Introduction To The PACE Mission For Water Quality Monitoring

0:00 Speaker 1: And there is an optional prerequisite if there is going to be a demonstration using Jupyter Notebook and Python. So if you are interested in kind of following along or do it later, then you need to install Jupyter Notebook and Python at least version 3 or larger or later. But that is actually optional. You will have a demonstration. So there will be three parts to this training. starting from today and there will be one next week and in the subsequent week. There will be one homework assignment that will be posted on 9th of October.

0:39 Speaker 1: That's the last day of the training. It will be posted on our website and it will be due on October 24th. A certificate of completion will be awarded to those who attend all live sessions and complete the homework assignment before the given due date. With that, we will start with today's session, Introduction to PACE Mission for Water Quality Monitoring. And specific objectives for this session are that by the end of this session, you should be able to review past and current hyperspectral missions useful for water quality applications, identify key features of the NASA PACE satellite and hyperspectral instruments useful for monitoring water quality of large lakes and estuaries, and identify advantages and limitations of using PACE ocean quality instrument data for water quality monitoring.

1:39 Speaker 1: Just a note about how to ask questions. Please put your questions in the questions box, and we will address them at the end of the webinar. And feel free to enter your questions as we go through this presentation. So we will try to get to all the questions during the question and answer session. after the webinar. The remainder of the questions, which we cannot cover today, they will be posted on the training website about a week after the training. Now, I want to introduce Dr. Antonio Manino, and he will be talking about PACE mission.

2:20 Speaker 1: Before that, I also want to make a note that all the acronyms that we use in this session They are posted at the end of this presentation. So there's a slide with all the acronyms. So please refer to those. So I'm just going to introduce Dr. Antonio Manino. He is a research oceanographer of the Ocean Ecology Laboratory at NASA Goddard Space Flight Center since 2002 and is currently Deputy Project Scientist for NASA Space Mission. and Deputy Principal Investigator on the upcoming NASA GLIMR mission.

3:01 Speaker 1: His scientific expertise is in coastal and ocean biogeochemistry, carbon cycle science, phytoplankton ecology, and remote sensing, thereof. Among his many accomplishments, Dr. Manino has served as Project PI, or Principal Investigator, Laboratory Manager, lead and co-lead for the GeoCAPE mission, a pre-formulation ocean science working group. Modis and we're ocean science team member, and he's a PI and lead for the ocean biology and biogeochemistry field support group. So I invite Dr. Manino to talk about the PACE mission.

3:46 Speaker 2: Dr.

3:47 Speaker 1: Manino, thank you.

3:50 Speaker 2: Thank you, Amita, for that wonderful introduction. I'm happy to be here with the RSAT community. I'm excited to introduce PACE and our hyperspectral observations for water quality monitoring. Okay, so what will PACE observe? One of the key things that PACE will observe are these invisible or microscopic things that are from space. And these include particles in the atmosphere, like aerosols, various types of aerosols, as well as microscopic organisms in the ocean surface, various phytoplankton, for example.

4:33 Speaker 2: So, you know, why are we interested in using PACE? Why satellites in general? And specifically, why phytoplankton aerosols? And this is one of the things that I will address today. So, phytoplankton on Earth are quite important. They they're distributed throughout natural water bodies throughout the Earth. And they have a very wide range in in diversity, as shown here from this picture on the left. They play an important role on Earth. They produce over half of the oxygen annually that enters the atmosphere.

5:22 Speaker 2: They're also, they form the base of the ocean's food web. So the phytoplankton are consumed by small animals and those are consumed by small fish and then on to larger fish so that their production cascades up the food web and ultimately can produce fish and shellfish that humans consume or larger fish and mammals in the ocean. Also, phytoplankton are important because as they produce oxygen, they also take up carbon dioxide. And so they do have a contribution to reducing atmospheric carbon dioxide to some extent.

6:08 Speaker 2: Here's an example of a satellite image of a phytoplankton bloom. In this case, it's a coccolithophore. This would be an organism that looks like this. It has this calcium carbonate armor. or chalk, essentially, shell. And that gives it a very bright turquoise coloration that we can easily see from space because it scatters the sunlight back out into space for the satellite to measure. So we can see these distributions here in the North Atlantic. And just to give you an example, a single coccolithophore is about a hundredth of a millimeter in diameter.

6:54 Speaker 2: And if you filled a thimble with seawater where these are present, their concentrations can reach about 1,000 cells per that thimble full of water or milliliter. Another feature that we can detect from space is the pigment, the primary pigment in phytoplankton called chlorophyll A. That gives plants their green coloration, and it gives phytoplankton a green coloration as well, which can be modified if they have other pigments, which they do. So not all phytoplankton look green. Some do, depending on their other pigments.

7:37 Speaker 2: Here's an example of a cyanobacteria bloom in Lake Erie. You can see the satellite image here. And in fact, in this This cyanobacteria blue-greenology does actually indeed look kind of greenish. And in 2011, there was a massive bloom. And these are considered harmful because they produce a toxin. They impacted the municipal water supply for the city of Toledo, Ohio. In fact, they shut off the municipal water supply to residences and commercial entities in Toledo because of this bloom and subsequent harmful algal blooms that have occurred since then.

8:20 Speaker 2: So it's important to monitor them. Also, here's an example from Oman and the Indian Ocean in which sardines were killed because of the massive phytoplankton blooms resulted in a large amount of organic matter that resulted in bacterial consumption of the oxygen present in the water. And so the sardines actually couldn't breathe. There was not enough oxygen present. And so that resulted in this massive sardine death here. A bit about aerosol. Again, the focus here is water quality, but I'd like to give you a broader perspective of the PEGS mission.

9:07 Speaker 2: So aerosols come from many sources and they're quite variable in size and shape. And an example here on the left is like a dust plume of aerosols here. You can have aerosols coming from plants that emit volatile organic matter that can rise into the atmosphere. Smoke from forest fires is an important source of aerosols, volcanic eruptions. Also, the ocean itself produces sulfate aerosols. It's essentially a salt aerosol. And here's some other images. of aerosols, pollen, for example, some salt crystals, and some smoke or soot, char-based aerosols as well.

10:01 Speaker 2: Clouds are technically an aerosol, but we generally classify them separately.

10:09 Speaker 2: What's important is that they impact climate, and a little bit more about that next. So aerosols and clouds help control how much energy is absorbed by the Earth. Some aerosols warm the atmosphere and some aerosols cool the atmosphere. It has to do whether they absorb the sunlight or they reflect the sunlight. So that's something that PACE will help with. By characterizing the aerosol properties and the types of aerosols that are present and their size and other characteristics, we can better understand how their presence and distribution composition affect the Earth's climate and long-term impacts on global warming as we better understand how aerosols change over time.

11:05 Speaker 2: Okay, now we'll move into ocean color. So the color that we see has to do with the from the ocean has to do with how the sunlight interacts with the constituent organisms and substances that are present in the water. For example, if you have this photon of light coming down into the water, of course, it's, you know, full spectrum of light, rainbow spectrum of light plus your ultraviolet and near-infrared that we can't see. It interacts with phytoplankton and other organisms and bits and pieces of material as well as dissolved substances in the water.

11:57 Speaker 2: And so light can be absorbed. It can be scattered in the forward direction. It can be scattered in the backward or to the side. And so the presence of these constituents alter life. the penetration and the color of the light that penetrates in the water column. And ultimately, the light that gets scattered out of the water or backscattered is what we call it, exemplified here, that is the actual signal that we actually see with our eye. And we call that water leaving reflectance. And it has its own light distribution or color distribution.

12:43 Speaker 2: In clear waters, in the clearest beaches that some of you may have experienced, the ocean looks very blue. And so that means that it's free of many of these constituents. But there's certainly always organisms present. But the blue color is what is prominently scattered out of the sea. And what we see is ultimately similar to what the satellite observes. So the satellite observes the full spectrum of light and beyond on ultraviolet and infrared as well. And so, and using the, and we use the satellites to quantify how much light leaves the ocean through the atmosphere and also the intensity across the full spectrum from ultraviolet into the infrared.

13:55 Speaker 2: So just to give you a little more background on what causes the variation in the ocean color, here's a satellite image of a coastal region. It has wide-ranging colors. This is a true color image. And as mentioned previously, it is a function of the light that's absorbed and scattered by the constituents that are in the water. And these include phytoplankton and their pigments, dissolved organic matter. To give an example of the dissolved organic matter, if you take a teabag and you steep it in water, the substance that comes out of the teabag, that is an example of dissolved organic matter.

14:34 Speaker 2: So across the landscape, when rain falls through vegetation, and through soils, it essentially steeps those plants in the soil and strips out dissolved organic matter that flows into the rivers that flow into the sea that we can detect from satellites. Other constituents are just basically bits and pieces of dead organisms, even fecal pellets of organisms as well, small animals, for example, and also sedimentary particles, especially in the coastal environment. And water itself absorbs light, so that also comes into play.

15:23 Speaker 2: And here on the right just gives you an idea of what the spectrum looks like from the violet all the way out into the red part of the spectrum for different locations in this image. So if we look here, right near the coast, the water looks very brown, very turbid. And you can see that the highest water, water lean reflectance is in the, is in this yellowish, orangish portion of the spectrum. And that gives, and this spectrum gives it this brownish color. As we move further off, we still see, we see some, some turbidity and really kind of opaque-ish green coloration gives us this type of spectrum with the peak occurring in the, greenish to yellow wavelengths.

16:09 Speaker 2: If we move further out, we see that the spectrum shifts further to the left and more towards the blue and still in the green, but the waters are still on the order are still green. We move further out, we move more into bluish waters, and we can see that the peak is sort of at the edge of the blue spectrum and towards the edge of the blue UV spectrum. And then further offshore in the clearest waters, we see that the peak is at the far blue end of this spectrum of water leaving reflectance. All right.

16:44 Speaker 2: So how do we take that information, that knowledge of, okay, so we know that different constituents impact the water color differently. We saw the turbidity in those brownish waters, the greener waters where you likely have high amounts of phytoplankton present in their pigment chlorophyll-A, among other pigments, and also the bluish water, where you have low concentrations of these constituents. And so if we break down the– this is just a very cartoonish notion of life. It's not necessarily a true spectrum of reflectance, of water-leaving reflectance.

17:25 Speaker 2: But if we look at the different portions of the spectrum, what information can we derive from it? If we look at, if we want to look at the color-dissolved organic matter, this is a, tea is an example of that because it's got that, you know, can have a yellowish to brownish color depending on your tea or green for that matter. But we can use information, especially in the lower end of the spectrum where color-dissolved organic matter or CDOM absorbs most intensely, we can see that because of its presence, it pushes down this reflection signal very low in the ultraviolet and blue end of the spectrum.

18:04 Speaker 2: As we move, as we look at other portions of the spectrum, we saw that peak in the roughly, you know, in the 550 to 600 nanometer range in the previous image. This is because of suspended solids. So, we can use information at this part of the spectrum as well as into the, beyond the red, into the near-infrared. about the presence and concentration of suspended solids. We can also look at specific pigments. Here are two examples. Here in this portion just above around 620 or so, we can detect this pigment called phycocyanin.

18:45 Speaker 2: It's present in some of those pigments. phytoplankton that were present in Lake Erie, the cyanobacteria or blue-green algae, they possess the spike of cyanantigna. Also, we can look at the signal for chlorophyll here at the red end of the spectrum. It's also present in the blue end of the spectrum, but in this example, we're just showing the red. Also, the full spectrum across from below 400 to over 700 nanometers can be used to derive the composition of phytoplankton pigments that are present in the water.

19:21 Speaker 2: And that helps us derive phytoplankton community composition. So here's the reflective spectrum. We use this information to get another piece of information. And we can break this spectrum apart to look at different individual constituents. And here's an example of absorption. of these different constituents here. We've got the CDOM in this orange line. For example, as I said, this observes intensely at the lower end in the blue and ultraviolet. The detigmented particles or the detritus has a spectrum that looks similar to CDOM, but it's less intense.

20:05 Speaker 2: So you can see it's less intense in the in the UV and blue than CDOM. And then water itself absorbs. And here's an example of the water spectrum. It absorbed more intensely in the higher wavelengths, especially as you go into the red and near infrared. And the green curve represents phytoplankton. This is like a mixture of phytoplankton communities, a mixture of pigments. But generally, this absorption spectrum represents the pigment composition of all the phytoplankton present in the water. And we can break that down further into different phytoplankton communities.

20:46 Speaker 2: And here's just an example of four very different phytoplankton species.

20:56 Speaker 2: Huxley has the calcium carbonate armor. Palacithera is a diatom. These are very, very common and widely distributed throughout the global oceans. And then there's this tank curve represents a cyanobacteria that's present in the open ocean. And percoccus is another cyanobacteria that's present and also in the open ocean, far offshore in very clear waters. Okay, so with PACE, we're moving from multispectral radiometry to spectroscopy. Historically, we worked with very few pieces of information in terms of the

21:45 Speaker 2: intensity of the ocean at different colors. We've only had, we started with four pieces of information of colors in the late 70s and through 80s with CZCS. With SeaWiz, we added several more, a few more bands. With MODIS, we added a couple more. With VIIRS, which are the most recent sensors, we actually went a little bit backwards. We actually had two fewer bands, as you can see here by comparison. So that limits the amount of information that we have to tease apart the phytoplankton communities that are present.

22:20 Speaker 2: And to give you an example from the Arabian Sea here in the Indian Ocean, we'll look at an example of a diatom here. This is a microscopic image, and also another a phytoplankton that can be both a phytoplankton and a microzooplankton. It can actually eat other organisms as well. So it's a combination, it's a myxotroph, is what we call it, Nucleuca. And if we look at what Veer sees for these two types of organisms, if we look at a water with the diatom, which we'll call fish food because diatoms are really a really good source of food for fish because it's very nutritious for them.

23:05 Speaker 2: We see the black dots, right? So that's because Deers has only these five pieces of information. And the Noctiluca, which is consumed by turtles and not at all by fish, we see the red dots here. Only one wavelength is there a slightly different signal here. this is really insufficient to really distinguish these organisms. What PACE will give us is, you know, well over 100 more pieces of information of light in terms of, in this case, it's absorption. And we have much more capabilities to distinguish these two kinds of organisms, whether we have fish food or whether we have turtle food.

23:52 Speaker 2: And that's obviously important for humans. I also wanted to share with you that while PACE is the first truly dedicated ocean color mission, hyperspectral ocean color mission, there are other hyperspectral missions that are present today and have been in space previously. So these are ones that are being applied for water quality purposes. And so HICO was the first Basically, it's an off-the-shelf sensor. They basically took parts that were available commercially, put something together, and launched it to the International Space Station.

24:37 Speaker 2: It was up there for five years, roughly, from 2009 to 2014. It collected 10,000 images over its lifetime. They're relatively small. The dimensions are given here at a footprint of about 90 meters. That's the pixel dimensions from each side. It provided some very nice information and data. It's limited in the sense that the data quality is not what we're accustomed to for performing both climate research as well as for applications that can directly benefit society. There's some other instruments that are currently in orbit and in use.

25:26 Speaker 2: There's PRISMA from the Italian Space Agency. It's high spatial, 30 meters. However, this is designed for land surface imagery, for atmospheric greenhouse gas retrievals. It is being applied for water quality, but it's coarser in spectral information. It's on the order of 12 nanometers, and we'll talk more about that for PACE later. But it has its limitations. But it's being applied, and it could be useful for those of you who require high spatial resolution data. Again, it's limited in coverage.

26:04 Speaker 2: It's very limited. They're only acquiring images in certain regions of the Earth. The same can be said for NMAP, which is launched by the German Space Agency. It's similar to PRISMA, in that it's got a 30-meter spatial footprint, but it also collects generally small scenes on the order of 30 kilometers by 390 kilometers. Again, designed for atmospheric properties, land surface imagery, but it is being applied for water quality currently.

26:41 Speaker 2: And the last one here is EMET. This is a NASA mission. It's on the International Space Station. So it's limited in coverage from about 50 north to roughly 50 south. And it has a 60-meter spatial footprint. Again, just as the previous two, this is designed for other science and applications. It's for land surface reflectance, to study mineralogy, to study atmospheric dust for climate radio forcing and greenhouse gases. But it is presently being explored for water quality. So there may be some water quality products coming out in the future.

27:21 Speaker 2: But again, because it's not designed for ocean color, it does not have the sensitivity, it doesn't have the complete attributes that would benefit for ocean color water quality applications.

27:39 Speaker 2: So now we come to PACE. PACE was launched on the 8th of February of this year. about nearly seven months ago. Here's a picture of the full observatory in the clean room at Goddard Space Launch Center where it was assembled. Actually two images. So there are three instruments on the PACE observatory. The ocean color instrument, which is the primary one, the largest one here on top. And that's what we'll just talk about primarily in this training session and subsequent sessions. There are also two multi-angle polarimeters.

28:17 Speaker 2: And these are shoebox-sized instruments, just to give you a sense of scale. We refer to them as small but mighty. HARP2 was contributed by the University of Maryland Baltimore County in Maryland. It is designed primarily to retrieve advanced cloud properties, but also capable to retrieve aerosol properties. FEX-1 contributed by a consortia and from the Netherlands. It is, it produces advanced aerosol properties and also advanced cloud properties. Okay, so I won't say much more about the multi-angle fluorimeters and I'll focus on the ocean color instrument, which is hyperspectral.

29:03 Speaker 2: It spans from the ultraviolet from the order of 315 nanometers all the way up to 895 nanometers. And we also have several discrete shortwave infrared bands spanning from 940 to 2260 nanometers. And we'll talk about global coverage later. There is a tilt mechanism that tilts the instrument forward and aft 20 degrees. So as we come up from the South Pole, as the PACE Observatory orbits from south to north, it views aft as it comes, as it images the southern hemisphere and then shifts to forward 20 degrees over the tropics as it starts to observe the northern hemisphere.

29:59 Speaker 2: The spatial footprint is about 1 kilometer, 1.1 kilometer. Okay.

30:07 Speaker 2: Just to give you a sense of how much spectral information is present, here's a screenshot of the near-infrared end of the spectrum. You can see that there's spectral information at every 2.5 nanometers. So, 801.9, 804.4, 806.9. So, the amount of special content is incredible. So briefly, just some unique aspects about that PACE will contribute. We're moving from multi to hyperspectral ocean color observations. No other current or plain hyperspectral radiometer provides one- to two-day global coverage.

30:52 Speaker 2: That is a key distinction between those other sensors that I mentioned earlier, the EMET PRISMA and MAP. For atmospheric, to improve the atmospheric correction for ocean color purposes, we have ultraviolet capability and also those shortwave infrared bands, including 2 at 2 micron. That's important for turbid coastal waters. It's challenging to apply atmospheric correction without those sure wave infrared bands and so those are those are very helpful in improving retrievals in these most turbid coastal waters and inland waters and then in multi-angle perimetry obviously that that helps with with the atmospheric properties and the tilt is essential because it increases coverage overall okay Here's a summary chart of the advances that PACE provides over current and prior satellite missions for ocean color purposes.

32:05 Speaker 2: Hyperspectral, spanning, as I mentioned, 315 to 895. The spectral resolution is 5 nanometers, so that we get information across the full spectrum from 315 to 895 for 5 nanometer bands. And that is a

32:30 Speaker 2: finer bandwidth than those other instruments that I've mentioned previously. Also, we spectrally sample at every one and a quarter or every two and a half nanometers. What that means is that the five nanometer bands are, we step across the full spectrum at 1.25 or every 2.5 nanometers. So we'll have a band at 380, 382.5, 385, 387.5 nanometers. That would be the band center. This gives us 184 total ocean color bands, which is incredible and unprecedented. The other amazing...

33:23 Speaker 2: aspect of the ocean color instrument is the incredible sensitivity as a very high signal to noise ratio. This is really important because if you recall that image where we were talking about that sunlight coming into the ocean and then coming back out, only a small portion of the signal sunlight coming in through the atmosphere into the ocean, back out of the ocean through the atmosphere to the satellite sensor is actually the ocean signal. And it ranges from about 3% to 10%. And so the ocean signal is very small compared to the atmospheric signal.

34:01 Speaker 2: In order to tease apart the ocean signal, we need very high sensitivity. Or in this case, in here, we define it as signal-to-noise ratio. We need a very high signal. And so here's just the actual... information as to the SNR of the ocean color instrument across the spectrum. So, we're mostly interested in the, you know, 350 to 700 nanometer range for ocean color. As you can see, the wider bands at 10 to 15 nanometers, we have SNR from 1400 to over 3000, which is unprecedented for ocean color. We also have high sensitivity in the ultraviolet, which makes those ultraviolet bands useful, and also the nine shortwave infrared bands.

34:55 Speaker 2: You can see them here as well, and three of them being sensitive enough for use for ocean color applications for atmosphere correction purposes. The nearly daily global coverage, and you'll see examples of that. And the polar eminars will also help with atmospheric correction. If we have better information about the aerosol properties and even cloud properties, we can do a better job at the atmospheric correction, improving the quality of the ocean color data. Limitations. The spatial resolution is 1.1 kilometer.

35:29 Speaker 2: We know that the water quality community, especially for inland waters, would benefit from much higher spatial resolution. Unfortunately, well, I should say, the goal of the PACE mission was to accomplish global imagery and climate quality data and climate change studies. Hence, tradeoffs have to be made. And spatial resolution was one that was kept at 1.1 kilometer. challenges.

36:12 Speaker 2: There is a lack of verified hyperspectral algorithms. So that's one thing that we need more of. They're becoming more available, but something to keep in mind. We also need more comprehensive hyperspectral field measurements. That helps us develop new algorithms as well as help us validate the data coming from the PACE mission. Okay, so here's an example that we are comparing remote sensing reflections data at 445 nanometer or a blue band from VIIRS with PACE-OCI. And you can see that generally these agree, they agree well.

36:58 Speaker 2: And, but the other thing you can see is that the overall coverage, there's more of the ocean surface that's filled in with OCI than for VIIRS. that is because of the tilt. That's why that tilt is so important. You see these ellipsoids here. At each orbit, you see these ellipsoids. And that is sun-impacted ocean that have to be masked because we can't retrieve high-quality ocean color data from those areas. And here, we don't see that. We do have We do lose a little bit because of the tilt right along the equator or off the equator.

37:38 Speaker 2: But something to keep in mind. So the tilt makes a huge benefit.

37:45 Speaker 2: An example of the spectral plot of Paces. Yeah, those are all the little diamonds. And then the circles represent what the Vier's values are for these five wavelengths that Vier's has and collects information on. And you can see they're right on top of each other. To demonstrate that right out of the box, PACE, PACE-OCI yields data comparable to a mission that's been in orbit for many years, which is very good. Here's an example of chlorophyll-A. They look quite similar. Again, you know, obviously you can see the difference in coverage, much more coverage.

38:29 Speaker 2: from OCI, and on average, it yields 31% greater coverage than VIIRS each day and 58% more spatial coverage than modus operandi. Okay. Now we'll go into a description from the data products. So we'll have the top of the atmosphere radiance, atmospheric contribution, oceanic contribution to these things, including the water leaving radiance, which are the fundamental products that are used to drive water quality and other ocean public products. We have a suite of atmospheric products. I won't go into details for the sake of time, but aerosol properties, cloud properties.

39:16 Speaker 2: Also, with the multi-angle perimeters, we'll retrieve some ocean properties, ocean reflectance, white cap fraction, angular light distributions, and oil slick detection. So there's some, data products that could be of use to the water quality community here. Now the ocean color products include things like light penetration, light transmission, absorption properties, scattering properties, photosynthetically available radiation. This is the light that Phytoplankton can use to perform photosynthesis.

39:49 Speaker 2: So, this is a very fundamental piece of fundamental observation. Also, with PACE, we'll have products on photosynthetic pigments, fluorescence, and plankton community composition, particulate organic carbon, suspended matter, such as suspended sediments. And also, the symmetry classification is planned. And there are some terrestrial products as well. One of the key advances with PACE's hyperspectral capabilities is to be able to quantify phytoplankton community composition. So if we show here an image of chlorophyll A concentrations, you can see the east coast of the U.S. and part of the Gulf Coast.

40:40 Speaker 2: You can see that chlorophyll concentrations are generally higher along the coastline and in the coastal ocean here. Now, we can use the information from OCI to derive information of the community composition. In this case, we're looking at three groups of phytoplankton using this Moana algorithm developed by Priscilla Lang et al. In this image here, we see the cyanobacteria perchlorococcus concentrations here. These are abundances in cells per milliliter, in cells per symbol full of seawater. And in this image, this gives us another concentration of another group of phytoplankton, another cyanobacteria called synechococcus.

41:30 Speaker 2: And in this image here on the top left are the concentrations of picoliteriops. These are very small cells, but they are, these are not cyanobacteria. classified as eukaryotic phytoplankton, include very small flagellates and diatoms, among others. What we'll show here is that the information from ocean color, even from chlorophyll, gives us some insight as to the physical circulation of the coastal ocean here. You can see the Gulf Stream front here, these filaments. These outflows from estuaries and rivers here and out here and throughout the southeastern U.S. for examples.

42:24 Speaker 2: So some information on data processing and release. So the PACE project began releasing data officially on the 11th of April of this year. This included level one radiometry data from all three instruments. as well as a heritage suite of ocean color products like chlorophyll-A remote sensing reflectances. We performed the first reprocessing, which we've identified as version two or V2. This was completed not long ago in early July, in which all the instruments were calibrated with the onboard calibration hardware that we have on the instruments.

43:13 Speaker 2: We also released a second wave of ocean color instrument products, all our test products. These include terrestrial and aquatic surface reflectance at every 10 nanometers, terrestrial vegetation indices, and some cloud properties. And you can access information about the reprocessing following this URL. We are planning a second reprocessing, which we're going to tag the three, version three, in the coming months, probably in the next three months. And this will include the first vicarious calibration.

43:47 Speaker 2: Basically, what we're going to do is take data from this in-water profiling buoy that measures radiometry

44:04 Speaker 2: as ground truth data or sea truth data and adjust the gains on the ocean color instrument to match to match that information from this HyperNav system. And that will improve the ocean color data quality even further. We also expect to really release a new wave of products with this secondary processing. These will be test products and you'll learn a little bit more about that later. Additional products will be released. but we don't have any, we may not have predefined deadlines for those. So a little bit more about algorithms.

44:46 Speaker 2: Phytoplankton community composition algorithms include the Moana that we mentioned earlier. Here's an image off of South Africa showing the different Senecococcus and the Pico Eukaryotes that I mentioned earlier. There are two other algorithms that will be implemented shortly that provide insight into phytoplankton community composition. One by Kramer et al. that relies on phytoplankton pigments and absorption, and one by Chase et al. that quantifies diatom carbon. Again, diatoms are excellent fish food.

45:20 Speaker 2: So that's a very important product for fisheries, among other things. And this algorithm relates absorption and phytoplankton imaging to derive a diatom carbon. So, just briefly about data levels. The module two of this training series will talk about this in more detail, but there are a range of data levels available. Level 1A is raw instrument data. Generally, most users do not work with level 1A. Level 1B would be the sort of the lowest level that people would work with. These are calibrated and geolocated instrument data.

46:03 Speaker 2: Level 1C just basically registers all the instruments, the three instruments on paste for common grid. If you're just working with the ocean color instrument, you need not worry about level 1C. Level 2 and level 3 are the primary data levels that most users will work with. Level 2 are the derived science data products, remote sensing reflectance, chlorophyll A, and so forth. Level three are basically level two products that are binned spatially or temporally. So we will take the one kilometer data and composite it to four kilometers.

46:40 Speaker 2: We also bin it temporally. So we'll have, in addition to having daily level three, we'll also have eight day and monthly level three data. And then level four products are based on level three inputs, and we could also bring in model output into the driving these level four products. Net primary production is an example of a level four product. And there's also different maturity levels from standard, like the heritage algorithms that have been available, such as chlorophyll-A and remittance and reflectance, provisional ones that have been reviewed, but not not formally validated or verified.

47:25 Speaker 2: Test products, which are just, have not yet been reviewed, so there could be high uncertainties. And diagnostic are just ones that are just taking a look, we're taking a look at and exploring, but not intended for science or for usage. I refer you to the data products availability table. There's a QR code here and a URL. This gives you insight as to what's currently available, what's coming soon, what we're looking at implementing and evaluating, or whether we don't have a plan for it yet, but we're interested in having that data product.

48:07 Speaker 2: This table runs for many, many pages, so I will only show you the very top of it. But I refer you to check it out online on your own. And we also are producing suites of products. Because the PACE OCI data is quite voluminous in terms of storage on people's hard drives, instead of making single large files with all the ocean color data products together, which would be very, very extreme and would take a long time to download, we subset them into different product suites so that people can choose which are most useful to them.

49:00 Speaker 2: And this includes the apparent optical properties, which are primarily the remote sensing reflectances. This is for 184 bands. And there's also discussions on having a subset of these It would be multispectral, but at least it might be for those people that are interested in a smaller subset and don't want to work with 184 spectral pieces of information, that'll be a different suite at some point. This also includes some aerosol, as well as apparent visible wavelength. This is basically what color is the ocean.

49:33 Speaker 2: It gives you a single wavelength value for representing the color of the ocean. So if it's in the 400s, it's blue. If it's in the upper 500 to 600, it's more brownish, reddish color. The ones that are crossed out are ones that are not yet available, but we do expect them to become available in the future, but they will be part of this product suite. We also have an inherent optical property suite. These are the absorption and scattering properties that were mentioned earlier. as well as the diffuse attenuation coefficient.

50:09 Speaker 2: So again, some of these are available now. Some of these will become available in the future. We have a biogeochemistry suite, which includes Perfil-A, particularly getting carbon concentrations, phytoplankton carbon, and some uncertainties of these products. And then there's also the PAR suite, the photosynthetically available radiations. And these are both daily and instantaneous. Some are above the ocean surface, the zero plus. the zero minus are just below the surface, and there's both square, scalar, and planar versions of these products.

50:44 Speaker 2: Again, the data table can be reached with this QR code or this, and the algorithms themselves, there's only a few algorithm descriptions available at this point, but you can access them through this URL and you can keep checking back to find new ones as they become available. And there's more to come. And here's some insight into the water quality suite that we are planning. This will include water clarity, such as diffuse attenuation coefficient, water fillet. Turbidity would be based on suspended particulate matter product.

51:21 Speaker 2: Colored dissolved organic matter absorption as well. And the algorithms that we are concurrently exploring include the Aquaverse algorithm, by Nima Palavan et al. These include chlorophyll-A, C-dimer absorption, total suspended matter, as well as that pycocyanin pigment that I mentioned earlier that's present in those freshwater cyanobacteria that cause those harmful algal blooms in Lake Erie, for example. There are also these cyan equivalent products. If you're familiar with the cyan project and the products that come out from them, we are working on implementing these products for PACE-SCI, Xenia product surface reflectance, cyanobacteria index, and the cyanobacteria apparent visible wavelength.

52:12 Speaker 2: And we are also looking forward to other harmful algal broom products as well. But those are TBD currently. There are a number of data issues under investigation. We have a website that describes this in detail. But there are things related to geolocation where we're working on, radiometric performance. We are continuing improvements on orbit calibration. There are also portions of the spectrum that we recommend avoiding for now. These are portions of the spectrum that are influenced by some instrument aspects and also atmosphere characteristics.

52:53 Speaker 2: And just to give you– and here are the ones for the ocean color instruments. So the red are the ones. to avoid. The gray are the use of caution. And the reason, you know, some of these have to do with oxygen and water vapor absorption, where we see these dips in the reference spectrum, solar reference, atmospheric transmittance spectrum here. Other issues pertain to the aerosol optical property suite in particular, and some of it has to do with not having the vicarious calibration applied. That will be resolved in the next couple of months.

53:44 Speaker 2: We've performed very limited validation of remote sensor reflections to date.

53:51 Speaker 2: There was an initial one performed recently, and the information are on the PACE website. So please take a look at that to get the first hard analysis results for that.

54:09 Speaker 2: We see some challenge, some problems with the performance and remote sensor reflectance below 400 nanometers. We need to spend quite a bit more time reviewing that and improving that. We expect that the vacuous calibration will improve this, but some of this is related to atmospheric gases absorbing. Again, we're refining some of the corrections for absorbing gases, particularly for oxygen and water vapor in these portions of the spectrum. The Asian color instrument has two detectors that overlap, and where these overlap, we have light coming, going to both detectors, and we're seeing some artifacts there.

54:54 Speaker 2: So, definitely avoid this 590 to 610 nanometer region. Also, we're processing to increase spatial coverage globally daily to beyond what we're accustomed to higher view angles. And so there are some artifacts at those very high view angles. So for each orbit of PACE data, the very extremes besides east to west, we offer caution in using that. And just to point out, chlorophyll fluorescence line height will be included in the feature. in this product suite. There are also some product issues on the IOP suite.

55:48 Speaker 2: There are some failures that have been noted in very productive nearshore waters. Those are being explored, largely having to do largely with atmosphere correction, among other things. Obviously, all the products are derived from the remote sensor reflective. If the remote sensor reflectance utilize, all these products utilize remote sensor reflectance, so they're impacted by those data qualities from the previous slide. And then PAR, no issues, and CloudMask is just a minor thing that's being resolved.

56:25 Speaker 2: Here's some resources that we point you to for further information on geotechnical memos, Lots of information and background descriptions of the PACE mission. And here's the final, another slide on accessing the data with a QR code. There are three options. That'll be described more in the subsequent training event. And to learn more about the PACE mission or to join the community of practice Please scan these QR codes and look into that. And I look forward to answering your questions. Thank you.

57:20 Speaker 1: Thank you so much, Dr. Manino, for the excellent introduction to the PACE mission, its sensors, data products, and applications. With that, we will summarize today's session.

57:35 Speaker 3: Dr.

57:35 Speaker 1: Manino described PACE-OCI, HARP2, and SPEX1. They're spectral, spatial, and temporal resolutions. PACE data products are available for oceans and estuaries, also for atmosphere and inland. Specifically, OCI observations are hyperspectral from 315 to 895 nanometer with spectral sampling of 2.5 nanometer. They will enable separation of aquatic constituents, and phytoplankton community composition. And this is the only hyperspectral observation mission with near daily global coverage. HARP-2 and SPECS-1 will aid in atmospheric correction.

58:20 Speaker 1: There's some things to note about that PACE has relatively low spatial resolution of one kilometer. And so there are constraints on using observations for inland and near shore waters. The hyperspectral algorithms, they need verification and require hyperspectral field measurements. Overview of available base data products and issues in level 2 data products that Dr. Manino described. Specifically, they're not completely calibrated and there is limited validation because it's relatively a new mission.

58:55 Speaker 1: So these things are going on. And then there's ongoing refinements to the atmospheric correction as well.

59:03 Speaker 1: So our next session is going to be about overview, access, and analysis of base ocean color data products. We will also have a short demonstration of NASA's open source SEDA software for analysis and visualization of selected OCI data products. So that's going to be next week. Again, as I mentioned earlier, there will be one homework that will be posted on 9th of October, the last day of the training. It will be accessible through our webpage, and the answers must be submitted via Google Forms. It will be due on 24th of October.

59:43 Speaker 1: And as I mentioned earlier, you will get a certificate of completion after about two months after the completion of the course. if you attend all three live webinars and complete the homework assignment by the deadline.

1:00:02 Speaker 1: This is the contact information for Dr.

1:00:04 Speaker 2: Manenu.

1:00:05 Speaker 1: If you have more questions, and of course you can always contact us at RSAT. This is our website for information, our social media links are given here. And these are our science capacity building sister programs, if you're interested in checking them out. So with that, I want to thank you for attending today's session. I also want to thank Dr. Manino once again for his excellent presentation. And we will now go to question and answers. Thank you. Thank you, Dr. Manino. And it looks like we have quite a few questions.

1:00:46 Speaker 1: And many of them have already been addressed. we're going to do is just go through questions, as many as we can go through in the given time. So we'll start with question one. And by the way, we have our entire PACE team here.

1:01:05 Speaker 2: Dr.

1:01:05 Speaker 1: Manino is here, Dr. Morgan McEwan, and Dr. Sanna, you know, she's here as well. So question one, is field data collected to verify the remote sensing data? And you can unmute and answer the question.

1:01:26 Speaker 2: Yes, NASA has funded a PACE validation science team that is out at sea at present and in the future to collect field observations to validate the various PACE data products. And actually this month NASA is completing An airborne and Etsy campaign called Pace Packs off the coast of California to validate the atmospheric products using the airborne, the airplane-based instrumentation and flying underneath Pace and also a ship, a NOAA ship that we are using to collect observations of the water as well as the atmosphere.

1:02:16 Speaker 1: Thank you. And question two is, how do you distinguish aerosols from algae?

1:02:22 Speaker 2: Yes. So I answered this question here myself. So because the ocean or water in general is very dark in the infrared, and because of the strong absorption by water in the infrared, we can use that dark background from the pace observations of the Earth to measure and quantify aerosol properties in the infrared because the ocean itself is essentially black or very, very dark. Things become more challenging in the case of absorbing aerosols. These are things that are products of fire or combustion, such as, you know, burning of gasoline or oil or coal.

1:03:14 Speaker 2: that produce soot, they're very absorbing. And then there's also brown carbon, which is sort of a range of different sources, including, you know, land processes. But those aerosols are absorbing. So that means they absorb in the UV and lower end of the blue spectrum. And those are, so they can compound, retrievals of ocean color properties. And so we generally mask those areas. But because of the PACE capabilities, we do expect to be able to better quantify those absorbing aerosols and then be able to separate out the ocean color and phytoplankton signal from that.

1:04:02 Speaker 2: But that is something that is in development and not currently available. At present, we can only just designate that there are absorbing aerosols present, which are very common in coastal urban areas throughout the Earth.

1:04:22 Speaker 3: Thank you.

1:04:23 Speaker 1: Question three is, if all these contributors to ocean color are present in a given location, can PACE detect several? or could a few potentially mask others? For example, a phytoplankton bloom happening in really turbid coastal water with a lot of dome and sediment, would PACE be able to distinguish all those particles?

1:04:46 Speaker 2: Oh, that's an excellent question. That is something that's a subject of a great deal of research. And part of the reason for the capabilities that were built into the PACE search and color instrument, the ultraviolet, the hyperspectral across the UV visible and infrared spectrum is to help us better distinguish these various constituents. That is the actual, one of the primary objectives. And so while the data quality will not likely be as good as in blue ocean water for detecting and quantifying chlorophyll A, for example, we do expect to be able to retrieve chlorophyll, a measure of phytoplankton in turbid waters.

1:05:40 Speaker 2: In fact, we included shortwave infrared bands such as the 1038 nanometer band and the 1235 nanometer band in particular so that we can drive an aquatic signal for turbid waters very turbid waters in that spectral range to help us better distinguish sediments from phytoplankton. Wonderful.

1:06:13 Speaker 1: Great. Question four is, since aerosols impact climate, is there a particular aerosol or constituent that we want more of or less of? And how can PACE help with that?

1:06:28 Speaker 2: Yeah, so whether we want more of one aerosol or another is sort of a philosophical question. What we do as scientists within the PACE project is to provide the data. And the scientists that are using the PACE data will help them understand how different aerosols cool and warm the Earth and what the implications are for global warming and climate change in the present and in the future. The decision whether to take action on, you know, in producing more cooling aerosols or warming aerosols, depending on your perspective, is a policymaker decision and also for Earth citizens.

1:07:16 Speaker 2: It's not something for the scientists to advocate for. We just study and present the results, but geoengineering is something that is beyond our purview.

1:07:32 Speaker 1: Question five is, can we use reflectance to identify phytoplankton functional types?

1:07:40 Speaker 2: Yes, yes. As indicated in the answer here, yes. we can use reflectance data to distinguish phytoplankton. And that's largely because phytoplankton have strong absorption features across the visible spectrum, and that helps us distinguish them from other constituents in the water. Generally, we see strong absorption signals by chlorophylls in the blue and the red end of the spectrum. And then there's also carotenoids that have strong absorption features in the blue and green portions, well, blue to low end of the green portions of the spectrum.

1:08:27 Speaker 2: And because different phytoplanktons have different pigments, it can help us, the hyperspectral data can help us distinguish different phytoplankton. For example, there are the freshwater algae microcystis has the spicobillin pigments, picocinins, that are strongly in the 620, 640 nanometer end of the spectrum towards the red. And these are fairly unique to those types of algae. So that'll help scientists distinguish those from diatoms in freshwater systems. Similarly, there's some unique pigments in cyanobacteria in the ocean, like synechococcus.

1:09:14 Speaker 2: They also have phycobilins, but they're different kind of phycobilin pigments. And they tend to absorb sort of in the mid-range of the visible spectrum. And so those are the approaches that are being implemented, such as in the Moana algorithm or the algorithm by Kramer et al. that is being implemented currently.

1:09:44 Speaker 1: Question six is, how does the presence of clouds affect images gathered by PACE?

1:09:51 Speaker 2: Yes. So for ocean color, we basically mask clouds. We're not able to see through clouds to actually retrieve ocean color properties. There are some studies by colleagues actually at our institution at Goddard Space Flight Center who are doing some work to see through the clouds. And so those are those are promising, but the PACE project is not producing ocean color products through clouds at present.

1:10:30 Speaker 1: Question seven is, are HICO, PRISMA, NMAP, and EMET data are freely available?

1:10:36 Speaker 2: Yes, and one of my colleagues has provided some details here as to where you can find um, um, the, the, these data, um, Heiko from NASA and then the, the other, um, and, uh, from NASA. Anyway, they're, they're listed there in the, in the, in the Q and a, uh, document, um, exactly where you can find them.

1:10:58 Speaker 1: Thank you for that information again. And question eight is can, uh, land surface reflectance be used to monitor fresh water quality?

1:11:09 Speaker 2: Um, Yes, and it is being used to study water quality in fresh waters. Oh, I'm sorry. Yes. So, yes, but this is in the preliminary stages. We expect we are in the process of implementing some of the cyan products, which are– Cyan is a program that uses the European OCHE sensors to produce water quality products in inland waters.

1:11:48 Speaker 2: They use something similar to land surface reflectance, but not exactly. However,

1:11:58 Speaker 2: the PACE project is developing cyan-like products for distribution in the near term. And, you know, the, but as I answered there, we have not yet validated any land surface reflectance products that is in the works and we hope to have some results in the coming months. And then for the water quality part, that will be, that's a longer term project, but we hopefully, the scientific community will provide some measurements to help us evaluate the quality of that product for water quality in freshwaters.

1:12:40 Speaker 2: Oh, so let me expand on this. There is active work by PACE validation science team members in the Great Lakes. So there are two projects funded. And so for the Great Lakes, which are freshwater bodies, there is active work to actually But they're not, it's unlikely that we would use land surface reflectance for the Great Lakes. We would just use our standard ocean color products. But just trying to give you a complete picture of what's happening from the PACE project and PACE science teams.

1:13:21 Speaker 1: Wonderful. So next question is, does anyone have an idea about phytoplankton bloom initiation, termination in beaks? Have you ever detected bloom initiation termination or peak using Python or MATLAB?

1:13:36 Speaker 2: Yes. So obviously, phytoplankton have been studied for well over a century. And so both in the lab and in the field. And so these things, for lab or field observations, if you monitor a population of phytoplankton over you know, a period of days, hours and days, you can clearly detect these processes of initiation, termination, and peak or, you know, what we call log growth, stationary phase where the growth is balanced and so there's no net change in populations, and then senescence when they essentially begin to die and decay.

1:14:24 Speaker 2: And so, in terms of, you know, remote sensing, That, you know, that has been detected and there are examples of, for example, when you've had a volcanic eruption and that feeds the ocean with nutrients that spurs initiation of phytoplankton blooms in those waters that are seeded by the, you know, the dust that falls out of the volcanoes. And so that's just one example, but there are many others in the literature. Regarding the Python or MATLAB, as I answered, those are software tools. But what you need is the algorithms.

1:15:13 Speaker 2: You need the coded algorithms, and then you can apply those software programming tools with the PACE data in order to study algorithms. you know, phytoplankton bloom evolution.

1:15:32 Speaker 1: Question 10 is, can we access PACE data in Google Earth Engine?

1:15:38 Speaker 2: No. We are not, PACE data is not currently in the Google Earth Engine catalog. MODIS Aqua can be added according to the answer here. Do you want me.

1:15:56 Speaker 1: To say something?

1:16:00 Speaker 4: Yeah, right now it's not in the Google Earth Engine data catalog, but it's possible for it to be added because NASA's MODIS Aqua data was added, but that's a decision made by Google.

1:16:10 Speaker 1: We don't have anything to do with that.

1:16:13 Speaker 4: And so users can request to add.

1:16:16 Speaker 3: Remote sensing data sets, and I added.

1:16:18 Speaker 4: A link there.

1:16:18 Speaker 2: So if.

1:16:20 Speaker 3: Enough people request it, maybe Google will decide.

1:16:23 Speaker 4: But yeah, Google makes that decision. Google Earth Engine, we don't.

1:16:29 Speaker 2: Thank you, Dr. Wendell.

1:16:30 Speaker 1: Yes. Thanks for the information. And question 11 is, could PACE be capable of detecting, measuring, and determining some ecologic communities as water lily blooms mangrove island evolution?

1:16:46 Speaker 2: Yes, it is possible. I'm not familiar with how extensive water lily blooms are, but PACE OCI spatial resolution is on the order of 1.2 kilometers by 1.2 kilometers. Therefore, if the water lily blooms are about that size or smaller, then PACE OCI would not be a useful source of satellite data to study them. Mangrove islands are obviously larger, and so PACE can be brought to bear for looking at changes in mangrove islands, size, and I guess the changes in the vegetation indices for changes that are greater than one kilometer in size and dimension.

1:17:41 Speaker 1: Next question is, how are hyperstructural algorithms used to better inform PACE? I'm not sure exactly what the question means, but.

1:17:51 Speaker 2: Sure. Yeah, so I guess I can answer in one way, in that we do expect to have better quality products using hyperspectral algorithms. A prime example would be distinguishing different phytoplankton communities. quantifying the proportion of diatoms and dinoflagellates and cyanobacteria present in a water parcel. So that's one area. I mentioned earlier how hyperspectral algorithms can help us, or using hyperspectral data can help us better distinguish the different aquatic constituents or help us distinguish aquatic constituents from atmospheric constituents such as aerosols.

1:18:48 Speaker 2: So, yes. And, yeah, it would be informing the scientific community or the PACE project, not necessarily PACE itself. It's just the satellite mission, obviously.

1:18:59 Speaker 1: So, question 14 is, how can we access the spectral information from PACE? For example, if we wanted to download a data frame which included a time series and the value of spectral irradiance measured for every wavelength for each time term for one geographic region, how could I do that?

1:19:21 Speaker 3: Yeah, so.

1:19:24 Speaker 2: So that's... Yeah, Anna, would you answer that? Did you answer this already or did someone else?

1:19:29 Speaker 1: Yeah, I can answer it. Yeah.

1:19:32 Speaker 3: So we will have Python code as in Jupyter notebooks in part three of this training on October 9th, I believe. So we'll provide Python code on how to download access and open paste files. And we'll even have an example of how to do a time series analysis. For the question of spectral irradiance, I put a link to.

1:19:58 Speaker 1: The data products table.

1:20:00 Speaker 4: We have PAR, which is photosynthetic available radiation data, which is similar to spectral irradiance, so you can easily pull that and do a time series using PAR data. You can also pull PACE data into CDAS to visualize the data if you're familiar using the CDAS software. Stay tuned to part three because hopefully some of this Python code can.

1:20:25 Speaker 1: Be helpful to do this. That's great. We look forward to the rest of the sessions also to learn more. Question 15 is, how are inland waters masked in base products? What water mask or what is used for determination of inland water?

1:20:50 Speaker 2: Yes, so as I'm, so you, the PACE project does produce data products for inland waters. So you can, if you, you know, go into Worldview, NASA Worldview or the Earth data, and you obtain, look at an image that includes inland waters that are large enough for OCI to measure, then you'll see ocean color, like chlorophyll-A, for example. With respect to masking, something we would have to research exactly if there's any masking done, these water quality, ocean color type of products. I don't know if the question is referring to terrestrial, so I won't go further unless someone follows up on that.

1:21:45 Speaker 2: As I mentioned earlier, we are working on implementing cyan-like products with PACE-OCI data, and those are more, those are algorithms that are more specific and for inland waters.

1:22:06 Speaker 1: Yeah, so, yeah, what we're going to do is address next two questions because we're almost at the end of today's session, and then remaining questions we will address and then post on our website. But let's go through next two questions, which are, can you better explain the tilt and sun glint downs for water quality retrievals and specific for PACE?

1:22:34 Speaker 2: Yes. So sun glint affects our retrievals of ocean color properties, water quality. And so we want to avoid sunglent if possible for our standard or typical ocean color products. And so a precursor sensor called CWIS back in the late 90s and early 2000s had a tilt mechanism, but the more recent missions, MODIS, like the MODIS instruments or in the VIIRS instruments, had no tilt. And so they were subject to a high amount of sunglent. And so if you look at a daily coverage for VIIRS or MODIS, you see a lot of areas are masked out because of sun glint.

1:23:19 Speaker 2: So for PACE, and the Ocean Coloring Instrument in particular, we implemented a tilt mechanism. And so the tilt mechanism tilts OCI in the aft direction as it comes over the South Pole, over the Southern Hemisphere. And then as it flies over the tropics, it shifts to tilting in the forward direction so that in either, for each hemisphere, it basically minimizes and avoids much of the sunlit so that over the course of the day, we accumulate much more of the global ocean color coverage than we would without the tilt.

1:24:05 Speaker 1: Next question is, Could you please elaborate further on the tilt feature? Does it imply that the scenes captured are not orthographic images taken at nadir?

1:24:20 Speaker 2: Yeah, that's correct. The PACE-OCI images are not nadir viewing. They are collected at 20-degree tilt in the aft or forward direction. And however, the multi-angle polarimeters, they view the Earth at different angles. many different angles. One of those is nadir viewing. So if you're interested in nadir viewing, you might consider looking at the multi-angle polarimeters, the SPEX-1 and the HARP-2 data.

1:24:51 Speaker 1: Great. We have several questions that we want to go through, but we're coming to the end of today's allotted time. So what we will do with the help of PACE team, they will all be answered and we'll post them on our website in about a week or so. So we really want to thank Dr. Manino for his presentation, his time, and explaining all the questions, answering all the questions. That's very helpful. Also want to thank the PACE team present today, Drs. Morgan McEwan, Anna Wendel, and Karina Pollin. Our next speaker is Dr. Morgan McEwan, and then In the last session, we'll have Drs.

1:25:38 Speaker 1: Hannah Windle and Karina Paulin talking about this data access. So next two sessions, we'll focus more on data products and how to access them and analyze them. So we thank you for joining today's session, and we hope to see you in the next session next Wednesday on 2nd October and that last one on 9th October. And again, we'll get to all the questions that we've received and post them on our website. So thanks again, Dr. Marino, for today's presentation.

1:26:10 Speaker 2: Thank you, Amita, and thank you all for joining. It's been a pleasure, and it's really exciting to see so much interest in the PACE mission, and look forward to more gains and Anna's presentations in the next couple weeks.