how VIIRS can be used to detect, monitor, or improve our understanding of the subject in question. Not today.

You see, VIIRS is constantly taking high quality images of the Earth (except during orbital maneuvers or rare glitches). There isn’t enough time in a day to show them all, or go into a detailed discussion as to their relevance. And, nobody likes to read that much anyway. So, as we busily prepare for the upcoming holidays, we’re going to skip the in-depth discussion and get right to the good stuff.

Here then is a sample of interesting images taken by VIIRS over the years that weren’t featured on their own dedicated blog posts. Keep in mind that they represent the variety of topics that VIIRS can shed some light on. Many of these images represent topics that have already been discussed in great detail in previous posts on this blog. Others haven’t. It is important to keep in mind… See, I’m starting to write too much, which I said I wasn’t going to do. I’ll shut up now.

Without further ado, here’s a VIIRS Natural Color image showing a lake-effect snow event that produced a significant amount of the fluffy, white stuff back in November 2014:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (18:20 UTC 18 November 2014)

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (18:20 UTC 18 November 2014)

As always, click on the image to bring up the full resolution version. Did you notice all the cloud streets? How about the fact that the most vigorous cloud streets have a cyan color, indicating that they are topped with ice crystals? The whitish clouds are topped with liquid water and… Oops. I’m starting to discuss things in too much detail, which I wasn’t going to do today. Let’s move on.

Here’s another Natural Color RGB image using the high-resolution imagery bands showing a variety of cloud streets and wave clouds over the North Island of New Zealand:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (02:55 UTC 3 September 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (02:55 UTC 3 September 2016)

Here’s a Natural Color RGB image showing a total solar eclipse over Scandinavia in 2015:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (10:06 UTC 20 March 2015)

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (10:06 UTC 20 March 2015)

Here’s a VIIRS True Color image and split-window difference (M-15 – M-16) image showing volcanic ash from the eruption of the volcano Sangeang Api in Indonesia in May 2014:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (06:20 UTC 31 May 2014)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (06:20 UTC 31 May 2014)

VIIRS split-window difference (M-15 - M-16) image (06:20 UTC 31 May 2014)

VIIRS split-window difference (M-15 – M-16) image (06:20 UTC 31 May 2014)

Here’s a VIIRS True Color image showing algae and blowing dust over the northern end of the Caspian Sea (plus an almost-bone-dry Aral Sea):

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (09:00 UTC 18 May 2014)

VIIRS True Color RGB composite of channels M-3, M-4 and M-5 (09:00 UTC 18 May 2014)

Here is a high-resolution infrared (I-5) image showing a very strong temperature gradient in the Pacific Ocean, off the coast of Hokkaido (Japan):

VIIRS I-5 (11.45 um) image (03:45 UTC 12 December 2016)

VIIRS I-5 (11.45 um) image (03:45 UTC 12 December 2016)

The green-to-red transition just southeast of Hokkaido represents a sea surface temperature change of about 10 K (18 °F) over a distance of 3-5 pixels (1-2 km). This is in a location that the high-resolution Natural Color RGB shows to be ice- and cloud-free:

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (03:45 UTC 12 December 2016)

VIIRS Natural Color RGB composite of channels I-1, I-2 and I-3 (03:45 UTC 12 December 2016)

Here’s a high-resolution infrared (I-5) image showing hurricanes Madeline and Lester headed toward Hawaii from earlier this year:

VIIRS I-5 (11.45 um) image (22:55 UTC 30 August 2016)

VIIRS I-5 (11.45 um) image (22:55 UTC 30 August 2016)

Here are the Fire Temperature RGB (daytime) and Day/Night Band (nighttime) images of a massive collection of wildfires over central Siberia in September 2016:

VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12 (05:20 UTC 18 September 2016)

VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12 (05:20 UTC 18 September 2016)

VIIRS Day/Night Band image (19:11 UTC 18 September 2016)

VIIRS Day/Night Band image (19:11 UTC 18 September 2016)

Here is a 5-orbit composite of VIIRS Day/Night Band images showing the aurora borealis over Canada (August 2016):

Day/Night Band image composite of 5 consecutive VIIRS orbits (30 August 2016)

Day/Night Band image composite of 5 consecutive VIIRS orbits (30 August 2016)

Here is a view of central Europe at night from the Day/Night Band:

VIIRS Day/Night Band image (01:20 UTC 21 September 2016)

VIIRS Day/Night Band image (01:20 UTC 21 September 2016)

And, finally, for no reason at all, here’s is a picture of Spain wearing a Santa hat (or sleeping cap) made out of clouds:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (13:05 UTC 18 March 2014)

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10 (13:05 UTC 18 March 2014)

There you have it. A baker’s ten examples showing a small sample of what VIIRS can do. No doubt it will be taking more interesting images over the next two weeks, since it doesn’t stop working over the holidays – even if you and I do.

Beginning of Autumn in the Great Lakes

Have you noticed it? The seasons are changing (for the mid- and high latitudes, at least). Days are getting shorter (or longer if you live in the upside-down hemisphere). This time of year, if you live in Alaska or Scandinavia or similar high latitude locations, you lose about 5-10 minutes of available daylight each day. (That’s between a half and one hour per week!) You may have noticed by the fact that your neighbor no longer mows the lawn at 11:00 PM because it’s still bright outside and hey, why not? I wasn’t going to sleep anyway.

Closer to home – in the mid-latitudes – loss of daylight is more like 1-3 minutes per day, which isn’t as noticeable. But, one day, you watch the sun set and look at the clock and realize that it’s only 6:30 PM and you think, didn’t it used to be light out later than this?

That’s not the only way to tell the seasons are changing. For one, there’s the arrival of snow. (Although parts of Montana, Wyoming and South Dakota received snow earlier this year while it was still technically summer.)  And, for two, there’s what VIIRS observed on 27 September 2014:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:57 UTC 27 September 2014

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:57 UTC 27 September 2014

In case it’s not obvious, here’s what VIIRS saw earlier in the month (8 September 2014):

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:13 UTC 8 September 2014

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:13 UTC 8 September 2014

Notice anything different between the two images? (Remember to click on the images, then on the “1735 x 1611″ links below the banner to see the images in full resolution.)

That’s right – the loss of daylight leads to one of the benefits of autumn: fall foliage. VIIRS True Color imagery shows, quite clearly, that the leaves of New England and eastern Canada have changed color. Forests that were green in early September have turned orange, red and brown by the end of the month.

Another thing you may have noticed comparing those two images: the change from green to beige in the area around Montreal, Quebec. This is another sign of autumn: the fall harvest. This is a productive agricultural region in eastern Canada, and what you are seeing is the green vegetation (crops) being harvested, leaving behind bare dirt.

True Color imagery is useful for observing the changing foliage and the harvest because it is designed to reproduce what we humans observe on the ground. The red, green and blue components of the RGB composite are channels in the red (M-5, 0.67 µm), green (M-4, 0.55 µm) and blue (M-3, 0.48 µm) portions of the electromagnetic spectrum. When leaves change from green to red, the True Color RGB detects that.

Now, you’ve probably known since elementary school (or at least middle school) that leaves change color because of chlorophyll. And, unless you became a botanist, that is probably the limit of your knowledge on the subject. But, there’s a lot of interesting chemistry that goes on inside a leaf (and the whole tree) that determines it’s color.

Of course, leaves are green because they contain chlorophyll. Chlorophyll is necessary for plants to convert sunlight into sugar. Chlorophyll, by necessity due to it’s job, is highly absorbing of visible-wavelength radiation, although it is slightly less absorbing of green wavelengths. Green light is therefore preferentially reflected out of the leaves and into your eye, and the leaves appear green.

When the sunlight goes away and the air becomes cold, deciduous trees go into hibernation. They break down the chlorophyll in their leaves, and send the remaining nutrients down into the trunk and roots. This exposes the carotinoids that were in the leaves and these carotinoids have a yellow or orange color – they preferentially reflect yellow and/or orange wavelengths. Red colors come from a pigment called anthocyanin, which was recently discovered to be a sort of “plant sunscreen”.

Now, utilizing sunscreen when you get all your energy from the sun may sound silly but, recent studies have shown that anthocyanin protects the leaves from sun damage once the chlorophyll is gone so that the tree has time to extract all the nutrients out of the leaves before they fall off. Trees in poor soil conditions are more likely to turn red in the fall as a natural defense mechanism – they need to store all the nutrients they can from their leaves, since they aren’t getting them from the soil.

Oak and other leaves turn brown in the fall because of a buildup of tannin (link to PDF file), which is a waste product. Brown leaves are full of plant poo! Think about that the next time you go on a fall color driving tour.

Now, back to the satellite science before the biologists come after me for grossly oversimplifying leaf chemistry. I’ve often talked about the Natural Color RGB composite as being similar to the True Color RGB in many instances (except for the detection of ice and snow). So, what does that look like here?

Here’s the VIIRS Natural Color RGB from 8 September 2014:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 17:13 UTC 8 September 2014

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 17:13 UTC 8 September 2014

And here’s the same RGB from 27 September 2014:

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 17:57 UTC 27 September 2014

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 17:57 UTC 27 September 2014

Why does the vegetation still appear green when the leaves have changed color? Because we’ve made vegetation artificially appear green. The Natural Color RGB uses the red wavelength visible channel (M-5, 0.67 µm) as the blue component. The green component is a near-infrared channel (M-7, 0.87 µm), where plants are their most reflective – leaves and other plant tissues don’t absorb radiation at this wavelength. The red component is a longer wavelength channel (M-10, 1.61 µm) where the water inside the leaves starts to absorb radiation and the reflectance goes down. Cellulose and lignin also weakly absorb at 1.61 µm. The bottom line is, plants are highly reflective at 0.87 µm regardless of how healthy the plant is, or what color the leaves are – so they will always appear green in the Natural Color images.

You might also note the one difference (apart from clouds) that shows up between the two Natural Color images is the lack of green surrounding Montreal in the 27 September image. This is another sign of the fall harvest: the highly reflective plants have been removed and all that’s left is dirt, which is not as reflective. That’s why those areas appear more brown in the later image.

If we look a bit further west in the True Color imagery from 27 September 2014, the fall color really stands out:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:57 UTC 27 September 2014

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 17:57 UTC 27 September 2014

Fall colors are visible from the Adirondacks of Upstate New York and Quebec to the Upper Peninsula of Michigan. The most vivid fall color is in Ontario – both in the area of Sault Ste. Marie and in the area of Algonquin Provincial Park, the oldest provincial park in Canada. Every autumn, the Friends of Algonquin Park post pictures of the fall colors, including this shot from 27 September 2014 showing just what VIIRS was seeing. Amazing colors!

We have sunny days, cool nights and plant survival techniques to thank for that.

 

BONUS:

Here’s a desktop wallpaper that’s zoomed in on the above image and cropped to the most popular screen resolution (1366×768):

VIIRS True Color RGB Composite Desktop Wallpaper (17:57 UTC 27 September 2014)

VIIRS True Color RGB Composite Desktop Wallpaper (17:57 UTC 27 September 2014). This image fits monitors with a 16:9 ratio and is optimized for 1366×768 screen resolutions.

Make sure you click on the image, then on the “1366 x 768″ link below the banner to get the full resolution image. Then you can right-click on the image and choose “Set as desktop background” to save it as your new desktop wallpaper.

Hell Froze Over (and the Great Lakes, too)

This has been some kind of winter. The media has focused a lot of attention on the super-scary “Polar Vortex” even though it isn’t that scary or that rare. (I wonder if Hollywood will make it the subject of the next big horror movie in time for Halloween.) Many parts of Alaska have been warmer than Georgia, with Lake Clark National Park tying the all-time Alaskan record high temperature for January (62 °F) on 27 January 2014. (Atlanta’s high on that date was only 58 °F.) Sacramento, California broke their all-time January record high temperature, reaching 79 °F three days earlier. In fact, many parts of California had record warmth in January, while everyone on the East Coast was much colder than average. Reading this article made me think of an old joke about statisticians: a statistician is someone who would say: if your feet are stuck in a freezer and your head is stuck in the oven, you are, on average, quite comfortable.

One consequence of the cold air in the eastern United States is that Hell froze over. No, not the Gates of Hell in Turkmenistan. This time I’m talking about Hell, Michigan. Hell is a nice, little town whose residents never get tired of people telling that joke.

It has been so cold in the region around Hell that the Great Lakes are approaching a record for highest percentage of surface area covered by ice. This article mentions some of the benefits of having ice-covered Lakes, including: less lake-effect snow, more sunshine and less evaporation from the Lakes, which would keep lake levels from dropping. Although, that is at the cost of getting ships stuck in the ice, and reducing the temperature-moderating effects of the Lakes, which allows for colder temperatures on their leeward side.

This article (and many other articles I found) uses MODIS “True Color” images to highlight the extent of the ice. Why don’t they show any VIIRS images? Well, I’m here to rectify that.

First off, I can copy all those MODIS images and show the “True Color” RGB composite from VIIRS:

VIIRS "True Color" RGB composite of channels M-3, M-4 and M-5, taken 17:27 UTC 11 February 2014

VIIRS "True Color" RGB composite of channels M-3, M-4 and M-5, taken 17:27 UTC 11 February 2014

While it was a rare, sunny winter day for most of the Great Lakes region on 11 February 2014, it’s hard to tell that from the True Color imagery. I mean, look at this True Color MODIS image shown on NPR’s website. Can you tell what is ice and what is clouds?

There are ways of distinguishing ice from clouds, which I have talked about before but, it doesn’t hurt to look at these methods again and see how well they do here. First, let’s look at my modification of the EUMETSAT “Snow” RGB composite:

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 17:27 UTC 11 February 2014

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 17:27 UTC 11 February 2014

This “Snow” RGB composite differs by using reflectances at 2.25 µm in the place of the 3.9 µm channel that EUMETSAT uses. (Their satellite doesn’t have a 2.25 µm channel.) It’s easy to see where the clouds are now. Of course, now the snow and ice appear hot pink, which you may not find aesthetically pleasing. And it certainly isn’t reminiscent of snow and ice.

If you don’t like the “Snow” RGB, you may like the “Natural Color” RGB composite:

VIIRS "Natural Color" RGB composite of channels I-01, I-02 and I-03, taken 17:27 UTC 11 February 2014

VIIRS "Natural Color" RGB composite of channels I-01, I-02 and I-03, taken 17:27 UTC 11 February 2014

This has the benefit of making snow appear a cool cyan color, and has the added benefit that you can use the high-resolution imagery bands (I-01, I-02 and I-03) to create it. There is twice the resolution in this image than in the Snow and True Color RGB images. Here’s another benefit you may not have noticed right away: the clouds, while still white, appear to be slightly more transparent in the Natural Color RGB. This makes it a bit easier to see the edge of the ice on the east side of Lake Michigan and the center of Lake Huron, for example.

If you’re curious as to how much ice is covering the lakes, here are the numbers put out by the Great Lakes Environmental Research Laboratory (which is about a 25 minute drive from Hell) from an article dated 13 February 2014:

Lake Erie: 96%; Lake Huron: 95%; Lake Michigan: 80%; Lake Ontario: 32% and Lake Superior: 95%. This gives an overall average of 88%, up from 80% the week before. The record is 95% set in 1979, although it should be said satellite measurements of ice on the Great Lakes only date back to 1973.

Why does Lake Ontario have such a low percentage? That last article states, “Lake Ontario has a smaller surface area compared to its depth, so it loses heat more slowly. It’s like putting coffee in a tall, narrow mug instead of a short, wide one. The taller cup keeps the coffee warmer.”  Doesn’t heat escape from the sides of a mug as well as the top? And isn’t Lake Superior deeper than Lake Ontario? Another theory is that “Lake Ontario’s depth and the churning caused by Niagara Falls means that it needs long stretches of exceptionally cold weather to freeze.”  Does Niagara Falls really have that much of an impact on the whole lake?

So, what is the correct explanation? I’m sorry, VIIRS can’t answer that. It can only answer “How Much?” It can’t answer “Why?”

 

BONUS UPDATE (17 February 2014):

It has come to my attention that the very next orbit provided better images of the Great Lakes, since they were no longer right at the edge of the swath. Here, then, are the True Color, Snow and Natural Color RGB composite images from 19:07 UTC, 11 February 2014:

VIIRS "True Color" composite of channels M-3, M-4 and M-5, taken 19:07 UTC 11 February 2014

VIIRS "True Color" composite of channels M-3, M-4 and M-5, taken 19:07 UTC 11 February 2014

 

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 19:07 UTC 11 February 2014

VIIRS "Snow" RGB composite of channels M-11, M-10 and M-7, taken 19:07 UTC 11 February 2014

 

VIIRS "Natural Color" composite of channels I-01, I-02, and I-03, taken 19:07 UTC 11 February 2014

VIIRS "Natural Color" composite of channels I-01, I-02, and I-03, taken 19:07 UTC 11 February 2014

 

UPDATE #2 (18 March 2014): The Great Lakes ice cover peaked at 92.2% on 6 March 2014, just short of the all-time record in the satellite era. March 6th also happened to be a clear day over the Great Lakes, and VIIRS captured these images:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 18:35 UTC 6 March 2014

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken 18:35 UTC 6 March 2014

 

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 18:35 UTC 6 March 2014

VIIRS Natural Color RGB composite of channels M-5, M-7 and M-10, taken 18:35 UTC 6 March 2014

Wild Week of Wildfires, Part II

Last time on “Wild Week of Wildfires“, we looked at the Little Bear Fire and High Park Fire, two lightning-ignited fires burning out west that were so hot they caused saturation in the two 3.7 µm channels on VIIRS (I-04 and M-12). There was mention of the Duck Lake Fire, a lightning-ignited fire in northern Michigan, which VIIRS also saw, and I couldn’t resist showing some more images.

On 9 June 2012, the same day the High Park Fire exploded (figuratively speaking), the Duck Lake Fire finally reached 100% containment after burning over 21,000 acres. The next day (10 June 2012), Suomi NPP passed over the Upper Peninsula of Michigan, and it was actually a clear day. (This joke comes courtesy of 20+ years experience of living in Michigan.) Even with 100% containment, the hot spot of the fire was still clearly visible in VIIRS channel I-04 (3.7 µm) that afternoon:

Channel I-04 image of the Duck Lake Fire from VIIRS, taken 18:18 UTC 10 June 2012

Channel I-04 image of the Duck Lake Fire from VIIRS, taken 18:18 UTC 10 June 2012

The highest brightness temperature in the burn area in this channel at this time was    ~331 K. As we saw before with the Lower North Fork Fire, the high resolution false color composite of channels I-01, I-02 and I-03 is useful in highlighting the burn area:

False color RGB composite of VIIRS channels I-01 (blue), I-02 (green) and I-03 (red), taken 18:18 UTC 10 June 2012

False color RGB composite of VIIRS channels I-01 (blue), I-02 (green) and I-03 (red), taken 18:18 UTC 10 June 2012

Notice the large, brown area that coincides with the hot spot in the I-04 image. The combination of wavelengths used in this composite (0.64 µm [blue], 0.865 µm [green] and 1.61 µm [red]) is quite sensitive to the amount (and health) of the vegetation.

You might have also noticed several other interesting features in the image that show up better when you zoom in:

False color composite of VIIRS channels I-01, I-02, and I-03 from 18:18 UTC 10 June 2012

False color composite of VIIRS channels I-01, I-02, and I-03 from 18:18 UTC 10 June 2012

The Upper Peninsula of Michigan was based on mining for most of its history, and several large mines and quarries still exist, which VIIRS can easily see.

If you didn’t know any better, you might confuse the iron mine southwest of Marquette, Michigan with a frozen lake, or miraculously un-melted snow leftover from winter, since that is just what snow and ice look like in this kind of RGB composite. Compare that with the true color view of the same area:

True color RGB composite of VIIRS channels M-3, M-4 and M-5, taken 18:18 UTC 10 June 2012

True color RGB composite of VIIRS channels M-3, M-4 and M-5, taken 18:18 UTC 10 June 2012

In this case, the iron mine stands out as a bright red. Why?

The true color composite uses wavelengths at 0.48 µm (blue), 0.55 µm (green) and 0.67 µm (red). The red channel in the true color composite is actually in the red portion of the visible spectrum. The blue channel in the false color composite (0.64 µm) is also in the red portion of the visible spectrum.

This example shows that the iron oxide (rust) produced at the iron mine is highly reflective in the red portion of the visible spectrum. That’s what gives it the characteristic rust color. Iron oxide is not nearly as reflective at shorter or longer wavelengths, so it shows up blue when red wavelengths are used as the blue channel (as in the false color composite) and red when they are used as the red channel (as in the true color composite).

Let this be a lesson to anyone who uses the false color composite as part of a snow and ice detection algorithm. Snow and ice are not the only things to show up that color. You may be looking at a really large iron mine.