Eyes on Earth Episode 74 – A Satellite Cross Calibration Mission

GREG STENSAAS:

We're going to see things that we never dreamed of in the next five years in terms of remote sensing data use. It will change the way we do business. It'll change our decision making for our government agencies. Great time to be involved. We're hitting 50 years on the Landsat program and we'll continue to see huge advancements, but that Landsat record will always continue. 

JOHN HULT:

Hello everyone, and welcome to another episode of Eyes on Earth. We're a podcast that focuses on our ever-changing planet and on the people here at EROS and across the globe who use remote sensing to monitor and study the health of Earth. I'm your host for this episode, John Hult. When the first Landsat satellite launched 50 years ago, it was the only game in town in terms of civilian land remote sensing. In the years that followed, more satellites launched to serve similar purposes. But all that data doesn't always play well together. Subtle differences between the measurements taken by satellites make it difficult to do apples to apples comparisons of land change, even for satellites as similar as Landsat and the European Space Agency's Sentinel-2A and 2-B. That's why specialists with the EROS Calibration and Validation Center of Excellence, or ECCOE, and the USGS representatives on the Joint Agency Commercial Imagery Evaluation, or JACIE, spend so much time cataloging, comparing and quality checking the measurements taken by the hundreds of commercial and civilian satellites that currently orbit the Earth. Landsat data are considered the gold standard for calibration, and plenty of newer satellites bootstrap their own data to Landsat to improve reliability. On today's show, we're talking about the international partnership that's behind a new mission, which takes the concept of cross-sensor calibration to the next level the satellite cross calibration radiometer, or SCR, as we're going to refer to it, it's going to launch this decade carrying an instrument that will serve as a standard reference for many satellites that they can use to bring themselves closer to the same level of calibration as Landsat. Now, the idea here is to create a baseline that will make it easier for satellite data sets to be used together by remote sensing scientists. With us today to talk about that is Greg Stensaas of the USGS. He's been working toward the goal of an SCR mission for decades. We also have EROS contractor John Christopherson, who's worked alongside Stensaas to help make that goal a reality. And he's also spearheaded efforts to compile a compendium of land remote sensing satellites, which is now updated annually. And we have Grant Mah, who is the USGS mission engineer for SCR. Greg, John, Grant, welcome to Eyes on Earth.

GRANT MAH:

Thanks. 

JON CHRISTOPHERSON:

Yeah. Thank you. 

GREG STENSAAS:

Thank you, John. 

HULT:

Greg, let's start with you. Start with some history, because SCR has been a very long term goal for you. Tell us a little bit about where this idea came from and how we've been building to this moment. Why is this so important? 

STENSAAS:

John, the importance of the SCR goes back to, almost the beginning of my career of working at EROS in the early 2000, from my perspective, but I'm sure it's gone back way further than that. The challenge that we've always had is that we've not ever been able to fully align the spectral bands within the satellite systems. The relative spectral response curves for the systems are the key to being able to understand how each sensor is looking at the Earth in a common way. 

HULT:

Greg, can I jump in here real quick? I'm jumping in because I, I want to ask ... when you're talking about the spectral radiometric response. I mean, you're talking about a measurement taken by one satellite of the near-infrared portion of the electromagnetic spectrum not aligning, the measurement doesn't necessarily align, with the same measurement taken by another satellite. Is that what we're talking about? 

STENSAAS:

That's correct. Each satellite has sensors that are onboard and they have radiometric spectral bands that those sensors can see. So each system is different. Even each Landsat system is different. We have to be able to adjust to understand how each system compares to each other. We've spent lots of time and effort to be able to do that, work with that process with our well-known Landsat systems and created sort of a gold standard reference point. But to be able to compare Landsat to the rest of the sensors out there, we needed a translation tool, or a way to compare the spectral bands from one system that the Landsat or another system. 

HULT:

We're talking about a translation tool here. I wanted to turn to Jon now, because he can kind of set the stage and talk a little bit about why it's so important to have a translation tool. Jon, talk to us about JACIE and the Land Remote Sensing compendium. As of this morning, the online compendium lists 412 satellites. That's a few. Talk to us about the buildup of the remote sensing industry and how that kind of factors into the need for this mission. 

CHRISTOPHERSON:

Yeah, the 400 and some satellites that the online compendium lists, there are actually more, but they're some of these constellations that we see launching where a company will launch many of the same kind of satellites to give it greater coverage and so on. We only count them as one. So there's actually well over 500 satellites orbiting the Earth right now imaging just the land that used to be really hard to launch, satellites, very expensive. It was for only the rich countries that had well-established space agencies and so on: NASA, ESA, are others like that, the Soviet Union and then the Russians. But about the middle of the last decade, around 2015, '16 or so, everything started to come together at once, not only in the technologies to build satellites, the ability to launch them more cheaply, and then also, very importantly, the ability to process the data on the ground, to use these data dense images from space. Back in the early days of EROS, very few people even had computers that could handle something as large as a Landsat scene. Well, nowadays, that's not a problem. The environment is right for this. But as Greg pointed out, those satellites are similar, but they're not exactly the same. They don't take exactly the same kinds of images.  Users, scientists, a lot of commercial companies starting up with applications and things like they would like to be able to use these data together in calibrating a satellite imagery like Landsat. We call it the gold standard. Landsat has a lot of onboard calibration methods. Plus, there's a team of people were constantly working with it. It's very expensive. A lot of companies and others choose not to use it. They'll say it's close enough, it works. But helping to get those satellites to the same level of calibration removes at least one difference between them and makes it easier to use those data together. Now instead of every 16 days, or because we have two Landsats we have data every eight days, you might be able to fill that in on the days in between to monitor growing seasons and important things happening there, or for disaster monitoring. So we're helping to get rid of one of those key differences between all the different satellites out there. We're giving them a common reference point to compare to. 

HULT:

And when you talk about using these data sets together, the unspoken thing here is you're talking about science level comparisons. When you say good enough, you're like, 'Well, I can look at a satellite scene before and after, and just see it on TV, and it maybe, maybe doesn't matter. But we're talking about science level quality, so that we can have a better understanding of what's going on on the Earth, and a little more confidence. That's kind of the idea here?

CHRISTOPHERSON:

Right. Right. We talk about our images because we look at them and we say, 'oh, it's a picture.' Too often we make it seem too simple. A picture, an image taken from a satellite, is actually not just simply a picture. It's a series of measurements. Every pixel in that image tells you information about that piece of ground that is represented by that pixel. So they're are actually matrix of measurements that we see as a picture, but they're all measurements. And making sure that those measurements are accurately recording and reporting to the same scale is what calibration is about. Like imagine looking at a rainbow. And if you were to hold your fingers up and say, 'I'm just going to make a gap with my fingers that covers just the blue part.' You, me, somebody else would all do that, but we'd interpret different parts of that as blue from the other and so on, some narrower or some wider and so on. And so that's what Greg was talking about. Those spectral differences, that's hard to compensate for. You can't really compare apples to apples then between them. Otherwise it'd be really simple, just calibrating a real answer. So we're putting up SCR. That's going to be a hyperspectral satellite, be able to simulate all sorts of other satellites and transfer an accurate calibration to them. 

HULT:

Let's talk about that, let's dive into that a little bit. Walk us through some of the steps leading up to the SCR. The Australian Space Agency is leading this effort. Australia is a long-term partner for Landsat, but this is their first satellite launch. Talk to us about who the partners are here and what their roles are. And maybe if you can, talk about a timeline. And anybody can take that. You can fight for it. 

CHRISTOPHERSON:

Ha! I'll let Greg handle that one.

STENSAAS:

The Australians, they've had long-term needs for coastal monitoring, and fire hazards, and drought, and just dealing (with) all the different climate variables across Australia. They've always been big in minerals and mining, so they've done a lot of remote sensing work, using other systems and airborne systems, and now they're trying to build up their capability, starting SCR as one of their main missions, but also to build that into regular operational Earth observing capabilities in the hyperspectral arena in the future. The advantage of Australia is, is they're bringing together their space agency, the Australian Space Agency, with Geoscience Australia, which is really a USGS for Australia, and then CSIRO, which is another science organization within the government agencies in Australia, along with USGS and NASA partnership. USGS will be providing a processing system based on what we use for processing Landsat data. And Australia will be leading that and taking that and turning it into a calibration, cross-calibration processing tool set. It's a great opportunity for Australia to be able to launch two SCR systems in the end of 2014, beginning of 25, and then also two years later to launch two more SCR systems, with a potential plan towards '28, '29, launching multiple hyperspectral instruments. 

HULT:

Right. So a lot of activity is going to be going on over the next ten years here. Just random curiosity, not a remote sensing question: What's it like to work with an agency that so many time zones away? I mean, the collaboration seems very close, but I mean, the time zones ... a little far off. They're a day away when you're making those calls, what's it been like to work with those people? 

CHRISTOPHERSON:

That's a little bit of a challenge. We typically meet very late in the afternoon, which is very early in their next day morning, late Monday afternoon, is their very early Tuesday morning. It also depends what part of Australia we're working with. People on the eastern part of Australia, Sydney, Canberra and so on, that's one thing. But by the time you get to Perth, they're about 11 hours difference from us. There's no time that we're both going to be at work at the same time. So we meet at night there, early morning.

HULT:

You watch your prime-time television and sometime around 9-9:30, you can get a call in with Perth as they have their coffee. 

CHRISTOPHERSON:

Hopefully not that late, but yeah. It's been a challenge, but it's been working. 

STENSAAS:

That's a good point. I know Grant has been dialed into a lot of those meetings with us, too, and it's sometimes a challenge. But you know, this whole environment that we've gone to with the video chat and the capability to just log in and dial in to a meeting from wherever you're at has actually, I think, sort of helped that whole process.

HULT:

Like with satellites, the technology for communication has advanced quite a bit in the past few years. Grant, let's talk about the nuts and bolts of SCR now. What will the SCR do, and how does that differ from what's available? I mean, if Landsat is considered the gold standard and other systems are bootstrapping the Landsat, why is this mission necessary? What's this going to bring to the table? 

MAH:

So SCR is intended to be something of a transfer radiometer that takes the gold standard reference from, say, a mission like Landsat, and then uses that to calibrate a client system. This allows us to use all the work that we've done calibrating Landsat and then leverage that to provide calibrated data from other systems. By having all this calibrated data, this allows data from these other commercial systems and other agencies to be used for science applications. As Greg noted, the way it works is we have a dense hyperspectral instrument, and we can pick various bands and in a cookie cutter way, assemble a simulation of each of the bands of the client systems we're trying to calibrate. We then compare the two, develop correction factors, and that is used to apply to the client system's data. 

HULT:

Can we take a step back just in case we've lost some folks who don't work with remote sensing every day? What do you mean by hyperspectral? How does that differ from just a typical satellite with a few bands? 

MAH: 

A hyperspectral instrument takes data in a series of very narrow bands. In our case, we're looking at 2- to 5-nanometer bands that are basically one after the other. So we take the entire spectrum from, say, 400 nanometers to 2,500 nanometers, we chop them into about 500 individual bands between 2 to 3 to 5 millimeters wide, and it has continuous sampling of that entire spectrum, and that gives us the ability to pick and choose the various narrow bands that we need to simulate the different sensors for a client system. 

HULT:

In other words, you're measuring more of the electromagnetic spectrum than you might typically look at in a satellite instrument. And you're using that as a, as a baseline. 

MAH:

Yeah, it basically measures the entire spectrum instead of in various discrete bands, say like Landsat or Sentinel do right now. In some ways it's like taking a rainbow, and then picking out the part of the rainbow that you need to make, say, the color red, or the color green or the color blue. 

HULT:

I want to pivot back to the importance of international and commercial partnerships here. How important have these been to the advancement of remote sensing science, to get us to this point where we're launching a hyperspectral instrument like this?

STENSAAS:

At EROS, we've been working with international partners since the beginning of Landsat. We've had the Landsat ground stations around the world that we continue to partner with today. Those partnerships have turned into a very good relationship and collaboration, but also have led us to engage with many countries, like Germany, the DLR, their space agency there, Brazil, ISRO in India, the Indian Space Research Organization. We're collecting data and downlinking it here at EROS and many, many other collaborations. Everybody has recognized that we need to move into an environment to get ready to use these data sets together. It's really a paradigm shift in Earth observation. Some of the information capabilities, the processing, the cloud network environment, and the amount of storage that we can have ... we've kind of solved those issues. The next paradigm shift is (that) the data is everywhere. It's across the electromagnetic spectrum, in the UV through the thermal, but it's also radar and SAR systems. Our challenge really today is using the SCR to get the visible and infrared systems aligned, but also to now start thinking about all these other sources. It's a great place to be, but it's a big change and a big challenge. A lot of people don't like change, but I think our whole science philosophy is going to go through a major change in the next few years.

HULT:

Right. And you're talking about the next few years. Greg, you're not going to be around for that. You're retiring now after three decades of federal service, as I understand it, three decades and some change between contract and federal service. Talk to us a little bit about what you've witnessed in that time. Where does SCR stand on your list of accomplishments? 

STENSAAS:

So Landsat was involved in my early career. We use Landsat data to develop simulations to evaluate electronic countermeasures for Army aviation and the Air Force. And as I came to USGS as a systems engineer of the Land Processes (Distributed) Active Archive Center, we did a lot of work to prepare for Landsat 7 and MODIS, getting ready to have those systems launched in 1999. That was probably one of the most exciting times that I've seen. Now, the growth of all the satellite systems that I've worked on has just been incredible. It's so exciting to see this environment. It's the environment that we've always dreamed of, and then having the SCR be able to do the comparison and allow a long-term record of multiple systems is kind of what I've always dreamed of getting to. I am retiring to go have fun with family and continue doing things that I want to do for enjoyment and fun, but I think it's a good time to go. I mean, we've gotten to the point where everybody's getting involved and integrated. I'm going to continue to watch from the sidelines. But John, it's definitely a great time to be involved in remote sensing.

HULT:

Walking away a winner, as they say. 

CHRISTOPHERSON:

Yeah, Greg's leaving at a high point, although I think it may even get higher from here. Greg captured it. It really is the most exciting time to be in remote sensing, ever. So many things are possible. And this SCR project is just going to try to help more things be possible, by making it easier to use this amazing amount of data that are suddenly available. 

HULT:

We've been talking to Greg Stensaas, Jon Christopherson, and Grant Mah about a mission meant to foster satellite data harmony and enable remote sensing scientists to study changes to the Earth's surface more easily. Greg, Jon, Grant, thank you so much for joining us today.

CHRISTOPHERSON

Thank you for having us. 

STENSAAS:

Thank you, John. There's a lot of fun. 

MAH:

Yeah, thanks for talking to us today.

HULT:

And thank you to the listeners, as well. Be sure to join us next time to learn more about satellites, remote sensing, land change and more. You can find all our shows on our website. That's usgs.gov/eros. That's U-S-G-S-dot-G-O-V, forward slash E-R-O-S. You can also subscribe on Apple Podcasts or Google Podcasts with the click of a button. This podcast, this podcast, this podcast is a product of the U.S. Geological Survey, Department of Interior.