When China Looks Like Canada

Posted on November 21, 2014 by Curtis_Seaman

OK, so there probably aren’t any “Canadatowns” in China like there are Chinatowns in Canada. (Now you’re probably wondering what a Canadatown in China would look like. Maybe stores and restaurants selling poutine and maple syrup? Hockey rinks and curling sheets everywhere? A Tim Hortons on every street corner?) But this isn’t about that!

Last time I made the comparison between Canada and China, it was because there were numerous fires, particularly in the Northwest Territories, that produced so much smoke that it choked the air, making it difficult to breathe. This smoke was visible all the way down to the Lower 48 United States. These huge smoke plumes looked a lot like Chinese super-smog. Today, we’re talking not about the smoke and smog… well, actually, smoke and smog will be mentioned… hmm. Uh, what I mean is we’re focusing on the zillions of fires that VIIRS saw over Manchuria – just like the zillions of fires in the Northwest Territories. Well, OK, not “just like” – those fires were caused by Mother Nature. These fires appear to be intentionally set by human beings and are much smaller.

A CIRA colleague was checking out a real-time loop of MTSAT 3.75 µm imagery over northeastern China and reported seeing bright spots (which are typically hot spots from fires) throughout the area for most of the last month. So what is going on there?

MTSAT has ~4 km spatial resolution, so it’s not the best for fire detection. (At the time of this writing, CIRA has access to MTSAT-2, aka Himawari-7, which has 4 km spatial resolution in the infrared channels. The Advanced Himawari Imager {AHI} was successfully launched on Himawari-8 on 7 October 2014 and, when the operational imagery becomes available, it will have 2 km resolution in this channel [and it will have many of the channels that VIIRS has]. CIRA has plans to acquire this data when it becomes available. Until then, you’ll have to deal with coarse spatial resolution.) To really see what is going on, you need the spatial resolution of VIIRS.

Of course, spatial resolution is not the only thing you need. Check out the VIIRS M-13 (4.0 µm) image below from 04:48 UTC 18 November 2014. How many hot spots can you see?

VIIRS M-13 image of northeastern China, taken 04:48 UTC 18 November 2014.

This image uses a color table specifically designed to highlight hot spots from fires. Any pixel above 317 K (44 °C or 111 °F) is colored. (As always, click on the image to see it in full resolution.) There aren’t that many colored pixels, even though we’re using a relatively low temperature threshold for fire detection. There are, however, a lot of nearly black pixels, which means they are warmer than the background, but not warm enough to be highlighted. (In case you’re not sure, I’m talking about the area between 45° and 48°N, 123° and 128°E.) If we used this temperature threshold in a summer scene, there would be a lot false alarm fire detections.

A situation like this is when the Fire Temperature RGB composite comes in handy. It can detect the small (or low temperature) fires with no problem, particularly since the background isn’t very warm. Try to count up all the red pixels in this image from the same time:

VIIRS Fire Temperature RGB composite of channels M-10, M-11 and M-12, taken 04:48 UTC 18 November 2014.

That’s a lot of fires! It’s probably because there are so many of them that they were visible in MTSAT. If you look closely at the full resolution image, there are two significant fires in North Korea, plus many more smaller fires/hot spots northwest and north of the Yellow Sea. Go back and compare the Fire Temperature RGB with the M-13 image. Admit it: fires in this scene are easier to see in the RGB composite.

If you don’t believe me, check out the M-13 and Fire Temperature RGB images that have been zoomed in on main concentration of fires. The Fire Temperature RGB has been lightened a little bit and the M-13 image has been darkened a little bit to highlight the hot spots better.

VIIRS M-13 image (as above) but zoomed in and slightly darkened.

VIIRS Fire Temperature RGB image (as above) but zoomed in and lightened slightly.

If you want to know why the Fire Temperature RGB composite works, go back and read this and this. Otherwise, stay put. If you’re familiar with the Fire Temperature RGB, because you are a loyal follower of this blog, you may be wondering why the overall image looks so dark.

All the previous cases where I’ve shown this RGB have been in the summer, typically under bright sunlight (since fires don’t tend to occur in winter). Here, it’s almost winter so there is less sunlight and the background surface is colder, which are going to make the image appear darker. Plus, there is some snow in the scene and snow appears black in this RGB composite. It’s not reflective at 1.61 µm (blue component) or 2.25 µm (green component) or at 3.74 µm (red component), plus it’s cold so it doesn’t emit much radiation at any of these wavelengths either.

The Natural Color RGB shows where the snow is. Look for the cyan signature of snow and ice here:

VIIRS Natural Color RGB composite of channels M-5, M-7, and M-10, taken 04:48 UTC 18 November 2014.

The Natural Color RGB shows that the fires are occurring in an area with a lot of lakes. Also, there isn’t a very strong green signature from vegetation in this area. So what is burning? Your guess is as good as mine. (Unless your guess is a bunch of Chinese children using magnifying glasses to burn ants. That’s not a very good guess – particularly because, as I said, there is less sunlight in the winter and it’s colder so the ants wouldn’t ignite easily. Also, that’s a cruel thing to suggest and my reasoned account of why that wouldn’t work should not be taken as an implicit admission that I ever did such a thing as a kid.)

A quick perusal of Google Maps reveals that it is an area full of agricultural fields. So my guess is that it’s some sort of end-of-year burning of agricultural waste. They are all small or low temperature fires and they’re not anything that made the news (I checked), so it’s doubtful that it’s a zillion uncontrolled fires.

How do we even know they’re fires? Besides the fact that they show up in the Fire Temperature RGB, we can also see the smoke. Check out this True Color RGB image and focus on the area where the majority of the fires are occurring:

VIIRS True Color RGB composite of channels M-3, M-4 and M-5, taken at 04:48 UTC 18 November 2014.

There are visible smoke plumes right where the greatest concentration of hot spots is located. There is also a long plume of gray along the base of the Changbai Mountains stretching southwest to the shores of the Yellow Sea, but it’s not clear if that is also smoke or simply smog. By the way, if you have respiratory ailments, don’t look at the southwest corner of the image (west of the Yellow Sea) because that’s definitely smog! The northern extent of that large area of smog is the Beijing metropolitan area.

What is most cough- and barf- inducing about that smog near Beijing is that it is thick enough to completely obscure the view of the surface. Last time we looked at that, it was record levels of smog that was receiving international attention. The thick, surface obscuring smog you see here isn’t record breaking or news-worthy – it’s simply a normal late fall day in eastern China!

If you can’t think of anything else to be thankful for on Thursday, you can be thankful that you don’t have to breathe air like that. Unless you live there. But, then, you wouldn’t be celebrating Thanksgiving anyway. And, if you live in Canada, you already had your Thanksgiving. You get to just sit back, relax, and watch Americans trample each other to death for discount electronics. Being able to avoid the Black Friday mob is something to be truly thankful for!

Hell Froze Over (and the Great Lakes, too)

Posted on February 14, 2014 by Curtis_Seaman

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

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

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

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 "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

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

B-31 and the Pine Island Glacier

Posted on December 31, 2013 by Curtis_Seaman

Nope. This post is not about a warplane, an alcoholic beverage or a “New Wave” band from the 1970s. (Those are all B-52s.) And I’m not talking about a county road in Michigan or a New York City bus line. B-31 is the rather bland name given to the massive iceberg that just broke off from the Pine Island Glacier in Antarctica. (Of course, if you tried to name every chunk of ice floating around Antarctica, how long would it take you to run out of names and just switch to random letters and numbers?)

This particular chunk of ice is special, however, as it has been described as the size of a city. Now, as a scientist, I have to say that the size of a city is a terrible unit of measurement. How big a city are we talking about? I suspect people who live in one of the ten largest cities in the world would laugh at what the people of Wyoming call a “city”. And are we talking the size of the greater metropolitan area or just what is within the city limits?

The article that describes B-31 as the size of city mentioned that it was roughly the size of Singapore, or twice the size of Atlanta. Those seem like odd choices for comparison. How many of you have a good idea of what the land area is of Singapore? And twice the size of Atlanta? They could have used New York City, which has just over twice the land area of Atlanta and people are probably more familiar with New York City. In any case, all of these size estimates have errors.

The original estimate came from this NASA MODIS image and associated caption, which put the size of B-31 as 35 km x 20 km. Now, that’s 700 km² assuming the iceberg is a perfect rectangle, which you can see in the image that it isn’t. Singapore has a land area of 714 km², while New York City is 768 km² and Atlanta is 341 km² (these are “within the city limits” numbers, not the size of the greater metropolitan area). Since the iceberg is actually smaller than the 35 km x 20 km rectangle based on the widest and longest dimensions of the iceberg, maybe “twice the size of Atlanta” is the most accurate estimate.

Anyway, MODIS is not the only satellite instrument out there capable of viewing B-31. Landsat-8 saw it in much higher resolution in another post from NASA. And, of course this entire blog is about what VIIRS can see. Now, VIIRS doesn’t have the resolution of Landsat or the highest-resolution channels on MODIS, but VIIRS has the Day/Night Band, allowing us to see the iceberg both day and night (at visible wavelengths).

To show why that is important, take a look at the infrared image (M-15, 10.7 µm) below. Images in the “infrared window” (the N-band window, according to this site) used to be the only way to detect surface features and clouds at night. At these wavelengths, the amount of radiation detected by the satellite is a function of the temperature of the objects the instrument is looking at. As always, to see the high resolution version of the image, click on it, then on the “1660×1706” link below the banner.

VIIRS IR image (M-15) taken 23:34 UTC 7 November 2013

See that slightly darker gray area near the center of the image? That’s open water in Pine Island Bay, which is only slightly warmer than the ice and low clouds surrounding it. Otherwise, there isn’t much detail in this picture. What really stands out are the cold, high clouds that are highlighted by the color scale. Contrast this with a visible wavelength image from the same time (M-5, 0.67 µm):

VIIRS visible (M-5) image, taken 23:34 UTC 7 November 2013

The open water in Pine Island Bay shows up clear as day because, well, it is daytime and the ice and snow reflect a lot more sunlight back to the satellite than the open water does. Icebergs can easily be distinguished from the low clouds now. You can even see through some of the low clouds to identify individual icebergs that are not visible in the infrared image. The difference in reflectivity between the ice and water at visible wavelengths is a lot greater than the difference in brightness temperature in the 10-12 µm infrared wavelengths, and that contrast is what makes things more easily visible.

Now, it is summer down there and at these latitudes, the sun is up for most of the day (actually, all day for everywhere in this scene on the Summer Solstice, which occurred on 21 December 2013), so you could say that using the VIIRS Day/Night Band to look at this stuff is unnecessary. But, since VIIRS is on a polar-orbiting satellite, it views the poles a lot more frequently than where you or I live: every 101 minutes on average, instead of every 12 hours in the low and mid-latitudes. That means it may occasionally capture a nighttime image here or there during the short nights and will frequently capture images where the day/night terminator crosses through the scene and we still want to be able to see what’s going on then. And you need the Day/Night Band to do that.

For the first time on this blog, however, we’re not going to show the Day/Night Band data exactly. We’re going to show the Near Constant Contrast imagery product, which is produced from the Day/Night Band. You can read up more on the Near Constant Contrast product and how it’s related to the Day/Night Band here. At this point, we’ll refer to NCC and DNB rather than having to type out Near Constant Contrast and Day/Night Band all the time.

Here’s a NCC image from 7 November 2013 at 20:15 UTC where the Pine Island Glacier has been identified. B-31 is still attached to the glacier – it’s sticking out into the bay and, if you look at the high resolution version of the image, you may be able to see the crack where it has started to calve.

VIIRS Near Constant Contrast image from 20:15 UTC 7 November 2013. The Pine Island Glacier is identified.

Keep your eye on that spot as you watch this zoomed-in animation of NCC images starting from the above image to 03:06 UTC 18 November:

Animation of VIIRS NCC images of the Pine Island Glacier from 7-18 November 2013

I should say that the above animation does not include images from every orbit. I’ve subjectively removed images that were too cloudy to see anything as well as images where the VIIRS swath didn’t cover enough of the scene. This left 25 images over the 11 day period. Even so, VIIRS captured the moment of B-31 breaking free quite well.

Imagine the sound that this 600+ km² chunk of ice made as it broke free. I bet it sounded something like this glacier calving event in Greenland:

One of the articles linked to above mentioned the importance of tracking such a large iceberg, because it could impact ships in the area. (Just this week a ship got stranded in ice off the coast of Antarctica.) So, I decided to see if VIIRS could track it. The results are in the MP4 video clip linked to below. You may need an appropriate browser plug-in or add-on (or whatever your browser calls it) to be able to view the video.

Animation of VIIRS NCC images from 7 November – 26 December 2013 (.mp4 file)

That’s 50 days of relatively cloud-free VIIRS NCC images (7 November – 26 December 2013), compressed down to 29 seconds. Go ahead, watch the video more than once. Each viewing uncovers additional details. Notice how B-31 doesn’t move much after 10 December. Notice how ice blocks the entrance to Pine Island Bay at the beginning of the loop, then clears out by the end of the loop. Notice all the icebergs near the shore that are pushed or pulled or blown out to sea from about 20 December through the end of the loop. Notice that B-31 isn’t even the biggest chunk of ice out there. Notice the large ice sheet on the west side of Pine Island Bay that breaks up right at the end of the loop. In fact, here’s another zoomed-in animated GIF to make sure you notice it:

Animation of VIIRS NCC images from 20-26 December 2013

That area of ice is much larger than B-31! (Dare I say, as large as the state of Rhode Island? Probably not, because then you’ll just think of how Rhode Island is the smallest US state, so it can’t be very impressive. It’s also not very accurate since that estimate is based on eye-balling it and thinking it looks like it could be four times the size of B-31.)

Of course, we are heading towards the middle of summer in the Antarctic when the ice typically reaches its minimum extent. So the ice breaking up isn’t unusual. Plus, large calving events occur on the Pine Island Glacier every few years. But, the B-31 event is noteworthy because Pine Island Glacier holds about 5% of the total freshwater contained on Antarctica. It’s also the site of an ongoing field experiment where researchers are investigating glacier-ocean interactions. You can read up on what it’s like to install instruments on a glacier while living in a tent on the coldest continent 1000 miles from any other human settlement in this article. (That article doesn’t say if any instruments are still stuck in B-31 and floating out to sea, though.) And, if you’re curious, Pine Island Glacier has its own Twitter account. So far, the conclusions are that Pine Island Glacier is thinning, receding and speeding up. Large calving events are just one piece of the puzzle, but an important piece to understand since they contribute to sea level rise.

The calving process of B-31 was first noticed by NASA researchers noticing a crack forming in Pine Island Glacier while flying over the area in October 2011 – before VIIRS was even launched. But, VIIRS was there to capture the end result of that crack two years later!

UPDATE (22 April 2014): B-31 has continued to drift towards the open ocean. Researchers at NASA have been monitoring the movement of the massive iceberg since it first calved, and have put together their own video here, which tracks B-31 from the time of my video above into mid-March 2014.

Record Russian Spring Snowmelt

Posted on May 7, 2013 by Curtis_Seaman

It seems that last year’s posts were all about fires. Fires in Colorado (multiple fires, in fact), the Canary Islands, Siberia, Australia – there was even that 40-year-old pit of burning natural gas that has been called the “Gates of Hell“. (It’s still burning, by the way.) Maybe this year’s theme will be all about flooding. We just looked at flooding in the U.S. Midwest. And now, we return back to Russia – the western part this time – where massive flooding has occurred this spring.

Moscow had 65 cm of snow on the ground on 1 April 2013. (That’s roughly 26 inches for any American readers.) That’s the most snow they’ve ever had on the ground that late in the spring, and it was all thanks to record snowfall during the month of March. This article from 26 March 2013 says they got 70 cm (28 inches) in a two day period, and forecasters were predicting another 8-10 cm by the end of the month.

What happens when record amounts of snow melt? It causes flooding. In this case, flooding that makes the Illinois River look like a creek you can hop across. The watershed of the Volga River has been hit especially hard. Here’s a picture that our resident Russian, Galina C., tells me is from near the city of Ryazan, so I assume it is the Oka River. (Refer back to the Volga River map I linked to.) There are more pictures here.

To bring this all together with VIIRS, here is what VIIRS saw on 28 March 2012, right after the region got 70 cm of snow:

False color RGB composite of VIIRS channels I-01, I-02 and I-03, taken 10:38 UTC 28 March 2013

Again, to see the full resolution image, click on it and then click on the “1793×2036 ” link below the banner. This is the false color combination that EUMETSAT refers to as “Natural Color“, where snow and ice appear cyan and liquid clouds appear white. The whole scene is snow, except for a few small clouds north of Moscow and anywhere there are trees sticking out above the snow, which appear green.

Notice that you can’t see any rivers. They’re all frozen over and covered with snow.

Here’s what VIIRS saw (same false color combination) a month later (29 April 2013):

False color composite of VIIRS channels I-01, I-02 and I-03, taken 10:39 UTC 29 April 2013

All the snow is gone. Plus, look at all the rivers you can see. The problem is that you shouldn’t normally be able to see all of these rivers. The flooding makes them visible.

What I think is more impressive is seeing a time-lapse loop of VIIRS images over this period:

Animation of false color composites of VIIRS channels I-01, I-02 and I-03 from 28 March 2013 to 2 May 2013.

Make sure you look at it in full resolution mode. Note that the time period between frames in the animation varies. Some days it was too cloudy to see anything, one or two days had missing data, etc., so this isn’t always one image per day.

The city of Ryazan is identified in the animation (remember the photo linked to earlier). To put it into perspective, check out the Google Maps satellite view of the city. The Oka River is normally ~200 m wide near the city. In the last two frames of the animation, the Oka River is over 10 km wide at its widest point near Ryazan! The same goes for a lot of the rivers visible at the end of the loop – rivers that are normally a few tens or hundreds of meters wide are up to a few kilometers wide.

The city of Tambov at 52°43′N, 41°26′E, which is outside of the domain of the animation, but in the southeastern portion of the larger static images, experienced its worst flooding in 130 years in early April. (That corner of the domain was the first to experience snowmelt.) One of the contributing factors at Tambov, according to that article, was that the ground below the snow was still frozen. The snowmelt occurred before the ground thawed. This meant that the meltwater couldn’t be absorbed into the ground – it simply collected in the low-lying areas or ran off into the rivers, which quickly filled as you can see.

Our resident Russian was also able to grab this plot of the Oka River stage at Novinky, just upstream of where the Oka empties into the Volga. The information comes from this website. This plot covers the time period from 7 April to 7 May 2013.

River stage of the Oka River at Novinky, Russia for April 2013. Data comes from gis.waterinfo.ru, with help from Galina Chirokova (CIRA).

The Oka River looks like it peaked at about 2.5 m above normal. (8 ft. for you Americans.)

All that water is going to end up in the Caspian Sea, whose water level is largely based on inflow from the Volga River’s watershed. Variations of sea level in the Caspian have been +/-3 m over the last century and, with this influx of snowmelt, it is sure to go up.

Land of Lincoln Underwater

Posted on April 23, 2013 by Curtis_Seaman

The week beginning on 14 April 2013 was a big week for weather across the United States. There were 30 reports of tornadoes. (Make sure you click on each link, and look at the filtered reports.) And, when our home base of Fort Collins, Colorado was in the middle of being buried under two feet of snow, large parts of the Midwest received 4-7 inches of rainfall. This is a lot of rain for an area with saturated ground caused by recent snowmelt. Unsurprisingly, it caused a lot of flooding – including a sinkhole in a Chicago neighborhood.

Now, we know VIIRS is good at detecting snow. But, flooding is a bit trickier, particularly river flooding. First, flooding usually occurs when it’s cloudy. (Not always, of course, since you can have flooding from snowmelt or heavy rains that occurred upstream or caused by ice jams when it isn’t cloudy. And, as we saw with Hurricane Isaac, flooding may linger long after the clouds are gone.) Second, flooding can have a huge impact over a small area that your satellite might not have the resolution to detect.

Well, I’m here to report that VIIRS has the resolution to detect the flooding that occurred over Illinois last week. And the flooding lasted until well after the clouds cleared. Take a look at the image below from 21 April 2013, where the flooding is visible:

VIIRS false color composite of channels I-01, I-02 and I-03, taken 18:13 UTC 21 April 2013

This is a “Natural Color” RGB composite of the high-resolution channels I-01 (0.64 µm, blue), I-02 (0.87 µm, green) and I-03 (1.61 µm, red). If you click on the image, then on the “3124×2152” link below the banner, you will see the full resolution image. If you’re wondering where the flooding is, notice the rivers I have labelled in the image. Now try to spot those rivers in this image from two weeks earlier (5 April 2013):

VIIRS false color composite of channels I-01, I-02 and I-03, taken 18:13 UTC 5 April 2013.

Those rivers are a lot more difficult to see. The Illinois, Sangamon, and Mississippi rivers are the only rivers easily visible in the before image. A lot more show up after the heavy rains because they grew beyond their banks and became big enough for VIIRS to see. You might also notice that the vegetation has become much greener over this two week period. To make it easier to compare, here are those images cropped and centered on the swollen rivers, side-by-side:

False-color RGB composites of VIIRS channels I-01, I-02 and I-03, taken on 5 April 2013 and 21 April 2013

False-color RGB composites of VIIRS channels I-01, I-02 and I-03, taken on 5 April 2013 (left) and 21 April 2013 (right)

There are a couple of important things to note about these images that are related to how VIIRS and its satellite (Suomi-NPP) work. One is that Suomi-NPP has an orbit with a 16-day repeat cycle. Every 16 days it should (if it’s in its proper orbit) pass over the same spot on the Earth at the same time of day. The images above were taken 16 days apart, and as you can see in the captions, were taken at the same time of day. The only difference in the area included in the images is the result of the start time of the data granules being 13 seconds off. This means that VIIRS is viewing all the same spots at the same viewing angles.

This leads to point #2: the VIIRS instrument has a constant angular resolution (recall that it uses a constantly rotating mirror to detect radiation across the swath) which, when projected onto the surface of the Earth, means that it does not have a constant spatial resolution. (See slide 12 of this presentation.) The spatial resolution of the high resolution channels shown here is ~375 m at nadir, and it degrades to ~750 m resolution at the edge of the swath. In the images above, the center of the VIIRS swath (nadir) is near the right edge of the data plotted. The left edge of the data plotted is about 80% of the distance from nadir to the edge of the swath. The loss in resolution over this distance may be enough to prevent VIIRS from detecting all the flooding that is occurring. But, the important thing is that we are viewing all these rivers at the same angles and the same resolution. This gives the best comparison between the before and after images.

A few more things to notice in the above images: there is snow in the northern part of Michigan’s Lower Peninsula, with ice on Green Bay and Lake Winnebago (all of which are easier to see in the image from 5 April 2013). Does anyone living there still remember last year’s record heat wave? Many places in this region had already had a number of +80 and +90 °F days, but it seems like a distant memory now. This year, winter doesn’t want to end.

One last thing for today: If you focus on Michigan again you might notice another area of flooding. This one is large enough it wouldn’t be impacted by any resolution degradation (even though it is near the center of the swath where you wouldn’t be worried about that anyway). I’ve zoomed in on the area here:

False-color composites of VIIRS channels I-01, I-02 and I-03 from 5 April 2013 and 21 April 2013

False-color composites of VIIRS channels I-01, I-02 and I-03 from 5 April 2013 (left) and 21 April 2013 (right)

This is along the Shiawassee River near the Shiawassee National Wildlife Refuge, a few miles southwest of Saginaw. This area of flooding is confirmed by these aerial photographs taken on 22 April 2013.

JPSS (SNPP and NOAA-20)

VIIRS Imagery and Visualization Team Blog

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