NASA ARSET NISAR Data Access and Tools Session 2-3

0:01 Speaker 1: Hello, and welcome to this webinar series harnessing NISAR, next generation radar observations for Earth applications. I'm Erica Potis, the scientist that NASA's Jet Propulsion Laboratory and also an instructor with the RSET program. Today's session will be an overview of NISAR data access and tools, which will be delivered by invited experts doctor Franz Meyer from the Alaska Satellite Facility and the University of Alaska Fairbanks, by Heidi Christiansen from the Alaska Satellite Facility, and by Anupama Sharma from the National Remote Sensing Center Israel.

0:45 Speaker 1: This is the training outline. Today is the second session of this three part webinar series. The third and last session will be next Thursday at the same time, and that's going to be focused on monitoring earthquakes, volcanoes, and landslides with nicar's InSAR capability. As a reminder, there is one homework associated with this training. It will open next week on the last day of the training on July sixteenth, and the homework is due on August six You will be able to access the homework through the training web page.

1:28 Speaker 1: There will be a Certificate of Completion awarded to all of those participants who attend all of the live sessions and complete the homework assignment by the do date. Next, I'll provide an overview of today's session. So these are the objectives for today's session. By the end of the session, participants will be able to identify where to access NISAR data, whether through the Bunity platform, is Rosebuniti platform, or the Alaska Satellite Facilities Platform. Participants will be able to recognize the data format and tools for reading the data, and apply tools for visualizing and analyzing NYSART data.

2:22 Speaker 2: How to ask questions. To ensure we see your question, please write your question in the Q and A box, which you can locate it by doing a click on the three dots in the bottom right of the window in the platform, and there you'll see a slider a Q and A option, and you can also see a standalone slide o, tap or app. Write your questions or all your questions there and we will answer them during the Q and A session at the end, and we will try to answer all the questions in the Q and A session. The remaining questions will be answered in a Q and A document which we will post on the training website about a week after the training.

3:14 Speaker 1: Today's guest instructors are doctor Franz Meyer, who's a professor of Remote sensing at the University of Alaska, Fairbanks and chief Scientists at the Alaska Satellite Facility. Heidi Christensen, who's a Senior GIS Specialist at the Alaska Satellite Facility, and Anupama Sharma, whose group lead with the Data Dissemination and Workflow Automation group at the National Remote Sensing Center at Israel. So we'll start out with doctor Franz Meyer, and welcome to all three of our guest instructors.

3:51 Speaker 1: It's a real privilege to have them be presenting here today. Welcome doctor Meyer, and the floor is yours.

4:04 Speaker 3: Thank you Erica for the introduction. Yes, so NISA data will be available in various places. One of the main ones is the Alaska Satellite Facility in its discovery services that Heide is going to present to you later on in this session. So ASF is an organization at the University of Alaska, Fairbanks, and our main goal is to make remote sensing data accessible, including those from NISAR, but including also other data sets that behold in the archive. ASF is a remote sensing center specializing in synthetic capture radar data sets, and we operate the NASA Data Center for SAR data, which is hosting data back to nineteen seventy eight and is also hosting managing NAR's L band data sets those that are being processed by JPL on a global scale.

5:01 Speaker 3: In addition to just hosting the data making them discoverable, we also provide a bunch of tools and services and additional resources, training materials and so on so on that help you working with our data sets, understand the content of the data, how to access the data, and how to do various applications with the data sets. All data that you discovered through ASF nicer of course, but also the other data sets we host are freely and openly accessible for you and for download and for doing your research work.

5:36 Speaker 3: So let's talk briefly about nicer data availability and accessibility. So currently nicer data there is a set of pre calibration data sets available through ASF. It's a little bit more than one hundred thousand products that were acquired prior to February twenty twenty six. The average map that you show on C on the slide shows you all of these data sets are located. These are really there to get you used to the data set, to get you used to how you read these data sets, what layers are contained, what types of products will become available.

6:16 Speaker 3: Information about these pre calibration products, including some known issues with this particular set of products, can be found on the following the link that's on this slide. Very very soon, in July of twenty twenty six, we expect the global release of forward process nicer data sets to become available. So this is when really starting from July twenty twenty six, all the observations going forward will be accessible on a global scale through ASF, all the l band nicer data sets. If you have questions about data releases and products that are available through ASF, you can follow this link here that says data user guide available nicer data sets.

7:02 Speaker 3: So this link will get you to a page where latest release information is being tracked. So nice are in contrast to many other traditional SAR missions, is providing a larger suite of data products. So of course we will provide the traditional level on SAR data products that you are used to. If you have used SAR data before this includes rslc's These are radar coded single or complex images. So these are data sets that contain both the full amplitude information of the image information, but also the underlying phase content, which you would need if you wanted to do nice InSAR type applications.

7:44 Speaker 3: The RSLC data are available on a global scale. Additionally, in selected regions, particularly the cryospheric regions, so the large ice caps and the large glaciers. There's also a radar coded in the paragram available. The are IFG product. It's arrange Doppler wrapped in the paragram and also arrange Doppler unwrapped into peragram, so if you're familiar within the parometry, these products might be useful for you to do some displacement analysis in these chryspheric regions. Lastly, there is an offset product available, so these are pairwise pixel offsets calculated between pairs of images.

8:25 Speaker 3: These offsets also useful to track motion in the cryosferic region. So these are level one data sets. Those are more familiar to folks that I've used CR before. But NISA will create additional products and make available additional products so called level two products. These are still radar observables, so amplitude and phase information and programetric information. But they are available now in a geocoded form, so a in a ground coordinate system, and so the products are listed here. On a global scale, we have the g E COUGH product, the geocoded covariance product.

9:03 Speaker 3: This is just a complicated word for saying. These are the normalized radar backscatter layers for each polarization, so you have in for most land areas, you have a horizontally polarized fully calibrated image available there and also a cross polarized polarized fully calibrated amplitude image that you can use for mapping or if you have sequences of them, as we'll show you later, you can use them to look at changes in the environment. The GSLC product is a geocoded single look complex product that's also globally available.

9:39 Speaker 3: Again, this is very useful for folks that want to get into in the parometry. It gets you very rapid and easy access to in the paragram generation. To create an in the paragram, you simply will have to cross multiply two repeated images two repeated GSLC images. Then there's a g unwrapped or gun w products that we often call it. It's a geocoded unwrapped in the pheragram. So this is one that's ready to go for your analysis of geophysical displacements. It captures displacements of the Earth's surface that occurred between two subsequent images, so within a twelve day time period.

10:22 Speaker 3: Associated with that are also coherence layers. They are really useful for coherent change detection, to identify damage or to identify deforestation or any kind of other change feature that you're interested in. And lastly, again over the christ Feric regions, there's another offset product, a geocoded offset product similar to the level one product that I talked about earlier, but this one's really good to give you offset estimates in a UTM or polar stereo projection. The image on the on the right hand side shows you one of these unwrapped in the pherogram products of a co eruptive in the paragram for an earthquake in Ethiopia, showing you the kind of phase quality and displacement quality nicer we'll be able to give you.

11:09 Speaker 3: Lastly, in terms of data products, there's a level three data product that's also being made available. So this is a science product it's called the SEME two or the moisture a soy moisture retrieval product. So this provides you soy moature information at a two hundred meter pixel spacing, derived using three independent algorithms. So there are three algorithms that are listed here that are used to derive soy moistures. So you get three different layers, and these algorithms that differ that they have different dependencies on time series, so some use more time series information, some less, Some use more ancillary data, some less, Some do more regularization, some less, So you get three solutions that you can compare and analyze for your soy moisture applications.

12:01 Speaker 3: There also additional layers included in this data set. You have the two hundred meters aggregated rate of brightness information for all porizations and additional reference information like incidents, angle maps, and statistics information. They must product has an accuracy goal of zero point zero six cubic meters over unflagged areas, you know, allowing for areas that have a vegetation water content below five kilograms per square meter. So there's a lot of calibration and validation that was done for this product.

12:41 Speaker 3: There were pre launch calibrations where alos two images were used to simulate nicer data sets and do the validation, and in post launch there's sparse soy much to networks that are being used for calibration as well as there are supersites that are being used to ensure that the product meets a require diamonds and user expectations. So these are all the data sets that are available through ASF the Alaska Satellite Facility on the NASA side, but there is additional data available through the Indian Space Research Organization ISRAEL.

13:15 Speaker 3: Remember that NISSA is a two frequency mission. It carries an L band radar but also an S band radar, and all NISAR spend data will be distributed through Israel through the discovery portals that ISRAE provides. In addition to that, Israel will also distribute some L band data. These are data collected concurrently with sband acquisitions over areas of interest to Israel, and so the Israel distributed L band data might have slight differences compared to the ones available through the NASA interfaces.

13:53 Speaker 3: They are independently processed and may look have some value differences compared to the ones that you get from NASA the locations where ESPAN data are available or shown in these maps, so you see some areas in the Antarctic, of course, lots of areas in India and around India, but also certain selected areas throughout the rest of the world. To discover the SPAN data hosted by Israel. Israel has a very nice discovery platform that's called Buniti. The link on this page, the one behind Nicer Information page, gets you to the Perniti page, specifically the one that's designed for nicer.

14:37 Speaker 3: It gives you some background information about the mission and if you scroll down there's then information about how to access and discover nicer data sets. You will hear more in this session about israe's capability from a presenter from Israel itself, and with that I am handing it off next to Heidi for the next second of the training.

15:05 Speaker 4: Thanks Ronz. There are a number of tools available that allow you to find and access nice our data. Vertex is ASF's data search portal. It's a map based interface that you open in your web browser and it allows you to explore and access all of ASF's star holdings. This interface is really optimized for looking for star data. There are customized filters that allow you to search for things like polarization, orbit direction, product types, or acquisition modes, and each data set that you can search for in Vertex has a custom suite of filters.

15:45 Speaker 4: Vertex allows you to view all of the available products for emission in the same search results and in the background. What's finding these data sets is actually the ASF search Python package. If you're interested in interacting with the data pro grammatically, you can use ASF search to build a script that will find an access in your ASF's data holdings. So let's take a look at vertexts. When you open Vertex, by default, it will be a geographic search for Sentinel one data. You can search for NISAR data by clicking the drop down menu and selecting NISAR.

16:26 Speaker 4: You can see that there are more than one hundred thousand files available, so it's important to apply search filters. One of the easiest ways to restrict your search is to draw an area of interest. So by default, I can just draw a rectangle, but I can also play as a point, a line, a polygon, draw a circle, or even upload a geospatial file. This brings my search down to one hundred and eighty one files. And I can also apply some additional filters, including a date range, and I can search for a particular product type.

17:05 Speaker 4: If I don't select anything in this menu, it will return all of the different products in my search, but I can restrict it to a single product, or if I have several products that I want to include, I can select all of the desired products. I'll just search for the cheek of products. Now, going back to the area of interest, you can see that the area of interest that I drew is indicated here. But I have this option to import a geospatial file. I can use a shape file, GeoJSON or KMAL if I want to use that to set my area of interest.

17:41 Speaker 4: If I scroll down, you can see that there are a number of additional filters, and as you become more familiar with the data set, you may want to use some of these filters to further restrict your search. If you're unsure of what the filter is used for, you can click the documentation link. So, for example, the scene name patterns. This document link tells you about the ability to use a wildcard query to search for any portion of a nice our file name. So I'll launch my search and I have thirty three results.

18:15 Speaker 4: If I click on an item in my results, you can see that it displays the footprint for that item in red on the map. I can also click on the map to select a footprint and it will highlight a product with that footprint in my results. If I find a product that covers my area of interest particularly well, I can use them more like this button to restrict my search to just items that have that same footprint. Essentially, what this is doing is adding a track and frame filter to my search, and that uses the track and frame from that granule that I selected.

18:55 Speaker 4: So now I have a list of seven files that all have that same footprint. If I click on one of these files, you can see a long list of associated files that I can download. For most people, the HDF five file for that product is what you're interested in, and I can either just click on this download file icon to launch the download directly in my browser, or I can choose to add the file to my download queue. If I want to explore the images a little bit more before selecting which ones to download, I can choose this option to open the image viewer and I can actually zoom into browse images and examine my area of interest at a bit more closely.

19:42 Speaker 4: That zoom extent is kept as I go through the different products, and if I find a product that I like, I can also launch a download directly from this interface, so I can either download it through my browser or add it to my queue. You can see that I now have two items in my download queue. If I decide that I actually want all seven of these and I want to do a time series analysis, I can use this que button at the top of the list and either download all associated files or just select the file types that I want.

20:20 Speaker 4: So I can select all seven g cuve HDF five files and add that to my downloads que. Now you'll see that I have seven items. It recognized that the two that I'd added already were already in the list and did not duplicate them, so I now have seven items ready to download. I could just go through and click each one of these download icons, or I could leverage the option to download a Python script. If I download a Python script, it saves that to my computer and I can simply launch that to download each one of the items automatically.

20:56 Speaker 4: In order, I can also use this option to copy the URLs. So if you have a script that you already use to access data based on their URL, you can copy that list, or if you want to use a direct S three access these products are stored in AWSS three, and so I can copy a list of the S three URLs as well. As I mentioned in the background, ASF search is doing the work to find these products, and there is the option for any search that you do, you can export a snippet, a Python snippet that shows you what was done to fund these data sets.

21:42 Speaker 4: And so if you're interested in getting started with the Python package, this is a great way to start as it shows you the particular syntax that would be needed to find those data sets. So that's Vertex. Let's take a look at Earth Data Search. Earth Data Search is similar to Vertex in that it's a map based interface that you run in your web browser, but rather than just having access to SAR data sets, you can search for any data set in the Earth Data system. Many of you may already be using Earth Data Search to find data, and you can now search for NISAR in that same location.

22:22 Speaker 4: Earth Data Search provides built in support for Harmony data transformation tools, which i'll talk a little bit more about later, and similar to asf search, the Earth Access Python package allows you to find and access data from any of the Earth data any of the Earth data collections. So let's launch Earth Data search. The landing page is simply a search bar. I can type in NISAR and you can see that there are forty nine collections that match my search. Not all of these are actually collected by the NISAR mission itself, so you may want to use this platforms filter to restrict the search to just NISAR.

23:08 Speaker 4: So also like space based platforms, Earth observation satellites and then NISAR, there are still quite a few collections, and so if you're just exploring the data set and you're not exactly sure what you're looking for, the processing levels may be helpful. Now, these descriptions are more specific to optical imagery, so they don't always translate to SAR, but level zero is still raw data for NICSAR. The level one data are any data sets that are still in the range Doppler coordinate system or the radar geometry.

23:44 Speaker 4: Level two data has been geocoded to a map projection, and then level three data are higher level of products generated from those Level two products. The only NICAR Level three data available are soil moisture products, so one A designation is used for any of the ancillary data sets for NYSAR. So this is mostly for users who are doing some advanced processing and want access to data sets such as the orbit e femerist files. So I'll restrict my search to just level two data, which are the geocoded products, and you can see that there are a number of options.

24:22 Speaker 4: I can apply again a spatial restriction, so I'll draw an area of interest on the map, but I can also set a date range to see the actual products. I need to select a collection, so I'll look for the data and if I click on an item in the results, it shows me the foot or again, I can click on a footprint on the map to highlight one of those products in the search results.

25:01 Speaker 4: If I find a product that I'm interested in, I can click on the download icon and this gives me a link to to download the download url. I also have the option to copy the S three access path, or I can add this item to a project. So this is similar to the download Quean vertex. So I'll add a couple of items to my project and then open it up. You can see that this is redirecting me to my Earth Data to use my Earth Data log in credentials and Earth Data log in credentials are required when downloading data from Earth Data, and this same log in applies to any data set across the Earth Observation platform, So you just need one set of credentials to download anything from Earth Data.

26:00 Speaker 4: If you are logging in and you don't already have Earth Data log in credentials, there will be a link where you can register. It's free and it's very fast, and then that gets you access to all of NASA's Earth observation data. The same thing applies in Vertex.

26:19 Speaker 3: Here.

26:19 Speaker 4: I have some options for downloading. I can either just download all of the HDF five files or I can choose to customize the data sets.

26:29 Speaker 3: Now.

26:29 Speaker 4: Harmony is Earth Data's data transformation tool. It allows you to extract or pull out data in the format that you want to use it in your application. So for the nice rg CUV products, the net to cog service is available, and this allows you to extract individual data sets from the HDF five file and output them as cloud optimized geotips. So if I select that option, I can click the edit Variables button and you'll see that there's a data structure inside of the HDF five file. I'll go into this more later, but there are a number of different data sets all packaged together, including from different frequencies.

27:14 Speaker 4: I can select individual products that I'm interested in, and rather than downloading the entire HDF five file, it will extract each one of these layers as a cloud optimized geotip and provide me a download link just for those products. So if I click the download data, instead of having a list of links ready, it shows me a status page where in the background, Harmony is extracting these layers and preparing download links for me to use, so I can come back later and there'll be something that looks like this, which is a list then of all of the different individual layers that have been extracted.

27:59 Speaker 4: The Harmony in Earth Data Search is currently being overhauled, so things may actually look a little bit different later this summer. The functionality will remain the same, but don't be surprised if the menu looks a little bit different. We will provide update guidance for how to use the new interface in our nice Our Data user Guide, which is a resource that I'll talk a little bit more about later on. So that's Earth data search. Now. As Franz mentioned, NASA serves the L band data from the NISER mission and the Indian Space Research Organization serves the S band data.

28:37 Speaker 4: I'm going to pass it over to USHA to introduce you to Israel's data discovery platform.

28:50 Speaker 5: Hello everyone, I'm a Nupamama from National Remote Sensing Center ISRO, and today I'll be talking on Buonidi, which is Arosio Data Hub, and also on the access mechanism of ESSA and ISRO interested ELSA data from Bunidi. My presentation will majorly cover the introduction to Bunidi, thensar information site, the data access mechanisms and tools available at Bumidi for analyzing the NISSA data.

29:28 Speaker 6: Bundi Isrozio data hub and it hosts an extensive virus and non IRIS catalog for the data which we have acquired since nineteen eighty eight, and daily acquisitions which we do at our ground station covering India and surrounding regions are also added to the catalog of Bunidi. We also facilitate the regional distribution of sentinel data and also the data of Lancet eight and nine which we acquire over our ground station. Bunidi provides a single window interface for both open and commercial that data access with a near real time visualization tool and the data dissemination at Bunidi is guided by the Indian Space Policy twenty twenty three and as for that, even the ESSA data and the ELSA data of ISTO interest will be available as an open data access at Bunidi.

30:22 Speaker 6: Like I said, Bunidi has a vast archive of data. To begin with the resource SAT series ranging from five meters to fifty six meters, Censor List three avivs and lispore wherein we also have global coverage. Then the SAR satellite which is RESET one.

30:43 Speaker 5: A and the acquisitions on the web site show the Medium Resolutions Cancer Sensors acquisition wherein it shows the global archives as well. Then, as I said said, we are the regional distributors of SET and one BI that this is how the archive is at Buonidi. Then the Lancet nine acquisitions the Nova SAR data which we have at our archives. Moving on to the OCEANSID series satellite which has the local area and global area coverage and in the global area coverage, the entire globe is covered in two days.

31:26 Speaker 5: Even that data is available at Boondi for access to the same archive. We will be adding the NISAR data as well. This image shows the Jikov product of n Essa, which we took for a smaller data range just to give a feel of how the SA data spread is available at Bonidi. We have also launched the Nissar Information website, wherein we have made every information available on the NASAR specification, the observation plan for SR data, the data products formats, the excess mechanisms, and the science and application and the associated resources.

32:12 Speaker 6: So from the bizarre page itself, since.

32:15 Speaker 5: We have also provided the data access mechanism details at the site, you can move on to the Data Download BII page directly from the information page itself. These are the products which will be available at Bunidi, the starting from the Level zero product which are the ross science products, to Level one which are the products in REDAR coordinates and the level two products in geographic coordinates.

32:44 Speaker 6: Eventually we will be also.

32:46 Speaker 5: Adding the Level three and Level four products which are the Large India Mosaic, Large Area Mosaic and the science products. These products are available in the H five format. This slide shows the type of products of NISAR both for SNLSR and the volumes of those corresponding products, wherein you can see the volume ranges from point five gbn it goes up to fifty.

33:14 Speaker 6: Four GB for the SR data products.

33:19 Speaker 5: Coming onto the NISSAR data access mechanisms, the data will be available as an open data and the two major access mechanisms are the browser order, wherein we have provided a web interface where the data can be searched and then adding on to the card with using an interactive map interface. The data can be then downloaded for programmatic access of NISAR data. The APIs are also available. We have enabled the Spatio Temporal Asset Catalog which makes our catalog interoperable and writing a Python program, the data can be daily taken from the catalog that Bunidi we have the data visualization tool as well, and the near future we plan to release the Vista tool, which will be augmented for NASAR data and based on the augmentation of resources which we do at Boonidi.

34:16 Speaker 5: We will be planning to release the code lab as well as the Guraster platform, which are for online coding as well as for readily available tools at Bunidi for NASAR data processing. So to begin with the direct download access of missile data. This is for the users who would wish to browse and download the data sets through a map based interactive web application. We have provided various search options to target the area of interest. Various advanced filters specific to NASA data ordering are also provided like track frame, Polarization, Radark, configure node.

34:56 Speaker 5: The data sets can be searched using these filters can be then downloaded after adding to the card. So login is mandatory that that means you have to register at Bunidi and once you get the user iety, you can access the data. So if we go to the steps which are to be followed in order to get the direct taxes of the data, after the registration is done and the login credentials are available on login, you have to then get the target area of interest by specifying the aois the period of interest and using various filters, the common as well as the NISSAR specific filters.

35:40 Speaker 5: So the video on the right side is actually depicting a small session of how the data can be downloaded from Boonidi.

35:50 Speaker 6: It shows various.

35:51 Speaker 5: Filters which are available and selection of those filters can be done and then based on the filter selected, the satellite and censored and product combinations are to be taken. We have added all the NASAR satellite product combinations at the filters and once the product is selected, you can also go for advanced filters which are specific.

36:18 Speaker 6: For NISAR data.

36:20 Speaker 5: Like I said the polarization, there are dark config node

36:26 Speaker 5: and once the satellite specific filters are selected, on clickoff submit button, BII fetches all the data products which are available at the archive for those filters. Besides every scene which we fetch, this is the sub sample view of the product. So besides every scene which we fetch, there are some tools using which the data can be either published onto the map, the metadata can be seen and the same metadata can be downloaded as well. The video is showing the same and once the product is added to the cart, the cart option is to be selected and you can see the product which was added to the cart.

37:18 Speaker 5: So suppose if the product is available at our live archive. Over the scene, we make a green window which is an information to the user that the product is available you can directly download. If there is a red window above it, then that means that is a delayed download. We will be bringing it from our cold archive, so this is how the product gets downloaded. Now, if the data is to be programmatically accessed, then we have made available the Boni the api is through our suce API. We have also enabled a specification page at Buonidi which gives every detail about the ap is right from authentication to data search to data download.

38:04 Speaker 5: Because it is available as a special temporal asset catalog, we have even given small programs which can be then seen and a Python program can be then written to directly download the data using the program. The right side shows the Nisar data collections which will be enabled for the Nissa data very soon. In near future, we plan to release first the visualization of Nissar data and then the code lab platform using which which is a Jupiter ub interface using which online coding can be done on the Bunidi platform.

38:48 Speaker 5: Now, in order to analyze these products, we have added few tools at Bonidi. To begin with is the HDF to TIFT conversion tool which converts this NISSA data sets in h BY format into geotif and it also supports the special subsetting which enables the users to extract only a smaller area of interest by providing by providing either a point or a location or a polygon as a shapewile. Then the next tool is the Sarpole tool, which is available as an integrated package with QGIS and it has a comprehensive suite of utilities for SAR data processing and analyzing by providing advanced hybrid polarimetic decaposition techniques, so it facilitates tools for leveraging the rich information content of NISI data sets and enables sarvea's application within an open source GIS environment.

39:47 Speaker 5: Very soon we are planning to release our nisard Reference Observation Plan visualization tool which will actually help the user to know how, when and where the NISAR data is actually planned and to be acquired, and then subsequently when it comes to our archive, we will try to bring the footprints of that data also on the tool. So by using filters such as observation cycle or a time range or a a dark configuration ID or the node, the users can see how the observations are being planned and over that even the track and frame footprints can be put and users will know exactly how or at a given time which observation was planned.

40:37 Speaker 5: We have released a limited set of Misar as band data sets at Bunidi.

40:43 Speaker 4: The information about.

40:45 Speaker 5: The release detail is available at the URL which is put on the slide at the bottom ride. In the first release, we have put RSLCGSLC and g coof products which are not only covering the Indian landmarks, but we have also selected few global locations. This release is for the users to understand the data structures, the product format and the characteristics of Nisar SR observations. This brings me to the end of my presentation, but before that, we have a resource page at boonide wherein we have added very many frequently asked questions in order to access the data.

41:30 Speaker 6: From Boonidi.

41:31 Speaker 5: There are various help videos which you can check and check the procedure through which the data can be accessed. The Nissar satellite can also be tracked through our Boni the uper tool.

41:44 Speaker 6: And in case of any queries or any.

41:46 Speaker 5: Feedback you have on Booni the application, please write to us at Boonidi at NRC dot gov dot ten or you can also connect with us at our forum application, the URL for which is also available in this slide.

42:00 Speaker 3: Thank you.

42:07 Speaker 4: Now that we've introduced you to some of the platforms that are available for finding and accessing the data. Let's talk a little bit about using the data. Some things to be aware of about the Nicer data format is that it's in hdfive format. You've got a bit of a preview of that with the Earth Data Search demonstration, but basically this allows the data producers to package together multiple data sets, along with metadata and ancillary files all together in a single file. Nice or uses a specific flavor of HDF five in that the coordinate system is encoded using CF conventions.

42:43 Speaker 4: This is more common for netCDF files, and so you may need to use netCDF drivers to read the data rather than the current HDF five drivers, otherwise it can't access the geospatial data. Nicer HDF five files are cloud optimized, so you can access them in small chunks, which allows users to stream the data into memory rather than downloading the entire file. So if you want to access just specific parts of the data from the file rather than downloading the whole thing, both ASF Search and Earth Access have workflows for doing that, and I've linked to some documentation that walks you through how you can stream the data rather than downloading it.

43:26 Speaker 4: NISAR has a large spatial footprint that the data is acquired in a very wide swath of about two hundred and forty kilometers, so the footprint of the individual files is quite large, and then with having many data sets packaged together in a single file, it does result in some very large file sizes. So for it varies depending on the type of file and the acquisition mode, but for example, many of the GCOV products over land tend to be on the order of about seven gigabytes each. If you're looking at single complex data sets, those are often close to thirty gigabytes each, so it is important to be aware of the options available for subsetting or just pulling out what you want from the data set, especially if you're limited by download bandwidth.

44:15 Speaker 4: There are a number of different projections used for the level two data sets. These diagrams show the range of projections, so between sixty degrees north and sixty degrees south the data is projected into the UTM zone for the location of the data set, So if your area of interest crosses multiple UTM zones, you may want to reproject some of your data so that it's all the same. And similarly, if you're working in above sixty degrees north, that area uses a north polar stereographic coordinate system, which may be less familiar, and so if you add your data set to a GIS software or take a look at it, it may be oriented differently than what you expect, so you might also consider reprojecting your data if you're working in those areas.

45:09 Speaker 4: Similarly, everything south of the sixty degrees so all of Antarctica is projected into a south polar stereographic system. There are a range of software platforms available that support nicar. Working with nicer data will cover just a handful of those today. For GIS software, RGIS pro has supported the NICRHDF five data format since version three point four, which was released back in November twenty twenty four. The most recent release, which is version three point seven, was just released this past May.

45:45 Speaker 4: Provides additional support specifically for the nicsrg Cuve products, so I'll be doing a demonstration of how to work with those products in the newest version. Users can also leverage QGIS to work with nicer data, but there are some workourounds currently required in order to view the data and QGIS basically you have to treat it like a net CDF file. JPL is developing a GDL driver for the NICs HDF five format, but it's not yet available in the main g distribution, so it's not accessible to the g doll installation that comes with QGIS.

46:22 Speaker 4: So I'll be demonstrating some of the GIS workflows in both pro and QGIS, and then we'll also talk about some of the open source software packages that are available for working with the data programmatically. So ICE three is the software package developed by JPL four specifically for the NISER mission, and then plant ICE three is kind of a wrapper software package for ICE three that makes it easier to use some of the ICE three functions. And then gmtsar is a commonly used SAR processing package which now supports nicer products.

46:57 Speaker 4: And open Sepo is a collection of open source tools developed by Earth Big Data that allows you to work with NICs our data, specifically for subsetting and transforming the data. So France will be going through a programmatic workflow after we've looked at GIS workflows. So for working it with nice our Data and RDIs PRO, we do have some tutorials that are available, so if you're working with earlier versions than three point seven, you can check out our NICRNGIS story map which goes over how to work with Nicer and RGIS versions three point four and newer, and it also links to content for older versions as well, and it covers many of the different products in the Nicer Data Suite.

47:47 Speaker 4: So if you're interested in working with any of those products, or if you're using a slightly older version of RGS Pro, I encourage you to check out that tutorial. And we also have a newly published tutorial that's focused on working with the g cuff products in RGIS Pro three point seven. So let's switch over to RGIS Pro. Since RGIS three point four, we've been able to treat NICs our data as a multidimensional raster. I can simply click the add data button and from the drop down menu select multi dimensional data, navigate to my NICs data file and select it to see the data sets that it contains.

48:34 Speaker 4: Now within the HDF five file, there are a number of different data sets, including two different frequencies. Most of the g cuff products have both frequency A and frequency B, and these are collected at slightly different frequencies and in general the Frequency A is the data set that most people will want to use for analysis because it is a pixel spacing of usually ten or twenty meters depending on the acquisition mode, whereas frequency B is generally add an eighty meter pixel spacing.

49:06 Speaker 4: So I'll add the two frequency B. You can see that there's a layer, a covariance layer for each one of the polarizations that are available, So I'll select those two and add them as multi dimensional rasters. They're each added as their own layer, and they display radiometrically terrain corrected backscatter values in GAMMAOT power starback scatter values are generally very close to zero, especially in the power scale, but if you have a few very bright pixels, it can really skew the dynamic range of your image.

49:43 Speaker 4: And because the nice footprints are so large, the chances of having some of those bright pixels is quite high. So often we'll have to adjust the symbology. So I'll take a look at my copolarized covariance product and change the symbol.

50:02 Speaker 4: So I'll just set a custom minimum and maximum zero and zero point three, and now we can see some features on the surface. So if I zoom in, I can see that surface water tends to be a dark blue, and then we can see agricultural fields and urban areas in varying brightnesses of the starback scatter. So this is the traditional way of working with the data as a multi dimensional data set, but there is now another option in when you're using our js pro three point seven. If I navigate to a g cuff product and this is specific to the g cuff products only, if I navigate to a G cuff product and expand it, I can see that there's an entry for each of the frequencies.

50:52 Speaker 4: So we have that frequency A and frequency B. If I just grab one of those and drag it into the map, I'm going to cancel the pyramids. It takes a while for these large data sets, and it does take a while to render the frequency A data, so it is added as it's a two band raster that has one band for each polarization. If it's a quadpole data set, then it would have four bands included, but it renders it as an RGB image, and by default, the hh is assigned to the red and the HV to the green and blue. So we can change that symbology.

51:35 Speaker 4: I'll switch this so that both the red and the blue show the HH. And again I'll set some custom ranges so that it's more in line with the values that we expect to see. So I'll use zero point three for both of the copol bands and then zero point five for the cross bow band, so we can see a color decomposition where the surface water is very dark, urban areas tend to be quite bright. And then we can see kind of the patchwork of agricultural regions in a mosaic of varying varying colors and intensities.

52:17 Speaker 4: So this is one way to interact with the GCUVE data set by integrating both polarizations into a into a single representation. But we can also work with these data sets one polarization at a time. So if I select one of these layers that I've added with by the new drag and drop GCUFF method, you can see in the menu there's this SAR tab that's available to me. If I select one of these layers that I added as a multi dimensional raster, that's not available. There are some multidimensional tools that I can access, but by selecting it by adding it as a SAR product, we then get access to this sar menu, so there's an option to visualize or just pull out one band of the raster to visualize.

53:08 Speaker 4: So I'll look at the HH and by default this shows a color bar that kind of goes from a rusty red to a yellow. Again, I could adjust the symbology to see the features better. I can also choose to look at the HV instead. Some processing tools are fine with a two band raster or a multiband raster, but for some analysis workflows you may want to have each polarization as available as a standalone raster. And so to do that, we can leverage the existing imagery and SARVE specific tools, either the geoprocessing tools or the raster functions that are available in RJS pro.

53:52 Speaker 4: And so we'll open the raster functions pain and search for extract to pull out just one of the two bands from that two band raster. So under the extract bands, I'll select my two band raster and I'll select the HH polarization.

54:18 Speaker 4: I can create a new layer and this just then shows me the HH from the frequency A and again I could adjust the symbology. But at this point I can select the right click and select the data option to export the raster to a standalone format, so I can output it as a geotiph for example, and then it's available for me to use just on its own.

54:46 Speaker 4: So I'll output this as the frequency AHH geotif. I have the option to clip my output to a specific geometry. I can either select an existing layer, or I could add a shape file and use that extent, or I can just use the current display extent. So I'll just clip out this part here, and under the settings I prefer for SAR data, I prefer nearest now nearest neighbor resampling rather than by linear, and I'll export that raster data set. So now I have a standalone geotif that I can use in other projects as well.

55:34 Speaker 4: And if I zoom out and compare it to the full footprint, I've extracted just a small part of that, and again I can adjust the symbology so that I can better see the features, and then I can use any of the analysis tools or geoprocessing tools or raster functions that I want to apply to that image. So that was urgs PRO. Let's take a look at QGIS. We do have some resources for working with QGIS as well. There's a really nice demonstration video that we'll go through some of the content that I'm I'll be covering in my demo as well, that was recorded after the last Nicear town hall meeting.

56:18 Speaker 4: And then we also have some step by step documentation for working with nice data in QGIS that covers quite a range of the different product types and different things you might want to do with them. So let's launch qgis. I. If I navigate to a nice data set, I've got this gcup data set, you can see that it recognizes the data. It can see that there are many layers included in that HDF live file, and it's very tempting to just want to drag and drop these right into my project. Unfortunately, QGIS does not recognize the geospatial coordinate system and so we can't do that.

57:01 Speaker 4: It just it can't place it on the map. So in order to work with the data we have to treat it like a net CDF file. One way to do that is simply rename the file so that instead of the dot h five extension you have dot NZ. But another way is to simply define the net CDF driver when adding the layer. So if you go to the layer menu, add layer, add a raster layer. I'll navigate to my nicer file and then I'll go to the very front of the path and just type in net CDF and a colon. And now if I click the AD button, it shows me all of those data sets that are possible to add.

57:45 Speaker 4: So again we've got the frequency A and the frequency B with one layer for each polarization. I'm going to select the frequency B just so that things render a little bit more quickly, and I'll add those layers to my project. So now there is a layer for each one of those polarizations. You can see that I've got HH and HV. Again, the symbology needs adjustment, so we can open the symbology pain and again we can just set our minimum and maximum to a custom number. Now there's a glitch in QGIS that sometimes doesn't allow you to type in decimals, but you can just copy and paste from a text pile and then it works.

58:39 Speaker 4: So that's a quick workaround if you wanted to use decimals for your maximum. All right, so we have this image of the starback scatter and we can work with it using whatever tools are available for dealing with five data sets. Or we may want to export this to be a standalone raster, and that can be done by opening the processing toolblocks and I'll search for clip and we can clip the raster by extent. So I'll select my HH layer and then again there are some options. You can calculate the extent from an existing layer, you can calculate it from a bookmark.

59:33 Speaker 4: You can even just draw an area of interest on the map, or we can just use the current map extent. So I'll choose that option. By default, it will just save it to a temporary file. But if you want it to actually have a standalone geotip that you could use in the future as well or share with somebody else, you can actually save it to a file. Then I'll tag it with a subset. All right, So now I've added my subset to the map. I'm going to change the color so that we can tell the difference between the original and the subset.

1:00:22 Speaker 4: And again i will set my max to zero point three. So now you can see I've got this nice subset image of the raster. So zoom in and see that this is the all right, And so that's a way to subset the nicer data. If you wanted to work with a smaller area of interest,

1:01:01 Speaker 4: I'll pass it over to France to talk about programmatic workflows.

1:01:08 Speaker 3: Thank you Heidi for handing it off one more time. Yes, so I want to show you a little bit more involved workflows. I'm going to give you a sneak peek to just show you how this would work if you want to dive in deeper into the data set, if you have graduate students that you want to spend more effort into diving deep into the processing algorithms. So first, if you wanted to work with data sets and develop algorithms around the data set, NASA is committed to make this easy for you, no matter what your access patterns are.

1:01:41 Speaker 3: If you are a person who's interested in downloading data sets to your local machine and work with the data sets locally, we are set up to support you fully. We have very high bandwidth data download capabilities. We can distribute up to ten petabytes per month of data sets through the ASF DOC interfaces, and we have very thick pipes to get large amounts of data to you quickly. So I fear not if you like downloading data sets locally. This is perfectly fine, and we have the systems in place to support this kind of access pattern.

1:02:18 Speaker 3: If, however, you're interested, or you are somebody that is interested in working in the cloud or moving basically your algorithm from your local machine to the archive. There's a variety of ways of how you can get started with cloud workflows. One of them is the ASF open Sarlab platform. There's a link here to the open sarlab platform. You can sign up for that platform and try to do your work. But you have also a team here at open sar Lab that can give you suggestions for how to start with your cloud based workflows.

1:02:52 Speaker 3: There's also NASA has a community that's called NASA openscapes. This community is particularly designed to help people with getting started in the cloud and moving their workflows into the cloud. So if you follow the link to the open scapes platform, there's a lot of information and material available for you to get started with your lifting your workflows into the cloud. And so whether you work locally on your local computer machine or whether you work in the cloud, there is a number of workflows available that you can use.

1:03:29 Speaker 3: It can help you get used to accessing nice our data sets and getting an idea how to use Python and Jupiter notebooks to interact and do scientific analysis with nicer data. You can follow this QR code or snap this QR code. Also follow the link that's on the slide here to navigate to a page that has a lot of information. So what I'm just briefly going to do with you is, I'm going to go to that page to show you where you can find nicer cookbooks and tutorials. We're wan to talk a little bit about who the target audience for these workflows is, and we already talked about where to run these.

1:04:09 Speaker 3: You can run them locally on your Linux machine, or you can use a platform like open star Lab. I'm going to use open sarlab to demonstrate one of those workflows for you at the very end. Briefly, so let's dive in. I'm gonna click this link for you. If you want to follow along, you can snap that QR code. If you have the slides already, you can also click that link and follow along with me. So you're landing again at the Nicer Data use a user guide that was referenced a number of times already, super useful place for you to find all things nice are and so here you see a section that's on nicer tutorials.

1:04:45 Speaker 3: There's a set of tutorials available, there's story maps, tutorials that you've already heard about, and then they are Python resources. So these are more Python based involved science workflows and cookbooks that help you get started with these workflows. So we'll look here quickly at the nicer cookbooks as it shows here that were developed for accessing and working with nicer data. So if you click that, you see a little bit more information. You see a link to the GitHub repository. Always important to say that all of our workflows are fully open source and openly available.

1:05:23 Speaker 3: So everything you find that ASF is exposed on public gitab and you can clone it to your local machine and work with these workflows yourself. For now, we'll go to the cookbook website. And so this website is really useful because it tells you a lot about what these cookbooks are about. So they are workflows that help you get started with accessing GKOV and gun W and SLC data sets. So how do you read these data sets? How do you subset the data sets? How do you stream files? And how do you do workflows using Python scripting.

1:06:03 Speaker 3: It's important that we are accepting community contributions, so we are envisioning this as a growing sort of collection of cookbooks that include contributions from all of you over the years. The motivation is that you know, nice Art provides an incredibly comprehensive data set, and we want to provide hands on examples that help you understand how to work with this growing archive over time. So we'll talk a little bit real quick on how to get how to run these cookbooks. These cookbooks are done as jupid and notebooks.

1:06:47 Speaker 3: You can run them on your local machine, or you can run them on platforms like open sarlab. That's what I will do to demonstrate those cookbooks to you. To get started, you would install the software by basically cloning the code from the guitub repository using commands that are listed here. So if you've never done interactive with gittub before, there's information in this in this document that helps you understand how to how to get started. And so in our case, I have already cloned h these cookbooks on my platform, my open sar lab platform, So I'm gonna switch over to open star lab, which is a Jupiter Hub in the cloud that is basically running in a web browser.

1:07:35 Speaker 3: It's it's you know, very easy to access and doesn't need any local install And in open sar lab, I have already cloned the Nicer Cookbooks. So let me show that to you real quick on my screen, just a second clicking away these boxes. So in my folder cloned a Nicer cookbooks. So I have a folder here that's called Nicer Cookbook. If I go there, you have a bunch of files in this in this folder, and I have a little symbol here that's called JB. It's a so called Jupiter book, a structure here that basically lets me look at all of the individual notebooks contained in this folder like they are chapters in a book.

1:08:26 Speaker 3: So here that if you go through this cookbook in the individual chapters will tell you a bunch of things about what these cookbooks are about. So this is a page that you've already seen. It tells you how to install the software. It tells you how to get an Earth Data login to be able to access Nicer data, and tells you how to actually access data sets. And then further down here in this section, g coov are a bunch of notebooks that let you play around with data sets where you can test some first examples of workflows.

1:08:58 Speaker 3: And so I'm going to look at one of the Amplitude backscattered tutorials, and so we just will load real quick a time series of nicer images into a notebook and do a simple time series animation of these data sets. So this here, the thing that you're seeing on the screen now is what's called a Jupiter notebook. A Jupiter notebook is basically a data recipe that's composed of individual cells that you have to look through in sequence. So there are two kinds of contents here. One of them is text based content in so called markdown cells.

1:09:39 Speaker 3: So this is instructions and graphics that you can embed here and then interspersed with the text content. Our individual cells shown here in gray. These are you know, Python based

1:09:55 Speaker 3: code cells that you can execute to run the code behind that's embedded in this particular notebook. To run these code cells, there's a little sideways triangle here which would run this particular cell. So when I click that, it executes the particular hot cell executes the code and thus the work that this particular cell is designed to do. So I'm going to do something real quick. I'm going to quickly run all these cells oops, and then show you what is happening by going through them real quick. So what this notebook does It fetches a small series of nicer g CoV products, so g COF again the calibrated amplitude products that have layers for the horizontal polarization in the cross polarized data sets.

1:10:47 Speaker 3: And we are fetching a bunch of data here that are constrained by a start in an end time. So I'm looking for data between November twenty second of twenty twenty five and January sixteenth of twenty twenty six, actually twenty twenty eight in this case, but the latest scenes is in twenty twenty six. Then I create an AOI sort of a geographic bounding box. So I'm looking here at an area that in this case is southern Nepal in the in the Himalaya mountains. And I'm searching then the ASF archive for areas that fit, you know, this area of interest and fit the start and end time time that I defined.

1:11:32 Speaker 3: And I'm looking only for these g COOV products now. And so in this case I found five repeated images. You see the URLs to these images. You could click those to download the files to the local machine if you wanted to. So we have five repeated data sets. The next cell is making sure that sometimes currently in the archive we may have certain products more than once processed with different processor version. You very likely will be interested in the one process with the most recent processor version, So this next code grabs the newest one.

1:12:08 Speaker 3: If there are identical products, grabs the one that was created with the most recent version of the processor. In Now, in case there are still five products available, so there were no duplicates in the archive for this particular area of interest, then we can start loading these data sets. In this case, we show you how you can stream data without actually having to download them, so nicer data is stored in a way that you can stream them from the archive like you would do in an IGIs platform, where you actually don't download the data sets, but you stream the data set through the Internet, and so we can do this using http S approaches.

1:12:49 Speaker 3: In order to do this, you need to log in, and you need to do this by retrieving what's called an earth data log in bearitoken. It is explained here how this works essentially, you go to your Earth Data login page. If you don't know how to get there, you can just google NASA Earth Data log In and then click on that first link that you find on the web. If you're not locked in, that means to log out real quick. To simulate that, you would get to a login page. In my case, my credentials are already here, so would log in.

1:13:26 Speaker 3: It gets me to my homepage and then here you see a variety of menu items and one of them is called create a token. If I click that, it will create for me at so comparatoken which allows me to access and stream data sets directly from the cloud. And so it has the token here you can either show it. Easiest thing is to just click on this symbol next to it and copy that co token into memory. And then in this packet the notebook, you then copy paste that beveraitoken into your notebook. Now you're ready to access and stream your data sets.

1:14:04 Speaker 3: So now we can access these data so we briefly fetch them and lazy load them, so you put them in memory so that we can work with the data. And then we extract real quick the date information that's associated with these files and can show them here. So we have five different dates. They are between November twenty third of twenty twenty five and January tenth of twenty twenty six. These are on the dates to this year, month, day and then the hour, minute and second time of the observation time. Then we can extract a layer from this data set.

1:14:48 Speaker 3: So we extracting only the horizontal polarized data set and we get some information about it. So we have extracted a small subset of the data set has about three thousand by three thousand or four thousand, four thousand samples and totally has a file of five size of a four hundred bligabytes. In this particular case, you can then visualize where this dated this subset that we created is located. So this just spins up a little map and and so the red boxier is where where the data set is located.

1:15:29 Speaker 3: So were in the southern Nepal region in the Hindukushimalaya, in sort of the foothills in front of the Himalayas and just south of the Himalayas. So now we are ready to potentially work with this data set. What you can do now once you extracted the subset you want. You can load only this subset to your local machine. This is useful because once it's on your local machine, you can access them again. So if you come back to this notebook later, you don't have to reload it. You can just start at this particular cell and load the data from your local storage.

1:16:04 Speaker 3: And because you already subseted it, it's a much smaller data volume and much easier to handle. So here we you know, created a folder and now we are saving the data set to this folder. And once the saving is done, we can close out the file and just work now on our local machine with the subset we've defined. So instead of downloading a huge frame and huge amount of data, what we did now is we access the data directly in place. We found the subset that we're interested in, and then only really saved that subset to your local machine, saving you lots of download time and lots of local memory.

1:16:44 Speaker 3: You see that this takes just a hot second for the data data set to save, and once it's saved, you could walk away from this notebook and come back, you know a few days later, and then start directly with section five, load that data set back into the notebook for your further research analysis. Okay, so now if you want to and you come back to this to this section of the notebook later, you can now reload this data set from your local machine, go super fast, and now you can start working with the data set.

1:17:23 Speaker 3: In this particular notebook, there's some instructional content in there that talks about the different ways how data sets are presented to you. These SAR images often are presented in what's called the decibel scale. The reason why that's done is because our data have a very large dynamic range from the very darkest pixel to the very brightest pixel. They are usually orders of magnitude of power difference, and so that's sometimes difficult to visualize in an image in like two hundred and fifty six Grave values.

1:17:58 Speaker 3: So what we often do is we do transformation to the decibel scale, which performs a logarithmic transformation which sort of squeezes the dynamic range down and makes it easier to visualize in sort of a gray scale image. This is super useful for visualization, it's not a good way of doing actual math with the data set. The decibel scaling is a nonlinear transformation and it actually causes a lot of biases in the mathematics. So this part of the notebook shows you how you can transform the data into the B scale.

1:18:34 Speaker 3: It shows you a little bit what kinds of values you would expect. So there's a histogram that is being plotted here that shows you, for a typical SAR image what kind of TB values you would expect. But there's somewhere between zero or maybe a value of one down to values of minus thirty TB. So this is the range that you would typically expect. But then it also shows you how you can go back to the power scale. If you want to do any kind of mathematics averaging or so off a data set, you should always do that in power scale and not in Dicipel scale.

1:19:08 Speaker 3: And so then it again shows you what you know the statistical distribution is in the power scale and the values that you would expect in power scaled. And so lastly this notebook just shows you how you can then take In this case, we have five repeated images to do a small time series animation. So this creates a little time series animator that you can use to start expecting your data set, and you can imagine after one year of nice er you have a you know, you get a nice and deep data stack that you can start analyzing over time.

1:19:42 Speaker 3: So you see here how a small image is being pulled up. I'm going to zoom on a little bit. So this is pirate in Nepal, and there is a little play button that you can click to sort of start looking at the variations in this data set. And one of the first things you notice that there is a lot of the ability in the data. SAR is a super repeatable data set. So if you take repeated observations from the same place in the orbit, the images should look identical unless the physical properties of the ground have changed.

1:20:15 Speaker 3: So if you have changes in soy moisture, changes in vegetation cover, or if there was a physical change like you know, harvesting a field or an earthquake or any kind of change that happened in the surface. So it's a great tool for change detection. So mostly there's stability if you look very carefully, the brightness goes down a little bit over time. But we can quantify that a bit more because we can't calculate mean values, and so here we do calculate a mean where we average across the AOI for each timestep, and we calculate sort of a mean value as a function of time.

1:20:54 Speaker 3: We can plot that here, and so what you see is that the average radio brightness is decreasing over time in this sequence. And we did the calculations of the mean in the power scale, and in this particular notebook, the calculated means then converted also to the B scale, so you see them in both in both sort of projections in the B scale and in the power scale annotated on this plot. But you see the slow degradation of brightness over time here in the power This has mostly to do with the drying out of the surface.

1:21:29 Speaker 3: The soil moisture is decreasing during this phase, which is the dry season in Nepal after the monsoon, and that typically corresponds to a slow lowering of the radio brightness. And it ends with a little two panel figure that is being created where you can see side by side the mean value development and the image itself. So this is going to just take a short second to pull up. Here we go, so you see on one side the image itself on on the on the other side, you see the mean value over time, and you can start, you know, linking the overall lowering of the brightness in the scene with sort of a change in the average radiar brightness value for that particular scene as well.

1:22:21 Speaker 3: And again that this can be really useful to just get used to a data set, you know, inspected, understand what is the dynamic of the data set, what what kind of signals are in there, and start to think about what may cause the changes that you see in a data set. So this is one of many data recipes that are already made available by the team. And again going back to the earlier slide, we are very interested in having you contribute to this set of tutorials and expand about on, you know, beyond this set of tutorials and give us feedback to some of the workflows that are currently available.

1:23:03 Speaker 3: And with that I handed back over to honey.

1:23:11 Speaker 4: ASF is developing a number of tools and services to help users work with nice our Data, and the first resource I'd like to mention is the nice our Data User Guide. This is intended to be an information clearing house where you can find what you need to know to work with the data. There are guidance and tutorials in the website but it also leaks out to a lot of existing content from JPL and other trusted sources. It's a living document, so we're frequently updating it with additional content. There are lots of things we'd like to add in as time allows, and we also keep it updated so that as new information, especially about which data is available, is known, we can update those pages with the new information.

1:23:55 Speaker 4: So let's take a quick tour. There is a REEF introduction to the nice Our mission, including the instrumentation, and then an important page is the available nice Our Data. This is where we talk about what is currently available, but also present the data release timeline so that you can know what's coming in the future. So we currently have pre calibration products available, as France mentioned at the beginning, but we are expecting calibrated data to arrive in the archives starting in July. If you are working with the pre calibration sample products, it's important to know that there are some known issues with these data sets, and so you may want to reference this page that goes through some of those issues.

1:24:43 Speaker 4: We expect that most of these will be fixed with the calibrated data release, but for the data that's currently available, this provides important information. We also link to nicer observation plan resources, including some interactive apps that are really handy if you want to see what's available in your area of interest, either for the pre calibration data set or if you're looking ahead to see what will be collected over your area of interest. The data products section introduces each one of the products and links to its product specification file, and it also goes over some of the naming conventions and the HDF five file format and more detail if you're interested in learning more.

1:25:27 Speaker 4: The Accessing nice our Data section goes over what we've demoed in today's presentation using Vertex Earth Data Search, the ASF search Python package, the Earth Access Python package, and then, if you want to interact with the data directly in s three, how you can access those data products. The using NISAR data section has guidance on rgis and qjis, and there are also a lot of tutorials available that either step by step tutorials that walk you through interactively, and we also have Python cookbooks that are linked here.

1:26:04 Speaker 4: I'll skip down to the contact information. If you do have questions, you can contact ASF, but if you have questions about nice our Data. In particular, we encourage you to post to the Earth Data Forum. This allows you access to a wide range of experts and specialists, so rather than just relying on ASF to answer your question, we can open it up to people at JPL and the nice our Science team, so it gives it broadcasts your question more widely, and it also allows us to have that information available for other people who may have the same question, so they can see the answer already.

1:26:37 Speaker 4: ASF is also developing Harmony services to work with nice our data sets. You saw a bit of the first effort in the Earth Data Search demo, but Harmony is Earth Data's data transformation tool and we have that option now to extract data sets from the GCUV products as cloud optimized youtifs. And the next thing on the docket is to use spatial subsetting so that you can clip that extracted data set to an area of interest. Next after that we'll be looking into clipping me in the full HDF file to find extent.

1:27:14 Speaker 4: So if you're interested in seeing what we're working on now and what's coming up, we do have our tools and services roadmap, and so you can check that out and see what we're working on and what's coming soon. Another great tool is NASA's Worldview, which is a visualization platform. There are hundreds of layers in there from different from different sensors and data sets across NASA's ecosystem, and we are really excited that we'll have daily NISAR GCUP mosaics available in Worldview. These will be available once calibrated data is released, so we expect going forward from July we'll have these daily mosaics available.

1:27:56 Speaker 4: But I'll give you a bit of a sneak peek the development environment. So here we've loaded thirteen days of data and you can see that we use an rgbdcomposition. You can click through the dates to see where acquisitions occurred and kind of what they look like, and you can also zoom in to these different products and there's actually quite a lot of detail available. The mosaics are posted to about a fifteen meter pixel spacing, and most of the g CUB products over land are either ten or twenty meter, so this is pretty close to the source data.

1:28:41 Speaker 4: So it allows you to take a look at your area of interest, see if there was an acquisition at the time that you're interested in, and if it captured the event that you want to know about. It also has the option to compare layers, either different layers, different data sets, or different dates from the same data set. I'll set the time slider to December twenty eighth and December twenty ninth,

1:29:12 Speaker 4: and you can see that this area has overlapping acquisitions, so I can swipe back and forth to compare the two acquisitions. So this is a really powerful visualization tool. It's a great way to see if there's data available for your area of interest that captured an event that you care about. And with that, I'll turn it over to France to talk about the Level three Signs algorithm notebooks.

1:29:52 Speaker 3: Thank you Heidi for handing it over to me one last time, and like in a demo earlier, I'm going to show you some additional notebooks. So now you know what the Jupiter notebooks are. And the Nicer Science team has actually developed a number of very nice notebooks that show you how to create Level three science products from NISAR. Nicer data is being useful, is useful for a variety of different disciplines, and notebooks are available for a variety of applications. In the Chris Feric sciences you see in the screenshot, in the ecosystem sciences and in the solid of sciences, there's also some data available for the soy moture products.

1:30:34 Speaker 3: So in each of these folders, if you follow this link to the get lab repository, you find a set of notebooks that you can, similarly to earlier, clone to your local machine and run. If you are interested in want to dive deeper into the science algorithms behind NISAR, you can run those similarly to what we've shown you before in the demo earlier. So if you're free to play with these following this link and by cloning the repository and playing with these notebooks. An additional service that might be of interest to you is the observational products for end users from Remote Sensing Analysis AL so called OPERA project has been is going to develop level three science products from nicer data sets.

1:31:25 Speaker 3: Specifically, it will use nicer data sets to create products such as surface water extent such as surface displacements. So this is going to be a North America wide nicer based surface displacement product as well as a vertical land motion product that will also integrate nicer data sets. So vertical land motion and surface displacements are available on a North America scale, while surface water extent will be available on a global scale derived from are data sets these there's a link to the Opera website on this web page.

1:32:05 Speaker 3: The displacement data sets will be hosted by ASF. The surface what A extent data set will be hosted by a different of the NASA dacks. But all these Level three data sets are freely and openly available to end users for a variety of applications, and so stay tuned for those data sets. Those will be created and come out soon.

1:32:30 Speaker 3: So here's a little rundown of what the different products are that Opera is creating overall. So you have the surface water extent on the left, there's a surface disturbance product basically a change detection product, and the second column then you have the displacement and the vertical land motion product. And out of all of those, it is these that are left that will be derived also from NYSA. So again surface water extent, displacement, vertical motion as well as an intermediary product which is also called co registered SLC product will also be made available through Opera to a global community.

1:33:10 Speaker 3: So against stay tuned for these data sets. There is Lady to come later in twenty twenty six for your analysis and your you know, download and research pleasure.

1:33:25 Speaker 3: So with that, I want to just wrap it up by summarizing a bunch of resources on this last website. On this last PowerPoint slide, you can see all of these links that are available here, anything from data discovery to a variety of tools that are available that are available to all of the you know, further research tools that we talked about throughout this session. So this is a one page, one stop shop for all the links that you found throughout this tutorial. Thank you all for your attention and I'm looking forward to all of your questions.

1:34:07 Speaker 1: Thank you very much to Dr Meyer and to Heidi for those great demonstrations and presentations. Next, I want to summarize the most important points discussed today and then we will start our question and answer session. So in summary, nice our data are freely and openly available. L Band data can be accessed through the Alaska Satellite Facility, while spand data along with coincident L band observations are available through Israel's Bounity platform. L band provides near global coverage every twelve days.

1:34:50 Speaker 1: With an exact repeat of every twelve days, whereas S band observations are acquired primarily over India and selected science and calibration validation sites outside India. NISAR data distributed in HDF five formats. Level two products are geocoded, allowing them to be readily overlaid with other geospatial data sets. Open source Jupiter notebooks implementing algorithms for the mission's different science disciplines are publicly available. The only global Level three product generated by NYSAR is soil moisture with a spatial resolution of twelve of two hundred meters.

1:35:36 Speaker 1: The GCOV product is a level two product and it's the most suited for those that are not so familiar with radar and that are working on ecosystems looking at things like land cover, biomass, agricultural growth, floods. So these are radiometrically terrain corrected backscatter images in gammon knots. The GUNW product is the most suited for those that don't have much experience with InSAR. Again, that's a geocoded product that contains both the wrapped and the unwrapped interferogram, which is generated every with pairs of images from every twelve days, and finally, NICSAR data can be downloaded for local analysis or accessed and processed in the cloud, depending on your workflow.

1:36:39 Speaker 1: Looking ahead to next week's session, Session three, which is focused on monitoring earthquakes, volcanoes, and landslides with nicsar's InSAR capability, participants will be able to identify the NICAR inser data products and their characteristics, recognize the uses of the nice are insert data products and their uses and limitations, and apply INSART data to assess ground surface displacement due to earthquakes and landslides. Homework and certificates. There is one homework assignment associated with this training.

1:37:19 Speaker 1: It opens on July sixteenth and the due date is August sixth. You can access the homework through the training web page. A certificate of completion will be awarded to those participants that attend all three live webinars and complete the homework assignment by the deadline. You'll receive a certificate via email approximately two months after completion of the course.

1:37:45 Speaker 1: And of course, for those of you that have any questions about the material that was presented today, please feel free to contact doctor Franz Meyer or Heidi Christensen through their emails listed here. So we reached the end of today's session and we will now be starting the question and answer session. Great, thank you so much to our guest speakers today, wonderful, wonderful presentations from doctor Meyer, Heidi Christensen and Appa Mama Sharma. And of course thank you to all participants for all of your questions and then so much enthusiasm for this amazing mission.

1:38:38 Speaker 1: So what we've been doing has been gathering your questions into a document that you see now on your screen, and we will start answering your questions. Our time is a little limited, so we will try to answer as many questions as possible. However, the questions that we do not have time to answer, we will answer on this Google doc which we will post on the web page on the training web page. So let's get started then with the first question here, which is question number one. Does NASA or ASF plan to provide cloud ready nice our products or APIs that facilitate large scale machine learning workflows or platforms such as Google Earth Engine, Open Data Cube or cloud computing environments.

1:39:32 Speaker 1: So go ahead, doctor Meyer or Heidi Yeah.

1:39:37 Speaker 3: So, as it says here, So the data products available through asf A. They are stored on the cloud, specifically on the Amazon Web Services cloud, and they are stored in the cloud. Optimize the HDA five format. Actually, the Nicer Project led by JPL has worked extensively on making sure that the HDA five data format is cloud ready so that folks that want to access data sets in place in stream data directly from the bucket can do so. The I it is, if you are a user of Google at Engine, I would inquire with the GE team directly to see what their plans are for adding nicer data sets.

1:40:23 Speaker 3: I am quite sure that they're interested in also making nicer data sets available through G but I can speak for Google in this case, but please direct your questions to the team and and but I would expect that there will be some of availability also through that platform, especially if the community requests it. There are some limited cloud computing environments available. Open Science Lab is one of those. There's also resources around NASA that have made, you know, offer cloud computing environments and and NASA has a group what is it called how do you Help Me?

1:41:12 Speaker 3: That that is sort of designed to make to teach you how to access data that's in the cloud. So I think I showed those on one of the slides, so you can go back to that slide to find it. And there's the Nicer Data User Guide is always a good place to go back to. This is, as highly said, a living document that will list resources as they become available, So just navigate back to that page over time for sort of a one stop shop with links and information available.

1:41:47 Speaker 1: Great, thank you very much, Franz. And just to add to that, the component of the question related to Google earth Engine. Right now, what you can do is upload your data onto Google earth Engine and work with that platform that way. Okay, the second question number two, is there an exact data available for nice r S r SLC.

1:42:16 Speaker 3: Yeah, yes, so was quite sure what the question was referring to here. But the r slcs are unofficial product that nicear is releasing, so once the global data release will occur, it will include also the r SLC products for every acquisition that that is being made, so it's it's one of the official products supported by ISA and will be available with the full data release. And then if you are referring to the data annotation, so every product will have its acquisition date and time annotated both in the fine name very long fine name and in the middle day to information.

1:43:04 Speaker 1: Great, thank you. Let's move on to question number three. Then, will we get the whole raw data set or preprocessed ones that we can clip to save RAM and storage for low end laptops.

1:43:16 Speaker 4: Yes, go ahead, So there are quite a range of products available you can so. There are not currently the level zero B products. They weren't included in the February release, but they will be included with the July release or the forearm processing.

1:43:37 Speaker 3: But you have the choice.

1:43:38 Speaker 4: If you want the raw data, you can access that or you can choose one of the higher level products whatever best suits your workflow.

1:43:49 Speaker 1: Great, thank you, Heidi. Question number four, are there recommended preprocessing pipelines for combining nice rstar products with optical data sets such as Sentinel two or on set before applying machine learning models for glacier monitoring?

1:44:07 Speaker 3: Ah, I'm not sure if you're at the question to the end, but yes, certain the nice thing is nice that provides fully radiometrically terrain corrected data products. So if you're interested in looking at appetitude change, the data sets are pretty ready to use alongside landset they're fully geocoded, but you may want to make sure that they are sampled the same way, So I would you may have to resample one of them to match the sampling of the other. And there is a valuid pixel mask and a calibration mask available for each nicer product that tell you which pixels are to be trusted and which ones you may want to mask out.

1:44:47 Speaker 3: But then if you're looking for like just looking at malprocesses on glaciers, you should be ready to use them jointly.

1:44:59 Speaker 1: Okay, Russian number five. Then I'm working in the archaeological field, and I'm studying GIS and its usages to apply them in archaeology. How can nyser data and its tools benefit this field in the sense of discovering and identifying loss structures that sometimes can be hard to identify because they're underground.

1:45:21 Speaker 3: Yeah, elband is a known source for doing sort of satellite archaeology. El BAND does have some ability to penetrate into vegetations and also into surfaces. How far and deep you can penetrate will depend, though strongly on the surface itself, what kind of soils or rock structures you have and what the soil moisture is high penetration you get in places like sandy deserts. The Sahara is a really good place, and there have been some past studies using elband to do archeology in the Sahara. And there's also been some work on looking like the large ice sheets and especially in places where you have deeper snow layers where people have looked for you know, down the aircraft or you know installations that over time were buried in snow.

1:46:10 Speaker 3: So there's some some literature that you can look through in some opportunities that alban Data will offer.

1:46:19 Speaker 4: Great.

1:46:20 Speaker 1: Question number six is the geak of product radiometrically corrected for topography and if so, which algorithm and d M is used for this correction.

1:46:30 Speaker 4: Yes, the gkv covariance products are RTC products and I've added a link to the product specification document for details. It uses essentially perdict is thirty meters dem with some modifications for the.

1:46:47 Speaker 1: RTC super Thank you, Heidi. Next question number seven, can we have more information on one forward processed data will be available? Is it in one week? Two weeks July thirty first?

1:47:04 Speaker 3: Yeah, so there is no firm a noun state as far as far as I know in Erica you can chime in here too, But we know that data will be flowing soon, and once data are becoming available, we will make sure that the community will know that data are rolling. I don't know if Erica, if you have any.

1:47:25 Speaker 1: Additional improt No, no additional information other that I think it will more likely be towards the second half of July, sometime in the second half of July. Okay, the next question number eight for nice arlband dual polarization products, how does the mission account for i anospheric Faraday rotation? Will do standard products include any correction quality flag metadata such as estimated Faraday rotation angle? And what best practices would you recommend for users analyzing two polarizations that may be affected by FR induced channel mixing.

1:48:10 Speaker 3: Yeah, there's some iOS information annotated in the metadata. Maybe Eric can help out with that too. I'm not sure if it's fair dirotation or TC in this particular case, but as most data are quite in dual pool, there isn't an easy way of correcting for fair deritation. There are some cordpot data available, but those currently are also not operationally corrected, partly because the acquisition happens at six am six pm, and the fair dirotation at these times is going to be comparably low, So no current operational correction, most likely low fare dirritation in most places around the globe because of the acquisition time and geometry.

1:48:58 Speaker 1: Great, and we do have doctor Eric Fielding online in case, Eric, if you like to shine in. Okay, so just fyi for everyone here. The third session will be focused on InSAR and there will be a discussion on ionospheric corrections. Okay. Question number nine, how should uncertainty and latency and NICE urgent response products be incorporated into disaster response decisions, especially when rapid action is required, but the data may not be yet fully refined.

1:49:44 Speaker 4: So we do have an urgent response section that goes through the latency and kind of what the trade offs in terms of processing for those urgent response products, and so it would really depend on the particular application whether that is a good tradeoff.

1:50:02 Speaker 1: Okay, great, thank you Heidi. And the next question number ten, how can the multiple multi algorithm nice are soul moisture data along with aggregated backscattering and ancillary layers be integrated into GIS and predictive models to support resilient infrastructure and climate responsive land new strategies in urban planning.

1:50:26 Speaker 4: Yeah, this is a very it sounds like a fairly specific workflow. As demonstrated, all of these products are supported in GIS, and so how you choose to use them that in that environment or your particular workflows will depend on your particular application.

1:50:48 Speaker 1: Great, thank you. Next question number eleven, where can I get more details of the NICs our products that we're mentioned like GSLC, GEOFF GU and w g CO, et cetera.

1:51:03 Speaker 4: And so the Data user Guide has a section that goes through the data product's overview, goes through kind of the different levels, and then there is a page for each one of the products that links to the product specification. So that's a great place to start to get a sense of the products that are available and what you might use them for. Yeah.

1:51:26 Speaker 1: Absolutely, The Nicer Data User Guide has an excellent description of the different data products, So that's the best place to start. Okay, the next question number twelve, I cannot find nice our data for Southeast Asia. When will it be available?

1:51:44 Speaker 3: Yeah, same as before, We expect the global release in the second half of July. Is Erica has mentioned before, and that will include Southeast Asia. It will cover all land masses plus some adjacent oceans around all the lamp assis.

1:52:04 Speaker 1: Okay. Question number thirteen, what minimum time period or number of nicer acquisitions what you recommend before attempting a meaningful deformation time series analysis?

1:52:16 Speaker 3: Yeah, that is a question, so if you know who have asked this, I would recommend going to the third session also where Eric is going to talk extensively about nicer InSAR. How many data you need will depend a bit on your application. If it's a large signal, like an earthquake signal, a single in the paragram is often enough. If you're looking for very small data signals that compete with like atmospheric noise, you may want to use a certain time series. How many you need is going to depend on how small your signal is relative to the noise.

1:52:52 Speaker 3: But Eric, I would expect, will be a better source to answer this in the third session.

1:52:59 Speaker 1: Yes, absolutely agree. The third session will be focused on this and Eric will be covering all of these things, and these are questions that he'll be addressing. All right, So the next question number fourteen, can i SF be used through API call to automate for multiple downloads.

1:53:20 Speaker 4: The asf search python package can be used to script workflows both to search for data and download it. The same thing with the earth access python package, so that's probably your best bet for a programmatic workflow.

1:53:37 Speaker 1: Okay, thank you. Number fifteen. Could you recommend a simple workflow for going from nicer data download to a basic deformation map? Yeah, I'll just okay, go ahead. I think in session three, Eric Fielding will be showing that workflow. But I see that you've included some additional information here, go ahead, fronts.

1:54:06 Speaker 3: Yeah, and I mean there are these gnw G unwrapped in the paragram products available that you can scale. For large events like earthquakes or stronger volcano displacement. You can scale to displacement line of side displacement right away. Time series analysis may be required at times. Eric will talk about this, and there's also a link for some additional materials that will share with this document.

1:54:36 Speaker 1: Okay, great, Next question number sixteen. Can a user download clip nysour products covered by the bounding box instead of the entire area?

1:54:47 Speaker 4: So, as the fronts demonstrated, there are ways to subset programmatically, but there are currently not tools available for spatial subsetting in Earth Data Search or Vertex. SF is working on that. That's kind of the next thing on our docket for Harmony services. So you can check out our development roadmap to see the efforts. Are development efforts that are planned, currently underweight and planned.

1:55:17 Speaker 1: Okay, great, thank you Heidi. Let's move on to the next question. Let's see any news on whether SPAN will have any availability within the US.

1:55:33 Speaker 3: Yeah. I linked a r g I S map here that shows the observation plan for a nice r and there are some SPAN data available also over the US over some some calibration sites. I showed it on one slide as sort of on a on a word map. But if you go to this link that will be in this document. Uh, you can explore which which sites are covered in in SPAN and also jointly in SML band.

1:56:05 Speaker 1: Great, thank you. The next question number eighteen downloading SLC files for interferometry purpose and time series analysis. Each scene is seven around seven and a half gigabytes. How do we deal with these big size images?

1:56:24 Speaker 3: Yeah? I think we showed some samples in a sample notebooks. How you can subset in place and download data smaller subsets of data sets. Again, also because of the to avoid you having to process the inter paragrams yourself, there are interferometic data products that Nicile delivers operationally, so you don't have to compute them yourself, at least not all of them, and they're also smaller in size. And there is a likelihood that the community over time will develop services that you can utilize to help you out with handling these large data volumes.

1:57:08 Speaker 1: Okay, great, Next question the question number nineteen. Will the acquisition grids be updated to include the mission the missing grids in the future, and if so, will it also be available from June twenty twenty six or the providers will only include some specific dates.

1:57:31 Speaker 3: So I'm assuming this refers to the initial release not having global coverage. NISA does provide global coverage of all land masses up to what is the northern latitude. There's a hole in the Arctic at I can't remember what the cutoff latitude is, but for the rest of it's it's global coverage every twelve days in ascending and descending.

1:57:58 Speaker 3: So all of these will become available starting with the release date coming soon. Then there is going to be a reprocessing campaign where some of the past data sets will also be reprocessed that made available since global acquisition has started, so you should be able to access most of these data sets on a global scale.

1:58:19 Speaker 1: Absolutely, all right. The next question number twenty, What opportunities exist within the NICs are community for contributing to collaborative projects, data driven publications or applied case studies, specifically in urban resilience and infrastructure systems.

1:58:40 Speaker 3: Yes, it is. If you're familiar with sort of the NASA solicitation, there's sort of an omnibus solicitation that comes out every year that's called ROSES, So just look for solicitations. There are many of them will mention NICSAR. There might be some that are specific to NISAR, but many of the other ones will mention nicer in the call. If you know somebody that works with nice already, feel free to reach out. There's a new selection of a nicer applications and a nicer dart team which used to be called the science team, and so who's on the science team will become publicly available.

1:59:19 Speaker 3: Feel free to reach out to them and see if they're interested in collaborating. It may last we're hoping for open codes, so we mentioned a few times, you can contribute workflows. All of the workflows that we do know that the community is developing, we will share with you through the nicer Data User's Guide, so you can contribute as a member of an open science community all the time.

1:59:53 Speaker 1: Great. Okay, the next question, are the data compatible with panoply?

2:00:00 Speaker 4: Yes, a nice our data can be used in Panently. There's a guidance document It was developed for working with the original nicar sample data, but it applies to the two newer nice art products as well, so if you're interested, you can refer to that documentation.

2:00:19 Speaker 1: Great. How about okay, let's move on to question number twenty two. Well, the nice sr data acquired over calibration sites in the US also be made available through Bundy.

2:00:30 Speaker 3: Yeah. So the simple answer is yes, all nice rs band data will be available through Bonniti, also those over the US.

2:00:39 Speaker 1: Okay, wonderful. So how about we take a couple more questions. I know we're at time, but we have had so many amazing questions, So how about about three more? The next question number twenty two? Well, sorry, twenty three. Do you know if it's possible to open the nice art data in snap?

2:00:59 Speaker 3: Yes, I say, he is Snap supports, but I think the better answer is will support nicer. So you can't already open snap data as an HDA five file or generic HDA five file in Snap, but it will have specific Nicer support I think, coming as far as I know with Snap version fourteen.

2:01:17 Speaker 1: Okay, great, the next question what is the number twenty four? What is the difference between ASF and earth access when pulling data programmatically.

2:01:29 Speaker 4: The ASF search package, It's similar to using Vertex versus Earth data Search in that ASF search is really optimized for finding data that ASF hosts. So there are a lot more parameters that are specific to the SR data sets, and so it may be easier to find what you're looking for or going in on what you're looking for using a SF search. But it really just depends on user preference, so you can find data using either package. It's just what you prefer.

2:02:03 Speaker 1: Okay, wonderful. How about question number twenty five? Is there an ETA for the GLHDF five driver that JPL is developing.

2:02:14 Speaker 3: Yeah, that'd be a question maybe for Marco. Maybe you can take that back to the team. I am not aware of a specific ETA, but maybe the JPL team is yeah.

2:02:25 Speaker 1: Absolutely, we'll update that answer here in the document, but we do have to consult with that team to see what is the what is the intended development timeline? All right, so how about something let's just skip question number twenty six. How about something more focused on the data itself and data access? So let's just go to question twenty eight. Can we work on these data sets in QGIS as well?

2:02:58 Speaker 4: Yes, and refer to the video or documentation. You just need to treat it currently. You just need to treat the data set as if it's a net CDF file. And as long as you do that, then you can access the data sets and use them in QTIS.

2:03:18 Speaker 1: All right, great, so let's go with let's go with three more questions, Okay, I promise just three more. Okay, So how about number twenty nine. If we would like to process the data and slant range geometry, how can we do it?

2:03:36 Speaker 3: Yeah? So there's I think we pointed at some resources. ICE three is a resource that's the software package that is also behind the operational NISA processing. There's some courses that I linked earlier on how to use ICE three. Snap is the software package that will support processing data and slant range geometry and very likely I mean commercial SAR data processing tools such as Gamma and similar tools will also support nice are in traditional NISER processing from the slant range geometry.

2:04:14 Speaker 1: Okay, great, okay, the next question number thirty, what is frequency A and frequency B?

2:04:23 Speaker 3: Yeah, we can tacting this. So NICER operates transmits two different frequencies, mostly to facilitate ionospheric correction. The main imaging band is frequency A, So if you have a if you hear of a twenty megahertz data set or forty megahad stata set, those are the bandwidths of the frequency A transmission, so that's on one side of the spectrum that NICER supports. Additionally, there's a signal transmitted on the other side of the spectrum, using then the frequency diversity to correct for ionospheric delay in the operational processed inside data.

2:05:01 Speaker 3: Frequency B is a narrow bandwidth, so lower spatial resolution, but still provides you a really nice image. And so both of those are available for you to analyze. So two products slightly different center frequency and slightly different resolution that you can average.

2:05:19 Speaker 1: Okay, great, thank you, that was a great explanation. All right, So how about question number thirty four. This is the last one. Is there documentation on how to use the earth access Python package and can we use it to search for available images in a specific area and time and download only for example gcov at frequency A.

2:05:42 Speaker 4: So you can refer to the earth Access section of the Nicer Data user guide which goes through the capabilities there. I'm not sure if you can excrapt specific variables. I think so, but I'm not certain.

2:06:01 Speaker 1: Okay, yeah, we will look into this and update this document. So with that, I thank everyone for their patients. I know we're over time. I first of all, I do want to thank our experts today are invited experts doctor Franz Meyer, Heidi Christensen, as well as our colleague from Israel,

2:06:22 Speaker 1: doctor Sharma, and of course the r set team who's been amazing and putting all of this together. And to all of you participants for your great interest and enthusiasm and amazing questions related to nissart today. So please stay tuned. There's one more amazing session and that'll be next Thursday. I will be focused on Instar and my colleague doctor Eric Fielding will be the invited expert for that session. So see you in a week, have a great day everyone, and I stay tuned. Bye bye