NASA ARSET NISAR Data Access and Tools Session 2-3

0:01 Speaker 1: Hello, and welcome to this webinar series harnessing NISAR, next

0:06 Speaker 1: generation radar observations for Earth applications. I'm Erica Potis, the

0:11 Speaker 1: scientist that NASA's Jet Propulsion Laboratory and also an instructor

0:16 Speaker 1: with the RSET program. Today's session will be an overview

0:19 Speaker 1: of NISAR data access and tools, which will be delivered

0:23 Speaker 1: by invited experts doctor Franz Meyer from the Alaska Satellite

0:28 Speaker 1: Facility and the University of Alaska Fairbanks, by Heidi Christiansen

0:33 Speaker 1: from the Alaska Satellite Facility, and by Anupama Sharma from

0:40 Speaker 1: the National Remote Sensing Center Israel. This is the training outline.

0:48 Speaker 1: Today is the second session of this three part webinar series.

0:53 Speaker 1: The third and last session will be next Thursday at

0:56 Speaker 1: the same time, and that's going to be focused on

0:59 Speaker 1: monitoring earthquakes, volcanoes, and landslides with nicar's InSAR capability. As

1:07 Speaker 1: a reminder, there is one homework associated with this training.

1:11 Speaker 1: It will open next week on the last day of

1:16 Speaker 1: the training on July sixteenth, and the homework is due

1:21 Speaker 1: on August six You will be able to access the

1:26 Speaker 1: homework through the training web page. There will be a

1:30 Speaker 1: Certificate of Completion awarded to all of those participants who

1:35 Speaker 1: attend all of the live sessions and complete the homework

1:39 Speaker 1: assignment by the do date. Next, I'll provide an overview

1:46 Speaker 1: of today's session. So these are the objectives for today's session.

1:52 Speaker 1: By the end of the session, participants will be able

1:55 Speaker 1: to identify where to access NISAR data, whether through the

2:01 Speaker 1: Bunity platform, is Rosebuniti platform, or the Alaska Satellite Facilities Platform.

2:09 Speaker 1: Participants will be able to recognize the data format and

2:12 Speaker 1: tools for reading the data, and apply tools for visualizing

2:18 Speaker 1: and analyzing NYSART data.

2:22 Speaker 2: How to ask questions. To ensure we see your question,

2:27 Speaker 2: please write your question in the Q and A box,

2:30 Speaker 2: which you can locate it by doing a click on

2:34 Speaker 2: the three dots in the bottom right of the window

2:41 Speaker 2: in the platform, and there you'll see a slider a

2:44 Speaker 2: Q and A option, and you can also see a

2:47 Speaker 2: standalone slide o, tap or app. Write your questions or

2:51 Speaker 2: all your questions there and we will answer them during

2:54 Speaker 2: the Q and A session at the end, and we

2:57 Speaker 2: will try to answer all the questions in the Q

3:00 Speaker 2: and A session. The remaining questions will be answered in

3:04 Speaker 2: a Q and A document which we will post on

3:07 Speaker 2: 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

3:18 Speaker 1: of Remote sensing at the University of Alaska, Fairbanks and

3:21 Speaker 1: chief Scientists at the Alaska Satellite Facility. Heidi Christensen, who's

3:26 Speaker 1: a Senior GIS Specialist at the Alaska Satellite Facility, and

3:31 Speaker 1: Anupama Sharma, whose group lead with the Data Dissemination and

3:37 Speaker 1: Workflow Automation group at the National Remote Sensing Center at Israel.

3:42 Speaker 1: So we'll start out with doctor Franz Meyer, and welcome

3:48 Speaker 1: to all three of our guest instructors. It's a real

3:52 Speaker 1: privilege to have them be presenting here today. Welcome doctor Meyer,

3:57 Speaker 1: and the floor is yours.

4:04 Speaker 3: Thank you Erica for the introduction. Yes, so NISA data

4:08 Speaker 3: will be available in various places. One of the main

4:10 Speaker 3: ones is the Alaska Satellite Facility in its discovery services

4:15 Speaker 3: that Heide is going to present to you later on

4:19 Speaker 3: in this session. So ASF is an organization at the

4:24 Speaker 3: University of Alaska, Fairbanks, and our main goal is to

4:28 Speaker 3: make remote sensing data accessible, including those from NISAR, but

4:32 Speaker 3: including also other data sets that behold in the archive.

4:37 Speaker 3: ASF is a remote sensing center specializing in synthetic capture

4:41 Speaker 3: radar data sets, and we operate the NASA Data Center

4:46 Speaker 3: for SAR data, which is hosting data back to nineteen

4:50 Speaker 3: seventy eight and is also hosting managing NAR's L band

4:56 Speaker 3: data sets those that are being processed by JPL on

5:00 Speaker 3: a global scale. In addition to just hosting the data

5:04 Speaker 3: making them discoverable, we also provide a bunch of tools

5:08 Speaker 3: and services and additional resources, training materials and so on

5:12 Speaker 3: so on that help you working with our data sets,

5:16 Speaker 3: understand the content of the data, how to access the data,

5:20 Speaker 3: and how to do various applications with the data sets.

5:24 Speaker 3: All data that you discovered through ASF nicer of course,

5:28 Speaker 3: but also the other data sets we host are freely

5:31 Speaker 3: and openly accessible for you and for download and for

5:34 Speaker 3: doing your research work. So let's talk briefly about nicer

5:39 Speaker 3: data availability and accessibility. So currently nicer data there is

5:46 Speaker 3: a set of pre calibration data sets available through ASF.

5:51 Speaker 3: It's a little bit more than one hundred thousand products

5:54 Speaker 3: that were acquired prior to February twenty twenty six. The

6:00 Speaker 3: average map that you show on C on the slide

6:02 Speaker 3: shows you all of these data sets are located. These

6:05 Speaker 3: are really there to get you used to the data set,

6:08 Speaker 3: to get you used to how you read these data sets,

6:10 Speaker 3: 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

6:21 Speaker 3: with this particular set of products, can be found on

6:24 Speaker 3: the following the link that's on this slide. Very very soon,

6:29 Speaker 3: in July of twenty twenty six, we expect the global

6:33 Speaker 3: release of forward process nicer data sets to become available.

6:37 Speaker 3: So this is when really starting from July twenty twenty six,

6:40 Speaker 3: all the observations going forward will be accessible on a

6:43 Speaker 3: global scale through ASF, all the l band nicer data sets.

6:49 Speaker 3: If you have questions about data releases and products that

6:55 Speaker 3: are available through ASF, you can follow this link here

6:58 Speaker 3: that says data user guide available nicer data sets. So

7:03 Speaker 3: this link will get you to a page where latest

7:06 Speaker 3: release information is being tracked. So nice are in contrast

7:10 Speaker 3: to many other traditional SAR missions, is providing a larger

7:14 Speaker 3: suite of data products. So of course we will provide

7:18 Speaker 3: the traditional level on SAR data products that you are

7:22 Speaker 3: used to. If you have used SAR data before this

7:25 Speaker 3: includes rslc's These are radar coded single or complex images.

7:31 Speaker 3: So these are data sets that contain both the full

7:33 Speaker 3: amplitude information of the image information, but also the underlying

7:38 Speaker 3: phase content, which you would need if you wanted to

7:41 Speaker 3: do nice InSAR type applications. The RSLC data are available

7:47 Speaker 3: on a global scale. Additionally, in selected regions, particularly the

7:53 Speaker 3: cryospheric regions, so the large ice caps and the large glaciers.

7:58 Speaker 3: There's also a radar coded in the paragram available. The

8:01 Speaker 3: are IFG product. It's arrange Doppler wrapped in the paragram

8:05 Speaker 3: and also arrange Doppler unwrapped into peragram, so if you're

8:08 Speaker 3: familiar within the parometry, these products might be useful for

8:12 Speaker 3: you to do some displacement analysis in these chryspheric regions. Lastly,

8:18 Speaker 3: there is an offset product available, so these are pairwise

8:22 Speaker 3: pixel offsets calculated between pairs of images. These offsets also

8:26 Speaker 3: useful to track motion in the cryosferic region. So these

8:31 Speaker 3: are level one data sets. Those are more familiar to

8:35 Speaker 3: folks that I've used CR before. But NISA will create

8:38 Speaker 3: additional products and make available additional products so called level

8:42 Speaker 3: two products. These are still radar observables, so amplitude and

8:47 Speaker 3: phase information and programetric information. But they are available now

8:51 Speaker 3: in a geocoded form, so a in a ground coordinate system,

8:56 Speaker 3: and so the products are listed here. On a global scale,

8:59 Speaker 3: 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

9:06 Speaker 3: the normalized radar backscatter layers for each polarization, so you

9:10 Speaker 3: have in for most land areas, you have a horizontally

9:14 Speaker 3: polarized fully calibrated image available there and also a cross

9:19 Speaker 3: polarized polarized fully calibrated amplitude image that you can use

9:24 Speaker 3: for mapping or if you have sequences of them, as

9:27 Speaker 3: we'll show you later, you can use them to look

9:29 Speaker 3: at changes in the environment. The GSLC product is a

9:35 Speaker 3: geocoded single look complex product that's also globally available. Again,

9:40 Speaker 3: this is very useful for folks that want to get

9:42 Speaker 3: into in the parometry. It gets you very rapid and

9:45 Speaker 3: easy access to in the paragram generation. To create an

9:49 Speaker 3: in the paragram, you simply will have to cross multiply

9:52 Speaker 3: two repeated images two repeated GSLC images. Then there's a

9:59 Speaker 3: g unwrapped or gun w products that we often call it.

10:03 Speaker 3: It's a geocoded unwrapped in the pheragram. So this is

10:06 Speaker 3: one that's ready to go for your analysis of geophysical displacements.

10:12 Speaker 3: It captures displacements of the Earth's surface that occurred between

10:17 Speaker 3: two subsequent images, so within a twelve day time period.

10:22 Speaker 3: Associated with that are also coherence layers. They are really

10:25 Speaker 3: useful for coherent change detection, to identify damage or to

10:29 Speaker 3: identify deforestation or any kind of other change feature that

10:34 Speaker 3: you're interested in. And lastly, again over the christ Feric regions,

10:38 Speaker 3: there's another offset product, a geocoded offset product similar to

10:42 Speaker 3: the level one product that I talked about earlier, but

10:46 Speaker 3: this one's really good to give you offset estimates in

10:49 Speaker 3: a UTM or polar stereo projection. The image on the

10:55 Speaker 3: on the right hand side shows you one of these

10:57 Speaker 3: unwrapped in the pherogram products of a co eruptive in

11:01 Speaker 3: the paragram for an earthquake in Ethiopia, showing you the

11:04 Speaker 3: kind of phase quality and displacement quality nicer we'll be

11:08 Speaker 3: able to give you. Lastly, in terms of data products,

11:11 Speaker 3: there's a level three data product that's also being made available.

11:15 Speaker 3: So this is a science product it's called the SEME

11:19 Speaker 3: two or the moisture a soy moisture retrieval product. So

11:23 Speaker 3: this provides you soy moature information at a two hundred

11:26 Speaker 3: meter pixel spacing, derived using three independent algorithms. So there

11:32 Speaker 3: are three algorithms that are listed here that are used

11:35 Speaker 3: to derive soy moistures. So you get three different layers,

11:39 Speaker 3: and these algorithms that differ that they have different dependencies

11:44 Speaker 3: on time series, so some use more time series information,

11:48 Speaker 3: some less, Some use more ancillary data, some less, Some

11:52 Speaker 3: do more regularization, some less, So you get three solutions

11:55 Speaker 3: 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

12:05 Speaker 3: have the two hundred meters aggregated rate of brightness information

12:08 Speaker 3: for all porizations and additional reference information like incidents, angle maps,

12:14 Speaker 3: and statistics information. They must product has an accuracy goal

12:20 Speaker 3: of zero point zero six cubic meters over unflagged areas,

12:28 Speaker 3: you know, allowing for areas that have a vegetation water

12:31 Speaker 3: content below five kilograms per square meter. So there's a

12:36 Speaker 3: 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

12:46 Speaker 3: used to simulate nicer data sets and do the validation,

12:50 Speaker 3: and in post launch there's sparse soy much to networks

12:54 Speaker 3: that are being used for calibration as well as there

12:56 Speaker 3: are supersites that are being used to ensure that the

12:58 Speaker 3: product meets a require diamonds and user expectations. So these

13:03 Speaker 3: are all the data sets that are available through ASF

13:06 Speaker 3: the Alaska Satellite Facility on the NASA side, but there

13:09 Speaker 3: 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

13:19 Speaker 3: an L band radar but also an S band radar,

13:22 Speaker 3: and all NISAR spend data will be distributed through Israel

13:27 Speaker 3: through the discovery portals that ISRAE provides. In addition to that,

13:32 Speaker 3: Israel will also distribute some L band data. These are

13:36 Speaker 3: data collected concurrently with sband acquisitions over areas of interest

13:42 Speaker 3: to Israel, and so the Israel distributed L band data

13:46 Speaker 3: might have slight differences compared to the ones available through

13:52 Speaker 3: the NASA interfaces. They are independently processed and may look

13:57 Speaker 3: have some value differences compared to the ones that you

14:00 Speaker 3: get from NASA the locations where ESPAN data are available

14:05 Speaker 3: or shown in these maps, so you see some areas

14:08 Speaker 3: in the Antarctic, of course, lots of areas in India

14:12 Speaker 3: and around India, but also certain selected areas throughout the

14:16 Speaker 3: rest of the world. To discover the SPAN data hosted

14:22 Speaker 3: by Israel. Israel has a very nice discovery platform that's

14:27 Speaker 3: called Buniti. The link on this page, the one behind

14:31 Speaker 3: Nicer Information page, gets you to the Perniti page, specifically

14:35 Speaker 3: the one that's designed for nicer. It gives you some

14:39 Speaker 3: background information about the mission and if you scroll down

14:42 Speaker 3: there's then information about how to access and discover nicer

14:46 Speaker 3: data sets. You will hear more in this session about

14:49 Speaker 3: israe's capability from a presenter from Israel itself, and with

14:56 Speaker 3: that I am handing it off next to Heidi for

14:59 Speaker 3: the next second of the training.

15:05 Speaker 4: Thanks Ronz. There are a number of tools available that

15:08 Speaker 4: allow you to find and access nice our data. Vertex

15:14 Speaker 4: is ASF's data search portal. It's a map based interface

15:19 Speaker 4: that you open in your web browser and it allows

15:21 Speaker 4: you to explore and access all of ASF's star holdings.

15:26 Speaker 4: This interface is really optimized for looking for star data.

15:30 Speaker 4: There are customized filters that allow you to search for

15:33 Speaker 4: things like polarization, orbit direction, product types, or acquisition modes,

15:39 Speaker 4: and each data set that you can search for in

15:42 Speaker 4: Vertex has a custom suite of filters. Vertex allows you

15:47 Speaker 4: to view all of the available products for emission in

15:50 Speaker 4: the same search results and in the background. What's finding

15:53 Speaker 4: these data sets is actually the ASF search Python package.

15:57 Speaker 4: If you're interested in interacting with the data pro grammatically,

16:01 Speaker 4: you can use ASF search to build a script that

16:04 Speaker 4: will find an access in your ASF's data holdings. So

16:09 Speaker 4: let's take a look at vertexts. When you open Vertex,

16:16 Speaker 4: by default, it will be a geographic search for Sentinel

16:18 Speaker 4: one data. You can search for NISAR data by clicking

16:22 Speaker 4: the drop down menu and selecting NISAR. You can see

16:27 Speaker 4: that there are more than one hundred thousand files available,

16:29 Speaker 4: so it's important to apply search filters. One of the

16:34 Speaker 4: easiest ways to restrict your search is to draw an

16:37 Speaker 4: area of interest. So by default, I can just draw

16:41 Speaker 4: a rectangle, but I can also play as a point,

16:44 Speaker 4: a line, a polygon, draw a circle, or even upload

16:48 Speaker 4: a geospatial file. This brings my search down to one

16:53 Speaker 4: hundred and eighty one files. And I can also apply

16:56 Speaker 4: some additional filters, including a date range, and I can

17:01 Speaker 4: search for a particular product type. If I don't select

17:07 Speaker 4: anything in this menu, it will return all of the

17:11 Speaker 4: different products in my search, but I can restrict it

17:14 Speaker 4: to a single product, or if I have several products

17:17 Speaker 4: that I want to include, I can select all of

17:20 Speaker 4: the desired products. I'll just search for the cheek of products. Now,

17:26 Speaker 4: going back to the area of interest, you can see

17:28 Speaker 4: that the area of interest that I drew is indicated here.

17:31 Speaker 4: But I have this option to import a geospatial file.

17:34 Speaker 4: I can use a shape file, GeoJSON or KMAL if

17:37 Speaker 4: 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

17:42 Speaker 4: a number of additional filters, and as you become more

17:46 Speaker 4: familiar with the data set, you may want to use

17:48 Speaker 4: some of these filters to further restrict your search. If

17:51 Speaker 4: you're unsure of what the filter is used for, you

17:54 Speaker 4: can click the documentation link. So, for example, the scene

17:58 Speaker 4: name patterns. This document link tells you about the ability

18:02 Speaker 4: to use a wildcard query to search for any portion

18:05 Speaker 4: of a nice our file name. So I'll launch my

18:10 Speaker 4: search and I have thirty three results. If I click

18:17 Speaker 4: on an item in my results, you can see that

18:20 Speaker 4: it displays the footprint for that item in red on

18:23 Speaker 4: the map. I can also click on the map to

18:29 Speaker 4: select a footprint and it will highlight a product with

18:32 Speaker 4: that footprint in my results. If I find a product

18:36 Speaker 4: that covers my area of interest particularly well, I can

18:39 Speaker 4: use them more like this button to restrict my search

18:42 Speaker 4: to just items that have that same footprint. Essentially, what

18:47 Speaker 4: this is doing is adding a track and frame filter

18:49 Speaker 4: to my search, and that uses the track and frame

18:53 Speaker 4: from that granule that I selected. So now I have

18:57 Speaker 4: a list of seven files that all have that same footprint.

19:02 Speaker 4: If I click on one of these files, you can

19:04 Speaker 4: see a long list of associated files that I can download.

19:09 Speaker 4: For most people, the HDF five file for that product

19:12 Speaker 4: is what you're interested in, and I can either just

19:15 Speaker 4: click on this download file icon to launch the download

19:19 Speaker 4: directly in my browser, or I can choose to add

19:22 Speaker 4: the file to my download queue. If I want to

19:27 Speaker 4: explore the images a little bit more before selecting which

19:30 Speaker 4: ones to download, I can choose this option to open

19:33 Speaker 4: the image viewer and I can actually zoom into browse

19:37 Speaker 4: images and examine my area of interest at a bit

19:40 Speaker 4: more closely. That zoom extent is kept as I go

19:45 Speaker 4: through the different products, and if I find a product

19:50 Speaker 4: that I like, I can also launch a download directly

19:53 Speaker 4: from this interface, so I can either download it through

19:56 Speaker 4: my browser or add it to my queue. You can

20:01 Speaker 4: see that I now have two items in my download queue.

20:05 Speaker 4: If I decide that I actually want all seven of

20:08 Speaker 4: these and I want to do a time series analysis,

20:11 Speaker 4: I can use this que button at the top of

20:13 Speaker 4: the list and either download all associated files or just

20:17 Speaker 4: select the file types that I want. So I can

20:20 Speaker 4: select all seven g cuve HDF five files and add

20:24 Speaker 4: that to my downloads que. Now you'll see that I

20:27 Speaker 4: have seven items. It recognized that the two that I'd

20:30 Speaker 4: added already were already in the list and did not

20:33 Speaker 4: duplicate them, so I now have seven items ready to download.

20:37 Speaker 4: I could just go through and click each one of

20:39 Speaker 4: these download icons, or I could leverage the option to

20:44 Speaker 4: download a Python script. If I download a Python script,

20:48 Speaker 4: it saves that to my computer and I can simply

20:52 Speaker 4: launch that to download each one of the items automatically.

20:56 Speaker 4: In order, I can also use this option to copy

21:02 Speaker 4: the URLs. So if you have a script that you

21:04 Speaker 4: already use to access data based on their URL, you

21:08 Speaker 4: can copy that list, or if you want to use

21:12 Speaker 4: a direct S three access these products are stored in

21:16 Speaker 4: AWSS three, and so I can copy a list of

21:20 Speaker 4: the S three URLs as well. As I mentioned in

21:25 Speaker 4: the background, ASF search is doing the work to find

21:28 Speaker 4: these products, and there is the option for any search

21:33 Speaker 4: that you do, you can export a snippet, a Python

21:36 Speaker 4: snippet that shows you what was done to fund these

21:41 Speaker 4: data sets. And so if you're interested in getting started

21:44 Speaker 4: with the Python package, this is a great way to

21:46 Speaker 4: start as it shows you the particular syntax that would

21:49 Speaker 4: be needed to find those data sets. So that's Vertex.

21:56 Speaker 4: Let's take a look at Earth Data Search. Earth Data

22:03 Speaker 4: Search is similar to Vertex in that it's a map

22:05 Speaker 4: based interface that you run in your web browser, but

22:08 Speaker 4: rather than just having access to SAR data sets, you

22:11 Speaker 4: can search for any data set in the Earth Data system.

22:15 Speaker 4: Many of you may already be using Earth Data Search

22:17 Speaker 4: to find data, and you can now search for NISAR

22:20 Speaker 4: in that same location. Earth Data Search provides built in

22:26 Speaker 4: support for Harmony data transformation tools, which i'll talk a

22:29 Speaker 4: little bit more about later, and similar to asf search,

22:33 Speaker 4: the Earth Access Python package allows you to find and

22:36 Speaker 4: access data from any of the Earth data any of

22:40 Speaker 4: the Earth data collections. So let's launch Earth Data search.

22:48 Speaker 4: The landing page is simply a search bar. I can

22:51 Speaker 4: type in NISAR and you can see that there are

22:55 Speaker 4: forty nine collections that match my search. Not all of

22:59 Speaker 4: these are actually collected by the NISAR mission itself, so

23:02 Speaker 4: you may want to use this platforms filter to restrict

23:06 Speaker 4: the search to just NISAR. So also like space based platforms,

23:10 Speaker 4: Earth observation satellites and then NISAR, there are still quite

23:17 Speaker 4: a few collections, and so if you're just exploring the

23:20 Speaker 4: data set and you're not exactly sure what you're looking for,

23:23 Speaker 4: the processing levels may be helpful. Now, these descriptions are

23:28 Speaker 4: more specific to optical imagery, so they don't always translate

23:31 Speaker 4: to SAR, but level zero is still raw data for NICSAR.

23:36 Speaker 4: The level one data are any data sets that are

23:39 Speaker 4: 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,

23:48 Speaker 4: and then level three data are higher level of products

23:51 Speaker 4: generated from those Level two products. The only NICAR Level

23:55 Speaker 4: three data available are soil moisture products, so one A

24:00 Speaker 4: designation is used for any of the ancillary data sets

24:04 Speaker 4: for NYSAR. So this is mostly for users who are

24:07 Speaker 4: doing some advanced processing and want access to data sets

24:11 Speaker 4: such as the orbit e femerist files. So I'll restrict

24:15 Speaker 4: my search to just level two data, which are the

24:17 Speaker 4: geocoded products, and you can see that there are a

24:20 Speaker 4: number of options. I can apply again a spatial restriction,

24:28 Speaker 4: so I'll draw an area of interest on the map,

24:32 Speaker 4: but I can also set a date range to see

24:37 Speaker 4: the actual products. I need to select a collection, so

24:40 Speaker 4: I'll look for the data and if I click on

24:44 Speaker 4: an item in the results, it shows me the foot

24:49 Speaker 4: or again, I can click on a footprint on the

24:51 Speaker 4: 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

25:04 Speaker 4: can click on the download icon and this gives me

25:07 Speaker 4: a link to to download the download url. I also

25:12 Speaker 4: have the option to copy the S three access path,

25:16 Speaker 4: or I can add this item to a project. So

25:20 Speaker 4: this is similar to the download Quean vertex. So I'll

25:26 Speaker 4: add a couple of items to my project and then

25:30 Speaker 4: open it up. You can see that this is redirecting

25:37 Speaker 4: me to my Earth Data to use my Earth Data

25:40 Speaker 4: log in credentials and Earth Data log in credentials are

25:44 Speaker 4: required when downloading data from Earth Data, and this same

25:48 Speaker 4: log in applies to any data set across the Earth

25:52 Speaker 4: Observation platform, So you just need one set of credentials

25:57 Speaker 4: to download anything from Earth Data. If you are logging

26:02 Speaker 4: in and you don't already have Earth Data log in credentials,

26:05 Speaker 4: there will be a link where you can register. It's

26:08 Speaker 4: free and it's very fast, and then that gets you

26:11 Speaker 4: access to all of NASA's Earth observation data. The same

26:15 Speaker 4: thing applies in Vertex.

26:19 Speaker 3: Here.

26:19 Speaker 4: I have some options for downloading. I can either just

26:22 Speaker 4: download all of the HDF five files or I can

26:27 Speaker 4: 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

26:35 Speaker 4: to extract or pull out data in the format that

26:39 Speaker 4: you want to use it in your application. So for

26:44 Speaker 4: the nice rg CUV products, the net to cog service

26:48 Speaker 4: is available, and this allows you to extract individual data

26:51 Speaker 4: sets from the HDF five file and output them as

26:55 Speaker 4: cloud optimized geotips. So if I select that option, I

26:59 Speaker 4: can click the edit Variables button and you'll see that

27:02 Speaker 4: there's a data structure inside of the HDF five file.

27:06 Speaker 4: I'll go into this more later, but there are a

27:08 Speaker 4: number of different data sets all packaged together, including from

27:13 Speaker 4: different frequencies. I can select individual products that I'm interested in,

27:19 Speaker 4: and rather than downloading the entire HDF five file, it

27:23 Speaker 4: will extract each one of these layers as a cloud

27:26 Speaker 4: optimized geotip and provide me a download link just for

27:30 Speaker 4: those products. So if I click the download data, instead

27:35 Speaker 4: of having a list of links ready, it shows me

27:39 Speaker 4: a status page where in the background, Harmony is extracting

27:42 Speaker 4: these layers and preparing download links for me to use,

27:46 Speaker 4: so I can come back later and there'll be something

27:50 Speaker 4: that looks like this, which is a list then of

27:53 Speaker 4: all of the different individual layers that have been extracted.

27:59 Speaker 4: The Harmony in Earth Data Search is currently being overhauled,

28:03 Speaker 4: so things may actually look a little bit different later

28:05 Speaker 4: this summer. The functionality will remain the same, but don't

28:09 Speaker 4: be surprised if the menu looks a little bit different.

28:13 Speaker 4: We will provide update guidance for how to use the

28:15 Speaker 4: new interface in our nice Our Data user Guide, which

28:19 Speaker 4: is a resource that I'll talk a little bit more

28:21 Speaker 4: about later on. So that's Earth data search. Now. As

28:28 Speaker 4: Franz mentioned, NASA serves the L band data from the

28:31 Speaker 4: NISER mission and the Indian Space Research Organization serves the

28:36 Speaker 4: S band data. I'm going to pass it over to

28:39 Speaker 4: 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,

28:56 Speaker 5: and today I'll be talking on Buonidi, which is Arosio

29:00 Speaker 5: Data Hub, and also on the access mechanism of ESSA

29:05 Speaker 5: and ISRO interested ELSA data from Bunidi. My presentation will

29:11 Speaker 5: majorly cover the introduction to Bunidi, thensar information site, the

29:19 Speaker 5: data access mechanisms and tools available at Bumidi for analyzing

29:24 Speaker 5: the NISSA data.

29:28 Speaker 6: Bundi Isrozio data hub and it hosts an extensive virus

29:32 Speaker 6: and non IRIS catalog for the data which we have

29:35 Speaker 6: acquired since nineteen eighty eight, and daily acquisitions which we

29:39 Speaker 6: do at our ground station covering India and surrounding regions

29:43 Speaker 6: are also added to the catalog of Bunidi. We also

29:47 Speaker 6: facilitate the regional distribution of sentinel data and also the

29:51 Speaker 6: data of Lancet eight and nine which we acquire over

29:54 Speaker 6: our ground station. Bunidi provides a single window interface for

29:59 Speaker 6: both open and commercial that data access with a near

30:03 Speaker 6: real time visualization tool and the data dissemination at Bunidi

30:08 Speaker 6: is guided by the Indian Space Policy twenty twenty three

30:12 Speaker 6: and as for that, even the ESSA data and the

30:15 Speaker 6: ELSA data of ISTO interest will be available as an

30:19 Speaker 6: open data access at Bunidi. Like I said, Bunidi has

30:25 Speaker 6: a vast archive of data. To begin with the resource

30:28 Speaker 6: SAT series ranging from five meters to fifty six meters,

30:34 Speaker 6: Censor List three avivs and lispore wherein we also have

30:39 Speaker 6: global coverage. Then the SAR satellite which is RESET one.

30:43 Speaker 5: A and the acquisitions on the web site show the

30:48 Speaker 5: Medium Resolutions Cancer Sensors acquisition wherein it shows the global

30:55 Speaker 5: archives as well. Then, as I said said, we are

30:58 Speaker 5: the regional distributors of SET and one BI that this

31:02 Speaker 5: is how the archive is at Buonidi. Then the Lancet

31:08 Speaker 5: nine acquisitions the Nova SAR data which we have at

31:12 Speaker 5: our archives. Moving on to the OCEANSID series satellite which

31:18 Speaker 5: has the local area and global area coverage and in

31:22 Speaker 5: the global area coverage, the entire globe is covered in

31:26 Speaker 5: two days. Even that data is available at Boondi for

31:29 Speaker 5: access to the same archive. We will be adding the

31:34 Speaker 5: NISAR data as well. This image shows the Jikov product

31:39 Speaker 5: of n Essa, which we took for a smaller data

31:42 Speaker 5: range just to give a feel of how the SA

31:46 Speaker 5: data spread is available at Bonidi. We have also launched

31:51 Speaker 5: the Nissar Information website, wherein we have made every information

31:56 Speaker 5: available on the NASAR specification, the observation plan for SR data,

32:02 Speaker 5: the data products formats, the excess mechanisms, and the science

32:07 Speaker 5: 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

32:19 Speaker 5: the site, you can move on to the Data Download

32:23 Speaker 5: BII page directly from the information page itself. These are

32:30 Speaker 5: the products which will be available at Bunidi, the starting

32:33 Speaker 5: from the Level zero product which are the ross science products,

32:37 Speaker 5: to Level one which are the products in REDAR coordinates

32:41 Speaker 5: 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

32:48 Speaker 5: the Large India Mosaic, Large Area Mosaic and the science products.

32:53 Speaker 5: These products are available in the H five format. This

32:58 Speaker 5: slide shows the type of products of NISAR both for

33:04 Speaker 5: SNLSR and the volumes of those corresponding products, wherein you

33:09 Speaker 5: can see the volume ranges from point five gbn it

33:13 Speaker 5: 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

33:24 Speaker 5: be available as an open data and the two major

33:27 Speaker 5: access mechanisms are the browser order, wherein we have provided

33:31 Speaker 5: a web interface where the data can be searched and

33:35 Speaker 5: then adding on to the card with using an interactive

33:38 Speaker 5: map interface. The data can be then downloaded for programmatic

33:42 Speaker 5: access of NISAR data. The APIs are also available. We

33:47 Speaker 5: have enabled the Spatio Temporal Asset Catalog which makes our

33:51 Speaker 5: catalog interoperable and writing a Python program, the data can

33:56 Speaker 5: be daily taken from the catalog that Bunidi we have

34:01 Speaker 5: the data visualization tool as well, and the near future

34:05 Speaker 5: we plan to release the Vista tool, which will be

34:09 Speaker 5: augmented for NASAR data and based on the augmentation of

34:14 Speaker 5: resources which we do at Boonidi. We will be planning

34:17 Speaker 5: to release the code lab as well as the Guraster platform,

34:21 Speaker 5: which are for online coding as well as for readily

34:24 Speaker 5: available tools at Bunidi for NASAR data processing. So to

34:30 Speaker 5: begin with the direct download access of missile data. This

34:34 Speaker 5: is for the users who would wish to browse and

34:36 Speaker 5: download the data sets through a map based interactive web application.

34:42 Speaker 5: We have provided various search options to target the area

34:46 Speaker 5: of interest. Various advanced filters specific to NASA data ordering

34:51 Speaker 5: are also provided like track frame, Polarization, Radark, configure node.

34:56 Speaker 5: The data sets can be searched using these filters can

35:00 Speaker 5: be then downloaded after adding to the card. So login

35:05 Speaker 5: is mandatory that that means you have to register at

35:09 Speaker 5: Bunidi and once you get the user iety, you can

35:13 Speaker 5: access the data. So if we go to the steps

35:16 Speaker 5: which are to be followed in order to get the

35:18 Speaker 5: direct taxes of the data, after the registration is done

35:22 Speaker 5: and the login credentials are available on login, you have

35:27 Speaker 5: to then get the target area of interest by specifying

35:31 Speaker 5: the aois the period of interest and using various filters,

35:36 Speaker 5: the common as well as the NISSAR specific filters. So

35:40 Speaker 5: the video on the right side is actually depicting a

35:44 Speaker 5: 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

35:57 Speaker 5: be done and then based on the filter selected, the

36:01 Speaker 5: satellite and censored and product combinations are to be taken.

36:05 Speaker 5: We have added all the NASAR satellite product combinations at

36:10 Speaker 5: the filters and once the product is selected, you can

36:16 Speaker 5: 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

36:33 Speaker 5: submit button, BII fetches all the data products which are

36:38 Speaker 5: available at the archive for those filters. Besides every scene

36:45 Speaker 5: which we fetch, this is the sub sample view of

36:48 Speaker 5: the product. So besides every scene which we fetch, there

36:52 Speaker 5: are some tools using which the data can be either

36:56 Speaker 5: published onto the map, the metadata can be seen and

36:59 Speaker 5: the same metadata can be downloaded as well. The video

37:05 Speaker 5: is showing the same and once the product is added

37:08 Speaker 5: to the cart, the cart option is to be selected

37:15 Speaker 5: and you can see the product which was added to

37:18 Speaker 5: the cart. So suppose if the product is available at

37:21 Speaker 5: our live archive. Over the scene, we make a green

37:24 Speaker 5: window which is an information to the user that the

37:27 Speaker 5: product is available you can directly download. If there is

37:31 Speaker 5: a red window above it, then that means that is

37:34 Speaker 5: a delayed download. We will be bringing it from our

37:37 Speaker 5: cold archive, so this is how the product gets downloaded. Now,

37:44 Speaker 5: if the data is to be programmatically accessed, then we

37:48 Speaker 5: have made available the Boni the api is through our

37:51 Speaker 5: suce API. We have also enabled a specification page at

37:56 Speaker 5: Buonidi which gives every detail about the ap is right

38:00 Speaker 5: from authentication to data search to data download. Because it

38:04 Speaker 5: is available as a special temporal asset catalog, we have

38:08 Speaker 5: even given small programs which can be then seen and

38:11 Speaker 5: a Python program can be then written to directly download

38:16 Speaker 5: the data using the program. The right side shows the

38:19 Speaker 5: Nisar data collections which will be enabled for the Nissa

38:24 Speaker 5: data very soon. In near future, we plan to release

38:32 Speaker 5: first the visualization of Nissar data and then the code

38:38 Speaker 5: lab platform using which which is a Jupiter ub interface

38:41 Speaker 5: using which online coding can be done on the Bunidi platform. Now,

38:48 Speaker 5: in order to analyze these products, we have added few

38:52 Speaker 5: tools at Bonidi. To begin with is the HDF to

38:56 Speaker 5: TIFT conversion tool which converts this NISSA data sets in

39:00 Speaker 5: h BY format into geotif and it also supports the

39:04 Speaker 5: special subsetting which enables the users to extract only a

39:09 Speaker 5: smaller area of interest by providing by providing either a

39:13 Speaker 5: point or a location or a polygon as a shapewile.

39:17 Speaker 5: Then the next tool is the Sarpole tool, which is

39:20 Speaker 5: available as an integrated package with QGIS and it has

39:24 Speaker 5: a comprehensive suite of utilities for SAR data processing and

39:29 Speaker 5: analyzing by providing advanced hybrid polarimetic decaposition techniques, so it

39:35 Speaker 5: facilitates tools for leveraging the rich information content of NISI

39:40 Speaker 5: data sets and enables sarvea's application within an open source

39:46 Speaker 5: GIS environment. Very soon we are planning to release our

39:51 Speaker 5: nisard Reference Observation Plan visualization tool which will actually help

39:57 Speaker 5: the user to know how, when and where the NISAR

40:01 Speaker 5: data is actually planned and to be acquired, and then

40:05 Speaker 5: subsequently when it comes to our archive, we will try

40:09 Speaker 5: to bring the footprints of that data also on the tool.

40:13 Speaker 5: So by using filters such as observation cycle or a

40:17 Speaker 5: time range or a a dark configuration ID or the node,

40:21 Speaker 5: the users can see how the observations are being planned

40:25 Speaker 5: and over that even the track and frame footprints can

40:28 Speaker 5: be put and users will know exactly how or at

40:32 Speaker 5: a given time which observation was planned. We have released

40:40 Speaker 5: 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

40:48 Speaker 5: put on the slide at the bottom ride. In the

40:53 Speaker 5: first release, we have put RSLCGSLC and g coof products

40:58 Speaker 5: which are not only covering the Indian landmarks, but we

41:01 Speaker 5: have also selected few global locations. This release is for

41:07 Speaker 5: the users to understand the data structures, the product format

41:12 Speaker 5: and the characteristics of Nisar SR observations. This brings me

41:19 Speaker 5: to the end of my presentation, but before that, we

41:23 Speaker 5: have a resource page at boonide wherein we have added

41:26 Speaker 5: 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

41:34 Speaker 5: check the procedure through which the data can be accessed.

41:39 Speaker 5: The Nissar satellite can also be tracked through our Boni

41:42 Speaker 5: 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

41:50 Speaker 5: us at Boonidi at NRC dot gov dot ten or

41:53 Speaker 5: you can also connect with us at our forum application,

41:56 Speaker 5: 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

42:09 Speaker 4: that are available for finding and accessing the data. Let's

42:12 Speaker 4: talk a little bit about using the data. Some things

42:16 Speaker 4: to be aware of about the Nicer data format is

42:19 Speaker 4: that it's in hdfive format. You've got a bit of

42:21 Speaker 4: a preview of that with the Earth Data Search demonstration,

42:26 Speaker 4: but basically this allows the data producers to package together

42:30 Speaker 4: multiple data sets, along with metadata and ancillary files all

42:34 Speaker 4: together in a single file. Nice or uses a specific

42:38 Speaker 4: flavor of HDF five in that the coordinate system is

42:41 Speaker 4: encoded using CF conventions. This is more common for netCDF files,

42:46 Speaker 4: and so you may need to use netCDF drivers to

42:48 Speaker 4: read the data rather than the current HDF five drivers,

42:53 Speaker 4: otherwise it can't access the geospatial data. Nicer HDF five

42:58 Speaker 4: files are cloud optimized, so you can access them in

43:02 Speaker 4: small chunks, which allows users to stream the data into

43:05 Speaker 4: memory rather than downloading the entire file. So if you

43:09 Speaker 4: want to access just specific parts of the data from

43:11 Speaker 4: the file rather than downloading the whole thing, both ASF

43:15 Speaker 4: Search and Earth Access have workflows for doing that, and

43:20 Speaker 4: I've linked to some documentation that walks you through how

43:22 Speaker 4: you can stream the data rather than downloading it. NISAR

43:27 Speaker 4: has a large spatial footprint that the data is acquired

43:31 Speaker 4: in a very wide swath of about two hundred and

43:34 Speaker 4: forty kilometers, so the footprint of the individual files is

43:37 Speaker 4: quite large, and then with having many data sets packaged

43:41 Speaker 4: together in a single file, it does result in some

43:44 Speaker 4: very large file sizes. So for it varies depending on

43:48 Speaker 4: the type of file and the acquisition mode, but for example,

43:52 Speaker 4: many of the GCOV products over land tend to be

43:55 Speaker 4: on the order of about seven gigabytes each. If you're

43:59 Speaker 4: looking at single complex data sets, those are often close

44:02 Speaker 4: to thirty gigabytes each, so it is important to be

44:05 Speaker 4: aware of the options available for subsetting or just pulling

44:10 Speaker 4: out what you want from the data set, especially if

44:12 Speaker 4: you're limited by download bandwidth. There are a number of

44:17 Speaker 4: different projections used for the level two data sets. These

44:21 Speaker 4: diagrams show the range of projections, so between sixty degrees

44:26 Speaker 4: north and sixty degrees south the data is projected into

44:31 Speaker 4: the UTM zone for the location of the data set,

44:35 Speaker 4: So if your area of interest crosses multiple UTM zones,

44:40 Speaker 4: you may want to reproject some of your data so

44:42 Speaker 4: that it's all the same. And similarly, if you're working

44:45 Speaker 4: in above sixty degrees north, that area uses a north

44:50 Speaker 4: polar stereographic coordinate system, which may be less familiar, and

44:55 Speaker 4: so if you add your data set to a GIS

44:58 Speaker 4: software or take a look at it, it may be

45:00 Speaker 4: oriented differently than what you expect, so you might also

45:05 Speaker 4: consider reprojecting your data if you're working in those areas. Similarly,

45:09 Speaker 4: everything south of the sixty degrees so all of Antarctica

45:13 Speaker 4: is projected into a south polar stereographic system. There are

45:19 Speaker 4: a range of software platforms available that support nicar. Working

45:23 Speaker 4: with nicer data will cover just a handful of those today.

45:29 Speaker 4: For GIS software, RGIS pro has supported the NICRHDF five

45:33 Speaker 4: data format since version three point four, which was released

45:37 Speaker 4: back in November twenty twenty four. The most recent release,

45:41 Speaker 4: which is version three point seven, was just released this

45:44 Speaker 4: past May. Provides additional support specifically for the nicsrg Cuve products,

45:49 Speaker 4: so I'll be doing a demonstration of how to work

45:51 Speaker 4: with those products in the newest version. Users can also

45:57 Speaker 4: leverage QGIS to work with nicer data, but there are

46:00 Speaker 4: some workourounds currently required in order to view the data

46:04 Speaker 4: and QGIS basically you have to treat it like a

46:06 Speaker 4: net CDF file. JPL is developing a GDL driver for

46:11 Speaker 4: the NICs HDF five format, but it's not yet available

46:14 Speaker 4: in the main g distribution, so it's not accessible to

46:17 Speaker 4: the g doll installation that comes with QGIS. So I'll

46:23 Speaker 4: be demonstrating some of the GIS workflows in both pro

46:27 Speaker 4: and QGIS, and then we'll also talk about some of

46:30 Speaker 4: the open source software packages that are available for working

46:33 Speaker 4: with the data programmatically. So ICE three is the software

46:37 Speaker 4: package developed by JPL four specifically for the NISER mission,

46:42 Speaker 4: and then plant ICE three is kind of a wrapper

46:46 Speaker 4: software package for ICE three that makes it easier to

46:49 Speaker 4: use some of the ICE three functions. And then gmtsar

46:52 Speaker 4: is a commonly used SAR processing package which now supports

46:55 Speaker 4: nicer products. And open Sepo is a collection of open

47:00 Speaker 4: source tools developed by Earth Big Data that allows you

47:03 Speaker 4: to work with NICs our data, specifically for subsetting and

47:07 Speaker 4: transforming the data. So France will be going through a

47:10 Speaker 4: programmatic workflow after we've looked at GIS workflows. So for

47:18 Speaker 4: working it with nice our Data and RDIs PRO, we

47:21 Speaker 4: do have some tutorials that are available, so if you're

47:24 Speaker 4: working with earlier versions than three point seven, you can

47:28 Speaker 4: check out our NICRNGIS story map which goes over how

47:32 Speaker 4: to work with Nicer and RGIS versions three point four

47:36 Speaker 4: and newer, and it also links to content for older

47:39 Speaker 4: versions as well, and it covers many of the different

47:44 Speaker 4: products in the Nicer Data Suite. So if you're interested

47:49 Speaker 4: in working with any of those products, or if you're

47:51 Speaker 4: using a slightly older version of RGS Pro, I encourage

47:54 Speaker 4: you to check out that tutorial. And we also have

47:57 Speaker 4: a newly published tutorial that's focused on working with the

48:00 Speaker 4: g cuff products in RGIS Pro three point seven. So

48:05 Speaker 4: let's switch over to RGIS Pro. Since RGIS three point four,

48:12 Speaker 4: we've been able to treat NICs our data as a

48:14 Speaker 4: multidimensional raster. I can simply click the add data button

48:19 Speaker 4: and from the drop down menu select multi dimensional data,

48:23 Speaker 4: navigate to my NICs data file and select it to

48:31 Speaker 4: see the data sets that it contains. Now within the

48:36 Speaker 4: HDF five file, there are a number of different data sets,

48:40 Speaker 4: including two different frequencies. Most of the g cuff products

48:43 Speaker 4: have both frequency A and frequency B, and these are

48:46 Speaker 4: collected at slightly different frequencies and in general the Frequency

48:50 Speaker 4: A is the data set that most people will want

48:53 Speaker 4: to use for analysis because it is a pixel spacing

48:56 Speaker 4: of usually ten or twenty meters depending on the acquisition mode,

49:00 Speaker 4: 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

49:10 Speaker 4: that there's a layer, a covariance layer for each one

49:14 Speaker 4: of the polarizations that are available, So I'll select those

49:17 Speaker 4: two and add them as multi dimensional rasters. They're each

49:23 Speaker 4: added as their own layer, and they display radiometrically terrain

49:26 Speaker 4: corrected backscatter values in GAMMAOT power starback scatter values are

49:33 Speaker 4: generally very close to zero, especially in the power scale,

49:38 Speaker 4: but if you have a few very bright pixels, it

49:40 Speaker 4: can really skew the dynamic range of your image. And

49:43 Speaker 4: because the nice footprints are so large, the chances of

49:46 Speaker 4: having some of those bright pixels is quite high. So

49:49 Speaker 4: often we'll have to adjust the symbology. So I'll take

49:53 Speaker 4: 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

50:09 Speaker 4: and zero point three, and now we can see some

50:13 Speaker 4: features on the surface. So if I zoom in, I

50:16 Speaker 4: can see that surface water tends to be a dark blue,

50:19 Speaker 4: and then we can see agricultural fields and urban areas

50:23 Speaker 4: in varying brightnesses of the starback scatter. So this is

50:28 Speaker 4: the traditional way of working with the data as a

50:31 Speaker 4: multi dimensional data set, but there is now another option

50:35 Speaker 4: in when you're using our js pro three point seven.

50:40 Speaker 4: If I navigate to a g cuff product and this

50:42 Speaker 4: is specific to the g cuff products only, if I

50:46 Speaker 4: navigate to a G cuff product and expand it, I

50:49 Speaker 4: 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

50:56 Speaker 4: I just grab one of those and drag it into

50:59 Speaker 4: the map, I'm going to cancel the pyramids. It takes

51:02 Speaker 4: a while for these large data sets, and it does

51:06 Speaker 4: take a while to render the frequency A data, so

51:10 Speaker 4: it is added as it's a two band raster that

51:13 Speaker 4: has one band for each polarization. If it's a quadpole

51:18 Speaker 4: data set, then it would have four bands included, but

51:21 Speaker 4: it renders it as an RGB image, and by default,

51:25 Speaker 4: the hh is assigned to the red and the HV

51:29 Speaker 4: 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

51:38 Speaker 4: blue show the HH. And again I'll set some custom

51:43 Speaker 4: ranges so that it's more in line with the values

51:46 Speaker 4: that we expect to see. So I'll use zero point

51:50 Speaker 4: three for both of the copol bands and then zero

51:55 Speaker 4: point five for the cross bow band, so we can

52:00 Speaker 4: see a color decomposition where the surface water is very dark,

52:05 Speaker 4: urban areas tend to be quite bright. And then we

52:07 Speaker 4: can see kind of the patchwork of agricultural regions in

52:11 Speaker 4: a mosaic of varying varying colors and intensities. So this

52:18 Speaker 4: is one way to interact with the GCUVE data set

52:21 Speaker 4: by integrating both polarizations into a into a single representation.

52:28 Speaker 4: But we can also work with these data sets one

52:31 Speaker 4: polarization at a time. So if I select one of

52:34 Speaker 4: these layers that I've added with by the new drag

52:37 Speaker 4: and drop GCUFF method, you can see in the menu

52:41 Speaker 4: there's this SAR tab that's available to me. If I

52:46 Speaker 4: select one of these layers that I added as a

52:48 Speaker 4: multi dimensional raster, that's not available. There are some multidimensional

52:52 Speaker 4: tools that I can access, but by selecting it by

52:56 Speaker 4: adding it as a SAR product, we then get access

52:59 Speaker 4: to this sar menu, so there's an option to visualize

53:03 Speaker 4: 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

53:11 Speaker 4: shows a color bar that kind of goes from a

53:14 Speaker 4: rusty red to a yellow. Again, I could adjust the

53:17 Speaker 4: symbology to see the features better. I can also choose

53:22 Speaker 4: to look at the HV instead. Some processing tools are

53:28 Speaker 4: fine with a two band raster or a multiband raster,

53:32 Speaker 4: but for some analysis workflows you may want to have

53:34 Speaker 4: each polarization as available as a standalone raster. And so

53:40 Speaker 4: to do that, we can leverage the existing imagery and

53:44 Speaker 4: SARVE specific tools, either the geoprocessing tools or the raster

53:49 Speaker 4: functions that are available in RJS pro. And so we'll

53:54 Speaker 4: open the raster functions pain and search for extract to

54:00 Speaker 4: pull out just one of the two bands from that

54:02 Speaker 4: two band raster. So under the extract bands, I'll select

54:08 Speaker 4: 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

54:21 Speaker 4: shows me the HH from the frequency A and again

54:26 Speaker 4: I could adjust the symbology. But at this point I

54:29 Speaker 4: can select the right click and select the data option

54:33 Speaker 4: to export the raster to a standalone format, so I

54:38 Speaker 4: can output it as a geotiph for example, and then

54:41 Speaker 4: 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

54:55 Speaker 4: have the option to clip my output to a specific geometry.

55:00 Speaker 4: I can either select an existing layer, or I could

55:03 Speaker 4: add a shape file and use that extent, or I

55:08 Speaker 4: can just use the current display extent. So I'll just

55:10 Speaker 4: clip out this part here, and under the settings I

55:16 Speaker 4: prefer for SAR data, I prefer nearest now nearest neighbor

55:20 Speaker 4: resampling rather than by linear, and I'll export that raster

55:27 Speaker 4: data set. So now I have a standalone geotif that

55:32 Speaker 4: I can use in other projects as well. And if

55:35 Speaker 4: I zoom out and compare it to the full footprint,

55:38 Speaker 4: I've extracted just a small part of that, and again

55:41 Speaker 4: I can adjust the symbology so that I can better

55:47 Speaker 4: see the features, and then I can use any of

55:51 Speaker 4: the analysis tools or geoprocessing tools or raster functions that

55:56 Speaker 4: I want to apply to that image. So that was

56:01 Speaker 4: urgs PRO. Let's take a look at QGIS. We do

56:04 Speaker 4: have some resources for working with QGIS as well. There's

56:08 Speaker 4: a really nice demonstration video that we'll go through some

56:11 Speaker 4: of the content that I'm I'll be covering in my

56:13 Speaker 4: demo as well, that was recorded after the last Nicear

56:17 Speaker 4: town hall meeting. And then we also have some step

56:20 Speaker 4: by step documentation for working with nice data in QGIS

56:23 Speaker 4: that covers quite a range of the different product types

56:27 Speaker 4: and different things you might want to do with them.

56:30 Speaker 4: So let's launch qgis. I. If I navigate to a

56:37 Speaker 4: nice data set, I've got this gcup data set, you

56:41 Speaker 4: can see that it recognizes the data. It can see

56:44 Speaker 4: that there are many layers included in that HDF live file,

56:50 Speaker 4: and it's very tempting to just want to drag and

56:53 Speaker 4: drop these right into my project. Unfortunately, QGIS does not

56:57 Speaker 4: 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

57:04 Speaker 4: in order to work with the data we have to

57:07 Speaker 4: treat it like a net CDF file. One way to

57:09 Speaker 4: do that is simply rename the file so that instead

57:12 Speaker 4: of the dot h five extension you have dot NZ.

57:15 Speaker 4: But another way is to simply define the net CDF

57:20 Speaker 4: driver when adding the layer. So if you go to

57:22 Speaker 4: the layer menu, add layer, add a raster layer. I'll

57:27 Speaker 4: navigate to my nicer file and then I'll go to

57:32 Speaker 4: the very front of the path and just type in

57:36 Speaker 4: net CDF and a colon. And now if I click

57:40 Speaker 4: the AD button, it shows me all of those data

57:42 Speaker 4: sets that are possible to add. So again we've got

57:47 Speaker 4: the frequency A and the frequency B with one layer

57:50 Speaker 4: for each polarization. I'm going to select the frequency B

57:55 Speaker 4: just so that things render a little bit more quickly,

57:59 Speaker 4: and I'll add those layers to my project. So now

58:05 Speaker 4: there is a layer for each one of those polarizations.

58:08 Speaker 4: You can see that I've got HH and HV. Again,

58:16 Speaker 4: the symbology needs adjustment, so we can open the symbology

58:20 Speaker 4: pain and again we can just set our minimum and

58:25 Speaker 4: maximum to a custom number. Now there's a glitch in

58:30 Speaker 4: QGIS that sometimes doesn't allow you to type in decimals,

58:35 Speaker 4: but you can just copy and paste from a text

58:38 Speaker 4: pile and then it works. So that's a quick workaround

58:41 Speaker 4: if you wanted to use decimals for your maximum. All right,

58:45 Speaker 4: so we have this image of the starback scatter and

58:53 Speaker 4: we can work with it using whatever tools are available

58:58 Speaker 4: for dealing with five data sets. Or we may want

59:03 Speaker 4: to export this to be a standalone raster, and that

59:08 Speaker 4: can be done by opening the processing toolblocks and I'll

59:13 Speaker 4: search for clip and we can clip the raster by extent.

59:21 Speaker 4: So I'll select my HH layer and then again there

59:26 Speaker 4: are some options. You can calculate the extent from an

59:29 Speaker 4: existing layer, you can calculate it from a bookmark. You

59:33 Speaker 4: can even just draw an area of interest on the map,

59:37 Speaker 4: or we can just use the current map extent. So

59:39 Speaker 4: I'll choose that option. By default, it will just save

59:44 Speaker 4: it to a temporary file. But if you want it

59:46 Speaker 4: to actually have a standalone geotip that you could use

59:49 Speaker 4: in the future as well or share with somebody else,

59:52 Speaker 4: you can actually save it to a file. Then I'll

59:57 Speaker 4: tag it with a subset. All right, So now I've

1:00:07 Speaker 4: added my subset to the map. I'm going to change

1:00:12 Speaker 4: the color so that we can tell the difference between

1:00:17 Speaker 4: the original and the subset. And again i will set

1:00:26 Speaker 4: my max to zero point three. So now you can

1:00:31 Speaker 4: see I've got this nice subset image of the raster.

1:00:34 Speaker 4: So zoom in and see that this is the all right,

1:00:52 Speaker 4: And so that's a way to subset the nicer data.

1:00:55 Speaker 4: 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,

1:01:12 Speaker 3: so I want to show you a little bit more

1:01:13 Speaker 3: involved workflows. I'm going to give you a sneak peek

1:01:16 Speaker 3: to just show you how this would work if you

1:01:19 Speaker 3: want to dive in deeper into the data set, if

1:01:21 Speaker 3: you have graduate students that you want to spend more

1:01:25 Speaker 3: effort into diving deep into the processing algorithms. So first,

1:01:31 Speaker 3: if you wanted to work with data sets and develop

1:01:33 Speaker 3: algorithms around the data set, NASA is committed to make

1:01:38 Speaker 3: 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

1:01:45 Speaker 3: sets to your local machine and work with the data

1:01:47 Speaker 3: sets locally, we are set up to support you fully.

1:01:52 Speaker 3: We have very high bandwidth data download capabilities. We can

1:01:55 Speaker 3: distribute up to ten petabytes per month of data sets

1:02:00 Speaker 3: through the ASF DOC interfaces, and we have very thick

1:02:03 Speaker 3: pipes to get large amounts of data to you quickly.

1:02:07 Speaker 3: So I fear not if you like downloading data sets locally.

1:02:11 Speaker 3: This is perfectly fine, and we have the systems in

1:02:15 Speaker 3: place to support this kind of access pattern. If, however,

1:02:19 Speaker 3: you're interested, or you are somebody that is interested in

1:02:21 Speaker 3: working in the cloud or moving basically your algorithm from

1:02:26 Speaker 3: your local machine to the archive. There's a variety of

1:02:29 Speaker 3: ways of how you can get started with cloud workflows.

1:02:33 Speaker 3: One of them is the ASF open Sarlab platform. There's

1:02:37 Speaker 3: a link here to the open sarlab platform. You can

1:02:41 Speaker 3: sign up for that platform and try to do your work.

1:02:43 Speaker 3: But you have also a team here at open sar

1:02:46 Speaker 3: Lab that can give you suggestions for how to start

1:02:49 Speaker 3: with your cloud based workflows. There's also NASA has a

1:02:54 Speaker 3: community that's called NASA openscapes. This community is particularly designed

1:02:59 Speaker 3: to help people with getting started in the cloud and

1:03:03 Speaker 3: moving their workflows into the cloud. So if you follow

1:03:05 Speaker 3: the link to the open scapes platform, there's a lot

1:03:08 Speaker 3: of information and material available for you to get started

1:03:12 Speaker 3: with your lifting your workflows into the cloud. And so

1:03:19 Speaker 3: whether you work locally on your local computer machine or

1:03:23 Speaker 3: whether you work in the cloud, there is a number

1:03:26 Speaker 3: of workflows available that you can use. It can help

1:03:29 Speaker 3: you get used to accessing nice our data sets and

1:03:33 Speaker 3: getting an idea how to use Python and Jupiter notebooks

1:03:38 Speaker 3: to interact and do scientific analysis with nicer data. You

1:03:43 Speaker 3: can follow this QR code or snap this QR code.

1:03:46 Speaker 3: Also follow the link that's on the slide here to

1:03:49 Speaker 3: navigate to a page that has a lot of information.

1:03:52 Speaker 3: So what I'm just briefly going to do with you is,

1:03:54 Speaker 3: I'm going to go to that page to show you

1:03:57 Speaker 3: where you can find nicer cookbooks and tutorials. We're wan

1:04:01 Speaker 3: to talk a little bit about who the target audience

1:04:04 Speaker 3: for these workflows is, and we already talked about where

1:04:08 Speaker 3: to run these. You can run them locally on your

1:04:11 Speaker 3: Linux machine, or you can use a platform like open

1:04:13 Speaker 3: star Lab. I'm going to use open sarlab to demonstrate

1:04:16 Speaker 3: one of those workflows for you at the very end. Briefly,

1:04:20 Speaker 3: so let's dive in. I'm gonna click this link for you.

1:04:23 Speaker 3: If you want to follow along, you can snap that

1:04:25 Speaker 3: QR code. If you have the slides already, you can

1:04:27 Speaker 3: also click that link and follow along with me. So

1:04:31 Speaker 3: you're landing again at the Nicer Data use a user

1:04:34 Speaker 3: guide that was referenced a number of times already, super

1:04:37 Speaker 3: useful place for you to find all things nice are

1:04:42 Speaker 3: 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

1:04:50 Speaker 3: that you've already heard about, and then they are Python resources.

1:04:53 Speaker 3: So these are more Python based involved science workflows and

1:04:58 Speaker 3: cookbooks that help you get started with these workflows. So

1:05:01 Speaker 3: we'll look here quickly at the nicer cookbooks as it

1:05:06 Speaker 3: shows here that were developed for accessing and working with

1:05:09 Speaker 3: nicer data. So if you click that, you see a

1:05:12 Speaker 3: little bit more information. You see a link to the

1:05:16 Speaker 3: GitHub repository. Always important to say that all of our

1:05:20 Speaker 3: workflows are fully open source and openly available. So everything

1:05:25 Speaker 3: you find that ASF is exposed on public gitab and

1:05:28 Speaker 3: you can clone it to your local machine and work

1:05:30 Speaker 3: with these workflows yourself. For now, we'll go to the

1:05:33 Speaker 3: cookbook website. And so this website is really useful because

1:05:38 Speaker 3: it tells you a lot about what these cookbooks are about.

1:05:43 Speaker 3: So they are workflows that help you get started with

1:05:49 Speaker 3: accessing GKOV and gun W and SLC data sets. So

1:05:53 Speaker 3: how do you read these data sets? How do you

1:05:55 Speaker 3: subset the data sets? How do you stream files? And

1:05:59 Speaker 3: how do you do workflows using Python scripting. It's important

1:06:05 Speaker 3: that we are accepting community contributions, so we are envisioning

1:06:09 Speaker 3: this as a growing sort of collection of cookbooks that

1:06:14 Speaker 3: include contributions from all of you over the years. The

1:06:20 Speaker 3: motivation is that you know, nice Art provides an incredibly

1:06:24 Speaker 3: comprehensive data set, and we want to provide hands on

1:06:30 Speaker 3: examples that help you understand how to work with this

1:06:34 Speaker 3: growing archive over time. So we'll talk a little bit

1:06:40 Speaker 3: real quick on how to get how to run these cookbooks.

1:06:44 Speaker 3: These cookbooks are done as jupid and notebooks. You can

1:06:47 Speaker 3: run them on your local machine, or you can run

1:06:49 Speaker 3: them on platforms like open sarlab. That's what I will

1:06:52 Speaker 3: do to demonstrate those cookbooks to you. To get started,

1:06:57 Speaker 3: you would install the software by basically cloning the code

1:07:03 Speaker 3: from the guitub repository using commands that are listed here.

1:07:07 Speaker 3: So if you've never done interactive with gittub before, there's

1:07:11 Speaker 3: information in this in this document that helps you understand

1:07:14 Speaker 3: how to how to get started. And so in our case,

1:07:19 Speaker 3: I have already cloned h these cookbooks on my platform,

1:07:24 Speaker 3: my open sar lab platform, So I'm gonna switch over

1:07:27 Speaker 3: to open star lab, which is a Jupiter Hub in

1:07:31 Speaker 3: 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

1:07:38 Speaker 3: need any local install And in open sar lab, I

1:07:43 Speaker 3: have already cloned the Nicer Cookbooks. So let me show

1:07:49 Speaker 3: that to you real quick on my screen, just a

1:07:53 Speaker 3: second clicking away these boxes. So in my folder cloned

1:08:00 Speaker 3: a Nicer cookbooks. So I have a folder here that's

1:08:02 Speaker 3: called Nicer Cookbook. If I go there, you have a

1:08:06 Speaker 3: bunch of files in this in this folder, and I

1:08:12 Speaker 3: have a little symbol here that's called JB. It's a

1:08:15 Speaker 3: so called Jupiter book, a structure here that basically lets

1:08:19 Speaker 3: me look at all of the individual notebooks contained in

1:08:21 Speaker 3: this folder like they are chapters in a book. So

1:08:26 Speaker 3: here that if you go through this cookbook in the

1:08:29 Speaker 3: individual chapters will tell you a bunch of things about

1:08:33 Speaker 3: what these cookbooks are about. So this is a page

1:08:35 Speaker 3: that you've already seen. It tells you how to install

1:08:39 Speaker 3: the software. It tells you how to get an Earth

1:08:42 Speaker 3: Data login to be able to access Nicer data, and

1:08:45 Speaker 3: tells you how to actually access data sets. And then

1:08:48 Speaker 3: further down here in this section, g coov are a

1:08:51 Speaker 3: bunch of notebooks that let you play around with data

1:08:55 Speaker 3: 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

1:09:03 Speaker 3: Amplitude backscattered tutorials, and so we just will load real

1:09:06 Speaker 3: quick a time series of nicer images into a notebook

1:09:11 Speaker 3: and do a simple time series animation of these data sets.

1:09:16 Speaker 3: So this here, the thing that you're seeing on the

1:09:19 Speaker 3: screen now is what's called a Jupiter notebook. A Jupiter

1:09:23 Speaker 3: notebook is basically a data recipe that's composed of individual

1:09:28 Speaker 3: cells that you have to look through in sequence. So

1:09:33 Speaker 3: there are two kinds of contents here. One of them

1:09:36 Speaker 3: is text based content in so called markdown cells. So

1:09:40 Speaker 3: this is instructions and graphics that you can embed here

1:09:43 Speaker 3: and then interspersed with the text content. Our individual cells

1:09:49 Speaker 3: 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

1:09:59 Speaker 3: behind that's embedded in this particular notebook. To run these

1:10:04 Speaker 3: code cells, there's a little sideways triangle here which would

1:10:08 Speaker 3: run this particular cell. So when I click that, it

1:10:11 Speaker 3: executes the particular hot cell executes the code and thus

1:10:15 Speaker 3: the work that this particular cell is designed to do.

1:10:18 Speaker 3: So I'm going to do something real quick. I'm going

1:10:20 Speaker 3: to quickly run all these cells oops, and then show

1:10:26 Speaker 3: you what is happening by going through them real quick.

1:10:31 Speaker 3: So what this notebook does It fetches a small series

1:10:35 Speaker 3: of nicer g CoV products, so g COF again the

1:10:40 Speaker 3: calibrated amplitude products that have layers for the horizontal polarization

1:10:45 Speaker 3: in the cross polarized data sets. And we are fetching

1:10:49 Speaker 3: a bunch of data here that are constrained by a

1:10:52 Speaker 3: start in an end time. So I'm looking for data

1:10:55 Speaker 3: between November twenty second of twenty twenty five and January

1:10:58 Speaker 3: sixteenth of twenty twenty six, actually twenty twenty eight in

1:11:02 Speaker 3: this case, but the latest scenes is in twenty twenty six.

1:11:06 Speaker 3: Then I create an AOI sort of a geographic bounding box.

1:11:12 Speaker 3: So I'm looking here at an area that in this

1:11:15 Speaker 3: case is southern Nepal in the in the Himalaya mountains.

1:11:21 Speaker 3: And I'm searching then the ASF archive for areas that fit,

1:11:27 Speaker 3: you know, this area of interest and fit the start

1:11:30 Speaker 3: and end time time that I defined. And I'm looking

1:11:33 Speaker 3: only for these g COOV products now. And so in

1:11:36 Speaker 3: this case I found five repeated images. You see the

1:11:42 Speaker 3: URLs to these images. You could click those to download

1:11:45 Speaker 3: the files to the local machine if you wanted to.

1:11:47 Speaker 3: So we have five repeated data sets. The next cell

1:11:51 Speaker 3: is making sure that sometimes currently in the archive we

1:11:55 Speaker 3: may have certain products more than once processed with different

1:11:59 Speaker 3: processor version. You very likely will be interested in the

1:12:02 Speaker 3: one process with the most recent processor version, So this

1:12:06 Speaker 3: next code grabs the newest one. If there are identical products,

1:12:13 Speaker 3: grabs the one that was created with the most recent

1:12:15 Speaker 3: version of the processor. In Now, in case there are

1:12:17 Speaker 3: still five products available, so there were no duplicates in

1:12:21 Speaker 3: the archive for this particular area of interest, then we

1:12:26 Speaker 3: can start loading these data sets. In this case, we

1:12:29 Speaker 3: show you how you can stream data without actually having

1:12:33 Speaker 3: to download them, so nicer data is stored in a

1:12:35 Speaker 3: way that you can stream them from the archive like

1:12:38 Speaker 3: you would do in an IGIs platform, where you actually

1:12:41 Speaker 3: don't download the data sets, but you stream the data

1:12:44 Speaker 3: set through the Internet, and so we can do this

1:12:46 Speaker 3: using http S approaches. In order to do this, you

1:12:52 Speaker 3: need to log in, and you need to do this

1:12:55 Speaker 3: by retrieving what's called an earth data log in bearitoken.

1:13:00 Speaker 3: It is explained here how this works essentially, you go

1:13:04 Speaker 3: to your Earth Data login page. If you don't know

1:13:06 Speaker 3: how to get there, you can just google NASA Earth

1:13:10 Speaker 3: Data log In and then click on that first link

1:13:14 Speaker 3: that you find on the web. If you're not locked in,

1:13:18 Speaker 3: that means to log out real quick. To simulate that,

1:13:21 Speaker 3: you would get to a login page. In my case,

1:13:23 Speaker 3: my credentials are already here, so would log in. It

1:13:26 Speaker 3: gets me to my homepage and then here you see

1:13:29 Speaker 3: a variety of menu items and one of them is

1:13:32 Speaker 3: called create a token. If I click that, it will

1:13:36 Speaker 3: create for me at so comparatoken which allows me to

1:13:39 Speaker 3: access and stream data sets directly from the cloud. And

1:13:43 Speaker 3: so it has the token here you can either show it.

1:13:46 Speaker 3: Easiest thing is to just click on this symbol next

1:13:49 Speaker 3: to it and copy that co token into memory. And

1:13:53 Speaker 3: then in this packet the notebook, you then copy paste

1:13:57 Speaker 3: that beveraitoken into your notebook. Now you're ready to access

1:14:02 Speaker 3: and stream your data sets. So now we can access

1:14:05 Speaker 3: these data so we briefly fetch them and lazy load them,

1:14:12 Speaker 3: so you put them in memory so that we can

1:14:16 Speaker 3: work with the data. And then we extract real quick

1:14:20 Speaker 3: the date information that's associated with these files and can

1:14:25 Speaker 3: show them here. So we have five different dates. They

1:14:29 Speaker 3: are between November twenty third of twenty twenty five and

1:14:32 Speaker 3: January tenth of twenty twenty six. These are on the

1:14:35 Speaker 3: dates to this year, month, day and then the hour,

1:14:39 Speaker 3: minute and second time of the observation time. Then we

1:14:45 Speaker 3: can extract a layer from this data set. So we

1:14:48 Speaker 3: extracting only the horizontal polarized data set and we get

1:14:54 Speaker 3: some information about it. So we have extracted a small

1:14:58 Speaker 3: subset of the data set has about three thousand by

1:15:01 Speaker 3: three thousand or four thousand, four thousand samples and totally

1:15:06 Speaker 3: has a file of five size of a four hundred bligabytes.

1:15:09 Speaker 3: In this particular case, you can then visualize where this

1:15:13 Speaker 3: dated this subset that we created is located. So this

1:15:17 Speaker 3: just spins up a little map and and so the

1:15:26 Speaker 3: 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,

1:15:33 Speaker 3: in sort of the foothills in front of the Himalayas

1:15:36 Speaker 3: and just south of the Himalayas. So now we are

1:15:40 Speaker 3: ready to potentially work with this data set. What you

1:15:44 Speaker 3: can do now once you extracted the subset you want.

1:15:47 Speaker 3: You can load only this subset to your local machine.

1:15:50 Speaker 3: This is useful because once it's on your local machine,

1:15:54 Speaker 3: you can access them again. So if you come back

1:15:56 Speaker 3: to this notebook later, you don't have to reload it.

1:15:59 Speaker 3: You can just start at this particular cell and load

1:16:02 Speaker 3: the data from your local storage. And because you already

1:16:06 Speaker 3: subseted it, it's a much smaller data volume and much

1:16:09 Speaker 3: easier to handle. So here we you know, created a

1:16:15 Speaker 3: folder and now we are saving the data set to

1:16:17 Speaker 3: this folder. And once the saving is done, we can

1:16:20 Speaker 3: close out the file and just work now on our

1:16:22 Speaker 3: local machine with the subset we've defined. So instead of

1:16:27 Speaker 3: downloading a huge frame and huge amount of data, what

1:16:30 Speaker 3: we did now is we access the data directly in place.

1:16:33 Speaker 3: We found the subset that we're interested in, and then

1:16:37 Speaker 3: only really saved that subset to your local machine, saving

1:16:40 Speaker 3: 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

1:16:47 Speaker 3: the data data set to save, and once it's saved,

1:16:52 Speaker 3: you could walk away from this notebook and come back,

1:16:56 Speaker 3: you know a few days later, and then start directly

1:16:58 Speaker 3: with section five, load that data set back into the

1:17:01 Speaker 3: notebook for your further research analysis. Okay, so now if

1:17:12 Speaker 3: you want to and you come back to this to

1:17:14 Speaker 3: this section of the notebook later, you can now reload

1:17:17 Speaker 3: this data set from your local machine, go super fast,

1:17:20 Speaker 3: 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

1:17:27 Speaker 3: that talks about the different ways how data sets are

1:17:31 Speaker 3: presented to you. These SAR images often are presented in

1:17:36 Speaker 3: what's called the decibel scale. The reason why that's done

1:17:40 Speaker 3: is because our data have a very large dynamic range

1:17:44 Speaker 3: from the very darkest pixel to the very brightest pixel.

1:17:47 Speaker 3: They are usually orders of magnitude of power difference, and

1:17:51 Speaker 3: so that's sometimes difficult to visualize in an image in

1:17:55 Speaker 3: like two hundred and fifty six Grave values. So what

1:17:58 Speaker 3: we often do is we do transformation to the decibel scale,

1:18:02 Speaker 3: which performs a logarithmic transformation which sort of squeezes the

1:18:08 Speaker 3: dynamic range down and makes it easier to visualize in

1:18:12 Speaker 3: sort of a gray scale image. This is super useful

1:18:16 Speaker 3: for visualization, it's not a good way of doing actual

1:18:20 Speaker 3: math with the data set. The decibel scaling is a

1:18:24 Speaker 3: nonlinear transformation and it actually causes a lot of biases

1:18:27 Speaker 3: in the mathematics. So this part of the notebook shows

1:18:30 Speaker 3: 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

1:18:36 Speaker 3: you would expect. So there's a histogram that is being

1:18:39 Speaker 3: plotted here that shows you, for a typical SAR image

1:18:43 Speaker 3: what kind of TB values you would expect. But there's

1:18:46 Speaker 3: somewhere between zero or maybe a value of one down

1:18:50 Speaker 3: to values of minus thirty TB. So this is the

1:18:53 Speaker 3: range that you would typically expect. But then it also

1:18:56 Speaker 3: shows you how you can go back to the power scale.

1:19:00 Speaker 3: If you want to do any kind of mathematics averaging

1:19:03 Speaker 3: or so off a data set, you should always do

1:19:05 Speaker 3: that in power scale and not in Dicipel scale. And

1:19:08 Speaker 3: so then it again shows you what you know the

1:19:12 Speaker 3: statistical distribution is in the power scale and the values

1:19:15 Speaker 3: that you would expect in power scaled. And so lastly

1:19:18 Speaker 3: this notebook just shows you how you can then take

1:19:21 Speaker 3: In this case, we have five repeated images to do

1:19:24 Speaker 3: a small time series animation. So this creates a little

1:19:29 Speaker 3: time series animator that you can use to start expecting

1:19:33 Speaker 3: your data set, and you can imagine after one year

1:19:35 Speaker 3: of nice er you have a you know, you get

1:19:38 Speaker 3: a nice and deep data stack that you can start

1:19:40 Speaker 3: analyzing over time. So you see here how a small

1:19:44 Speaker 3: image is being pulled up. I'm going to zoom on

1:19:46 Speaker 3: a little bit. So this is pirate in Nepal, and

1:19:50 Speaker 3: there is a little play button that you can click

1:19:52 Speaker 3: to sort of start looking at the variations in this

1:19:55 Speaker 3: data set. And one of the first things you notice

1:19:57 Speaker 3: that there is a lot of the ability in the data.

1:20:01 Speaker 3: SAR is a super repeatable data set. So if you

1:20:04 Speaker 3: take repeated observations from the same place in the orbit,

1:20:09 Speaker 3: the images should look identical unless the physical properties of

1:20:14 Speaker 3: the ground have changed. So if you have changes in

1:20:16 Speaker 3: soy moisture, changes in vegetation cover, or if there was

1:20:20 Speaker 3: a physical change like you know, harvesting a field or

1:20:24 Speaker 3: an earthquake or any kind of change that happened in

1:20:26 Speaker 3: the surface. So it's a great tool for change detection.

1:20:30 Speaker 3: So mostly there's stability if you look very carefully, the

1:20:34 Speaker 3: brightness goes down a little bit over time. But we

1:20:37 Speaker 3: can quantify that a bit more because we can't calculate

1:20:41 Speaker 3: mean values, and so here we do calculate a mean

1:20:46 Speaker 3: where we average across the AOI for each timestep, and

1:20:51 Speaker 3: we calculate sort of a mean value as a function

1:20:53 Speaker 3: of time. We can plot that here, and so what

1:20:56 Speaker 3: you see is that the average radio brightness is decreasing

1:21:02 Speaker 3: over time in this sequence. And we did the calculations

1:21:06 Speaker 3: of the mean in the power scale, and in this

1:21:08 Speaker 3: particular notebook, the calculated means then converted also to the

1:21:13 Speaker 3: B scale, so you see them in both in both

1:21:16 Speaker 3: sort of projections in the B scale and in the

1:21:19 Speaker 3: power scale annotated on this plot. But you see the

1:21:22 Speaker 3: slow degradation of brightness over time here in the power

1:21:26 Speaker 3: This has mostly to do with the drying out of

1:21:28 Speaker 3: the surface. The soil moisture is decreasing during this phase,

1:21:33 Speaker 3: which is the dry season in Nepal after the monsoon,

1:21:38 Speaker 3: and that typically corresponds to a slow lowering of the

1:21:41 Speaker 3: radio brightness. And it ends with a little two panel

1:21:47 Speaker 3: figure that is being created where you can see side

1:21:50 Speaker 3: by side the mean value development and the image itself.

1:21:56 Speaker 3: So this is going to just take a short second

1:21:58 Speaker 3: to pull up. Here we go, so you see on

1:22:03 Speaker 3: one side the image itself on on the on the

1:22:06 Speaker 3: other side, you see the mean value over time, and

1:22:09 Speaker 3: you can start, you know, linking the overall lowering of

1:22:14 Speaker 3: the brightness in the scene with sort of a change

1:22:18 Speaker 3: in the average radiar brightness value for that particular scene

1:22:21 Speaker 3: as well. And again that this can be really useful

1:22:24 Speaker 3: to just get used to a data set, you know, inspected,

1:22:28 Speaker 3: understand what is the dynamic of the data set, what

1:22:30 Speaker 3: what kind of signals are in there, and start to

1:22:32 Speaker 3: think about what may cause the changes that you see

1:22:35 Speaker 3: in a data set. So this is one of many

1:22:40 Speaker 3: data recipes that are already made available by the team.

1:22:45 Speaker 3: And again going back to the earlier slide, we are

1:22:50 Speaker 3: very interested in having you contribute to this set of

1:22:53 Speaker 3: tutorials and expand about on, you know, beyond this set

1:22:57 Speaker 3: of tutorials and give us feedback to some of the

1:23:00 Speaker 3: workflows that are currently available. And with that I handed

1:23:05 Speaker 3: back over to honey.

1:23:11 Speaker 4: ASF is developing a number of tools and services to

1:23:14 Speaker 4: help users work with nice our Data, and the first

1:23:18 Speaker 4: resource I'd like to mention is the nice our Data

1:23:20 Speaker 4: User Guide. This is intended to be an information clearing

1:23:23 Speaker 4: house where you can find what you need to know

1:23:25 Speaker 4: to work with the data. There are guidance and tutorials

1:23:28 Speaker 4: in the website but it also leaks out to a

1:23:31 Speaker 4: lot of existing content from JPL and other trusted sources.

1:23:36 Speaker 4: It's a living document, so we're frequently updating it with

1:23:38 Speaker 4: additional content. There are lots of things we'd like to

1:23:41 Speaker 4: add in as time allows, and we also keep it

1:23:44 Speaker 4: updated so that as new information, especially about which data

1:23:48 Speaker 4: is available, is known, we can update those pages with

1:23:52 Speaker 4: the new information. So let's take a quick tour. There

1:23:59 Speaker 4: is a REEF introduction to the nice Our mission, including

1:24:03 Speaker 4: the instrumentation, and then an important page is the available

1:24:07 Speaker 4: nice Our Data. This is where we talk about what

1:24:09 Speaker 4: is currently available, but also present the data release timeline

1:24:13 Speaker 4: so that you can know what's coming in the future.

1:24:17 Speaker 4: So we currently have pre calibration products available, as France

1:24:21 Speaker 4: mentioned at the beginning, but we are expecting calibrated data

1:24:26 Speaker 4: to arrive in the archives starting in July. If you

1:24:32 Speaker 4: are working with the pre calibration sample products, it's important

1:24:35 Speaker 4: to know that there are some known issues with these

1:24:37 Speaker 4: data sets, and so you may want to reference this

1:24:40 Speaker 4: page that goes through some of those issues. We expect

1:24:43 Speaker 4: that most of these will be fixed with the calibrated

1:24:46 Speaker 4: data release, but for the data that's currently available, this

1:24:51 Speaker 4: provides important information. We also link to nicer observation plan resources,

1:24:57 Speaker 4: including some interactive apps that are really handy if you

1:25:01 Speaker 4: want to see what's available in your area of interest,

1:25:04 Speaker 4: either for the pre calibration data set or if you're

1:25:07 Speaker 4: looking ahead to see what will be collected over your

1:25:10 Speaker 4: area of interest. The data products section introduces each one

1:25:15 Speaker 4: of the products and links to its product specification file,

1:25:19 Speaker 4: and it also goes over some of the naming conventions

1:25:22 Speaker 4: and the HDF five file format and more detail if

1:25:25 Speaker 4: you're interested in learning more. The Accessing nice our Data

1:25:29 Speaker 4: section goes over what we've demoed in today's presentation using

1:25:34 Speaker 4: Vertex Earth Data Search, the ASF search Python package, the

1:25:38 Speaker 4: Earth Access Python package, and then, if you want to

1:25:42 Speaker 4: interact with the data directly in s three, how you

1:25:44 Speaker 4: can access those data products. The using NISAR data section

1:25:50 Speaker 4: has guidance on rgis and qjis, and there are also

1:25:54 Speaker 4: a lot of tutorials available that either step by step

1:25:58 Speaker 4: tutorials that walk you through interactively, and we also have

1:26:01 Speaker 4: Python cookbooks that are linked here. I'll skip down to

1:26:05 Speaker 4: the contact information. If you do have questions, you can

1:26:08 Speaker 4: contact ASF, but if you have questions about nice our Data.

1:26:11 Speaker 4: In particular, we encourage you to post to the Earth

1:26:14 Speaker 4: Data Forum. This allows you access to a wide range

1:26:18 Speaker 4: of experts and specialists, so rather than just relying on

1:26:21 Speaker 4: ASF to answer your question, we can open it up

1:26:23 Speaker 4: to people at JPL and the nice our Science team,

1:26:26 Speaker 4: so it gives it broadcasts your question more widely, and

1:26:30 Speaker 4: it also allows us to have that information available for

1:26:33 Speaker 4: other people who may have the same question, so they

1:26:35 Speaker 4: can see the answer already. ASF is also developing Harmony

1:26:40 Speaker 4: services to work with nice our data sets. You saw

1:26:42 Speaker 4: a bit of the first effort in the Earth Data

1:26:46 Speaker 4: Search demo, but Harmony is Earth Data's data transformation tool

1:26:51 Speaker 4: and we have that option now to extract data sets

1:26:55 Speaker 4: from the GCUV products as cloud optimized youtifs. And the

1:27:00 Speaker 4: next thing on the docket is to use spatial subsetting

1:27:04 Speaker 4: so that you can clip that extracted data set to

1:27:06 Speaker 4: an area of interest. Next after that we'll be looking

1:27:10 Speaker 4: 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

1:27:18 Speaker 4: now and what's coming up, we do have our tools

1:27:21 Speaker 4: and services roadmap, and so you can check that out

1:27:23 Speaker 4: and see what we're working on and what's coming soon.

1:27:28 Speaker 4: Another great tool is NASA's Worldview, which is a visualization platform.

1:27:32 Speaker 4: There are hundreds of layers in there from different from

1:27:35 Speaker 4: different sensors and data sets across NASA's ecosystem, and we

1:27:41 Speaker 4: are really excited that we'll have daily NISAR GCUP mosaics

1:27:45 Speaker 4: available in Worldview. These will be available once calibrated data

1:27:50 Speaker 4: is released, so we expect going forward from July we'll

1:27:53 Speaker 4: have these daily mosaics available. But I'll give you a

1:27:57 Speaker 4: bit of a sneak peek the development environment. So here

1:28:04 Speaker 4: we've loaded thirteen days of data and you can see

1:28:08 Speaker 4: that we use an rgbdcomposition. You can click through the

1:28:13 Speaker 4: dates to see where acquisitions occurred and kind of what

1:28:17 Speaker 4: they look like, and you can also zoom in to

1:28:22 Speaker 4: these different products and there's actually quite a lot of

1:28:26 Speaker 4: detail available. The mosaics are posted to about a fifteen

1:28:32 Speaker 4: meter pixel spacing, and most of the g CUB products

1:28:35 Speaker 4: over land are either ten or twenty meter, so this

1:28:38 Speaker 4: is pretty close to the source data. So it allows

1:28:42 Speaker 4: you to take a look at your area of interest,

1:28:44 Speaker 4: see if there was an acquisition at the time that

1:28:47 Speaker 4: you're interested in, and if it captured the event that

1:28:49 Speaker 4: you want to know about. It also has the option

1:28:53 Speaker 4: to compare layers, either different layers, different data sets, or

1:28:59 Speaker 4: different dates from the same data set. I'll set the

1:29:02 Speaker 4: 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,

1:29:20 Speaker 4: so I can swipe back and forth to compare the

1:29:23 Speaker 4: two acquisitions. So this is a really powerful visualization tool.

1:29:32 Speaker 4: It's a great way to see if there's data available

1:29:35 Speaker 4: for your area of interest that captured an event that

1:29:37 Speaker 4: you care about. And with that, I'll turn it over

1:29:45 Speaker 4: 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

1:29:54 Speaker 3: last time, and like in a demo earlier, I'm going

1:29:57 Speaker 3: to show you some additional notebooks. So now you know

1:30:01 Speaker 3: what the Jupiter notebooks are. And the Nicer Science team

1:30:05 Speaker 3: has actually developed a number of very nice notebooks that

1:30:09 Speaker 3: show you how to create Level three science products from NISAR.

1:30:14 Speaker 3: Nicer data is being useful, is useful for a variety

1:30:18 Speaker 3: of different disciplines, and notebooks are available for a variety

1:30:22 Speaker 3: of applications. In the Chris Feric sciences you see in

1:30:25 Speaker 3: the screenshot, in the ecosystem sciences and in the solid

1:30:29 Speaker 3: of sciences, there's also some data available for the soy

1:30:33 Speaker 3: moture products. So in each of these folders, if you

1:30:36 Speaker 3: follow this link to the get lab repository, you find

1:30:41 Speaker 3: a set of notebooks that you can, similarly to earlier,

1:30:45 Speaker 3: clone to your local machine and run. If you are

1:30:47 Speaker 3: interested in want to dive deeper into the science algorithms

1:30:51 Speaker 3: behind NISAR, you can run those similarly to what we've

1:30:55 Speaker 3: shown you before in the demo earlier. So if you're

1:30:58 Speaker 3: free to play with these following this link and by

1:31:02 Speaker 3: cloning the repository and playing with these notebooks. An additional

1:31:07 Speaker 3: service that might be of interest to you is the

1:31:10 Speaker 3: observational products for end users from Remote Sensing Analysis AL

1:31:15 Speaker 3: so called OPERA project has been is going to develop

1:31:21 Speaker 3: level three science products from nicer data sets. Specifically, it

1:31:26 Speaker 3: will use nicer data sets to create products such as

1:31:31 Speaker 3: surface water extent such as surface displacements. So this is

1:31:36 Speaker 3: going to be a North America wide nicer based surface

1:31:39 Speaker 3: displacement product as well as a vertical land motion product

1:31:44 Speaker 3: that will also integrate nicer data sets. So vertical land

1:31:49 Speaker 3: motion and surface displacements are available on a North America scale,

1:31:53 Speaker 3: while surface water extent will be available on a global

1:31:57 Speaker 3: scale derived from are data sets these there's a link

1:32:02 Speaker 3: to the Opera website on this web page. The displacement

1:32:06 Speaker 3: data sets will be hosted by ASF. The surface what

1:32:11 Speaker 3: A extent data set will be hosted by a different

1:32:14 Speaker 3: of the NASA dacks. But all these Level three data

1:32:17 Speaker 3: sets are freely and openly available to end users for

1:32:21 Speaker 3: a variety of applications, and so stay tuned for those

1:32:25 Speaker 3: 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

1:32:34 Speaker 3: are that Opera is creating overall. So you have the

1:32:37 Speaker 3: surface water extent on the left, there's a surface disturbance

1:32:41 Speaker 3: product basically a change detection product, and the second column

1:32:46 Speaker 3: then you have the displacement and the vertical land motion product.

1:32:50 Speaker 3: And out of all of those, it is these that

1:32:53 Speaker 3: are left that will be derived also from NYSA. So

1:32:56 Speaker 3: again surface water extent, displacement, vertical motion as well as

1:33:01 Speaker 3: an intermediary product which is also called co registered SLC

1:33:05 Speaker 3: product will also be made available through Opera to a

1:33:09 Speaker 3: global community. So against stay tuned for these data sets.

1:33:13 Speaker 3: There is Lady to come later in twenty twenty six

1:33:17 Speaker 3: 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

1:33:28 Speaker 3: by summarizing a bunch of resources on this last website.

1:33:32 Speaker 3: On this last PowerPoint slide, you can see all of

1:33:37 Speaker 3: these links that are available here, anything from data discovery

1:33:41 Speaker 3: to a variety of tools that are available that are

1:33:43 Speaker 3: available to all of the you know, further research tools

1:33:48 Speaker 3: that we talked about throughout this session. So this is

1:33:53 Speaker 3: a one page, one stop shop for all the links

1:33:56 Speaker 3: that you found throughout this tutorial. Thank you all for

1:34:00 Speaker 3: 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

1:34:10 Speaker 1: for those great demonstrations and presentations. Next, I want to

1:34:17 Speaker 1: summarize the most important points discussed today and then we

1:34:23 Speaker 1: will start our question and answer session. So in summary,

1:34:30 Speaker 1: nice our data are freely and openly available. L Band

1:34:33 Speaker 1: data can be accessed through the Alaska Satellite Facility, while

1:34:37 Speaker 1: spand data along with coincident L band observations are available

1:34:42 Speaker 1: through Israel's Bounity platform. L band provides near global coverage

1:34:48 Speaker 1: every twelve days. With an exact repeat of every twelve days,

1:34:53 Speaker 1: whereas S band observations are acquired primarily over India and

1:34:58 Speaker 1: selected science and calibration validation sites outside India. NISAR data

1:35:05 Speaker 1: distributed in HDF five formats. Level two products are geocoded,

1:35:10 Speaker 1: allowing them to be readily overlaid with other geospatial data sets.

1:35:16 Speaker 1: Open source Jupiter notebooks implementing algorithms for the mission's different

1:35:21 Speaker 1: science disciplines are publicly available. The only global Level three

1:35:26 Speaker 1: product generated by NYSAR is soil moisture with a spatial

1:35:31 Speaker 1: resolution of twelve of two hundred meters. The GCOV product

1:35:39 Speaker 1: is a level two product and it's the most suited

1:35:43 Speaker 1: for those that are not so familiar with radar and

1:35:48 Speaker 1: that are working on ecosystems looking at things like land cover, biomass,

1:35:55 Speaker 1: agricultural growth, floods. So these are radiometrically terrain corrected backscatter

1:36:03 Speaker 1: images in gammon knots. The GUNW product is the most

1:36:08 Speaker 1: suited for those that don't have much experience with InSAR. Again,

1:36:13 Speaker 1: that's a geocoded product that contains both the wrapped and

1:36:18 Speaker 1: the unwrapped interferogram, which is generated every with pairs of

1:36:23 Speaker 1: images from every twelve days, and finally, NICSAR data can

1:36:30 Speaker 1: be downloaded for local analysis or accessed and processed in

1:36:34 Speaker 1: the cloud, depending on your workflow. Looking ahead to next

1:36:42 Speaker 1: week's session, Session three, which is focused on monitoring earthquakes, volcanoes,

1:36:47 Speaker 1: and landslides with nicsar's InSAR capability, participants will be able

1:36:53 Speaker 1: to identify the NICAR inser data products and their characteristics,

1:36:58 Speaker 1: recognize the uses of the nice are insert data products

1:37:01 Speaker 1: and their uses and limitations, and apply INSART data to

1:37:07 Speaker 1: assess ground surface displacement due to earthquakes and landslides. Homework

1:37:14 Speaker 1: 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

1:37:22 Speaker 1: August sixth. You can access the homework through the training

1:37:25 Speaker 1: web page. A certificate of completion will be awarded to

1:37:29 Speaker 1: those participants that attend all three live webinars and complete

1:37:34 Speaker 1: the homework assignment by the deadline. You'll receive a certificate

1:37:38 Speaker 1: 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

1:37:48 Speaker 1: questions about the material that was presented today, please feel

1:37:52 Speaker 1: free to contact doctor Franz Meyer or Heidi Christensen through

1:37:57 Speaker 1: their emails listed here. So we reached the end of

1:38:05 Speaker 1: today's session and we will now be starting the question

1:38:10 Speaker 1: and answer session. Great, thank you so much to our

1:38:19 Speaker 1: guest speakers today, wonderful, wonderful presentations from doctor Meyer, Heidi

1:38:27 Speaker 1: Christensen and Appa Mama Sharma. And of course thank you

1:38:31 Speaker 1: to all participants for all of your questions and then

1:38:35 Speaker 1: so much enthusiasm for this amazing mission. So what we've

1:38:39 Speaker 1: been doing has been gathering your questions into a document

1:38:44 Speaker 1: that you see now on your screen, and we will

1:38:48 Speaker 1: start answering your questions. Our time is a little limited,

1:38:51 Speaker 1: so we will try to answer as many questions as possible. However,

1:38:55 Speaker 1: the questions that we do not have time to answer,

1:38:58 Speaker 1: we will answer on this Google doc which we will

1:39:02 Speaker 1: post on the web page on the training web page.

1:39:05 Speaker 1: So let's get started then with the first question here,

1:39:12 Speaker 1: which is question number one. Does NASA or ASF plan

1:39:18 Speaker 1: to provide cloud ready nice our products or APIs that

1:39:22 Speaker 1: facilitate large scale machine learning workflows or platforms such as

1:39:28 Speaker 1: 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

1:39:40 Speaker 3: through asf A. They are stored on the cloud, specifically

1:39:45 Speaker 3: on the Amazon Web Services cloud, and they are stored

1:39:48 Speaker 3: in the cloud. Optimize the HDA five format. Actually, the

1:39:53 Speaker 3: Nicer Project led by JPL has worked extensively on making

1:39:57 Speaker 3: sure that the HDA five data format is cloud ready

1:40:01 Speaker 3: so that folks that want to access data sets in

1:40:04 Speaker 3: place in stream data directly from the bucket can do so.

1:40:10 Speaker 3: The I it is, if you are a user of

1:40:14 Speaker 3: Google at Engine, I would inquire with the GE team

1:40:18 Speaker 3: directly to see what their plans are for adding nicer

1:40:22 Speaker 3: data sets. I am quite sure that they're interested in

1:40:26 Speaker 3: also making nicer data sets available through G but I

1:40:30 Speaker 3: can speak for Google in this case, but please direct

1:40:33 Speaker 3: your questions to the team and and but I would

1:40:38 Speaker 3: expect that there will be some of availability also through

1:40:42 Speaker 3: that platform, especially if the community requests it. There are

1:40:47 Speaker 3: some limited cloud computing environments available. Open Science Lab is

1:40:52 Speaker 3: one of those. There's also resources around NASA that have made,

1:41:00 Speaker 3: you know, offer cloud computing environments and and NASA has

1:41:06 Speaker 3: 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

1:41:15 Speaker 3: you how to access data that's in the cloud. So

1:41:18 Speaker 3: I think I showed those on one of the slides,

1:41:22 Speaker 3: so you can go back to that slide to find it.

1:41:25 Speaker 3: And there's the Nicer Data User Guide is always a

1:41:27 Speaker 3: good place to go back to. This is, as highly said,

1:41:30 Speaker 3: a living document that will list resources as they become available,

1:41:37 Speaker 3: So just navigate back to that page over time for

1:41:41 Speaker 3: 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

1:41:50 Speaker 1: to that, the component of the question related to Google

1:41:56 Speaker 1: earth Engine. Right now, what you can do is upload

1:42:00 Speaker 1: your data onto Google earth Engine and work with that

1:42:05 Speaker 1: platform that way. Okay, the second question number two, is

1:42:12 Speaker 1: 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

1:42:21 Speaker 3: referring to here. But the r slcs are unofficial product

1:42:25 Speaker 3: that nicear is releasing, so once the global data release

1:42:30 Speaker 3: will occur, it will include also the r SLC products

1:42:36 Speaker 3: for every acquisition that that is being made, so it's

1:42:42 Speaker 3: it's one of the official products supported by ISA and

1:42:45 Speaker 3: will be available with the full data release. And then

1:42:48 Speaker 3: if you are referring to the data annotation, so every

1:42:52 Speaker 3: product will have its acquisition date and time annotated both

1:42:56 Speaker 3: in the fine name very long fine name and in

1:42:59 Speaker 3: the middle day to information.

1:43:04 Speaker 1: Great, thank you. Let's move on to question number three. Then,

1:43:07 Speaker 1: will we get the whole raw data set or preprocessed

1:43:10 Speaker 1: ones that we can clip to save RAM and storage

1:43:13 Speaker 1: for low end laptops.

1:43:16 Speaker 4: Yes, go ahead, So there are quite a range of

1:43:22 Speaker 4: products available you can so. There are not currently the

1:43:26 Speaker 4: level zero B products. They weren't included in the February release,

1:43:30 Speaker 4: but they will be included with the July release or

1:43:34 Speaker 4: 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

1:43:41 Speaker 4: or you can choose one of the higher level products

1:43:43 Speaker 4: whatever best suits your workflow.

1:43:49 Speaker 1: Great, thank you, Heidi. Question number four, are there recommended

1:43:53 Speaker 1: preprocessing pipelines for combining nice rstar products with optical data

1:43:58 Speaker 1: sets such as Sentinel two or on set before applying

1:44:01 Speaker 1: machine learning models for glacier monitoring?

1:44:07 Speaker 3: Ah, I'm not sure if you're at the question to

1:44:09 Speaker 3: the end, but yes, certain the nice thing is nice

1:44:13 Speaker 3: that provides fully radiometrically terrain corrected data products. So if

1:44:18 Speaker 3: you're interested in looking at appetitude change, the data sets

1:44:22 Speaker 3: are pretty ready to use alongside landset they're fully geocoded,

1:44:27 Speaker 3: but you may want to make sure that they are

1:44:28 Speaker 3: sampled the same way, So I would you may have

1:44:31 Speaker 3: to resample one of them to match the sampling of

1:44:34 Speaker 3: the other. And there is a valuid pixel mask and

1:44:37 Speaker 3: a calibration mask available for each nicer product that tell

1:44:42 Speaker 3: you which pixels are to be trusted and which ones

1:44:45 Speaker 3: you may want to mask out. But then if you're

1:44:49 Speaker 3: looking for like just looking at malprocesses on glaciers, you

1:44:52 Speaker 3: should be ready to use them jointly.

1:44:59 Speaker 1: Okay, Russian number five. Then I'm working in the archaeological field,

1:45:04 Speaker 1: and I'm studying GIS and its usages to apply them

1:45:07 Speaker 1: in archaeology. How can nyser data and its tools benefit

1:45:10 Speaker 1: this field in the sense of discovering and identifying loss

1:45:14 Speaker 1: 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

1:45:24 Speaker 3: satellite archaeology. El BAND does have some ability to penetrate

1:45:29 Speaker 3: into vegetations and also into surfaces. How far and deep

1:45:33 Speaker 3: you can penetrate will depend, though strongly on the surface itself,

1:45:37 Speaker 3: what kind of soils or rock structures you have and

1:45:41 Speaker 3: what the soil moisture is high penetration you get in

1:45:43 Speaker 3: places like sandy deserts. The Sahara is a really good place,

1:45:48 Speaker 3: and there have been some past studies using elband to

1:45:52 Speaker 3: do archeology in the Sahara. And there's also been some

1:45:55 Speaker 3: work on looking like the large ice sheets and especially

1:45:59 Speaker 3: in places where you have deeper snow layers where people

1:46:03 Speaker 3: have looked for you know, down the aircraft or you

1:46:06 Speaker 3: know installations that over time were buried in snow. So

1:46:10 Speaker 3: there's some some literature that you can look through in

1:46:13 Speaker 3: 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

1:46:23 Speaker 1: for topography and if so, which algorithm and d M

1:46:27 Speaker 1: is used for this correction.

1:46:30 Speaker 4: Yes, the gkv covariance products are RTC products and I've

1:46:36 Speaker 4: added a link to the product specification document for details.

1:46:40 Speaker 4: It uses essentially perdict is thirty meters dem with some

1:46:45 Speaker 4: modifications for the.

1:46:47 Speaker 1: RTC super Thank you, Heidi. Next question number seven, can

1:46:54 Speaker 1: we have more information on one forward processed data will

1:46:57 Speaker 1: be available? Is it in one week? Two weeks July

1:47:00 Speaker 1: thirty first?

1:47:04 Speaker 3: Yeah, so there is no firm a noun state as

1:47:08 Speaker 3: far as far as I know in Erica you can

1:47:09 Speaker 3: chime in here too, But we know that data will

1:47:14 Speaker 3: be flowing soon, and once data are becoming available, we

1:47:17 Speaker 3: will make sure that the community will know that data

1:47:20 Speaker 3: 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

1:47:29 Speaker 1: it will more likely be towards the second half of July,

1:47:33 Speaker 1: sometime in the second half of July. Okay, the next

1:47:39 Speaker 1: question number eight for nice arlband dual polarization products, how

1:47:44 Speaker 1: does the mission account for i anospheric Faraday rotation? Will

1:47:50 Speaker 1: do standard products include any correction quality flag metadata such

1:47:54 Speaker 1: as estimated Faraday rotation angle? And what best practices would

1:47:58 Speaker 1: you recommend for users analyzing two polarizations that may be

1:48:05 Speaker 1: affected by FR induced channel mixing.

1:48:10 Speaker 3: Yeah, there's some iOS information annotated in the metadata. Maybe

1:48:15 Speaker 3: Eric can help out with that too. I'm not sure

1:48:17 Speaker 3: if it's fair dirotation or TC in this particular case,

1:48:22 Speaker 3: but as most data are quite in dual pool, there

1:48:26 Speaker 3: isn't an easy way of correcting for fair deritation. There

1:48:29 Speaker 3: are some cordpot data available, but those currently are also

1:48:32 Speaker 3: not operationally corrected, partly because the acquisition happens at six

1:48:36 Speaker 3: am six pm, and the fair dirotation at these times

1:48:41 Speaker 3: is going to be comparably low, So no current operational correction,

1:48:50 Speaker 3: most likely low fare dirritation in most places around the

1:48:54 Speaker 3: 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,

1:49:03 Speaker 1: if you like to shine in. Okay, so just fyi

1:49:14 Speaker 1: for everyone here. The third session will be focused on

1:49:17 Speaker 1: InSAR and there will be a discussion on ionospheric corrections. Okay.

1:49:25 Speaker 1: Question number nine, how should uncertainty and latency and NICE

1:49:29 Speaker 1: urgent response products be incorporated into disaster response decisions, especially

1:49:35 Speaker 1: when rapid action is required, but the data may not

1:49:38 Speaker 1: be yet fully refined.

1:49:44 Speaker 4: So we do have an urgent response section that goes

1:49:47 Speaker 4: through the latency and kind of what the trade offs

1:49:50 Speaker 4: in terms of processing for those urgent response products, and

1:49:54 Speaker 4: so it would really depend on the particular application whether

1:49:57 Speaker 4: that is a good tradeoff.

1:50:02 Speaker 1: Okay, great, thank you Heidi. And the next question number ten,

1:50:06 Speaker 1: how can the multiple multi algorithm nice are soul moisture

1:50:10 Speaker 1: data along with aggregated backscattering and ancillary layers be integrated

1:50:15 Speaker 1: into GIS and predictive models to support resilient infrastructure and

1:50:19 Speaker 1: climate responsive land new strategies in urban planning.

1:50:26 Speaker 4: Yeah, this is a very it sounds like a fairly

1:50:29 Speaker 4: specific workflow. As demonstrated, all of these products are supported

1:50:34 Speaker 4: in GIS, and so how you choose to use them

1:50:38 Speaker 4: that in that environment or your particular workflows will depend

1:50:42 Speaker 4: on your particular application.

1:50:48 Speaker 1: Great, thank you. Next question number eleven, where can I

1:50:51 Speaker 1: get more details of the NICs our products that we're

1:50:53 Speaker 1: 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

1:51:06 Speaker 4: goes through the data product's overview, goes through kind of

1:51:10 Speaker 4: the different levels, and then there is a page for

1:51:13 Speaker 4: each one of the products that links to the product specification.

1:51:16 Speaker 4: So that's a great place to start to get a

1:51:20 Speaker 4: sense of the products that are available and what you

1:51:23 Speaker 4: might use them for. Yeah.

1:51:26 Speaker 1: Absolutely, The Nicer Data User Guide has an excellent description

1:51:31 Speaker 1: of the different data products, So that's the best place

1:51:33 Speaker 1: to start. Okay, the next question number twelve, I cannot

1:51:38 Speaker 1: find nice our data for Southeast Asia. When will it

1:51:41 Speaker 1: be available?

1:51:44 Speaker 3: Yeah, same as before, We expect the global release in

1:51:49 Speaker 3: the second half of July. Is Erica has mentioned before,

1:51:52 Speaker 3: and that will include Southeast Asia. It will cover all

1:51:56 Speaker 3: 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

1:52:08 Speaker 1: of nicer acquisitions what you recommend before attempting a meaningful

1:52:12 Speaker 1: deformation time series analysis?

1:52:16 Speaker 3: Yeah, that is a question, so if you know who

1:52:19 Speaker 3: have asked this, I would recommend going to the third

1:52:21 Speaker 3: session also where Eric is going to talk extensively about

1:52:25 Speaker 3: nicer InSAR. How many data you need will depend a

1:52:28 Speaker 3: bit on your application. If it's a large signal, like

1:52:33 Speaker 3: an earthquake signal, a single in the paragram is often enough.

1:52:37 Speaker 3: If you're looking for very small data signals that compete

1:52:41 Speaker 3: with like atmospheric noise, you may want to use a

1:52:45 Speaker 3: certain time series. How many you need is going to

1:52:48 Speaker 3: 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

1:52:55 Speaker 3: to answer this in the third session.

1:52:59 Speaker 1: Yes, absolutely agree. The third session will be focused on

1:53:01 Speaker 1: this and Eric will be covering all of these things,

1:53:04 Speaker 1: and these are questions that he'll be addressing. All right,

1:53:10 Speaker 1: So the next question number fourteen, can i SF be

1:53:14 Speaker 1: used through API call to automate for multiple downloads.

1:53:20 Speaker 4: The asf search python package can be used to script

1:53:23 Speaker 4: workflows both to search for data and download it. The

1:53:27 Speaker 4: same thing with the earth access python package, so that's

1:53:30 Speaker 4: probably your best bet for a programmatic workflow.

1:53:37 Speaker 1: Okay, thank you. Number fifteen. Could you recommend a simple

1:53:41 Speaker 1: workflow for going from nicer data download to a basic

1:53:45 Speaker 1: deformation map? Yeah, I'll just okay, go ahead. I think

1:53:54 Speaker 1: in session three, Eric Fielding will be showing that workflow.

1:54:00 Speaker 1: But I see that you've included some additional information here,

1:54:04 Speaker 1: go ahead, fronts.

1:54:06 Speaker 3: Yeah, and I mean there are these gnw G unwrapped

1:54:09 Speaker 3: in the paragram products available that you can scale. For

1:54:14 Speaker 3: large events like earthquakes or stronger volcano displacement. You can

1:54:18 Speaker 3: scale to displacement line of side displacement right away. Time

1:54:21 Speaker 3: series analysis may be required at times. Eric will talk

1:54:24 Speaker 3: about this, and there's also a link for some additional

1:54:27 Speaker 3: materials that will share with this document.

1:54:36 Speaker 1: Okay, great, Next question number sixteen. Can a user download

1:54:39 Speaker 1: clip nysour products covered by the bounding box instead of

1:54:43 Speaker 1: the entire area?

1:54:47 Speaker 4: So, as the fronts demonstrated, there are ways to subset programmatically,

1:54:51 Speaker 4: but there are currently not tools available for spatial subsetting

1:54:55 Speaker 4: in Earth Data Search or Vertex. SF is working on that.

1:54:59 Speaker 4: That's kind of the next thing on our docket for

1:55:01 Speaker 4: Harmony services. So you can check out our development roadmap

1:55:05 Speaker 4: to see the efforts. Are development efforts that are planned,

1:55:10 Speaker 4: currently underweight and planned.

1:55:17 Speaker 1: Okay, great, thank you Heidi. Let's move on to the

1:55:21 Speaker 1: next question. Let's see any news on whether SPAN will

1:55:27 Speaker 1: have any availability within the US.

1:55:33 Speaker 3: Yeah. I linked a r g I S map here

1:55:39 Speaker 3: that shows the observation plan for a nice r and

1:55:42 Speaker 3: there are some SPAN data available also over the US

1:55:45 Speaker 3: over some some calibration sites. I showed it on one

1:55:47 Speaker 3: slide as sort of on a on a word map.

1:55:51 Speaker 3: But if you go to this link that will be

1:55:53 Speaker 3: in this document. Uh, you can explore which which sites

1:55:57 Speaker 3: 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

1:56:10 Speaker 1: files for interferometry purpose and time series analysis. Each scene

1:56:15 Speaker 1: is seven around seven and a half gigabytes. How do

1:56:17 Speaker 1: we deal with these big size images?

1:56:24 Speaker 3: Yeah? I think we showed some samples in a sample notebooks.

1:56:28 Speaker 3: How you can subset in place and download data smaller

1:56:32 Speaker 3: subsets of data sets. Again, also because of the to

1:56:37 Speaker 3: avoid you having to process the inter paragrams yourself, there

1:56:40 Speaker 3: are interferometic data products that Nicile delivers operationally, so you

1:56:46 Speaker 3: don't have to compute them yourself, at least not all

1:56:48 Speaker 3: of them, and they're also smaller in size. And there

1:56:53 Speaker 3: is a likelihood that the community over time will develop

1:56:57 Speaker 3: services that you can utilize to help you out with

1:57:01 Speaker 3: handling these large data volumes.

1:57:08 Speaker 1: Okay, great, Next question the question number nineteen. Will the

1:57:12 Speaker 1: acquisition grids be updated to include the mission the missing

1:57:16 Speaker 1: grids in the future, and if so, will it also

1:57:18 Speaker 1: be available from June twenty twenty six or the providers

1:57:24 Speaker 1: will only include some specific dates.

1:57:31 Speaker 3: So I'm assuming this refers to the initial release not

1:57:35 Speaker 3: having global coverage. NISA does provide global coverage of all

1:57:39 Speaker 3: land masses up to what is the northern latitude. There's

1:57:44 Speaker 3: a hole in the Arctic at I can't remember what

1:57:48 Speaker 3: the cutoff latitude is, but for the rest of it's

1:57:53 Speaker 3: 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

1:58:01 Speaker 3: release date coming soon. Then there is going to be

1:58:05 Speaker 3: a reprocessing campaign where some of the past data sets

1:58:08 Speaker 3: will also be reprocessed that made available since global acquisition

1:58:12 Speaker 3: has started, so you should be able to access most

1:58:15 Speaker 3: of these data sets on a global scale.

1:58:19 Speaker 1: Absolutely, all right. The next question number twenty, What opportunities

1:58:24 Speaker 1: exist within the NICs are community for contributing to collaborative projects,

1:58:30 Speaker 1: data driven publications or applied case studies, specifically in urban

1:58:35 Speaker 1: resilience and infrastructure systems.

1:58:40 Speaker 3: Yes, it is. If you're familiar with sort of the

1:58:43 Speaker 3: NASA solicitation, there's sort of an omnibus solicitation that comes

1:58:48 Speaker 3: out every year that's called ROSES, So just look for solicitations.

1:58:53 Speaker 3: There are many of them will mention NICSAR. There might

1:58:56 Speaker 3: be some that are specific to NISAR, but many of

1:59:00 Speaker 3: the other ones will mention nicer in the call. If

1:59:04 Speaker 3: you know somebody that works with nice already, feel free

1:59:06 Speaker 3: to reach out. There's a new selection of a nicer

1:59:10 Speaker 3: applications and a nicer dart team which used to be

1:59:13 Speaker 3: called the science team, and so who's on the science

1:59:17 Speaker 3: team will become publicly available. Feel free to reach out

1:59:21 Speaker 3: to them and see if they're interested in collaborating. It

1:59:28 Speaker 3: may last we're hoping for open codes, so we mentioned

1:59:32 Speaker 3: a few times, you can contribute workflows. All of the

1:59:36 Speaker 3: workflows that we do know that the community is developing,

1:59:40 Speaker 3: we will share with you through the nicer Data User's Guide,

1:59:45 Speaker 3: so you can contribute as a member of an open

1:59:47 Speaker 3: 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.

2:00:02 Speaker 4: There's a guidance document It was developed for working with

2:00:06 Speaker 4: the original nicar sample data, but it applies to the

2:00:10 Speaker 4: two newer nice art products as well, so if you're interested,

2:00:14 Speaker 4: you can refer to that documentation.

2:00:19 Speaker 1: Great. How about okay, let's move on to question number

2:00:22 Speaker 1: twenty two. Well, the nice sr data acquired over calibration

2:00:26 Speaker 1: 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

2:00:33 Speaker 3: band data will be available through Bonniti, also those over

2:00:36 Speaker 3: the US.

2:00:39 Speaker 1: Okay, wonderful. So how about we take a couple more questions.

2:00:43 Speaker 1: I know we're at time, but we have had so

2:00:46 Speaker 1: many amazing questions, So how about about three more? The

2:00:50 Speaker 1: next question number twenty two? Well, sorry, twenty three. Do

2:00:53 Speaker 1: you know if it's possible to open the nice art

2:00:56 Speaker 1: data in snap?

2:00:59 Speaker 3: Yes, I say, he is Snap supports, but I think

2:01:01 Speaker 3: the better answer is will support nicer. So you can't

2:01:04 Speaker 3: already open snap data as an HDA five file or

2:01:07 Speaker 3: generic HDA five file in Snap, but it will have

2:01:11 Speaker 3: specific Nicer support I think, coming as far as I

2:01:14 Speaker 3: know with Snap version fourteen.

2:01:17 Speaker 1: Okay, great, the next question what is the number twenty four?

2:01:22 Speaker 1: What is the difference between ASF and earth access when

2:01:25 Speaker 1: pulling data programmatically.

2:01:29 Speaker 4: The ASF search package, It's similar to using Vertex versus

2:01:34 Speaker 4: Earth data Search in that ASF search is really optimized

2:01:38 Speaker 4: for finding data that ASF hosts. So there are a

2:01:42 Speaker 4: lot more parameters that are specific to the SR data sets,

2:01:47 Speaker 4: and so it may be easier to find what you're

2:01:49 Speaker 4: looking for or going in on what you're looking for

2:01:51 Speaker 4: using a SF search. But it really just depends on

2:01:54 Speaker 4: user preference, so you can find data using either package.

2:02:00 Speaker 4: It's just what you prefer.

2:02:03 Speaker 1: Okay, wonderful. How about question number twenty five? Is there

2:02:06 Speaker 1: 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

2:02:17 Speaker 3: can take that back to the team. I am not

2:02:20 Speaker 3: aware of a specific ETA, but maybe the JPL team

2:02:24 Speaker 3: is yeah.

2:02:25 Speaker 1: Absolutely, we'll update that answer here in the document, but

2:02:29 Speaker 1: we do have to consult with that team to see

2:02:32 Speaker 1: what is the what is the intended development timeline? All right,

2:02:37 Speaker 1: so how about something let's just skip question number twenty six.

2:02:42 Speaker 1: How about something more focused on the data itself and

2:02:46 Speaker 1: data access? So let's just go to question twenty eight.

2:02:53 Speaker 1: 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

2:03:06 Speaker 4: need to treat it currently. You just need to treat

2:03:08 Speaker 4: the data set as if it's a net CDF file.

2:03:10 Speaker 4: And as long as you do that, then you can

2:03:13 Speaker 4: 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

2:03:21 Speaker 1: three more questions, Okay, I promise just three more. Okay,

2:03:24 Speaker 1: So how about number twenty nine. If we would like

2:03:28 Speaker 1: to process the data and slant range geometry, how can

2:03:32 Speaker 1: we do it?

2:03:36 Speaker 3: Yeah? So there's I think we pointed at some resources.

2:03:39 Speaker 3: ICE three is a resource that's the software package that

2:03:43 Speaker 3: is also behind the operational NISA processing. There's some courses

2:03:47 Speaker 3: that I linked earlier on how to use ICE three.

2:03:51 Speaker 3: Snap is the software package that will support processing data

2:03:55 Speaker 3: and slant range geometry and very likely I mean commercial

2:04:00 Speaker 3: SAR data processing tools such as Gamma and similar tools

2:04:04 Speaker 3: will also support nice are in traditional NISER processing from

2:04:09 Speaker 3: the slant range geometry.

2:04:14 Speaker 1: Okay, great, okay, the next question number thirty, what is

2:04:18 Speaker 1: frequency A and frequency B?

2:04:23 Speaker 3: Yeah, we can tacting this. So NICER operates transmits two

2:04:28 Speaker 3: different frequencies, mostly to facilitate ionospheric correction. The main imaging

2:04:33 Speaker 3: band is frequency A, So if you have a if

2:04:36 Speaker 3: you hear of a twenty megahertz data set or forty

2:04:38 Speaker 3: megahad stata set, those are the bandwidths of the frequency

2:04:42 Speaker 3: A transmission, so that's on one side of the spectrum

2:04:47 Speaker 3: that NICER supports. Additionally, there's a signal transmitted on the

2:04:52 Speaker 3: other side of the spectrum, using then the frequency diversity

2:04:55 Speaker 3: 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,

2:05:06 Speaker 3: but still provides you a really nice image. And so

2:05:09 Speaker 3: both of those are available for you to analyze. So

2:05:12 Speaker 3: two products slightly different center frequency and slightly different resolution

2:05:16 Speaker 3: that you can average.

2:05:19 Speaker 1: Okay, great, thank you, that was a great explanation. All right,

2:05:22 Speaker 1: So how about question number thirty four. This is the

2:05:25 Speaker 1: last one. Is there documentation on how to use the

2:05:28 Speaker 1: earth access Python package and can we use it to

2:05:31 Speaker 1: search for available images in a specific area and time

2:05:35 Speaker 1: and download only for example gcov at frequency A.

2:05:42 Speaker 4: So you can refer to the earth Access section of

2:05:44 Speaker 4: the Nicer Data user guide which goes through the capabilities there.

2:05:49 Speaker 4: I'm not sure if you can excrapt specific variables. I

2:05:55 Speaker 4: think so, but I'm not certain.

2:06:01 Speaker 1: Okay, yeah, we will look into this and update this document.

2:06:05 Speaker 1: So with that, I thank everyone for their patients. I

2:06:09 Speaker 1: know we're over time. I first of all, I do

2:06:11 Speaker 1: want to thank our experts today are invited experts doctor

2:06:16 Speaker 1: 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

2:06:27 Speaker 1: been amazing and putting all of this together. And to

2:06:31 Speaker 1: all of you participants for your great interest and enthusiasm

2:06:35 Speaker 1: and amazing questions related to nissart today. So please stay tuned.

2:06:42 Speaker 1: There's one more amazing session and that'll be next Thursday.

2:06:46 Speaker 1: I will be focused on Instar and my colleague doctor

2:06:50 Speaker 1: Eric Fielding will be the invited expert for that session.

2:06:56 Speaker 1: So see you in a week, have a great day everyone,

2:07:00 Speaker 1: and I stay tuned. Bye bye