I'm Doctor Sharon. Uh It's a pleasure to be with you today. I'll be speaking with you about small radar satellites and, and why they're useful or if not critical for rapid climate response. And now I am thinking the quality of our quantity. Thank you. Yes, that's fantastic.Um I'm thinking I should have had a bit of a sexier name here, but, but that's quite all right. Um I'll tell you a little bit about, I, I, I'm, I'm in Finland right now, so I, I is a Finnish company. It's a beautiful day. Finally, it's almost summer here. Uh You know, arguably, I guess, day to day. Um But just a little bit about me, my career path was maybe a bit nontraditional. Uh I started in art, in fact, I still do a lot of art um but kind of switched more into the technical domain. So did my undergrad in physics and then went to grad school in astronomy, realized there are no astronomy jobs and then went to work in the US space defense sector. Um and, and also on some NASA programs, but then got a little tired of the government side of things I moved into geospatial machine learning um in the start up space and was there for quite a few years and then decided, you know, this was all kind of in the US.

But I relocated to Finland um about a year ago to work as the Vice President of Analytics at I I, which is a space satellite company. And the cool thing about, you know, where I am today is that I have this lovely multidisciplinary team. Uh I think we did account for uh 40 about 40 people, lots of different types of engineers between machine learning and data scientists and remote sensing software developers. And we, you know, out of the 40 people, we have 20 different nationalities. So it's fairly diverse and um I don't know, it's, it's pretty exciting to, to be at. So more about I I um as I launched the world's first small SAR satellites and I'll talk to you a little bit about what S A is if you, you're not aware, it's synthetic aperture radar. Um But typically, you know, many years this technology has been around um and it has largely been government oriented, so very expensive satellites and airborne sensors, um very limited access to the information, but it has a fantastic opportunity for a lot more open source and, and open community development.

So anyways, I, I was able to miniaturize the technology um headquarters based in Finland, but then we have four subsidiaries, you know, around the, the world where total in total about 450 people with 60 nationalities. I told you my team alone has 20 of those nationalities. So, so it's a pretty nice team to work with. Um And we just closed a recent round of funding or CD. So, uh you know, we're a advancing quite a bit um in the technology domain, but we're also very interested in how can we use this data. And so really, you know, one of the things I'll, I'll show you here is that Sara is fantastic for being able to essentially look through clouds, look through darkness, these are active systems. And so, unlike optical sensors, where you really need the the sunlight to illuminate the surface of something to see it, these sensors, these radar sensors have their own source of radiation and they're able to use that to observe and, and create two dimensional and three dimensional images.

And so the cool thing about that, especially where it is in terms of its wavelength, you know, you can see through also things like smoke and volcanic ash. And so when it comes to being able to have really responsive information in a very timely way, you know that that is, you know, essentially where it shines. And you know, one of the things, one of the focus areas that we have been very much concentrating on is flood and wildfire. And you know, in the case of flood, we've only se seen an uptick in the massive extent of floods um globally. And then, you know, also the, the amount of impact in terms of the, the human impact of these floods and having to wait, you know, to look through and you know, the clouds to assess the damage is, is just not an option in order to mobilize and, and either help people, you know, make decisions, relocate people or, or just to assess the situation.

Um a little small but hopefully you can read, I this is, you know, maybe the, the nerdy slide, but just to show you, um if we think about the atmosphere where we are on earth and, and why like the the wavelength of light that we used to observe is very important, you know, we can kind of plot it out as a function of um kind of the wavelength itself.

And so on this end, we have things like gamma rays and x rays you can see here in everything in the blue is essentially like a completely transparent atmosphere. So if we're sitting on earth and we're looking up um or we're like similarly sitting in space looking down a, a blue line of sight means that we can see through everything and, and observe what's going on, either looking out or, or looking down, this brown indicates that it's blocked, you know, there are gasses in the atmosphere or particulates in the atmosphere, aerosols that prevent us from either looking up or looking down.

And so gamma rays, x rays, you know, were blocked, which is probably a good thing because these are, you know, highly, you know, um uh you know, high energy particles in the visible light here in the rainbow, you know, this is where we observe optical, you know, this is where the optical domain is where our eye is sensitive to.

And it's a very small window. And you can see down here, you know, there's some, some barrier for us to see that's, you know, clouds or, or smoke or haze. But, but overall, you know, it's OK, you can see these other windows um kind of in the near infrared kind of mid infrared gets really rough again to see through. Um but the cool thing where we are, I I it's an Xan microwave um sensor, we can see we have kind of this literally a blue sky view which makes it perfect for observing, like I said through clouds and smoke. And one of the interesting things about the idea of synthetic aperture radar is that from a nerdy perspective, um the resolution. So, so kind of the the spacing, the thing that you want to resolve is proportional to the si or the inverse of the size of we'll call it the aperture. So in our case, it's kind of the length of the antenna. So if you want to see something super small, you need a massively large antenna. And so in this kind of example, if we wanted to resolve something that was like a quarter meter um at our wavelength, which is this 0.031 m.

And assuming we launch, you know, we're in space, we're in kind of a leo a low earth orbit say about 2000 kilometers, we would need an aperture or an antenna 248 kilometers, which is completely not possible, right? Like this is why for the longest time radar sensors are very hard to build because they have to be big. The beauty of synthetic aperture radar is that you can create really small radars and and very small, you know, systems that are basically the size of maybe a a dishwasher or sometimes even a microwave. And as you traverse, you know, in space as it's traversing over a point down on the earth, you can integrate and take kind of an image that is, you know, kind of is all along that track, essentially creating more of a synthetic aperture. So that's why it's synthetic aperture radar. It's a very small radar that's taking lots of images as it moves across the earth and kind of integrating that into a bigger picture. So it allows us to launch really small satellites, be able to observe, you know, very actively and and and through a lot of the atmosphere and they're, they're pretty cheap to, to launch as well.

So as I mentioned, kind of the difference, you know, between optical and radar, radar. Again, they own their own light source. Um I think of it often as kind of a, it's not that dissimilar from maybe echolocation of a bat where a batt sends a pulse and then it gets back the scattered, you know, kind of information that's very similar to conceptually what we're doing with radar where we send the pulse back, we collect that information and we use it to create a two dimensional or a three dimensional image.

And so, you know, we're really completely liberated from the sun, you know, having that day night capability and, and we can view a lot of different things. And so then in terms of I I again, we have a constellation of 21 of these satellites about dishwasher size. Like I mentioned, we just launched five m most recent ones last week on the SpaceX launch. So we have five new editions. Um They're very agile because they're small. So they can kind of pivot and observe lots of different locations. We also have this really cool ability that every single day we can observe the same point on earth in exactly the same way. So something we call faster visit, but we also have what we say one day ground track repeat. And so one of the cool things about S A is that it's not only that you're getting kind of this image, you know this two dimensional or even three dimensional image. But you also have this incredible information having to do with the coherence of the waves that you that have returned. And so we can use that kind of coherence and that phase information to measure things like subsidence. So ha has the earth sunk um or you know, have there been some kind of changes in the structure or this, you know, anything kind of on the surface of the earth? And we can measure that down to the the millimeter centimeter level.

So you can get really high fidelity, not only like general image information, kind of traditional image information, but also this idea of like the change in depth. Um you know, of specific locations on earth, we have pretty high resolution. So you can kind of see these boxes represent different resolutions of our sensors. The biggest one is scan um you know, that's 10,000 square kilometers. So you can imagine like, you know, this here is the English channel. You you get a lot of, you know, context but the resolution is is maybe not quite that great. And then a lot of things, you know, when we're trying to observe flood, for instance, often this strip mode is what we use. So it's kind of a, you know, it's a trade off between relatively good coverage but has a bit higher resolution. And then kind of the highest resolution we have are these, these spot resolute or the spot modes where you know down here, five by five kilometers, up to 15 by 15 kilometers um with 0.25 m resolution. So this is the kind of the highest resolution we have. But of course, you know, the higher the resolution we we go the smaller the images. So, so really it has to be a a mode that we use that adapts to the type of use case or problem that we have.

So some, some cool pictures that, you know, hopefully the movement doesn't make you irritated and dizzy at some point. But um these are just some recent use cases and this is the synthetic aperture radar imagery. So on this side, we have the La Palma volcano eruption that happened kind of the end of last year, maybe about November time frame or so. Um the red here represents the changing in the lava. It's a little hard to read. But like, you know, it's kind of the differences over time, I guess in this case, it was in December. So we did a lot of monitoring of lava flows. Um We've done a few others, we had uh an Icelandic volcano as well where we could measure both the subsidence and understand, you know, kind of what was going on underneath. So the lava substructure which was pretty cool as well as the dynamic day to day change and then maybe a little bit more on the manmade side, this is in Rotterdam, you can see kind of this cool dynamic motion of these oil tanks. What is happening here. So oil is stored often in, in harbor locations in these types of oil storage, you know, cylindrical things, these have floating lids and they go up and down depending on how full the tank is.

So, you know, it would be all the way up if the tank is full and then it decreases. And so one of the things you can do with S A is, you know, you can measure how much oil is being imported or exported. And that of course, has implications on the economic demand and, and the commodity side of things. And then this other image here is for deforestation. So this is in the Amazon, we do quite a few um uh analytical products based on monitoring the change in forest. And, and then typically this is kind of illegal deforestation or unsanctioned deforestation done in the wild Amazon. And so, you know, as you can see, you kind of like it, it's fairly easy to see the change, the dynamic change from day to day of this forest and, and how quickly large swaths of, of vegetation are, are f um Here's another cool example we studied muldrow glacier last year as well.

So this is an Alaskan glacier. Um I believe it was a glacier that moves 100 times faster than your typical, your average glacier. Um This is just kind of a zoom in that, that I had found online. Um But, you know, the cool thing that you can see here is that, you know, we're not only just, you know, capturing the information for one specific time, we're creating a time series of information. And so that enables us to look at the dynamics and, and perhaps even ask new scientific questions or, you know, you know, think about how else we might uh model or, or observe this kind of um information. Now, this is a great one. I think this is an example of flood. And so I, I maybe have a couple of um slides here on the flood side of things. Um As I mentioned, it's one of our focus areas for the reasons that I mentioned with, you know, s a really benefiting from um uh or S A being really perfect for flood analysis and observation. So this is an, this is Honduras, I think it was last year. Um And essentially every color that you see is a different day. Um So, you know, blue is one day, red is another day, green is another day.

Anything that's white kind of lined up over those days is not really changing, but anything that's a color is something that changed on that specific day. And so you can see like, you know, there's this large river system here snaking through this um kind of developed and and agricultural area, you can see on specific sides uh specific locations. If you kind of look uh you know, rather carefully, there's we can observe kind of where the flood breached. Um you know, the banks of the river, so kind of these red areas where the flood kind of went over um the river bank itself and started flooding these these different regions. And so it's, you know, a picture like this is incredibly profound when we can start monitoring, you know, when exactly an event happened, when did the peak of a flood happen? How did it migrate and how did it change and who did it impact? And so, you know, this is kind of our core focus, our core product. We had um another big flood recently in Queensland, Australia just earlier in April this year. Um perhaps one of the largest floods we have mapped.

And when we map, it's kind of a combination of, you know, autonomous uh methodology, a little bit of machine learning, but also a lot of humans in the loop because this is a, a pretty precise um product that we're creating and what we do within 24 hours is we provide the extent.

So where was the blood and then also the depth? So how deep and this particular one, I mean, we see, you know, particularly in Australia, pretty significant flooding in the same locations consistently. And so, and that the uptick of that has definitely increased over the last couple of years. This was hurricane IDA at the end of last year that affected the United States. But just to also give you an example, we can intersect the buildings. So the different residential or commercial buildings with this flood depth and extent layer. And we start to understand ok, which buildings were impacted by a high degree of flooding versus a very low or minimal degree of flooding. And as you can imagine, this kind of information is, is really useful for government insurance applications. So maybe my last slide before I run out of time here. Um This is Peterman glacier and of course, it has a funky color going on, but what you see, so Peterman glacier is a glacier in Greenland. Um It is actually, you know, I think one of the largest glaciers in the northern hemisphere and has contributed a lot, you know, it's it's melting and changing a lot. So it's contributing probably pretty substantially to sea level rise and the different colors.

Again, this is kind of showing what we call coherence, but kind of the, you know, what is same day to day versus what is different, different day to day. And you can kind of see cracking and, and the various crevices that occur over a period of time, um kind of changing the morphology and the structure of this particular glacier. But that's just an example, you know, really what I maybe want to end with is like, you know, why S A is useful? I mean, of course, it's a part of a larger more comprehensive space of, of space based observations and sensors. But there's kind of these, these three areas that I find super valuable, you know, from an economic perspective, it you know, it enables us to quantitatively quantitatively observe our earth and assess kind of the risk and the impact. So, so who's at risk and, and who is something going to be, you know, impacting and then, you know, from a societal perspective again, because it is quantifiable verifiable, like we're, we're not making it up, we can observe through clouds, we can observe through, you know, the weather situation.

And so it provides you with that snapshot of exactly what is happening. And so that, you know, that being able to confirm that uh you know, does bring with it a degree of trust and from an environmental perspective, you know, through some of the different use cases that I showed you, we're able to, you know, hopefully highlight, you know, or enable responsiveness when it comes to disaster.

So empowering people with money and resources and obligation and responsibility to act. But then also, you know, asking the bigger questions, the longer term questions about what can we be doing that, that could enable more sustainability from a, from a um a climate perspective.

So that's my last slide and I think um I have one minute to go. So I will just look and see if there's any questions. Um I haven't seen any yet, but thank you all so much. I really appreciate you coming. I think we at least ended with double the part precipitate participation than when we started. So thank you so much for joining and listening to me. Awesome. And Bia from Brazil. Yes, I see that. Good morning from Canada. That's awesome. Great to see so many people here and do feel free to reach out to me. I think I had provided in the first slide, you know, linkedin or, or Twitter or um also had a medium post as well. How did, how did I get into the industry? Um I think I accidentally got into the industry. I think, like I mentioned, I was going to be an astronomer um and then an artist before that. And I think I, I've gotten lucky just by largely be just not being afraid to like explore new avenues. Um And maybe, you know, to, to use maybe the antiquated term imposter syndrome, like fighting against that, like just going into it if it's interesting, just go after it. Um I think, you know, from a practical perspective, focusing on data science, machine learning, really good tool sets to have for sure.