NASA ARSET Overview and Applications of OPERA-DISP to Monitor Groundwater Changes Part 3

0:01 Speaker 1: Hello, everyone, Welcome back to this training on monitoring groundwater changes for Water Resources Management. This is Part three and the topic today is overview and applications of OPERA disp to monitor groundwater changes. OPERA is observational products for end users from remote sensing analysis and this is a splacement product, so we will be talking about that, and our guest picker for that today is doctor Eric Fielding from NASA Jet Proposion Laboratory. This is the final part of this training and there's one homework that's posted today.

0:38 Speaker 1: It opens today and it's available from the training website and the homework will be due on fifteenth of May. A certificate of completion will be awarded to those who attend all live sessions and complete the homework assignment before the two date.

0:57 Speaker 1: His brief review of what we saw in the last set. In Part two, we talked about Global Land Data Assimilation System version two point two. Land data assimilation systems, or altas. They integrate data from various sources using algorithms that represent knowledge of physical processes to fuel special and temporal gaps. Gilda's two point two integrates grace and grace followen observations we talked about these in part one of the Straining and so Gildas assimilates terrestrial water storage with other information using data assimilation techniques enabling spatial and temporal downscaling and vertical desegregation.

1:41 Speaker 1: Child's two point two soil moisture and groundwater output has been evaluated extensively using ground based observations. There are a couple of limitations to this data. The model does not simulate water management, that is, groundwater pumping, so it cannot effectively downscale grace or grace follow on observations, but direct human impacts are significant, and the model does not simulate confined a prefer storage changes, which may be an important components of total water storage changes observed by Grace and greasewallowing in certain regions.

2:21 Speaker 1: Gilda's two point two output are updated monthly on GEES disc site, and associated wetness and drought indicator maps and monthly forecasts are updated weekly on the National Drought Mitigation Center website. This brings us to today's session and so objectives for today are that by the end of part three you will be able to identify characteristics of opera surface displacement, product access and visualize opera dis data to infragroundwater change is at regional scale for applications. As in the previous two sessions, Please put your questions in the questions box and we will address them at the end of the webinar.

3:12 Speaker 1: Feel free to enter your questions as we go. We will try to get to all of the questions during the Q and A session after the webinar, and the remainder of the questions will be answered in the Q and A document which will be posted on the training website about a week after the training. With that, I want to introduce our guest speaker for today, doctor Eric Fielding.

3:38 Speaker 1: Doctor Fielding is a senior research scientist at NASA's Jet Proportional Laboratory and California Institute of Technology. He received his undergraduate degree in Earth sciences from Dartmouth College and his PhD in geological sciences from Cornell University. His expertise in processing synthetic aperture radar data and especially SAR interferometry. His research interests include mechanics of landslides, including transition from stable to unstable sliding, and effects of climate variations active tectonics, especially in the alpine Nimalayan Tibetan system on a variety of time scales, then fault interaction via the transfer of stresses through the lithosphere, and the possible implications for earthquake risk assessment.

4:31 Speaker 1: Doctor Fielding has received several awards including agu geotes, Ivan Muller Award for Service and Leadership, several NASA Exceptional Scientific Achievement Medals, and Edward Stone Award for Outstanding Research Publications. Doctor Fielding has more than one hundred peer defeat publications and so we invite doctor Fielding to talk about Opera disk Eric.

5:02 Speaker 2: Thanks Amitia for the introduction. I'm going to be covering the use of the Opera displacement products to monitor groundwater changes, and I'm using some slides from Grace Ponto, who is also a JPL and part of the Opera Displacement Validation team. So I'm going to be doing an overview of the Opera displacement products and show some specific applications to understand how this particular type of product can be used for groundwater mount during. These are the objectives. I'm going to do a very quick overview of sorrow defrometry just to get the basic concepts we have previous rset trainings that cover this topic in much more detail, so I will to those as a good play to get the more details about the background.

6:05 Speaker 2: A little bit about what the interferometric phase, which is the basic measurement of soorro intofrometry tells us about the land surface and surface subsidence. And then understand what the opera which OPERA stands for the observational products for end users from Remote Sensing Analysis program displacement product measurements mean. And then we'll in the demonstration show some examples of what the OPERA displacement time series reveals about land subsidence in one of the larger subsidence areas in the United States, the California Central Valley.

6:56 Speaker 2: These are the previous are set, so these are good places to start if you want more detailed information on the process InSAR and SORROW analysis. So here's a review of the synthetic APPETU radar or sorrow interfrometry. The twenty seventeen R set training and twenty nineteen are set training on sorrow defrometry. I have much more details. I've reduced that it here to make it fit into the time that we have available. So the key thing there is that what we measure with sorrow defrometry is the phase of the radar signal.

7:48 Speaker 2: You may have seen in other sor analysis training or papers that there's the radaritude, but for interferometry we don't really pay attention to the amplitude when you look at the phase of the SAR signal, and the SAR phase is a measurement of how many oscillations of the radar wave there are between the antenna and the satellite and the ground. And one of the things to keep in mind is that we have the radar resolution and all the elements are objects within that resolution element which is roughly ten by ten or twenty by twenty meters, are added together into one phase measurement, and we also have this very large number of cycles of the radar wave between the satellite in the ground.

9:01 Speaker 2: But by taking the difference or interferogram, we basically subtract the phase from one observation from them the phase of another observation, and by that cancels out almost all of these effects, and we then get a measurement that's just the surface displacement. So basically we can simplify the phase as just being due to the distance, which is the letter row. Here at some other consonants that are due to the the radar of propagation and distance from the satellite and noise. And by taking the same measurement of a second image uh these other constants, we assume that these other constants are are the same.

10:06 Speaker 2: And when we then subtract phase one from phase to these other constants, then cancel out and therefore get rid of those and the remaining signal is the difference between the distances between of the two satellite images plus some amount of noise. So this is just a graphical representation of the same thing. At we take two different images at time one and time two, and the distance to the ground can change up by a small amount, and the phase tells us how much that distance changed between time one and time two.

11:00 Speaker 2: One of the key things about the sarfrometry. I didn't have time to get into the fact that we can also measure topography with SAR in defrometry. But here we're using SARN difframetry to measure displacement. That's this equation here, This this equation the second equation here, and you can see that the sensitivity to displacement is depending on the wavelength of the radar lambda four pi over lambda. That's the sensitivity to displacement, and we were using here today. We're going to be talking about data from the Sentinel one satellite that the radar wavelength is six centimeters.

11:51 Speaker 2: Other satellites, including the the nice Are NASA is SAR satellite that was just launched last year and now being calibrated, has a radar wave length of twenty four centimeters. So the Sentinel one satellite has a greater sensitivity because the radar wavelength is shorter. There is a sensitivity to topography, but we're not going to talk about that here. So the Opera Observational Products for end Users from Remote Sensing Analysis Project does systematic measurement of displacements from the Sentinel one project, but they also do several other types of analysis.

12:51 Speaker 2: It's a large scale project that's supported by the NASA Satellite Needs Working Group, which gets money directly from the Office of Management and Budget to support these products that are requested by other US government agencies. And all of the data is free and open, and we've been working hard to make this data what we call analysis ready, which means that the data is processed to a level where you can start using the data without having to do your own sophisticated processing. So there's these different products and I'll go into more in the next slide and Offergos uses data from a wide variety of missions, including a lance AT eight, lance AT nine, Sentinel one the SAR from the Copernicus European constellation, and Sentinel two is the optical imaging from the Copernicus uh constellation, and then NISAR, which is the new NASA satellite that is obviously a SAR mission.

14:14 Speaker 2: So these are the products that are that Opera is producing. H there's level two products. These are basically intermediate products that we don't that we are producing for the as part of the processing, but could be useful for other people. They're not so easy to use directly. There. Level three products are what most people are going to be using. There's the dynamics surface water extent that's a map of the surface water over time. It's dynamic because it's over time, and there's two different versions of the surface water extent.

15:03 Speaker 2: One is made from the the harmonize HLS, the harmonized Lancet Sentinel two data sets. So that's optical imaging and then a second version that's been It's made from the Sentinel one SAR imagery and in the future will be made from ANSAR. Those products are near global wherever the data is acquired, and they're updated as often as we get new images from Sentinel one and the harmonized Lancet Sentinel two. Similarly, the disturbance product. This is a measurement primarily of a disturbance to vegetation and other

15:51 Speaker 2: other things that happen to the surface, could be due to fires or people cutting down tree or whatever. These are very similar in there both there's two different versions, one made from the harmonized LANSATS Sentinel two and one made from the Sentinel one, and again these are near global, available on land almost everywhere. Today I'm going to be talking about the surface displacement product. This is mapping surface displacement from the Sentinel one and soon from Sentinel from NYSAUR. The coverage of this data set is in North America, not global, because of the large amount of cost of doing the more advanced processing for necessary for the displacements.

16:49 Speaker 2: In the future, the Opera Project is going to be making a vertical land motion product. The present placement product is in the radar line of sight, not UH separated into vertical or horizontal motion, and it's not tied to any any datum, whereas the vertical land motion product will be separated into vertical and horizontal displacements and it will be tied to GPS or other absolute datum. To make a product that's directly usable for measuring vertical land motion. UH from North America, similarly to the displacement product, and just recently just UH released is a troposphere e zenith delay.

17:54 Speaker 2: One of the effects that I didn't mention on radar you know from tree is that as the radar beam goes through the atmosphere, it can be slowed down slightly by the presence of water vapor in the atmosphere. And this tropospheric zeenus delay product can be used to correct for those atmospheric delays, and it is useful for applying to the opera displacement product. But it's actually a generic global estimate for any place in the world and can be used for any sore mission. I won't have time to go into that today.

18:42 Speaker 2: So this is the map of the displacement products for North America. This is the the the descending track. Now this is the assending track map. The assending is where the satellite is going north relative to the Earth's surface. Due to the way that the Copernicus system operates the Sentinel one satellite, they have good coverage of the eastern part of North America. Only with the assnding tracks. They have limited capacity to do two different look directions there, so for the eastern part of the US, only assending data is available.

19:38 Speaker 2: You'll notice that the the displacement product is only available for the part of Canada that's within two hundred kilometers of the border with the United States. Due to the wide spread a snow cover in northern that we decided to not include that in the processing. But the processing also goes down through Central America to the southern border of Panama. The coverage is North America with an asterisk because it doesn't cover the northern part of Canada. The Sentinel one data coverage varies with time.

20:31 Speaker 2: In some time intervals time periods, the passes were acquired with six day separation, other times twelve days, or another times that's twenty four days, and that depends on also on the location. For the western part of the US, especially the west coast because of the larger amount of deformation due to groundwater and earthquakes and other processes. They tend to they acquire bold assending and descending data and generally have more frequent coverage. And the opera processing starts in July twenty sixteen.

21:26 Speaker 2: The satellite Sentinel one A, the first Sentinel one satellite, was operating before that, but the coverage was less, so we just decided to start with July twenty sixteen. Nice Are data processing is waiting on the final calibration of the nice ART data, which should be completed by June this year, and the Opera team is working hard on getting that ready. And this is the first insur product from anywhere that's been certified by the Committee on Earth Observation Systems COOS as application ready data.

22:16 Speaker 2: So OPERA products have a number of layers. I'm going to be talking about some of these in detail. The frame number here is part of the final name because the OPERA products are processed on a frame on a frame basis using a frame definition that was created by the ASM. So I'm going to be specifically talking about the main primary data later the displacement and meters, but there's a second version of the displacement, which is what we call the short wavelength displacement. The short wavelength displacement layer has the the larger scale variations in displacement filtered out.

23:20 Speaker 2: Everything over about twenty five kilometers in spatial scale has been filtered out, and that short wavelength displacement is what they use for display in the ASF displacement portal, which I'll be talking about later. And then there's a whole set of these different quality metrics. I'm not going to go into this in great detail because it's kind of an advanced topic, but these years can be used to determine which which pixels of the of the product have good quality measurements and which may have a more noisy measurement.

24:15 Speaker 2: Okay, so that's the we're ready to go to the demonstration. I'm going to be giving a demonstration of that displacement portal and then doing some analysis. The displacement portal, as I mentioned, uses these short wavelength displacement filter versions. Then I'm going to show a Jupiter notebook for more into this analysis of the displacement products. And when you go to the displacement portal which is here, the displacement dot A SF DOT Alaska dot E d u uh. It comes up with this. This a disclaimer, which is just a reminder that what they're showing is the filtered displacement, which can work.

25:12 Speaker 2: It works well to highlight small scale deformation features, but large features like the Central Valley that I'm going to be talking about later are not very well image by this short wavelength. Okay, So now I'm going to go to the demo. Okay, So now I'm in the displacement portal. Right now, I'm looking at the descending track upward displacement products from Sentinel one. As I mentioned earlier, there's kind of a gap here in the eastern part of the United States where they have not been acquiring the data with the descending tracks.

26:06 Speaker 2: That's going to be one of the big advantages of nice Are is the nice Are has global coverage with both ascending and descending tracks. The nice Are engineers spent a lot of time developing a whole hardware and data system and downlink it did downlink to be able to cover the whole world with assending and descending data. There is coverage in the very northeast part of the United States and also around Houston, but most of the eastern part of the US is not available in the descending tracks, So I'm going to zoom in here.

26:53 Speaker 2: You could just zoom in with a with a scroll bar or or the plus button over here. The scroll bar works for a nicely if you have a scroll a wheel on your mouse. And here we can see that these are groundwater subsidence areas in Arizona, in New Mexico and Texas, and also I think northern Mexico. Because these are relatively small areas of subsidence, the short wavelength filter is not changing the values too much. You can see that there's kind of a halo of what looks like apparent uplift around the edges.

27:43 Speaker 2: That's a filtering artifact. But we can click on a point here and the displacement portal goes out and gets the opera displacement products for that point and will show us a time series for the whole data set at that point. Takes a little bit of time because they have to go through a lot of files. And here we can see the time series from July twenty sixteen or August twenty sixteen through the last point here is September twenty twenty two.

28:30 Speaker 2: They are working on continuously updating this time series, so they're trying to get up to the very most recent data soon. And one of the things to notice here is that this is subsidence due to groundwater extraction, and it's highly seasonal. During the winter, when it's raining and they're not trying to grow so many crops, the surface is relatively stable. And then in the summer, when they're using lots of groundwater to irrigate their crops and extracting it out of the subsurface, then the ground surface subsides.

29:16 Speaker 2: So that's why we can see this very systematic level and then subsidence pattern over the whole almost almost ten years here, and we can see the same thing on other groundwater basins. It's a very common feature. That first point had a total subsidence of zero point four meters that's forty centimeters in nine years, and this new point.

29:58 Speaker 2: So the portal has a way of showing points that are not well determined as these kind of open or dash circles. Because I clicked on a point that has probably some agricultural fields in it during the time that they're plowing those fields. The opera displacement product cannot be measured, so then it ends up being these dashed circles. But the way that the opera displacement product is processed is able to get an overall time series even when there's these gaps where the measurements are not good quality?

30:55 Speaker 2: Can we erase those points? Uh? Oh before I go away from this area, I'm gonna just switch. You could use this little uh flight direction button here to change the flight direction. So these are This is now the assnding track data. Yeah, we can see that the groundwater line of sight displacement in the radar line of sight is

31:28 Speaker 2: very similar for the ascending and descending tracks. In this particular area, it seems that there's more gaps in the ascending track than they are in the descending track.

31:41 Speaker 1: Uh.

31:42 Speaker 2: Where you don't see a dot, that means that they didn't actually turn on the satellite on that date. So I'm gonna zoom out again. This is the ascending track. As I showed earlier in the slide. It has much more complete coverage, including all of the eastern US. Most of the eastern US doesn't have so much groundwater extraction, but there are some hotspots. Houston Texas in Galveston is one of the areas where there's a lot of groundwater extraction. Yeah, we can look at a point here and you can actually zoom in here to see the full resolution thirty meter pixels if you want to get the pixel that includes your house or whatever or some other feature.

32:59 Speaker 2: So here we can see that the ground the groundwater extraction seems to be more constant without that strong seasonal variation, and the total subsidence here is a little bit more than it's around twelve zero point one one two or meters or twelve centimeters. Now I'm going to take a quick look at the California Central Valley and here you'll notice that it looks very well. If you know what the subsidence in the Central Valley looks like, this is going to look very strange. And that's because the overall subsidence over a long spatial scale has been filtered out, and what we're seeing is just local variations.

34:05 Speaker 2: So it looks like there's little areas that are going up and down. But that's because the overall subsidence has been subtracted out. And that's why I'm going to show you next how to look at the full the full unfiltered data with a notebook Jupiter notebook. This other feature over here is the signal from the twenty nineteen Ridgecrest earthquake. It was a nineteen seven point one earthquake that was actually preceded by a six point four earthquake. This line here was the six point four and this was the rupture of the seven point one.

34:53 Speaker 2: This is also a little bit affected by the filtering, but it shows the earthquake signal. Okay, so I'm going to switch to a different tab now. Okay, So this is a Jupiter notebook that was Yes, this is a Jupiter notebook that we're going to use to download a subset of offer our displacement products for a part of the Central Valley and then look at the full unfiltered displacement products. I'm not going to go into a lot of detail on this first set up part. It's possible to run. You can run this by setting up a condo environment and then running these commands here here.

36:06 Speaker 2: So this next, this is the cell where we actually specify what area we want to look at. You'll remember earlier I said that the opera products are processed on frames. I was about to mention that the in the displacement portal, when you click on a point, it shows you what frame I D that point is part of. So that's the kind of the easiest way to get the frame I D. But you can also get it by searching for yeah upper products in the regular Earth data or

36:52 Speaker 2: or asf vertex search. Then I specify a bounding that's just a pulling on with the four corners of the rectangle that I want to analyze this particular notebook. I'm using just the data from August twenty twenty two to August twenty twenty four. This is a time interval that was very I had had a lot of rainfall. The twenty twenty two twenty twenty three winter in California was extremely wet, one of the wettest in the last century, and the twenty twenty three twenty twenty four winter was also well above normal.

37:39 Speaker 2: So this time interval is a very wet period for California rainfall. So once you've specified your area, then the system will prompt you to enter your NASA Earth Data log in. You can't downlo load than there uper displacement products without having a NASA Earth Data log In account. This is free, but it requires registration and you have to enter your username and password. Here you can set up a file called net rc in your home directory with that Earth Data log in information, which is what I do to avoid having to enter this every time.

38:29 Speaker 2: It just reads from that file in your home directory. So this sell checks to make sure that I have the credentials for Earth Data, and then it's going to go out and download the data. Because I've already downloaded this data. It's going to very quickly realize that I've already downloaded it. So then it finishes. It can take quite a while, depending on your internet speed. It could take twenty minutes or or longer too, and depending on how big of an area you're trying to download. So it's now put those files in this subfolder called subset NCS.

39:21 Speaker 2: That's net CDFs and frame thirty eight five h two. So these are all the files. It's fifty nine files for that two year time interval. This this just checks to make sure there's nothing wrong with the files. So the next step looks at the files and gives basic information about each file. I didn't have a chance to I didn't make a slide earlier but the opera data is process and what we call mini stacks, about fifteen dates in a row are combined into a mini stack. This is a key part of the processing efficiency because we can't process the whole time series together.

40:33 Speaker 2: But in this case, over those two years, there's four different mini stacks. Each one has fifteen scenes except for this one that seems to be missing one scene, and within each mini stack, the reference date is the first date in the mini snack, but the last date of a mini snack is the same as the first date to the next mini stack, So this notebook is able to then reconstruct the whole time series without any any real additional calculation. So we can see here the first date is August tenth, twenty twenty two, and the last date is remember fifty eight, it's between.

41:31 Speaker 2: So the first day here is the reference date of that mini stack. This mini stack goes from February to July, and the last date is July thirtieth, twenty twenty four. So this then quickly does the referencing of those mini snacks. It sees all the files and then we need to choose a reference point.

42:04 Speaker 3: As MH the opera products and as with most in is our products.

42:15 Speaker 2: They're not referenced to any absolute reference system. They have their own internal it's just basically relative to the satellite orbit. So we need to choose a point that we think is not moving. In this case, I chose a point. It makes this plot here. This is the whole time series. I made a point that over here on the side of the of the central valley where it's stable ground and not not affected by the subsidence, as the reference point. But you can pick one here and it'll tell you what lat long.

43:06 Speaker 2: What the lat long of the point that you picked is here, and then you can enter this value into this next cell. I already entered in the reference point here that I had chosen earlier. But you can also use this tool to choose another point that's in an area of subsidence and get that latitude and longitude. And then this next cell. If you enter in the point for the plumbing the time series, then this next cell shows the full time series for that point. First, it shows the map it's from. Now re reference the map.

43:59 Speaker 2: No, that's the that's the w's this one. We're going to use that reference point.

44:10 Speaker 3: Uh.

44:10 Speaker 2: The opera products are actually in UTM coordinates. But the notebook is is converting the long point that I gave it into UTM coordinates to uh use with the Opera data product. Yeah, it's now re referenced the time series of that reference point that I gave it. It's going to make a new map here. Yeah, ok yeah, Okay,

44:56 Speaker 2: So this is now the accumulative displacement over the over the four years relative to the reference point. And the reference point is this point up here with the yellow dot. You can see it's at the edge of the Central Valley. This is all the big Central Valley of California, near the city of Fresno, and there's a small city here called Corcoran. This point here is near the city of Corcoram, and we can see the time series for that point in this next plot. In this case, I chose a point which in this time in a whole actually had very little net displacement.

45:51 Speaker 2: There was a little bit of displacement here in twenty twenty two before the heavy rains of the winter of twenty twenty two twenty twenty three started, and then it started moving back up later. So this is one of the things that is very which is a little bit unusual about this time interval, you can see this is the fully This is not the filtered time series. This is the full time series. And you can see that the eastern part of the central Valley here has continued to subside, but this western part over here has actually rebounded, and that's because there was enough rainfall that it started recharging the shallow aquifers enough that the ground surface actually started moving back up during this time at aval we have seen this before.

46:53 Speaker 2: There was a previous time between twenty fifteen and twenty nineteen where we also got a lot of rainfall, and there was a previous time when this western part of the Great Valley also moved upwards. So this is one of the This is one of the key pieces of information that the InSAR can provide where Grace is going to be averaging over this whole area and showing that maybe there's a reduction in the water groundwater extraction, but it won't show you the spatial distribution of the surface effects of what the groundwater is doing.

47:38 Speaker 2: And with the ins are we here we can see that the western part of the Grade Valley is actually moved up in these two years due to significant groundwater recharge, and this eastern part is still moving down, but as I'll show you shortly, the rate of subsidences much less than in a previous time at all.

48:05 Speaker 1: Yeah.

48:09 Speaker 2: So then there's a bunch of more notebooks that show more information about the details of the equality metrics. This first one is the there's a number of different quality metrics. I don't really have time to get into all of them. Here there is a layer that you could look at. It's the short wavelength displacement. I'm not gonna we already saw that in the displacement portal, ah, one of.

48:48 Speaker 4: The yeah, yeah, yeah, the main it's.

49:04 Speaker 2: So I'm gonna show the here.

49:08 Speaker 1: Yeah.

49:09 Speaker 2: So this is showing the whole set of time of layers. This is the overall displacement, with the subsidence in the eastern part of the valley and in the a slight uplift in the western part. This is the short wavelength displacement, which, as I mentioned in the in the portal, it has this very strange fact where there's very large scale long wavelength displacement has been removed. One of the layers is called the recommended mask, and this is a starting mask to mask out the less accurately measured locations, and you can see that there are many of these fields that were not well measured int This area here was actually flooded for almost a year due to the extremely high precipitation in twenty twenty three.

50:12 Speaker 2: It formed a lake there they called Lake Tulari, which was a historical lake but then was drained for agriculture, but came back because there was enough rainfall. These are some of the other quality metrics. Temporal coherence is showing how coherent the measurements are with time. Corcorant is this in cities show up as very highly coherent because the buildings reflect the radar very well. Estimated. Phase quality is similar to the temporal coherence, and you can see this area here with the low phase quality is the area of that lake that formed in twenty twenty three, Yeah, causing the measurements to be inaccurate during that time in the hole.

51:06 Speaker 2: And then there's there is a water mask where permanent water is massed out, but there's very few permanent lakes here. This Lake Tulare is a temporary lake. There are some other types of measurements that I'm not going to go into here for the sake of time. So Finally, this cell here does a velocity estimation, and it's just going to take a linear fit to all the points in each pixel to estimate the average volney over the two year interval. And this particular step can actually take a lot of time to run and and quite a bit of memory.

52:14 Speaker 2: So I wouldn't run this unless your computer has at least thirty two gigabytes of round for for this very large area. For smaller areas, it doesn't use so much memory. It's something that I want to improve later. And then down here, this is the estimated UH average velocity for the for the two year time in a hole. And here we can see UH this western part where the recharge occurred with velocity on the order of six to six centimeters a year, or a little bit more in some places. And over here in the in the east part of the central valley it was subsiding around six centimeters per year during these two years.

53:08 Speaker 2: So this is a this is a significant a product that you can extract from the opera time series using this notebook. Then this later part is actually taking these products that we generated, the velocity and other other products and exploiting them to file formats that we can load into a time series program called mint pie or q GIS. And I'm not going to go through all these details the QGIS products.

54:01 Speaker 1: It.

54:01 Speaker 2: Actually there's different versions of the recommended mask. This is the most strict. It removes the most area, especially in this area of Lake Chuwari, whereas this one over here is a more lenient mask that allows it to try to estimate the velocity in that area from the points before and after the formation of the lake. And so it actually exports three these different files and it writes out the geotfs here. They can be loaded into h GIS. It also makes images for all the dates, so those export files go into this export directory.

55:03 Speaker 2: UH. These are the different files.

55:12 Speaker 1: UH.

55:12 Speaker 2: These are the as As I mentioned, there's three different versions of the recommended mask.

55:22 Speaker 1: UH.

55:27 Speaker 2: And then there's a velocity UH file here. It's based on the recommended mask converted to sentimeters. So now I'm going to bake a quick jump to the UH pretty popular q g I s g I S program. UH. This is available for Mac, PC Linux and so it's a very multi purpise international, free and open source GIS package. UH. It's can also be the same thing kind of things can be done with r g I S for those who have the commercial r GIS package or or other GIS packages. Okay, okay, So this is the the average velocity map loaded into q g i S. It's the notebook actually reprojected it from the UTM coordinates to allow long coordinates to make it easier to look at.

56:53 Speaker 2: But i've I'm actually using QGIS to to re display it in the in UTM coordinates, so it's not as a more accurate map coordinate system. And here I have also added in QGIS a Google satellite image, which is one of the layers that q I GIS offers as a background. It's a I can wait a minute. This is This is the twenty twenty four velocity map with the rebound in the western part of the Central Valley. And if we turn this off you can see the all the agriculture and you can see that they Google Earth actually

57:54 Speaker 2: captured this when there was so a significant part of the Tolare Lake here. So this is the twenty twenty twenty four average velocity. And I did the similar analysis with the time interval from August twenty twenty nineteen to twenty twenty two. This was a three year time interval of precipitation, and here you can see that the average subsidence rate is much much higher. This is the the color scheme here is that the the notebook converted the subsidence rate into centimeters per month, and these areas of deep blue here are subsiding at one and a half centimeters per month, or over thirty centimeters per year.

59:05 Speaker 2: That's that's a big amount. Whereas if we go back to the twenty twenty two twenty twenty four we can see that these subsidence rates are are much much lower in the area that's still subsiding and the western part is rebounding. So that's just a very quick introduction to how we could load these products into QGIS or other GIS programs to have a better view of what the results look like. And these particular versions of the are using the recommended mask with a pretty with a moderately strict coverage.

59:53 Speaker 2: So the Tlare lake is largely massed out here in the twenty twenty two twenty twenty four image, but more complete in the twenty nineteen to twenty twenty two image. There's also a lake down here that actually stayed even longer. I forget the name of that lake. So now I think that's a very quick demonstration, but I hope that's informative. And I'm just going to go back to these slides to show some resources of web pages that you can use to go back and look at the background for Opera and the displacement products.

1:00:43 Speaker 2: Okay, so these are some web page resources about the NASA Earth Data System, that is where all the Opera of Data products are stored, Satellite Needs Working Group, which supports the Opera project and other projects. In fact, the Harmonized landsat Sentinel to data products are another Satellite Needs Working Group product that we in the Opera project build on their product for some of those displacement I mean disturbance and water extent products. This is the displacement portal that I demonstrated.

1:01:31 Speaker 2: There's some product information about OPERA in general. There's a link to the GitHub page where the Opera Jupiter notebooks are shown, and in fact, you can also look at the Opera products in NASA Worldview. It's not as effective for the displacement products, but is very effective for the other OPERA products such as the water extent and disturbance products and I'd be happy to answer questions and I'll hand it back to a reader.

1:02:14 Speaker 1: Thank you so much, Eric for your presentation and information about opera disp especially for your demonstration about ASF disc portal and sharing your Jupiter notebook code with everyone. Just to summarize, we had a review of SAR interferometry, especially that phase of SAR signal is very important when you consider interferometry. So when two observations are made from the same location in space but at different times, the interferometric phase is proportional to any change in the range or distance of a surface feature, and that is used in producing opera DISP.

1:02:54 Speaker 1: It is derived from sentinel ones are imagery using interferometry. Opera disk products provide information or movements of the Earth's surface such as subsidence due to groundwater or oil and guest extraction, uplift due to water injection, and ground motion from tectonic faults, landslides and volcanoes. We also had an overview of opera displacement visualization portal available from Alaska sur Facility and after filling demonstrated Jupiter notebook code for in depth analysis of opera disc products.

1:03:32 Speaker 1: We saw how subsidence in Central Valley, California, looked at from disp indicated groundwater changes. This brings us to the end of this training. And in this training we focused on three different ways of looking at groundwater. So we first looked at Grace and Grace follow On, then Gilda's model and then Opera data products and we saw their characteristics. So Grace and Grace follow On they provide measurements of variations in mass or gravity changes over Earth surface and these gravity variations are interpreted in terms of changes in equivalent water thickness or terrestrial water storage t WS.

1:04:20 Speaker 1: Gled's version two point two integrates Grace and Grace follow on observations of t WS with other information using data assimilation enabling spatial and temporal downscaling of soil, moisture and groundwater. And finally Opera which uses central ones are as displacement product or disks that is also useful for monitoring groundwater depletion and recovery. As we saw, these are the characteristics of these data sets. Grace and Grace follow on missions they provide terrestrial water storage change data.

1:04:57 Speaker 1: These are global data sets. Resolution original is approximately three degree by three degree. Let you to longitude, but they're reprocessed at one degree by one degree and also at half a degree resolution. Temporal coverage is February two thousand and three to present, and there is a gap between July twenty seventeen to May twenty eighteen. That's the gap between Grace and Grace Fallen data are available on monthly timescale. Gilda's groundwater storage data that's also global and it's available at higher resolution quarter degree by quarter degree latitude longitude the same time period as Grace and Grace follow on as they're assimilated in here, and these data are available on daily timescale.

1:05:50 Speaker 1: Finally, Operatis, which is based on Centinel one data as a higher resolution thirty meters. It's right now away label only for North America and the duration of the data is July twenty sixteen to twenty twenty five and revisit time is about six to twenty four days depending on the location.

1:06:17 Speaker 1: We also had demonstrations and you had exercises for data access, analysis and visualization for Grace and Grace fall on terrestrial water storage changes. We looked at Grace data analysis tool. I want to bring another visualization tool which we did not have time to go through. But this mask on visualization tool is similar, but it also includes a GSFC mask on data at half a degree so you can explore that. But we looked at maps and time series based on this original Grace data analysis tool. Then we used GEO one n QGIS to look at Gilda's groundwater data.

1:07:05 Speaker 1: We had maps and time series and also looked at difference in groundwater so Groundwotter story change over different periods. Finally, today we had a demonstration on opera disc using ASF displacement portal and we will have some exercise based on that.

1:07:30 Speaker 1: There's one homework that's posted today on the training web page and answers must be submitted via Google forms. The homework is due on fifteenth of May. The certificate of completion will be awarded to those who attend all three live webinars, complete the homework assignment by the deadline, and then you will receive a certificate email approximately two months after completion of the course. Just to note here that for each train session we had exercise and some questions are based on those exercises, so please go through the exercises before you go to the homework.

1:08:10 Speaker 1: Once again, we want to thank our guest speakers, doctor Matthew Rottel and doctor Eric Fielding for their presentations and information about different data sets. You can see the trainer information here Doctor Rodell and doctor Fielding. You can contact them if you have any questions. This is the ourset team, my colleague Sean McCartney and edicup bodies. Along with that, you can contact any of us if you have any questions. This is our set website, our set YouTube link and for questions, comments, or to share how you have applied our training to your work or studies, please email at NASA dot R, set at gmail dot com and join our quarterly newsletter to stay up to date on our latest trainings.

1:08:59 Speaker 1: And for that you can send an email with no subject line to this email address and follow the instruction sending the response. Here are some useful resources used in this training.

1:09:27 Speaker 1: Thank you all, and we'll start with our question and answer session. We have a doctor Fielding here and so we can start with the question answer session. Welcome America and thank you for being here. So we'll start with question one. Is downscaling important for looking at groundwater drought index in a particular region? And Eric, you can unmute and answer the question.

1:09:58 Speaker 2: Please hello, Yes, this is a The drought index that the National Oceanic the National Weather Service in the United States makes is a estimate of the availability of surface water and shallow saw moisture. It does not directly measure ground water, so uh downscaling of that would would be possible, but they they estimate that based on the weather including precipitation and temperature, which are not which they use only a large scale measurements. The conversion of the drought index to the drought index is not directly measured groundwater, but areas of drought are often places where cities or farms, especially farms, will extract groundwater to continue their farming operations.

1:11:15 Speaker 2: So it's not the same thing. It's a different type of measurement, but they are related to.

1:11:24 Speaker 1: Great Thank you. Question two is what happens during a storm if opera has difficulty due to distance to capture data.

1:11:34 Speaker 2: So the opera surface water and disturbance products that use the optical harmonized landset Sentinel two inputs will be blocked by cloud cover and they will not have any measurement for the dates and pixels. Where there's cloud cover, the OPERA products based on SORROW measures, including the Sentinel one and soon Nice hours are images are not affected by clouds, and they have measurements for every past.

1:12:09 Speaker 1: Great question three. Can we apply OPERA DISP anywhere? For example the Middle East?

1:12:21 Speaker 2: So NASA only has funding to process the displacement products for North America, but the software that Opera uses is fully open so people could use the same software to process other areas.

1:12:38 Speaker 1: Thank you. Question for is I want to process Sentinel one data into displacement for small area of interest in different regions outside North America. Since Opera DISP is limited geographically, how difficult would it be to build a similar workflow?

1:12:56 Speaker 2: As I mentioned, the Opera software is available. Well, it's open source and on a GitHub. Processing a small area should be relatively easy. The main time series program is a program called Dolphin, and it can be used with Sentinel one or other SOUR data to analyze the time series and using the same algorithm that is used by Opera. The OPERA system does have a sophisticated workflow to enable continental scale processing, but you don't need that for processing a small area.

1:13:43 Speaker 1: Thank you. Question five. If we generate s pas insur time cities for a region using tools like by GMTSR, MINTBI or list s POS. How can we distinguish groundwater driven defamation from other processes like oil and gas extraction, tectonics, earthquakes or snow loading. How should we handle intracomparison and validation, and how to operate up products ensure consistency between seabands andinel one and ALB and NYSAR for long term monitoring.

1:14:24 Speaker 2: So the full separation of different geophysical and groundwater effects is beyond the scope of this training, but the time series data that's provided by the just Opera displacement product is very helpful for distinguishing different effects within the earth. The groundwater extraction in most places is typically seasonal, more in the dry seasons and less in the wet seasons, So if you see a seasonal variation in the displacements, that's a strong clue that the displacement is due to groundwater and not tectonic effects.

1:15:14 Speaker 2: Cut The second part of the question is about the how to opera products ensure consistency. The opera displacement products are processed separately for each Sentinel one track or and soon each nice our track. There is no additional processing to ensure consistency between the tracks, but the future Opera vertical land motion products, so that will have an additional processing to make to combine different sentinel one displacement tracks ascending and descending and with calibration with the local GNSS or g GPS data to make a consistent map of the vertical land vertical and horizontal land mush.

1:16:14 Speaker 1: Question six is for the Python demonstration, what is the velocity accuracy of this result? See five millimeter per year.

1:16:24 Speaker 2: Yes, the Opera products have a requirement to have an accuracy of five millimeters per years and that has been validated for places where the data has high quality. There are some areas where the InSAR coherence is low due to farm plowing fields or flooding or dense vegetation that is incoherent in central one seed bend data, and those are markets unreliable in the recommended matter that's included with the products, and there's no useful velocity measurements in those areas.

1:17:10 Speaker 1: Question seven for the Python demonstration are subsidence and delish and equal.

1:17:19 Speaker 2: There's a quite complex relationship between the volume of groundwater extracted and the amount of service. Subsidence This is determined by the characteristics of the groundwater reservoir, which varies greatly in different places, even within the same sedimentary basin, and the situation could be especially complex in places where there's groundwater reservoirs at different depths. They are not equal, but they're closely related it and require some advanced hydrological modeling of the ground border reservoir to better understand what the the reservoir characteristics and response are.

1:18:14 Speaker 1: Thank you. Question eight is I'm seeing some missing monthly data while working with the ASF data. What is the reason for this?

1:18:24 Speaker 2: The opera products are only available when the source data was acquired. The various satellites, especially Sentinel one, sometimes miss a few acquisitions or or change their change their acquisition plans. The Sentinel one constellation has had some changes over time. Initially they had just one satellite, they Sentinel one A. Then they added Sentinel one B, but then the Sentinel one B satellite stopped working in December twenty twenty one, so then they were back to having only one satellite until twenty twenty five when they got Sentinel one C in operation and they just I think earlier this month, declared Sentinel one d to be operational.

1:19:27 Speaker 2: So they're now back to full operational strength with two Sentinel one satellites. But during the especially during the time in the world where there was only one Sentinel one satellite, they had to reduce their data coverage because if they had less half the acquisition capacity, and if they didn't acquire the data, then Opera couldn't has no data point.

1:19:57 Speaker 1: Great, thank you. Question nine is when we downloaded that disk files, what is the best way to assign the reference based on a point or border of the mounting box? How is a reference point selected for measurement of displacement? Is there any specific point we should select?

1:20:17 Speaker 2: So you want to choose a reference point in a place that you know or have good reason to believe that the ground surface is not moving or stable. For groundwater studies, you typically want to have a point that's outside of the sedimentary basin nearby but not in the not likely to be affected by the subsidence. And the reference point also needs to be coherent in all the dates of the ins our time series. So for the Sentinel one display some products, you can't choose a point in an area where there's dense vegetation that will not have a good measurement and being coherent.

1:21:05 Speaker 1: The next question is question ten. What's the difference between a line of sight velocity deformation along slope horizontal and vertical deformation and how are they estimated using SAR?

1:21:20 Speaker 2: So the fundamental measurement that we get from the InSAR is the line of sight velocity. The single a single line of sight velocity from one track of data is not sufficient to be able to separate horizontal and vertical displacements.

1:21:39 Speaker 2: There are ways to combine two or more line of sight displacements to estimate what component of the displacement for each point is vertical and what part is horizontal. This is what the opera vertical land motion processing we'll be doing in the future. The if you only have two line of sight directions like you get from the ascending and descending sentinel one, then you only the only horizontal component that can be estimated is the east west direction. You can't estimate the north south component.

1:22:25 Speaker 1: Question eleven is is there any atmospheric correction applied to the level three or raw results when analyzing the displacement time series? Should we apply corrections like ionosphere delay, solid or tides et cetera.

1:22:41 Speaker 2: So the operator displacement products, the raw products that you get with from the time series files or do not have the atmospheric directions applied, but they do have extra layers in the in the file that include the ionospheric delay and solid earth tide estimation.

1:23:05 Speaker 1: The UH.

1:23:06 Speaker 2: These effects are really quite small for the c bend Sentinel one data, so for most purposes this is doing that those two corrections are not necessary. As I mentioned briefly, the opera product is providing a separate tropospher delay estimation that can be used to estimate and apply a troposfer delay correction. And these are much much larger than the amosphere or solid earth tide corrections, but requires some a little bit of extra work to add to your analysis group not covered here, just to.

1:23:55 Speaker 1: Make a point that the simple file that you have in your exercise will show a different layers that includes what doctor Filling is mentioned and question twelve in the displacement portal, there are many pixels over a water body. Why why were the pixels not removed by the water mask.

1:24:19 Speaker 2: The AESF displacement portal does include a water mask and a mask for areas of low InSAR coherence. There should not be pixels over a water body unless unless that water body was dried up for some reason. The Tu Laari Lake in the Central Valley of California is typically dry most of the time, but it did get water in it during the very heavy rainfall of the winter of twenty twenty three, so in that area we get a we had we could see that there was a measurements in the time in twenty three, and then during twenty twenty three when there was water there there's no measurements.

1:25:19 Speaker 1: Interesting question thirteen. In the time series that you shoot, does each point represent deformation with respect to the reference data or to the data immediately before. If we have deformation in time T times T one, T two, T three, the shown deformation in T three is deformation with reference to T two or T one. Assuming T one is a reference date.

1:25:46 Speaker 2: The time series is with reference to the reference date.

1:25:52 Speaker 2: So when I the opper products are process in these mini snacks, so there is a separate reference date for each mini stack. But the the Jupiter notebook that I had ties together the reference states for each mini stack, so that the whole time series is relative to the reference date of the first mini snack.

1:26:20 Speaker 1: That's great, And if you again look at the supplemental document in today's exercise, you will see that the file LIMB convention is explained in there. And then question fourteen, when analyzing displacement time series data, should we use unfiltered displacement or shortwave displacement?

1:26:46 Speaker 2: For almost every analysis you want to use the unfiltered displacement. The short wavelength displacement is just a visualization tool that makes it easy to show the displacement over a large area without worrying about local reference points, and it's not really usable for detailed analysis.

1:27:19 Speaker 1: Next question fifteen, is the displacement time series comparable with s PASS derived displacement time series?

1:27:27 Speaker 2: Yes, it's it should be comparable. The OPERA time series analysis uses a more sophisticated what we call persistent scatterer and distributed scatter analysis to get higher quality measurements, but the fundamental result should be similar to s PASS for most.

1:28:04 Speaker 1: So the next question also is like what algorithms? What algorithm uses opera to create the time series PSI or s BUS.

1:28:15 Speaker 2: The Opera algorithm is what we call it's a phase linking. It's called phase linking. It actually uses both persistent scatterer PSI InSAR and distributed scatterer in SAR, so it's kind of a combination of PSI and s PASS and the two measurements are combined into each thirty meter by thirty meter pixel.

1:28:56 Speaker 1: Next question is I received this message time series service earn no upera ice on burst frame IDs were found over the given AOI and the point is given here, what should I do? This is twenty nine longitude, which was.

1:29:20 Speaker 2: Yeah, this is outside of North America. There's not going to be any opera data outside of North America.

1:29:25 Speaker 1: I think it's not in North America. So question eighteen are the velocity maps and average or total for years?

1:29:37 Speaker 2: The calculation that I showed in the notebook is the average over the time and of ALE. So I showed two different calculations. One was the average velocity from twenty nineteen to twenty twenty two, the dry years, and the other was the average velocity from twenty twenty two to twenty twenty four, which was the wet years. So those were the average velocities over those two time intervals. You can't you can do other types of analysis. That's what the full time series is available for other types of estimation.

1:30:21 Speaker 2: I also showed some maps that were the total or cumulative displacement.

1:30:32 Speaker 1: Question nineteen is the velocity map still in line of site direction or does the script download both directions and decompose the vertical element.

1:30:44 Speaker 2: The script the analysis that I showed in the notebook is still in the line of site direction. I only have it only used one track of the opera displacement products, and that's just one line of sight. There's no decomposition.

1:31:06 Speaker 1: Question twenty is what should be the minimum time being able to measure surface displacement for groundwater applications?

1:31:16 Speaker 2: Well, that's going to the minimum time interval is going to depend on how fast the groundwater displacements are happening in a given area. In the Central Valley, the displacements can be up to thirty centimeters per year, so you can get a good measurement in just a few months. Other places, the displacements are much slower, maybe only a centimeter a year, and you might need to have two or three years of data to get a good estimate of what the rates are if the rates are quite slow.

1:32:02 Speaker 1: Question twenty one, why are these why are there positive subsidence points?

1:32:08 Speaker 2: Yes, that's quite a surprising effect there in the twenty twenty two to twenty twenty four time interval, because this was a time interval where the California received a huge amount of rainfall. The groundwater actually was recharged and the surface rebounded due to the very large amount of rainfall during those two years.

1:32:46 Speaker 1: Thank you. Question twenty two. While excluding the ASF portal, in my region, I'm seeing localized pockets of subsidence a few hundred meters per yards across. Is it reasonable to interpret this as subsidence? What sort of factor should be wary of for misinterpreting At fine scales.

1:33:13 Speaker 2: The displacement products are indicating that there's some type of ground motion. It's probably a subsidence, but it could be some other effect like such as landslides moving or in California, we have earthquakes also in Nevada and other places. But most of the measurements should be useful. Uh, it's it's just a measurement of the surface displacement. It doesn't tell you necessarily the cause of that surface displacement. So you do have to do some interpretation as to what would I actually is causing the displacement.

1:34:04 Speaker 2: In other places, there's some displacement where landfills or former landfills that the material keeps compacting with time, and you can see that on the on the opera products in urban areas where there's large landfills that are been they've been covered over with because they stopped using them, but the material keeps compacting with time. Mhm H.

1:34:38 Speaker 1: It's interesting. Question twenty three. Land displacement can be caused by multiple factors such as erosion to be separate them from groundwater subsidence in the dispretas.

1:34:53 Speaker 2: Sold land surface modification by erosion or other. You know, construction that that changes the land surface in a major way will become incoherent in the InSAR measurements and therefore it'll it won't have a valid measurement. So the InSAR measurements in the opera displacement products are only in places where the surface is not being changed significantly over time and major erosion. You can't measure erosion directly with insert.

1:35:38 Speaker 1: Thank you. Question twenty four. Could you provide key references documentations that could guide us to produce opera land displacement or vertical land motion, et cetera output data sets for study area located in Africa, for example.

1:35:57 Speaker 2: Yet the main uh opera algorithm is UH. All the software is online at gitab. The main there is a paper that's coming out shortly on the whole opera algorithm and methodology. It's not yet published, so I can't give you that link yet.

1:36:30 Speaker 1: Okay. Question twenty five is there a way to enter the codinates of my point of interest directly into the opera disc search bar?

1:36:42 Speaker 2: The displacement portal doesn't have that capability at this time.

1:36:48 Speaker 1: In NASA Earth Data, you can do that. You can just click on a point and then modify the let alone exactly the way you like.

1:36:56 Speaker 2: Ah, yes you can. You can. You can search for the opera displacement products, but not in the displacement portal.

1:37:04 Speaker 1: Not in the portal, Yes, thank you. Question twenty six, how can operate this time series be quantitatively integrated with Grace and Chielda's data to improve groundwor the storage estimation considering the different special and physical sensitivities.

1:37:23 Speaker 2: This is important question, but it's really beyond the scope of this training that you have to consider the whole response of the reservoirs in the areas, and the spatial resolution is a major effect that requires some computer modeling or data assimilation techniques beyond the scope of this training. But people have done this work and there's a number of papers on combining INSERT and GRACE and grace follow on data with insert to get a better estimation of what the local variations in the ground order extraction.

1:38:33 Speaker 1: Question twenty seven is, since OPERA disc uses insert radar from satellites, does the radar beam actually penetrate underground and see the water table directly?

1:38:46 Speaker 2: Now, the radar beam does not see the water table. It penetrates into the ground surface at most a few centimeters.

1:39:00 Speaker 1: In twenty eight, when OPERA data is processed, are there different reference points for the North America region or is there a unique point? Can we know the location of such point?

1:39:13 Speaker 2: The Opera displacement products are are not processed to any

1:39:20 Speaker 2: global reference point for all of North America, and that's what the for the future vertical land motion product will you be So each each track is basically each frame has its own local reference system and you need to provide your own reference points and when you do your analysis, there's no absolute reference point for the Opera displacement products.

1:39:57 Speaker 1: Question twenty nine for insult, what are some best practices if studying groundwater related subsidence in a seismically active area, in addition to analyzing both ascending and descending data sets. Do you have further recommendations.

1:40:18 Speaker 2: Yeah, Well, earthquakes are our fault motions. Gradual fault motions are a concern in most cases that the faults are are not in the middle of the groundwater basins, but in other places there are. There's an additional effect where many times the groundwater reservoirs are actually have an edge right at the location of a fall, and you may see that there's a bigger ground water variation on one side of the fault on the other side of the fault. It doesn't mean that the fault itself is moving. It just means that the aquifer layers in the groundwater are of different thickness on the two sides of the fault.

1:41:17 Speaker 2: We've seen. We saw that very clearly in the area of what's called the Santa Clara Valley that include San Jose in California. There's a few papers on that.

1:41:36 Speaker 1: Question. Thirty is InSAR measures displacement relative to a reference point, but no point is truly stable due to tectonic strain. And other factors in a basin with both regional tectonics and groundwater pumping. What the minimum temporal baseline is needed to separate these signals.

1:42:00 Speaker 2: Yes, the the InSAR measurements are are always relative to a reference point. In some cases, you may have a GPS station or or g n SS station that you can use to get the full absolute three dimensional UH displacement time series for that point and use that as your stable reference for the InSAR. That's a that's an effective way to try to separate the two signals. UH different signals, but you would have to do some your own analysis of the tectonic signal to see how it might be affecting the groundwater.

1:42:51 Speaker 2: Typically they're they're on different spatial scales, So just by looking at the spatial resolute, the spatial area a special pattern, you can separate different effects by using what when you have a local reference point, it's basically saying that that local reference point has zero displacement. So if there's some kind of you know, large scale plate motion, that that plate motion has already been subtracted out because you set that you have that local reference point.

1:43:40 Speaker 1: So next question, let's see if it's relevant. What if we want to do groundwater temporal changes as one of the environmental factor to demonstrate a casual relationship coursal relationship to the lake geo hazard, which data set is the best available option place as its own limitations.

1:44:04 Speaker 2: I'm not sure what they mean by the lake gudo hazard, but typically the in Sorrow or other just InSAR is going to give you the higher spatial resolution that you can more easily tie to or local effects. The grace data is only going to give you a regional scale look at what's happening in the given area.

1:44:36 Speaker 1: Question thirty two is if the area of selection is bigger than one frame, does that this Jupiter script music them and fix the phrase jump in between?

1:44:47 Speaker 2: No, this Jupiter script only works with one frame. In fact, you have to make sure that your area of interest fits within frame boundary or it'll it'll stop running.

1:45:08 Speaker 1: Thirty three is pont Opera process Canada even by removing wintertime data? Is there a limitation in processing six months of data every year?

1:45:19 Speaker 2: It was a decision that they made some years ago.

1:45:25 Speaker 2: I don't know the exact reason, but they decided not to process the part of Canada out north of more than two hundred meters two hundred kilometers from the border. That's just what they decided to do, mostly because of the snow cover issues. But so there is a snowcover issue in Alaska, and when you look at the displacement data in Alaska, you'll see that there's large gaps in the time series during the winters when there's snow cover. It is possible to do that kind of processing, but NASA, the NASA funding wasn't sufficient to do that for the large area of northern Canada.

1:46:13 Speaker 1: Thank you. Question thirty four. How can OPERA disc data be used at the local scale to assess groundwater level variations linked to vegetation eveport transpiration, particularly in comparing water use between fast growing and slow growing forestry species.

1:46:33 Speaker 2: So the OPERA displacement products are measured with the whatever temper resolution is available from the Centinel one data and in the future NISER data Sentinel one. As I mentioned earlier,

1:46:52 Speaker 2: some of the time they've had two satellites, in which case they in some places they actually acquired data every six days, especially over the West coast of the United States, but in other places they acquire data only every twelve days. And NISAR will also be twelve days. So the temporal resolution is determined by the input data, so you're not going to see variations at time intervals less than the

1:47:30 Speaker 2: data acquisition.

1:47:34 Speaker 1: Great, we just have time for a couple of more questions and then we'll stop. We're almost at the end of the time. What's the cause of the positive effect artifacting around bodies of water in the ASF.

1:47:53 Speaker 2: Yes, So this is due to the way that they calculate the short wave length filtered version of the placements. To calculate a short wavelength displacement, you take a window it's about twenty five kilometers across, take the average of that window and subtract it from the data, and that means that the center of the subsidence ball gets still has the subsidence, but then the areas next nearby end up having an apparent uplift. That's just due to the way the filter was calculated.

1:48:36 Speaker 1: Next question, when calculating the linear recurrence velocity of pixels, what is the improvement to accuracy of using a polynomial fitting instead, At least over smaller area where computation time is not a major concern.

1:48:52 Speaker 2: You can use higher order polynomial fitting, but then you may be fitting a noise in the data. There is a significant noise in each measurement. This can be reduced if you do the tropospheric corrections that I mentioned earlier.

1:49:14 Speaker 2: So you don't want to overfit and stay fitting noise in the data. That's why we typically use averages over at least a few months.

1:49:31 Speaker 1: Yeah. The last question is it is commonly known that Mexico City is sinking due to the compression of the clay beneath it. In our exercise, this negative lend displacement was surrounded by a ring of positive displacement. Why is this?

1:49:47 Speaker 2: Yeah, this is the same thing that I just described. It's due to the way the filter was applied. When you do the short wave length filter, it shows up as positive around the edges the subsidence in the middle. If you look at the unfiltered displacements from Mexico City, you will see that there is no uplift around the edges. It's only subsidence inside the basin.

1:50:20 Speaker 1: Thank you so much, Eric. We thank Doc right Fielding for his presentation and answering all the questions today and he's sharing his expertise on this subject. So thank you all for attending this webinar series. This is the last part of this series and we hope to see you in our future training, so please stay updated with our SAT listener you'll get information about our upcoming trainings. Also,

1:50:55 Speaker 1: please take a few minutes to complete the survey that you receive so that we get feedback from you. And we want to thank our set team for their help. Our coordinator Natasha Johnson Griffin, our editor Maria Marapito and other coordinators Roc Plevins, Salvin Hadsondoy, Shry Morris, and our editors Jonathan O'Brien and Sarah Corschel. Also want to thank our institutional designer Susan Monthy and my colleagues Sean McCartney and Erica Potters for everybody's help. And once again, Doctor Fielding, we thank you for your help and time with this training and it looks like it generated so many questions and people are very interested in this topic.

1:51:47 Speaker 1: So hopefully through the exercise and through the jupitter notebook you can explore this data and then, as as we told you earlier, the homework is you on fifteenth of May, and we hope to see you in our future training.

1:52:05 Speaker 2: Thank you all, Thank you, and you're welcome