Eyes on Earth Episode 111 – Mendenhall Fellow’s Drought Forecasting

TOM ADAMSON:
Hello everyone and welcome to another episode of Eyes on Earth, a podcast produced at the USGS EROS Center. Our podcast 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. My name is Tom Adamson. In this episode, we're talking with Mendenhall Postdoctoral fellow and research physical scientist Doctor Mikael Hiestand. Mikael works for EROS out of the University of California, Santa Barbara. The Mendenhall Fellowship is named after the 5th director of the USGS, Walter Mendenhall. It allows people who have just completed their PhD an opportunity to work on research with USGS scientists and prepare for their career. The two-year postdoctoral fellowships span the USGS mission areas.

Mikael, what drew you to remote sensing research in the first place? What's your background?

MIKAEL HIESTAND:
So my background, a combination of ecology and Earth science. I started as an environmental and Earth science major and then decided I wanted to focus on climate in particular. I decided I wanted to go more of the observational route as opposed to the modeling route in terms of climate science, and I was thinking about different types of observations and how I could look at big picture sorts of changes that are happening in the environment in relation to climate change. And that led me into remote sensing.

ADAMSON:
What's your bachelor's degree?

HIESTAND:
So that's the environmental and Earth science.

ADAMSON:
OK.

HIESTAND:
Yeah, and it was basically a double major—It was like almost a double major in Earth science and biology.

ADAMSON:
OK. And then I assume you went to graduate school?

HIESTAND:
Yes.

ADAMSON:
And what was your degree in then?

HIESTAND:
So I did my masters in geography and my PhD was a dual title PhD in geography and climate science.

ADAMSON:
And then how did you hear about this Mendenhall fellowship?

HIESTAND:
So that's kind of a funny story. I was applying for jobs in my last year of the PhD. And I saw this and I was like, oh, that's not quite the best fit for me. I'm gonna skip that one. And then my—I was working as a research assistant and my boss for that project was like, hey, you should apply for this. Let me get you in touch with Chris, who's my postdoc advisor here at the University of California, Santa Barbara. So I had a meeting with Chris. That went well and then I had a meeting with my postdoc advisor, Heather, at EROS.

ADAMSON:
Heather Tollerud, who is a scientist at EROS right now, right.

HIESTAND:
Yeah. Yeah, so I've got two postdoc advisors, one at EROS and one at the Climate Hazard Center here in California.

ADAMSON:
What was your idea when you applied for the Mendenhall?

HIESTAND:
So my initial idea was to use the continuous change detection and classification algorithm to provide a prognostic approach to drought prediction based off of land cover conditions. Initially I was thinking I could use that to detect slight changes in vegetation stress that would be indicative of a forthcoming drought. So that was my initial proposal. Once I started it got morphed around a little bit and now we're trying to use those land cover conditions to predict evapotranspiration, the idea is that if you can see, like, there's going to be a big influx in evapotranspiration, that could be indicative of a forthcoming drought. So instead of focusing solely on land cover conditions and vegetation stress, it's switched a little bit to focusing more on predicting evapotranspiration as a means of drought prediction and monitoring.

ADAMSON:
OK. And you mentioned one of the tools that you're using is an algorithm called CCDC. Can you just extrapolate real quick what that means?

HIESTAND:
Yeah, so it's the continuous change detection and classification algorithm. This algorithm was developed by EROS to classify different land cover types and detect land coverage change. And the way it works is it fits like a harmonic sine wave to the Landsat data and I'm trying to apply this harmonic sine wave to the Landsat data to get evapotranspiration through another model called SSEBop.

ADAMSON:
OK, there's another model we'll have to mention real quick. I know the acronym is S-S-E-B-o-p. People call it SSEBop. What does it stand for, first of all, and then what does it do?

HIESTAND:
Yeah. So, SSEBop is the operational simplified surface energy balance model, and the idea behind SSEBop is to get evapotranspiration estimates from Landsat data. And it works by finding the hot and cold pixel, and doing some math with some meteorological variables and then you get the evapotranspiration estimate.

ADAMSON:
And this is another algorithm developed by EROS scientists. I think that one was Gabriel Senay developed that. So you're working with that.

HIESTAND:
Yeah, I'm working with Gabriel Sanay as well.

ADAMSON:
I understand there's also something involved in your project called synthetic Landsat data. Can you describe what that is as well?

HIESTAND:
Yeah. So the idea behind the synthetic Landsat data is we're taking the results from CCDC and we're stitching them together to make Landsat images based off of that particular model. Right now we're just comparing the synthetic data to the Landsat data because when you look at the harmonic wave and the CCDC algorithm, you can extrapolate values from that on a per pixel basis and generate some very pretty looking Landsat images that aren't actually real Landsat images but they look like real Landsat images. Can get data in each one of the different bands and you can switch it around however you would want for whether you're trying to make it a true color Landsat image, or you could do false color infrared images. In this paper, I've got some images of evapotranspiration estimates from the synthetic data and the Landsat observations, and they're a pretty good match. And then right now the paper that just about to submit any day now or in the next week, hopefully, is trying to understand the accuracy of the synthetic Landsat data in relation to the Landsat observations. And then because that's a pretty good fit, eventually we're gonna use this continuous sine wave to try and fill the gaps between Landsat overpasses and eventually make predictions going back into the whole drought forecasting aspect of the research.

ADAMSON:
One of the great things with Landsat data that everybody talks about is the deep archive. We have data going back to 1972. And right now we have 2 Landsats operating and it's great that we have an 8-day repeat cycle between the two of them. We get picture of the globe, all of the land on the Earth, every eight days between the two Landsats that are operating. But what you're working with, you're concerned about those gaps even in between that 8-day cycle. You want to be able to estimate evapotranspiration even more frequently than that, is that what's going on?

HIESTAND:
The real goal of this project is to eventually produce what's known as near-term forecasts. So the idea is, is we're really good at weather and like three to five days and we're good at like long term, multi decadal climate predictions. But if you're a farmer, you really want to know, like, what's this summer going to be like, and that's the near-term that we're currently not very good at.

ADAMSON:
OK. That's that near term, more like seasonal or even subseasonal, that's what is going to be more helpful.

HIESTAND:
Yeah, we're trying to get like six-week to a year's forecast in terms of evapotranspiration and that's really hard and nobody's been able to find a good way to do that yet.

ADAMSON:
What would be the value in having evapotranspiration forecasts at that scale?

HIESTAND:
I think one of the main uses of this would be to help farmers make informed decisions about what varieties of crops to plant at the start of the growing season. If we could predict that there's going to be a high evapotranspiration across the summer, that might mean the farmer might want to bet on a more drought resistant variety of crop that year. Whereas if it looks like there's going to be below average evapotranspiration across this coming growing season, maybe the farmer could go for a more productive strain that's less drought tolerant. Additionally, like if you're a rancher, it can help inform where the optimal area is to graze your cattle might be this coming year.

ADAMSON:
That's really interesting. That's a really practical use for this data. What you're working on now is verifying that the accuracy of it—is that what your paper was about?

HIESTAND:
Yeah, it's seeing that the synthetic data actually produces usable results or accurate results to the observations before we start trying to develop the forecasts. Yeah, you have to see prove that it works well with the data we have already before we start doing other things with it that I wasn't intended to do initially.

ADAMSON:
Once you submit the paper, what happens next?

HIESTAND:
Well, there’s probably going to be a lengthy peer review process. That's never a fun thing.

ADAMSON:
That's how it works though, isn't it?

HIESTAND:
Yeah. When it's—while it’s in review, I'm going to immediately start on the next paper on this line trying to get one last publication out before my postdoc ends. And the idea behind this next paper is going to try to develop some hindcasts. So I'll look at 2020 and see if I can extend the CCDC harmonic algorithm out into the future a little—into the quote unquote, future—a little bit. So say 2021. So I’m going to make this imaginary forecast for 2021 and then compare that to the actual 2021 data and see how well my pretend forecast does.

ADAMSON:
OK, so how much time is left on your Mendenhall fellowship?

HIESTAND:
I've got until the end of April.

ADAMSON:
OK, so you're hoping to get this second project finished up by then?

HIESTAND:
Correct.

ADAMSON:
OK. And like you said, submit a paper and then that process obviously goes on Beyond that. You'll just be able to continue working on that hopefully.

HIESTAND:
I hope so because I think this near-term forecasting is the real cutting edge of climate science at the moment. The other main goals of this is to, like, try to like help people make informed decisions about targeting where they irrigate. Because if you can be like, OK, this side of the county is going to dry out a lot faster than that county over there, you know you can maybe make better decisions about using limited water resources for irrigation.

ADAMSON:
I'd like to thank Mikael for joining us for this episode of Eyes on Earth, where we talked about his Mendenhall Fellowship project, near-term evapotranspiration estimates for drought forecasting. And thank you listeners, check out the USGS EROS social media accounts to watch for our newest episodes, and you can also subscribe to us on Apple and Google Podcasts.

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HIESTAND:        
I mean, there’s the snarky reason why I got into remote sensing.

ADAMSON:
Let's hear that.

HIESTAND:
I didn't want to take time off from my band to be in the middle of nowhere keeping instruments running in terms of field observations.

ADAMSON:
That's great, not snarky at all. That sounds like a good excuse.