NASA ARSET An Introduction to NISAR

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

0:06 Speaker 1: Generation Radar Observations for Earth applications. My name is Erica

0:11 Speaker 1: Podest and I'm a scientist at NASA's Jet Propulsion Laboratory,

0:15 Speaker 1: where I study terrestrial ecosystems using ZAR. I'm also an

0:20 Speaker 1: instructor with the RSET program. We're entering a really exciting

0:25 Speaker 1: new era of Earth observation with the launch of NYSAR.

0:29 Speaker 1: This mission will provide an unprecedented view of how Earth's

0:33 Speaker 1: surface is changing, enabling routine monitoring of ecosystems, natural hazards,

0:40 Speaker 1: water resources, agriculture, and the cryosphere at a global scale.

0:45 Speaker 1: Although this webinar series is an introduction to NYSAR, we

0:49 Speaker 1: will also cover KEYSAR fundamentals to provide the context needed

0:54 Speaker 1: to understand the mission's capabilities, data products.

0:57 Speaker 2: And potential applications.

1:00 Speaker 1: The end of this training, you'll have a foundation for

1:02 Speaker 1: understanding how NICE data can be used to address a

1:07 Speaker 1: wide range of Earth science and decision support challenges.

1:14 Speaker 2: Before I begin, I'd like.

1:15 Speaker 1: To provide a bit of background about the RSET program.

1:22 Speaker 1: NASA's Applied Remote Sensing Training Program or r SET, offers

1:27 Speaker 1: free trainings on the use of remote sensing and model data,

1:31 Speaker 1: along with methods and tools for the global science and

1:34 Speaker 1: application communities. Our trainings are tailored to different experienced levels,

1:39 Speaker 1: ranging from introductory to intermediate to advanced. Ourset trainings focus

1:44 Speaker 1: on six thematic areas agriculture, disasters, ecological conservation, health and

1:51 Speaker 1: air quality, water resources, and wildland fires. Ourset trainings are

1:59 Speaker 1: either online or in person. All are free of charge

2:02 Speaker 1: and use open source data and software. Some of our

2:05 Speaker 1: trainings are delivered in both English and Spanish like this one,

2:09 Speaker 1: and all of our training sessions have a Spanish presentation available.

2:17 Speaker 1: Now I'll provide an overview of this webinar series. NISAR

2:24 Speaker 1: marks a new era of Earth observation. A fundamental challenge

2:29 Speaker 1: in Earth observation is monitoring change consistently across the globe,

2:34 Speaker 1: and NYSAR was designed to address this challenge by providing frequent,

2:39 Speaker 1: systematic radar observations that are independent of sunlight and that

2:44 Speaker 1: are largely unaffected by cloud cover. So these are the

2:50 Speaker 1: training learning objectives. By the end of this webinar series,

2:54 Speaker 1: participants will be able to identify the characteristics, capabilities, and

2:59 Speaker 1: limitations of NISAR data, recognize how NISAR data can be

3:04 Speaker 1: applied to decision making related to flooding, earthquakes, and landslides.

3:09 Speaker 1: Differentiate between the different types of NICSAR data products and

3:14 Speaker 1: their applicability for different use cases. Demonstrate how to access

3:19 Speaker 1: NICSAR data and tools via the ASFT platform to search, visualize,

3:24 Speaker 1: and analyze data provided case studies related to floods, earthquakes,

3:31 Speaker 1: and landslides. Visualized flooding with gcov files and generate landslide

3:37 Speaker 1: risk and volcano deformation maps for events of interest using

3:42 Speaker 1: the interfarogram, the GUNW files and qgis. Demonstrate how to

3:49 Speaker 1: access NICARSPAN data via the Israebunity platform and use available

3:55 Speaker 1: tools for basic visualization and analysis. Prerequisites for this training

4:03 Speaker 1: we suggest the fundamentals of remote sensing as well as

4:07 Speaker 1: an introduction to synthetic apeture radar and its applications. So

4:12 Speaker 1: there was one in twenty seventeen and there was also

4:15 Speaker 1: one in twenty twenty four. So these both of these

4:19 Speaker 1: contain background reviews of radar as well as InSAR. We

4:26 Speaker 1: will be focusing on InSAR in this training. The session

4:30 Speaker 1: three we'll discuss InSAR and as you all probably already know,

4:36 Speaker 1: InSAR is all about the face of the star signal.

4:42 Speaker 2: And this is a training outline.

4:44 Speaker 1: There are three sessions associated with this webinar series. Today

4:49 Speaker 1: is session one an introduction to NISAR. The next session

4:55 Speaker 1: will be on July ninth and it will be focused

4:58 Speaker 1: on NISAR data access and tools. That'll be led by

5:03 Speaker 1: the ASF the Alaska Satellite Facility DOC, which is the

5:10 Speaker 1: data archive center that holds all of the NISAR data.

5:14 Speaker 1: And then the third session will be on the sixteenth

5:17 Speaker 1: of July focusing on monitoring earthquakes, volcanoes, and landslides with

5:22 Speaker 1: nisar's InSAR capability, and that'll be led by my colleague,

5:27 Speaker 1: doctor Eric Fielding from JPL. All sessions are at the

5:31 Speaker 1: same time. There is one homework associated with this webinar

5:36 Speaker 1: series and it will open on the last session on

5:40 Speaker 1: the day of the last session on July sixteenth. The

5:43 Speaker 1: due date for the homework is August sixth. A certificate

5:48 Speaker 1: of completion will be awarded to those participants who attend

5:52 Speaker 1: all live sessions and complete the homework assignment by the

5:55 Speaker 1: do date. Now, let's focus on in this session and

6:01 Speaker 1: introduction to NISAR. I will be your instructor for this training.

6:07 Speaker 1: I'm a scientist at NASA's Jet Propulsion Laboratory, where I

6:11 Speaker 1: use microwave remote sensing, particularly SAR, to study wetlands vegetation,

6:16 Speaker 1: growing season in the northern high latitude regions, and land

6:21 Speaker 1: cover and land use change. I also serve as the

6:24 Speaker 1: community Engagement and Training coordinator for the NISAR mission, and

6:29 Speaker 1: I'm a member of nisar's science team supporting the Wetlands

6:33 Speaker 1: Science Area. Through these roles, I help connect the science

6:37 Speaker 1: applications and user communities with NISAR data and capabilities. The

6:44 Speaker 1: objectives for this first session. By the end of this session,

6:48 Speaker 1: participants will be able to identify the characteristics, capabilities, and

6:52 Speaker 1: limitations of NISAR data, recognize how NISAR data can be

6:57 Speaker 1: applied to decision making related to earthquakes and landslides, and

7:03 Speaker 1: identify the different NISAR data products. How to ask questions.

7:11 Speaker 1: To ensure we see your question, please write your question

7:16 Speaker 1: in the Q and A box, which you can locate

7:18 Speaker 1: it by doing a click on the three dots in

7:22 Speaker 1: the bottom right of of the window in the platform,

7:29 Speaker 1: and there you'll see a slider, a Q and A option,

7:33 Speaker 1: and you can also see a standalone slide o, tap

7:36 Speaker 1: or app. Write your questions are all your questions there,

7:40 Speaker 1: and we will answer them during the Q and A

7:42 Speaker 1: session at the end, and we will try to answer

7:46 Speaker 1: all the questions during the Q and A session. The

7:49 Speaker 1: remaining questions will be answered in the Q and A document,

7:53 Speaker 1: which we will post on the training website about a

7:56 Speaker 1: week after the train. Okay, so let's get started providing

8:02 Speaker 1: an overview of the NYSAR mission. So let's start out

8:08 Speaker 1: with why NYSAR. Many of the most important changes on

8:12 Speaker 1: Earth occur continuously and often in areas that are either

8:16 Speaker 1: cloudy or under darkness. And these changes occur at either

8:21 Speaker 1: very small scales on the order of centimeters or very

8:25 Speaker 1: large scales on the order of meters. And some of

8:28 Speaker 1: these changes that occur on the Earth's surface are earthquakes

8:34 Speaker 1: and tectonic deformation, glacier and ice sheet motion, landslides, surface

8:42 Speaker 1: subsidence due to groundwater extraction, and there are other changes

8:46 Speaker 1: to things like forest change and biomass, floods and wetland dynamics,

8:52 Speaker 1: and so to monitor these processes globally, we need a

8:56 Speaker 1: sensor that works day and night. I can see through

8:59 Speaker 1: clouds and can measure small to large changes in the.

9:03 Speaker 2: Earth surface.

9:07 Speaker 1: NASA stands for NASA ISRA which is the Indian Space

9:10 Speaker 1: Research Organization Synthetic Aperture Radar. It's a joint mission between

9:15 Speaker 1: NASA and ISRAE and it uses SAR to measure and

9:20 Speaker 1: understand changes in Earth's land, ice, water, and vegetation. The

9:25 Speaker 1: origins of NYSAR can be traced to CISAT in nineteen

9:29 Speaker 1: seventy eight, which was the first civilian l band T

9:32 Speaker 1: SAR mission in space, and it demonstrated the tremendous potential

9:37 Speaker 1: of radar observations for Earth science and applications. Although the

9:42 Speaker 1: mission operated for only a few months, it transformed our

9:46 Speaker 1: understanding of what TSAR could reveal about Earth surface. In

9:51 Speaker 1: two thousand and seven, the National Research Council de Catal

9:56 Speaker 1: Survey identified NYSAR as one of the highest part already

10:00 Speaker 1: Earth observation missions needed to advance our understanding of the

10:04 Speaker 1: Earth system through systematic global observations of Earth's changing surface

10:10 Speaker 1: and building. Then on decades of scientific and technological advances,

10:15 Speaker 1: discussions between NASA and Israel began in twenty eleven to

10:20 Speaker 1: explore a potential partnership.

10:22 Speaker 2: After several years.

10:23 Speaker 1: Of planning and coordinating, the agencies formalized their collaboration in

10:29 Speaker 1: twenty fourteen, and what followed was more than a decade

10:33 Speaker 1: of joint development, integration, testing, and preparation. Those efforts culminated

10:39 Speaker 1: in the successful launch of NYSAR from India on July thirtieth,

10:44 Speaker 1: twenty twenty five, and the animation that you see here

10:48 Speaker 1: illustrates the deployment sequence following launch. One of the mission's

10:53 Speaker 1: most impressive engineering features is its twelve meter deployable reflector antenna,

10:58 Speaker 1: which enables the collect action of high quality radar observations

11:02 Speaker 1: over a wide swath. The scientific motivation for NISAR spans

11:07 Speaker 1: multiple disciplines. Its observations supports studies of ecosystems and global biomass,

11:15 Speaker 1: I sheet and SIS dynamics, natural hazards, agriculture, hydrology, and

11:21 Speaker 1: many other processes that are critical to understanding our changing

11:24 Speaker 1: planet and supporting societal hazards and resilience. NISAR is the

11:32 Speaker 1: result of a highly complex and collaborative partnership between NASA

11:36 Speaker 1: and ISRAE, requiring close coordination across all aspects of the mission,

11:41 Speaker 1: from instrument development and spacecraft integration to operations and science.

11:47 Speaker 1: The table here shows the contributions from NASA and from Israel.

11:53 Speaker 1: NASA provided the alband star system, operating at a wavelength

11:57 Speaker 1: of twenty four centimeters.

11:59 Speaker 2: The radar system.

12:00 Speaker 1: Includes a twelve meter deployable reflector, a nine meter boom,

12:04 Speaker 1: and an approximately four meter long octagonal cylinder that houses

12:09 Speaker 1: the L band and SPAN radar electronics. And you saw

12:12 Speaker 1: this in the animation from the previous slide. And the

12:16 Speaker 1: two radar systems, the L and S ban radar systems

12:19 Speaker 1: can work simultaneously together. ISRAE provided the SPAN Star, which

12:25 Speaker 1: operates at a wavelength of nine point four centimeters, as

12:28 Speaker 1: well as the spacecraft bus and launch vehicle. NASA's Jet

12:33 Speaker 1: Proportion Laboratory also developed key electronics that enable communication between

12:38 Speaker 1: the spacecraft boss and the radar instruments, along with a

12:42 Speaker 1: high rate downlink system to transmit the very large volume

12:46 Speaker 1: of science data collected by the mission. The L band

12:51 Speaker 1: radar alone generates approximately four point four terabytes of raw

12:57 Speaker 1: data each day. ZO provided a separate high rate downlink

13:02 Speaker 1: system for the S band radar, meaning the mission operates

13:07 Speaker 1: two dedicated telecommunication systems to support the transmission of this

13:12 Speaker 1: very large.

13:13 Speaker 2: Volume of data to the ground.

13:16 Speaker 1: The partnership extends Beyond the hardware, NASA and Israel also

13:20 Speaker 1: share mission operations responsibilities and jointly support the science team

13:26 Speaker 1: and user community. Overall, NYSAR is one of the most

13:30 Speaker 1: capable Earth observing radar missions ever developed. The spacecraft has

13:35 Speaker 1: a total mass of twenty seven hundred kilograms and requires

13:40 Speaker 1: about six point two kilowatts of power, making it a

13:45 Speaker 1: high capacity, high performance system designed to support continuous global

13:50 Speaker 1: observations of the Earth's surface. NISAR has several unique characteristics

13:58 Speaker 1: that distinguish it from other spaceborn star missions. First, NISAR

14:03 Speaker 1: carries both an L band and an S band radar

14:06 Speaker 1: that can operate simultaneously, and because these radars operate at

14:11 Speaker 1: different wavelengths, so it would be twenty four centimeter wavelength

14:14 Speaker 1: at L band and a nine point four centimeter wavelength

14:18 Speaker 1: at S band, this means that they are sensitive. Each

14:23 Speaker 1: sensor is sensitive to different characteristics of the Earth's surface.

14:28 Speaker 1: Another key innovation is nisar's use of the sweepstar technology,

14:32 Speaker 1: and this represents a significant advancement over traditional spaceborn star systems.

14:39 Speaker 1: Sweepsar enables NISAR to achieve a wide two hundred and

14:44 Speaker 1: forty kilometer imaging swath while maintaining high spatial resolution and

14:49 Speaker 1: pleurometric measurement capabilities. NISAR also incorporates highly precise pointing and

14:56 Speaker 1: orbit control, both of which are essential for measuring subtle

15:00 Speaker 1: changes in the Earth's surface through a technique known as

15:04 Speaker 1: interferometric SAR or InSAR. Pointing control ensures that the radar

15:10 Speaker 1: beam is directed at the correct location on the earth

15:12 Speaker 1: surface with the precision required for repeat observations, and orbit

15:18 Speaker 1: control maintains the satellite's flight path so that the same

15:21 Speaker 1: locations can be observed repeatedly from nearly identical viewing geometries.

15:28 Speaker 1: NISAR is capable of repeating its orbit to within one

15:31 Speaker 1: hundred meters throughout the orbits, providing the stability needed for

15:36 Speaker 1: accurate surface deformation measurements. Another distinctive feature is nisar's high

15:42 Speaker 1: observation duty cycle, which refers to the fraction of the

15:46 Speaker 1: orbit during which the radar is actively collecting data. NISAR

15:51 Speaker 1: is designed for systematic global mapping, and the L band

15:55 Speaker 1: radar operates for approximately fifty percent of each orbit, while

15:59 Speaker 1: the SAN operates for about ten percent. This enables NISAR

16:04 Speaker 1: to routinely observe nearly all of Earth's land and ice

16:08 Speaker 1: covered surfaces. Finally, NISAR is a left looking radar system.

16:13 Speaker 1: I'll talk a little bit more about this in the

16:15 Speaker 1: upcoming slides, but this means that the viewing geometry provides

16:20 Speaker 1: comprehensive coverage of Antarctica and complements the observations from many

16:25 Speaker 1: existing SAR missions, which are predominantly right looking. And all

16:30 Speaker 1: of these unique features combined together make NISAR a powerful

16:35 Speaker 1: science and applications tool. NISAR operates at an altitude of

16:44 Speaker 1: seven hundred and forty seven kilometers in a near polar

16:49 Speaker 1: down dusk sun synchronous orbit. The satellite has a twelve

16:53 Speaker 1: day exact repeat cycle. However, by combining observations from both

16:58 Speaker 1: ascending and decent repeat orbits, the same area can be

17:02 Speaker 1: observed twice within each twelve day period. The acending pass

17:08 Speaker 1: crosses the equator at approximately six am local time, while

17:12 Speaker 1: the descending pass crosses at approximately six pm. Depending on

17:16 Speaker 1: the acquisition mode. NISAR provides spatial resolutions ranging from three

17:21 Speaker 1: to ten meters, and this animation illustrates the acquisition plan

17:25 Speaker 1: for the a sending portion of the orbits. The different

17:29 Speaker 1: colors represent distinct acquisition modes, which are selected based on

17:33 Speaker 1: the characteristics of the target being observed, scientific objectives, and

17:38 Speaker 1: practical resource constraints. For example, CIS is shown in pink

17:44 Speaker 1: and is imaged using a lower resolution mode because frequent

17:48 Speaker 1: observations are required and the features of interest generally occur

17:53 Speaker 1: at larger spatial scales. In contrast, North America is observed

17:58 Speaker 1: using the highest resolution mode with full polarimetric capability, enabling

18:03 Speaker 1: the most precise measurements.

18:06 Speaker 2: Most of the rest of the.

18:07 Speaker 1: World is imaged using a moderate resolution dual polarization mode

18:12 Speaker 1: that balances measurement quality with global coverage requirements. So that

18:19 Speaker 1: provides a little bit of background on nisar. However, before

18:23 Speaker 1: I start diving into the data and the different science

18:28 Speaker 1: and applications it can address, I want to provide some

18:32 Speaker 1: context about what radar is actually seeing and measuring and

18:37 Speaker 1: what radar.

18:38 Speaker 2: Is sensitive to.

18:39 Speaker 1: So this next portion will give a brief overview of

18:44 Speaker 1: radar remote sensing. So let's start with the very basics,

18:49 Speaker 1: and this is the electromagnetic spectrum. It's the range of

18:53 Speaker 1: electromagnetic energy that spans from very long wavelengths such as

18:57 Speaker 1: radio waves, which can be the length of a football field,

19:01 Speaker 1: to very short wavelengths such as gamma rays, which are

19:04 Speaker 1: the length of an atomic nucleus. Remote sensing sensors are

19:09 Speaker 1: designed to operate at specific regions of the electromagnetic spectrum

19:14 Speaker 1: according to their intended application.

19:18 Speaker 2: Microwave sensors operate within.

19:20 Speaker 1: The range delineated in this figure in black, and this

19:25 Speaker 1: range is at a much lower frequency range than optical

19:29 Speaker 1: and infrared sensors. So to put things into context, the

19:33 Speaker 1: wavelength of light is about three hundred and ninety to

19:36 Speaker 1: seven hundred nanometers, while for microwaves it's on the order

19:41 Speaker 1: of zero point three to one hundred centimeters. And because

19:46 Speaker 1: of this huge disparity in wavelengths, the features on the

19:52 Speaker 1: Earth's surface appear differently in the microwave range than in

19:56 Speaker 1: the optical range, and so is fundamentally different. The information

20:03 Speaker 1: content is fundamentally different than optical images. So what are

20:10 Speaker 1: the advantages and disadvantages of radar or optical remote sensing?

20:14 Speaker 1: First of all, radar has a nearly all weather capability,

20:18 Speaker 1: so you can see through clouds and most weather conditions.

20:21 Speaker 1: And I do talk about nearly because if you have

20:25 Speaker 1: very heavy thunderstorms that might affect the signal. It has

20:31 Speaker 1: a day or night capability, so it's an active sensor.

20:34 Speaker 1: We're sending out a signal and we can therefore acquire

20:38 Speaker 1: images of the Earth's surface, whether it's day or whether

20:42 Speaker 1: it's night. It can penetrate through the vegetation canopy or

20:46 Speaker 1: through a medium, so that signal that we're sending from

20:50 Speaker 1: the sensor can penetrate through vegetation, through soil, through snow.

20:58 Speaker 1: The radar signal has minimal atmospheric effects, and the most

21:05 Speaker 1: important part is that radar is sensitive to two things

21:09 Speaker 1: structure and moisture.

21:11 Speaker 2: Surface moisture.

21:13 Speaker 1: Optical is sensitive to the chemical properties of the surface,

21:17 Speaker 1: so very different information disadvantages of radar is that the

21:24 Speaker 1: information content is different than optical, so it's not necessarily intuitive,

21:29 Speaker 1: and sometimes it's difficult to interpret. There's the speckle effect

21:33 Speaker 1: that you've all heard about or know about. That's the

21:38 Speaker 1: kind of the salt and pepper effect in the images.

21:43 Speaker 1: It's not noise, it's actually part of the signal, but

21:46 Speaker 1: it is that graininess in the images that you have

21:49 Speaker 1: to deal with, and then the effects of topography that

21:52 Speaker 1: you need to deal with as well. So let's begin

21:57 Speaker 1: with some basic concepts of radar. It stands for radio

22:01 Speaker 1: detection and ranging, and, as the acronym suggests, radar functions

22:05 Speaker 1: as a ranging or distance measuring device. Essentially, a radar

22:09 Speaker 1: sensor emits a signal towards the Earth's surface and measures

22:13 Speaker 1: the time it takes for the signal to return. All

22:17 Speaker 1: imaging radars are side looking. If a radar were to

22:20 Speaker 1: look straight down, as depicted in the figure on the left,

22:24 Speaker 1: it would not be able to differentiate between two points

22:28 Speaker 1: A and B. In this case, the signal would reach

22:31 Speaker 1: points A and B at the same time and return

22:34 Speaker 1: to the sensor at the same time, which is why

22:37 Speaker 1: you wouldn't be able to differentiate them. However, when the

22:40 Speaker 1: radar is side looking, as shown in the figure on

22:43 Speaker 1: the right, the time it takes for the signal to

22:46 Speaker 1: reach points A and B differs along, allowing four points

22:51 Speaker 1: A and B to be differentiated. Synthetic aperture radars are

22:56 Speaker 1: side looking systems with an illumination direction, usually perpendicular to

23:02 Speaker 1: the flight line. So nisar is a left looking radar,

23:07 Speaker 1: and that means it's looking to the left of the

23:11 Speaker 1: satellite's direction of travel, and as a consequence, coverage at

23:16 Speaker 1: high latitudes differs between the Arctic and Antarctic in the

23:20 Speaker 1: northern hemisphere. The left looking geometry creates a larger gap

23:24 Speaker 1: around the North Pole, leaving regions north of approximately seventy

23:29 Speaker 1: seven point five degrees latitude outside the standard imaging swath,

23:35 Speaker 1: and this unobserved area is shown by the red circle

23:38 Speaker 1: in the North Pole coverage map. In contrast, the coverage

23:42 Speaker 1: gap around the South pole is substantially smaller, as illustrated

23:47 Speaker 1: in the Antarctic map. Let's begin by reviewing how radar

23:53 Speaker 1: images are formed. NYSAR measures to fundamental properties of the

23:58 Speaker 1: returned radar signs, the amplitude and the phase, so radar

24:03 Speaker 1: poses travel at the speed of light, and the sensor

24:06 Speaker 1: records only the portion of the transmitted energy that is

24:10 Speaker 1: scattered back toward the antenna. The amplitude represents the strength

24:16 Speaker 1: of the return signal and provides information about the reflective

24:22 Speaker 1: properties of the Earth surface. The signal power, which is

24:26 Speaker 1: proportional to the square of the amplitude, is calibrated and

24:30 Speaker 1: normalized to produce the radar backscattering coefficient or sigma not

24:36 Speaker 1: and this quantity is a measure of radar backscatter and

24:39 Speaker 1: is typically expressed in decibels using a logarithmic scale, so

24:45 Speaker 1: backscatter values can range from approximately minus thirty to minus

24:49 Speaker 1: twenty five deb for surfaces that reflect very little energy

24:54 Speaker 1: back to the radar, and these areas appear as dark

24:58 Speaker 1: tones in the radar images. And for example, the one

25:02 Speaker 1: you see here, there's a river that goes through the

25:05 Speaker 1: middle of the image, and that river has low backscatter

25:09 Speaker 1: because the smooth water surface reflects most of the radar

25:13 Speaker 1: energy away from the satellite. In contrast, surfaces that strongly

25:18 Speaker 1: scatter radar energy back to the sensor have higher backscatter values,

25:24 Speaker 1: often exceeding zero dB, and these areas appear as bright

25:28 Speaker 1: tones in the image and are typically associated with inundated

25:33 Speaker 1: vegetation and urban areas. The second measurement is phase, which

25:39 Speaker 1: describes the position of the radar wave within its cycle.

25:43 Speaker 2: At the moment the signal is received.

25:48 Speaker 1: Phase is usually measured in angular units such as degrees

25:52 Speaker 1: or radiance, and describes, as mentioned, the position of a

25:55 Speaker 1: wave within its waveform cycle. So for nysar, the radar

25:59 Speaker 1: wavelength is approximately twenty four centimeters for L band and

26:03 Speaker 1: nine point four centimeters for S band, and as the

26:06 Speaker 1: radar signal travels from the satellite to the Earth surface

26:10 Speaker 1: and back. It completes as you can imagine millions of

26:14 Speaker 1: wave cycles, and it's just not possible to count all

26:18 Speaker 1: of these cycles. Now viewed on its own, phase image

26:22 Speaker 1: often appears noisy and difficult to interpret, such as the

26:25 Speaker 1: example shown in the upper right here.

26:28 Speaker 2: However, one of.

26:29 Speaker 1: The most powerful applications of sarfase is interferometry or InSAR

26:35 Speaker 1: interferometric synthetic aperture radar, which measures the phase difference between

26:39 Speaker 1: two radar images acquired at different times or different viewing angles. Now,

26:46 Speaker 1: in the case of NYSAR InSAR, it's acquired the images

26:50 Speaker 1: are acquired at different times, and that's also known as

26:53 Speaker 1: repeat pass interferometry, and these phase differences between two different

26:58 Speaker 1: images can be converted it into extremely precise measurements of

27:02 Speaker 1: surface deformation, often detecting displacements of just millimeters to centimeters.

27:09 Speaker 1: Because nysar's radar wavelength is relatively short, as mentioned twenty

27:13 Speaker 1: four centimeters for lband nine point four four S band,

27:17 Speaker 1: even very small changes in the distance between the satellite

27:21 Speaker 1: and the ground produce measurable shift phase shifts, and this

27:25 Speaker 1: sensitivity enables scientists to monitor subtle surface movements associated with

27:31 Speaker 1: for example, earthquakes, volcanic activity, groundwater extraction, landslides, infrastructure deformation,

27:39 Speaker 1: and permafrost thought to name a few. In addition, the

27:43 Speaker 1: consistency or the coherence of the phase over time provides

27:48 Speaker 1: valuable information about surface stability. So if the phase does

27:52 Speaker 1: not change, that means the surface is stable and areas

27:57 Speaker 1: with stable phase behavior tend to remain unchanged, while a

28:01 Speaker 1: loss of phase coherence can indicate some sort of change,

28:06 Speaker 1: whether it's vegetation growth, surface disturbance, tectonic movement, or any

28:11 Speaker 1: other dynamic process. So now let's discuss the radar scattering mechanisms.

28:25 Speaker 1: So we discuss amplitude phase and nisar's interferometric capabilities. In

28:30 Speaker 1: session three of this training, we will take a deeper

28:33 Speaker 1: dive into nisar's insart data products and explore the different

28:38 Speaker 1: applications they enable. For now, let's focus on how the

28:43 Speaker 1: radar signal interacts with the earth surface. So, when the

28:47 Speaker 1: radar transmits a signal towards the ground, the way that

28:50 Speaker 1: signal is reflected or scattered depends on the physical properties

28:55 Speaker 1: of the surface. Different types of surfaces produce distinct scattering

29:00 Speaker 1: mechanisms which are fundamental to interpreting radar imagery the amplitude imagery. Now,

29:07 Speaker 1: if the surface is very smooth, such as a.

29:10 Speaker 2: Calm water body or a paved road.

29:12 Speaker 1: Most of the radar energy is reflected away from the satellite,

29:16 Speaker 1: much like a mirror reflecting light, and this is known

29:21 Speaker 1: as specular reflection and results in very little energy returning

29:26 Speaker 1: to the sensor, and as a result, these areas typically

29:30 Speaker 1: appear dark in radar images. As surface roughness increases, the

29:36 Speaker 1: radar signal is scattered in many different directions. Some of

29:40 Speaker 1: that energy is scattered back toward the satellite, producing a

29:43 Speaker 1: stronger return signal than say, a smooth specular surface, and

29:50 Speaker 1: this type of interaction is often referred to as surface scattering.

29:57 Speaker 1: When the radar signal encounters a volume, such as a forest, canopy,

30:02 Speaker 1: a snowpack, or even soil, the signal can penetrate into

30:07 Speaker 1: the material up to a certain point and interact with

30:10 Speaker 1: multiple components within it. So in a forest, for example,

30:15 Speaker 1: the radar wave may scatter from leaves, branches, stems, trunks,

30:19 Speaker 1: and the ground surface, and the combined contribution of these

30:23 Speaker 1: many interactions is known as volume scattering. A fourth important

30:30 Speaker 1: mechanism is a scattering mechanism is double bound scattering, and

30:35 Speaker 1: this occurs when the radar signal reflects between two surfaces

30:40 Speaker 1: that are roughly perpendicular to one another before returning to

30:44 Speaker 1: the satellite. So common examples include tree trunks standing in

30:48 Speaker 1: flooded areas, or buildings adjacent to a road, or flat

30:54 Speaker 1: ground surfaces. In these cases, a large fraction of the

30:58 Speaker 1: radar energy is redirected back toward the sensor. So the

31:02 Speaker 1: smooth surface causes the radar signal to be reflected away

31:07 Speaker 1: from the sensor, but then that perpendicular feature or surface

31:15 Speaker 1: causes that energy to be redirected back toward the sensor,

31:19 Speaker 1: producing a very strong return that appears very bright in

31:23 Speaker 1: the radar imagery. So in the next light, I'll show

31:26 Speaker 1: you these different backscattering mechanisms how they appear in a

31:31 Speaker 1: radar amplitude image. So this is an example of the

31:39 Speaker 1: different scattering mechanisms that I just discussed and how they

31:42 Speaker 1: look like in a radar image. So this here what

31:46 Speaker 1: you're seeing. This is a SMAP radar mosaic of the

31:48 Speaker 1: Amazon basin. It's all band HH polarization. What I want

31:54 Speaker 1: you to focus on are the different circles colored circles

31:59 Speaker 1: so black.

32:01 Speaker 2: Sorry.

32:01 Speaker 1: The blue circle is an example of a speculat surface.

32:06 Speaker 1: That's the Amazon River, so it's an open water body

32:10 Speaker 1: and the energy is scattered away from the satellite, which

32:13 Speaker 1: is why it appears very dark. There's a rough surface

32:17 Speaker 1: that's the red circle. These are areas where there's probably

32:21 Speaker 1: been cut down of trees due to grazing or due

32:26 Speaker 1: to agriculture, so there is some sort of roughness on

32:30 Speaker 1: the ground and part of the energy is scattered back

32:34 Speaker 1: towards the satellite. So these areas are not as dark

32:37 Speaker 1: as open water bodies, but there's some return to the sensor.

32:45 Speaker 1: And then there's the green circle that's volume scattering, So

32:50 Speaker 1: the signal is penetrating through the forest canopy, interacting with

32:53 Speaker 1: components within the canopy. And then there's double bounce that's

32:57 Speaker 1: the purple circle. And then this case it's inundated vegetation.

33:01 Speaker 1: The signal is penetrating through the canopy, it's hitting the

33:06 Speaker 1: standing water underneath the vegetation. That standing water is a

33:10 Speaker 1: specula reflector. It directs the energy away from the satellite,

33:15 Speaker 1: but then the tree trunks redirect the energy back towards

33:18 Speaker 1: the satellite. So as you can see. The radar can

33:24 Speaker 1: provide valuable information for different different things, different science and

33:30 Speaker 1: application areas. It's great for detecting inundated vegetation, it's great

33:36 Speaker 1: for looking at forest degradation, biomass measures of biomass, looking

33:44 Speaker 1: at areas that have been deforested, as well, looking at

33:49 Speaker 1: flooded areas, and looking at for example, if you have

33:54 Speaker 1: time series, you can look at agricultural growth. So these

33:58 Speaker 1: are some of the things that NICE can address. Now

34:03 Speaker 1: let's talk about radar and surface parameters as related to NYSAUR.

34:10 Speaker 2: So let's start with wavelength.

34:12 Speaker 1: That's kind of the most important parameter upfront, right, So

34:18 Speaker 1: wavelength is the length of the peak of one wave

34:20 Speaker 1: to the peak of the next wave, or really the

34:23 Speaker 1: position of any point in the wave to the same

34:25 Speaker 1: point in the next wave. In radar remote sensing, we

34:29 Speaker 1: often talk about wavelength rather than frequency, and this is

34:32 Speaker 1: because the length of the wave defines the interaction of

34:37 Speaker 1: the signal with the surface or the medium, and it's

34:40 Speaker 1: therefore easier to associate the capability of the sensor. Wavelength

34:47 Speaker 1: is inversely related to frequency. It is the speed of

34:49 Speaker 1: light divided by frequency, and the higher the frequency, the

34:52 Speaker 1: shorter the wavelength and vice versa. So the table on

34:56 Speaker 1: the right lists the common wavelength bands in radar and

35:01 Speaker 1: the letter codes were have been arbitrarily assigned, and these

35:07 Speaker 1: were developed during for military security, during the early stages

35:13 Speaker 1: of radar developments, so they're not in alphabetical order. But

35:20 Speaker 1: nisar operates at L band, so that's L band means

35:23 Speaker 1: there's a range of frequencies that define that band. Nisar

35:27 Speaker 1: operates specifically at a specific frequency.

35:31 Speaker 2: Within that range.

35:33 Speaker 1: That would be one point two five seven gigahertz with

35:39 Speaker 1: L band and three point two gigaherts with S band.

35:47 Speaker 2: There are two important.

35:48 Speaker 1: Things to remember about wavelength. The first is that the

35:52 Speaker 1: longer the wavelength, the greater the penetration through the medium

35:55 Speaker 1: that's whatever that is, vegetation, snow, soil. And the figure

36:00 Speaker 1: on the lower left shows that a shorter wavelength such

36:04 Speaker 1: as X band in this case, which is around three centimeters,

36:07 Speaker 1: will see the top part of a forest, probably the

36:10 Speaker 1: top third or fourth of the canopy, while cband will

36:14 Speaker 1: penetrate halfway through and L band will penetrate all the

36:18 Speaker 1: way through. Now this is limited by a vegetation density,

36:22 Speaker 1: but that's the general idea.

36:24 Speaker 2: So with nysar that has.

36:26 Speaker 1: L band and S band, S band will have less

36:30 Speaker 1: penetration through the canopy than L band, and the same

36:35 Speaker 1: goes for soil. So the second important thing about wavelength

36:42 Speaker 1: is that the length of the wave will determine the

36:45 Speaker 1: interaction with surface objects. In general, if an object or

36:49 Speaker 1: surface roughness is comparable to the size of the wave,

36:53 Speaker 1: then there will be interaction, the surface will appear rough

36:56 Speaker 1: and there will be energy scattered back. For example, little

37:01 Speaker 1: energy is scattered back from a surface with a high

37:04 Speaker 1: fluctuation on the order of nine centimeters if L band

37:08 Speaker 1: is used, and therefore that surface will appear smooth to

37:11 Speaker 1: the radar signal and dark in the radar image. However,

37:15 Speaker 1: that same surface will appear bright to the S band

37:20 Speaker 1: SAR image because it's on the order of of the

37:27 Speaker 1: wavelength the S band wavelength, which is nine point four centimeters.

37:32 Speaker 1: The table on the right shows examples of applications most

37:37 Speaker 1: relevant to specific bands.

37:39 Speaker 2: So if you're.

37:39 Speaker 1: Interested in forest type studies and you want deep penetration,

37:43 Speaker 1: L band is better than S band. S band can

37:47 Speaker 1: still be used for forest studies, but it's better for

37:52 Speaker 1: forests that are not as donce, so L band and

37:55 Speaker 1: P band are much more suited for, say, tropical forests

37:59 Speaker 1: where you have high biomass high density. The other radar

38:07 Speaker 1: parameter is polarization, and it refers to the plane of

38:10 Speaker 1: propagation of the electric field of the signal. So, irrespective

38:14 Speaker 1: of wavelength, radar signals can be transmitted and or received

38:19 Speaker 1: in different polarizations. The figure illustrates these planes of propagation.

38:24 Speaker 1: If you had a long string attied to an object

38:27 Speaker 1: and you stand at the very end of the string

38:30 Speaker 1: and move it up and down in a vertical manner,

38:32 Speaker 1: such as the figure in the top, then the signal

38:35 Speaker 1: is vertically polarized. If you move the string horizontally from

38:39 Speaker 1: side to the side, then that signal is horizontally polarized.

38:43 Speaker 1: And similarly, the antenna can receive either the horizontally or

38:47 Speaker 1: vertically polarized backscattered energy. By applying specific filters, NYSAR can

38:54 Speaker 1: receive both, so it can transmit and receive both. So

38:57 Speaker 1: there can be four combinations of both transmit and received polarizations.

39:03 Speaker 1: HH that means horizontally transmitted and horizontally received, VV, vertically

39:09 Speaker 1: transmitted and vertically received HV horizontally transmitted and vertically received

39:15 Speaker 1: and VH vertically transmitted and horizontally received. So nisar's polarimetric

39:21 Speaker 1: capability is important because it allows the radar to do

39:24 Speaker 1: more than just see the surface. It helps understand the

39:27 Speaker 1: orientation of the structures on the surface, and NISAR has

39:33 Speaker 1: different acquisition modes. Depending on the acquisition mode, either all

39:38 Speaker 1: polarizations are collected or just a subset of these polarizations.

39:46 Speaker 1: So we discussed two important radar parameters in the context

39:49 Speaker 1: of NISAR, that's wavelength and polarization, and the radar backscatter

39:55 Speaker 1: is in part driven by these two characteristics. The reflection

39:59 Speaker 1: of the signal is also driven by the characteristics of

40:02 Speaker 1: the surface, so this is super important. Radar is sensitive

40:06 Speaker 1: to two things. One is structure and the other one

40:09 Speaker 1: is moisture. So let's start with structure. In general, there

40:13 Speaker 1: are three parameters related to structure that's density, size relative

40:17 Speaker 1: to wavelength, and size and orientation. So we discuss size

40:22 Speaker 1: relative to wavelength. Here we have an example of an

40:24 Speaker 1: Austrian pine and that wavelength will or that wave that

40:30 Speaker 1: signal will interact with components in the surface that are

40:33 Speaker 1: approximately the size of the wave. Right, So in this example,

40:38 Speaker 1: at X band, which is about three centimeters. We're primarily

40:41 Speaker 1: seeing the needles in the Austrian pine at the top

40:45 Speaker 1: of the canopy. And with L band we're seeing more

40:49 Speaker 1: of the longer branches. So lband for nisar twenty four centimeters,

40:55 Speaker 1: we're looking at the longer branches, the trunk and so.

41:01 Speaker 1: And with PEA band even the longer structures that are

41:06 Speaker 1: more similar to the wavelength at PEA band.

41:09 Speaker 2: Also, the longer the wavelength.

41:11 Speaker 1: The greater the penetration through the medium. Then there's density

41:16 Speaker 1: that signal will penetrate through the forest canopy all the

41:20 Speaker 1: way down, depending on how dense your vegetation is. And

41:24 Speaker 1: there are many different studies that have looked at the

41:26 Speaker 1: saturation point in terms of the above ground biomass tons

41:33 Speaker 1: per hector, and this value differs where that saturation point is.

41:39 Speaker 1: It differs between different types of forests, tropical forests and

41:42 Speaker 1: boil forests, but the signal will definitely saturate at a

41:47 Speaker 1: larger tonnage per hector the longer the wavelength as opposed

41:50 Speaker 1: to the shorter wavelengths so at lband. L band is

41:54 Speaker 1: more suitable to look at dense forest s band forests

41:58 Speaker 1: that are like less dense and cband tends to be

42:03 Speaker 1: a better sensor for agriculture. And then their size and

42:07 Speaker 1: orientation we discuss polarization, So polarization provides information on the

42:14 Speaker 1: orientation of the components on the ground. The other surface

42:22 Speaker 1: parameter is the dielectric constant, which is controlled by the

42:26 Speaker 1: amount of moisture in the surface, and radar backscatter is

42:30 Speaker 1: influenced by the amount of moisture in the vegetation and

42:34 Speaker 1: the soil and the snowpack, and the larger the water

42:37 Speaker 1: content in the medium, the less the penetration. So the

42:45 Speaker 1: magnitude of the radar backscatter is proportional to the dielectric

42:48 Speaker 1: constant of the surface, So for dry surfaces it's on

42:54 Speaker 1: the order of three to eight the dielectric constant for

42:56 Speaker 1: liquid water it's around forty to eighty. This is frequency dependent,

43:01 Speaker 1: so the amount of moisture in the soil or in

43:04 Speaker 1: the medium can greatly influence an increase.

43:08 Speaker 2: In radar reflectivity.

43:10 Speaker 1: And so what you're seeing here on the right is

43:12 Speaker 1: an example of an L band. This is a JRS

43:16 Speaker 1: one image time series near Fairbanks, Alaska, in a place

43:21 Speaker 1: called Bonanza Creek, and you're seeing the transition of the

43:26 Speaker 1: land surface for the same area from early from mid

43:30 Speaker 1: September through late from mid February to late September. And

43:36 Speaker 1: what I want you to note here is the change

43:39 Speaker 1: in reflectivity when things are frozen in this part of

43:44 Speaker 1: the world. In Alaska, things are fully frozen in February,

43:48 Speaker 1: and then you've got a spring transition and then you've

43:51 Speaker 1: got summer when things are thought. So the radar backscatter

43:55 Speaker 1: increases when things go in this case from frozen, which

43:59 Speaker 1: is it has a very low dielectric to springtras when

44:04 Speaker 1: there is a large amount of water in the soil

44:06 Speaker 1: and things are thought. Now, let's focus on the science

44:14 Speaker 1: and applications that NYSAR can address, as well as some

44:18 Speaker 1: early results. Summarizing some of the key characteristics of NISAR

44:24 Speaker 1: include its dual frequency radar system operating in both L

44:28 Speaker 1: and S band. The satellite provides near global coverage with

44:32 Speaker 1: the exception of the high latitude regions, especially the Arctic.

44:36 Speaker 1: NISAR has a twelve day exact repeat cycle. However, it

44:39 Speaker 1: acquires two observations within each cycle, one in a sending

44:43 Speaker 1: orbit and the other one in d sending orbit. The

44:46 Speaker 1: spatial resolution of the data varies between three and ten

44:49 Speaker 1: meters depending on the acquisition mode. The mission has a

44:53 Speaker 1: baseline duration of three years for lband and five years

44:57 Speaker 1: for S band, and all data are or will be

45:00 Speaker 1: freely and openly available. L band data is distributed through

45:05 Speaker 1: the Alaska Satellite Facility Distributed Active Archive Center, also known

45:11 Speaker 1: as the ASF DOC, while the S band data, along

45:14 Speaker 1: with some L band data products, will be available through

45:18 Speaker 1: isroe's Bounity platform. Now given these capabilities, NISAR will support

45:24 Speaker 1: a wide range of science and application areas, including solid

45:29 Speaker 1: Earth processes, ecosystems, the cryosphere, soil moisture, and applied remote

45:36 Speaker 1: sensing applications. The mission has key science objectives organized into

45:44 Speaker 1: three main areas. The first is the cryosphere, which focuses

45:47 Speaker 1: on the dynamics of Earth, cis and major ice sheets

45:51 Speaker 1: in Greenland and Antarctica, as well as glaciers, and this

45:55 Speaker 1: includes examining how these components contribute to sea level rise.

46:00 Speaker 1: The second is ecosystems and this includes understanding the dynamics

46:04 Speaker 1: of carbon storage, landscape disturbance, wetland inundation dynamics, and agricultural systems.

46:10 Speaker 2: And it also provides a.

46:12 Speaker 1: Unique capability for estimating standing biomass and carbon stocks as

46:17 Speaker 1: they are distributed across the globe. The third is solid

46:21 Speaker 1: Earth science, which focuses on processes such as earthquakes, plate tectonics,

46:26 Speaker 1: volcanic activity and landslides, as well as related geohazards and together,

46:32 Speaker 1: these three science domains are supported by dedicated science teams

46:35 Speaker 1: working across each area. In addition, the mission includes important

46:40 Speaker 1: applications objectives, and these include understanding societal impacts related to hazards,

46:46 Speaker 1: water dynamics, ecosystem restoration and degradation, as well as their

46:50 Speaker 1: links to broader societal needs. In agriculture, the mission supports

46:55 Speaker 1: food security applications by monitoring land use and crop activity,

46:59 Speaker 1: distinct fishing between actively managed farmland, fallow areas and crops

47:04 Speaker 1: intended for food production, as well as monitoring soil moisture.

47:09 Speaker 1: There's also an urgent response capability enabling rapid tasking for

47:13 Speaker 1: events such as oil spills, storms, earthquakes and other natural hazards,

47:18 Speaker 1: and this allows the mission to acquire additional targeted observations

47:23 Speaker 1: and deliver data quickly to the user community, which is

47:27 Speaker 1: a particularly powerful aspect of the mission and in particular

47:33 Speaker 1: for India. There is a very strong focus on coastal processes.

47:40 Speaker 1: This is an example of a complete twelve day cycle

47:43 Speaker 1: at lband AHH polarization. The data used to create this

47:47 Speaker 1: mosaic have not yet been fully calibrated, and as a result,

47:51 Speaker 1: some radio frequency interference interference is present in certain areas. However,

47:58 Speaker 1: this example deserves as an illustration of nysar's L band

48:02 Speaker 1: coverage over land and ice, and it also provides a

48:06 Speaker 1: sense of the mission's ability to generate and manage a

48:11 Speaker 1: very large data value on the order of approximately thirteen

48:14 Speaker 1: hundred frames per day, about sixteen thousand frames per twelve

48:19 Speaker 1: day cycle and roughly half a million frames per year.

48:26 Speaker 1: This is a preliminary result of biomass with NISAR, and

48:30 Speaker 1: I mentioned previously that biomass dynamics are extremely important and

48:35 Speaker 1: these results illustrate the relationship between polarized backscatter and biomass.

48:41 Speaker 1: This information can be used at the calibration and validation

48:45 Speaker 1: sites shown on the left to generate biomass maps as

48:49 Speaker 1: illustrated on the right, and as you can see, the

48:52 Speaker 1: system is able to estimate biomass across a wide range

48:55 Speaker 1: of conditions, including very high biomass levels exceeding to one

49:00 Speaker 1: hundred metric tons per hector, and this capability is also

49:04 Speaker 1: important for improving climate cycle modeling. This is an example

49:10 Speaker 1: of wetland inundation, So this is a false color composite,

49:14 Speaker 1: and what you're seeing here in purple are inundated areas

49:19 Speaker 1: vegetated inundated area. So whatever is dark, that's river, that's

49:24 Speaker 1: open water, and all of the pink areas are inundated vegetation.

49:29 Speaker 1: And this inundated vegetation is something you would not see

49:32 Speaker 1: with optical data. Another example is a striking image related

49:39 Speaker 1: to ice sheets, and on the left you can see

49:42 Speaker 1: a series of ice streams flowing across Antarctica. The feature

49:47 Speaker 1: highlighted in the red box is shown in greater detail

49:50 Speaker 1: on the right, and this area corresponds to a topographic

49:54 Speaker 1: mound beneath the outsheet, and as ice flows through the region,

49:59 Speaker 1: it's diverted around the obstruction, which causes the formation and

50:04 Speaker 1: fracturing of the ice, and this process produces extensive crevass

50:08 Speaker 1: fields sometimes referred to as hairline crevasses, although they are

50:13 Speaker 1: far from small, spanning as you can see in the

50:18 Speaker 1: scale here several kilometers and so these features are clearly

50:23 Speaker 1: visible in the radar imagery and NYSAR, and they arise

50:27 Speaker 1: in part because the radar signal penetrates to some depth

50:32 Speaker 1: into the ice and it scatters from internal and surface

50:36 Speaker 1: structures and returns to the sensor. So measurements like these

50:41 Speaker 1: are used to study ice dynamics, including ice flow behavior

50:45 Speaker 1: and its interactions with both oceanic and atmospheric processes. So

50:51 Speaker 1: here's another ice related example where we directly measure velocity

50:55 Speaker 1: using interferometric techniques. On the left is a rate image

51:01 Speaker 1: of an ice field and you can see some areas

51:04 Speaker 1: appear slightly to focus and that's due to signal penetration effects,

51:08 Speaker 1: but the rougher regions clearly reveal the location of the

51:12 Speaker 1: ice stream and this example is from Jakobsham Glacier in Greenland.

51:19 Speaker 1: On the right, you can see a time series that's

51:22 Speaker 1: been generated by combining one image with another acquired twelve

51:26 Speaker 1: days later and forming an interferometric pair. So from this

51:30 Speaker 1: we can estimate ice surface displacement and derive velocity. In

51:34 Speaker 1: this case, the NYSAR data have also been combined with

51:37 Speaker 1: sentinel one data from a different viewing geometry to generate

51:44 Speaker 1: a full velocity vector field that shows ice motion of

51:48 Speaker 1: approximately three kilometers per year in this region.

51:52 Speaker 2: That's very fast flowing ice.

51:55 Speaker 1: Tracking the variability of this dynamic behavior over time is

52:00 Speaker 1: one of the primary objectives of NYSAR. The mission enables

52:04 Speaker 1: seasonal scale monitoring, whereas other systems have required up to

52:08 Speaker 1: a decade to produce a single velocity map. So this

52:13 Speaker 1: represents a significant advance in our ability to understand the

52:16 Speaker 1: ice dynamics and their relationship to climate. So here's a

52:23 Speaker 1: solid Earth example. Another important application of interferometry is tracking earthquakes,

52:28 Speaker 1: volcanic activity, and other forms of surface deformation to better

52:32 Speaker 1: understand processes that are these dynamic processes occurring within the

52:37 Speaker 1: Earth's crust. And this example shows a volcanic event in

52:41 Speaker 1: Ethiopia captured using.

52:44 Speaker 2: A time series.

52:45 Speaker 1: So in the lower right you can see the difference

52:47 Speaker 1: between two images acquired November ten and twenty second of

52:55 Speaker 1: last year, and the subtle color variations reveal surface deformation.

53:01 Speaker 1: So these are areas of subsidence and uplift, indicating subsurface

53:06 Speaker 1: magma movements, and so in some areas the magma chambers

53:10 Speaker 1: are losing material, while in other areas they're being recharged

53:14 Speaker 1: and may be approaching eruption. So in the subsequent twelve

53:18 Speaker 1: day interview, the data showed that an eruption did occur,

53:22 Speaker 1: did in fact occur, the magma that accumulated in the

53:26 Speaker 1: system was released and the diet that dyke effectively closed.

53:31 Speaker 2: So the colored.

53:32 Speaker 1: Fringes that you see are also known as interferometric fringes,

53:36 Speaker 1: and they represent the magnitude of ground displacement and in

53:40 Speaker 1: this case it's on the order of half a meter

53:43 Speaker 1: to a meter. And remarkably, we're able to measure this

53:47 Speaker 1: level of information deformation from space with centimeter scale precision

53:52 Speaker 1: using InSAR, and this capability is repeated every twelve days

53:57 Speaker 1: over the lifetime of the mission, providing, as you can imagine,

54:00 Speaker 1: a very powerful tool for monitoring these dynamic geophysical processes

54:06 Speaker 1: in near real time. Soil moisture is another key variable

54:12 Speaker 1: that is being measured by NISSAR. It's critically important for

54:16 Speaker 1: things like agriculture, hydrological models of epablic transporation, drought monitoring,

54:22 Speaker 1: weather forecasting, and related applications. And here you can see

54:25 Speaker 1: an example of the sole moisture product at field scale.

54:30 Speaker 1: Previous satellite based soil moisture measurements have generally been at

54:34 Speaker 1: relatively core spatial resolutions on the order of nine kilometers

54:39 Speaker 1: or more. In contrast, these observations that are approximately two

54:44 Speaker 1: hundred meter spatial resolution, and this represents field scale variability

54:50 Speaker 1: in soil moisture, which is highly valuable for the agricultural community.

54:57 Speaker 1: And of course, there are a number of applications that

54:59 Speaker 1: NISSAR and address. Here are some examples and you can

55:03 Speaker 1: access these white papers through the.

55:05 Speaker 2: Link on this slide.

55:10 Speaker 1: Now let's focus on the science and applications that NYSAR

55:14 Speaker 1: can address, as well as some early results. So this

55:19 Speaker 1: figure illustrates nisar's observation plan, which defines how the satellite

55:23 Speaker 1: acquires radar data around the globe. So rather than using

55:28 Speaker 1: a single imaging mode everywhere, the mission employs multiple radar

55:32 Speaker 1: acquisition modes optimized for different science objectives. And these modes

55:37 Speaker 1: also provide different spatial resolutions and polarizations, allowing NISAR to

55:43 Speaker 1: balance detailed observations with efficient global coverage. L band observations

55:49 Speaker 1: provide near global coverage averts land surface including islands, as

55:54 Speaker 1: well as the major ice sheets and cis. In contrast,

55:58 Speaker 1: the s BAN acquisition strategy is more targeted. Israel's primary

56:03 Speaker 1: focus is on the Indian subcontinent other regions of South

56:07 Speaker 1: Asia Antarctica, so S band data are not collected globally,

56:14 Speaker 1: just over targeted over specific areas, and in addition to

56:18 Speaker 1: those areas, S band observations are also acquired over global

56:23 Speaker 1: network of calibration and validation sites, and these locations provide

56:28 Speaker 1: them unique opportunities to combine L band and S band observations,

56:33 Speaker 1: helping scientists better understand the complementary information provided by the

56:39 Speaker 1: two frequencies and the benefits of using dual frequency radar measurements.

56:45 Speaker 1: The observation plan is reviewed and updated approximately every six

56:50 Speaker 1: months based on science team member recommendations, available mission resources,

56:55 Speaker 1: and other programmatic considerations to ensure that the mission contain

57:00 Speaker 1: unities to meet its evolving science objectives. These are the

57:07 Speaker 1: different NISAR data product levels, and like most NASA Earth

57:11 Speaker 1: observing missions, NISAR organizes its data into a series of

57:15 Speaker 1: processing levels, so level zero consists of the raw telemetry

57:20 Speaker 1: and signal data. Level one data products have undergone some

57:24 Speaker 1: initial processing and calibration, but remain in the radar's native

57:30 Speaker 1: or in the satellites native geometry also known as range

57:34 Speaker 1: Doppler rather than geographic coordinates. Level two products are geocoded,

57:40 Speaker 1: meaning they have been projected into geographic coordinates latitude and longitude,

57:45 Speaker 1: making them easier to integrate with other geospatial data sets.

57:50 Speaker 1: These products include calibrated radar backscatter, interferometric phase, unwrapped interferograms,

57:57 Speaker 1: and other geophysical measurements, so these are considered an ready

58:01 Speaker 1: Then Level three products are the higher level geocoded derived

58:08 Speaker 1: science products expressed in physical units, so the only global

58:12 Speaker 1: level three products generated by NYSAR is soul moisture. There

58:17 Speaker 1: are algorithms that are known as atbds in which stands

58:22 Speaker 1: for an algorithm theoretical basis document. These are algorithms that

58:26 Speaker 1: have been generated by the different disciplines and they have

58:33 Speaker 1: been validated over calibration validation sites and so these algorithms

58:39 Speaker 1: are focused on generating biomass vegetation disturbance, wetlands, in undation,

58:44 Speaker 1: crop area, surface deformation damage proxy, I sheet glacier velocity

58:49 Speaker 1: and c ice velocity and all of these atbds are

58:53 Speaker 1: openly available. So NISAR data products because system of backscatter

59:01 Speaker 1: and phase as well as interferograms that contain information on

59:06 Speaker 1: surface deformation as well as coherence, which is the stability

59:09 Speaker 1: of a surface on the order of centimeters. There are

59:13 Speaker 1: also pixel offset products generating using the backscattered data, and

59:18 Speaker 1: these identify large shifts in the surface on the order

59:21 Speaker 1: of meters. And finally there's the sole moisture product, which

59:24 Speaker 1: is a two hundred meter spatial resolution. You'll notice that

59:29 Speaker 1: many of these data products are derived only over Chrispear

59:33 Speaker 1: regions and the rest is global.

59:37 Speaker 2: Or near global.

59:39 Speaker 1: Now, if you're new to radar and you're doing, for example,

59:44 Speaker 1: ecosystem type studies lamp cover classification, or you're looking at

59:50 Speaker 1: fire burned scars, or you're looking at agricultural growth through time,

59:57 Speaker 1: you'll want to use the g COVE data products. So

1:00:00 Speaker 1: that is the geocoded covariance matrix. So that's basically the

1:00:06 Speaker 1: backscattered data at multiple polarizations, and that dataset has been

1:00:12 Speaker 1: radiometrically and terrain corrected, so it's analysis ready. If you're

1:00:17 Speaker 1: new to instar, then you'll want to use the gun

1:00:22 Speaker 1: W which is the geocoded unwrapped and it contains also

1:00:28 Speaker 1: the wrapped interferogram. So these are both level two data

1:00:34 Speaker 1: products that are produced globally. Nice art data status. In

1:00:42 Speaker 1: February of twenty twenty six, a large number of pre

1:00:45 Speaker 1: calibrated global data products were made available, and this release

1:00:50 Speaker 1: included over one hundred thousand Level one to three products

1:00:53 Speaker 1: totaling over five hundred terabytes of science data. In July

1:01:00 Speaker 1: day of twenty twenty six, the calibrated forward processing data

1:01:06 Speaker 1: will be released, so after the calibration phase concludes and

1:01:10 Speaker 1: planned software improvements are completed, the nice OUR project will

1:01:15 Speaker 1: begin forward processing of the newly acquired data. So forward

1:01:19 Speaker 1: processing is targeted to begin in sometime in July of

1:01:24 Speaker 1: twenty twenty six. Sometime during this month, the fully calibrated

1:01:28 Speaker 1: level zero to three products will be continuously generated from

1:01:33 Speaker 1: all data acquired from the start of forward processing into

1:01:37 Speaker 1: the future, and new data will be continuously published to

1:01:41 Speaker 1: ASF and made publicly available. Level one to three products

1:01:46 Speaker 1: will have a nominal latency of thirty six to seventy

1:01:49 Speaker 1: two hours from data acquisition to availability the Q three

1:01:56 Speaker 1: to four, so there's a calibrated back process. So after

1:02:01 Speaker 1: the start of the forward processing, the nice OAR project

1:02:04 Speaker 1: will produce calibrated level zero to three products from the

1:02:09 Speaker 1: backlog of all data collected from September twenty third, twenty

1:02:14 Speaker 1: twenty five, through the start of the forward processing in

1:02:17 Speaker 1: July twenty twenty six, and this processing will be completed

1:02:23 Speaker 1: sometime before the end of calendar year twenty twenty six.

1:02:31 Speaker 1: So the Alaska Satellite Facility is the data archives center

1:02:35 Speaker 1: for the L band data and Israel's Bounty Platform is

1:02:40 Speaker 1: the data archives center for the S band data as

1:02:43 Speaker 1: well as for overlapping.

1:02:46 Speaker 2: L band data.

1:02:48 Speaker 1: If you like to learn more, you can scan thequre

1:02:51 Speaker 1: code on this slide. Data can be downloaded through ASF's

1:02:55 Speaker 1: web based graphical user interface, or it can be accessed

1:02:58 Speaker 1: programmatically using an API, making it easy to incorporate nice

1:03:03 Speaker 1: our data into automated workflows. All nice our data are

1:03:07 Speaker 1: freely and openly available and are hosted in the Amazon

1:03:11 Speaker 1: Web Services Cloud, providing fast and scalable access for users

1:03:16 Speaker 1: around the world. Now, I'll show you a short demo

1:03:20 Speaker 1: on how to use the gcoff product to visualize flooding

1:03:26 Speaker 1: between two acquisitions, one during a high flood period and

1:03:33 Speaker 1: another one during the low flood period. All right, so

1:03:40 Speaker 1: the first thing we're going to do is we are

1:03:42 Speaker 1: going to go to the Alaska Satellite Facility asf Vertex tool.

1:03:47 Speaker 1: So go to Google and do a search on your

1:03:50 Speaker 1: web browser and we'll select. Here are our first results. Now,

1:03:59 Speaker 1: this is the Verse text platform and the default is

1:04:02 Speaker 1: Sentinel one. What we want is NICAR data. Note that

1:04:05 Speaker 1: this is uncalibrated data. So in February of twenty twenty six,

1:04:10 Speaker 1: there was a release of over one hundred thousand Nicar

1:04:13 Speaker 1: data products.

1:04:14 Speaker 3: These are all.

1:04:15 Speaker 1: Uncalibrated, so today we are working with the uncalibrated data.

1:04:18 Speaker 1: There are issues with the data, and I'll.

1:04:21 Speaker 3: Show you what some of those are.

1:04:24 Speaker 1: The plan, as mentioned is that sometime in July of

1:04:27 Speaker 1: twenty twenty six, late July twenty twenty six, there will

1:04:31 Speaker 1: be a release of the calibrated data NICEAR data.

1:04:37 Speaker 3: So let's just select NISAR.

1:04:39 Speaker 1: And again there are over one hundred thousand data products available.

1:04:46 Speaker 3: Let's do a specific search.

1:04:48 Speaker 1: This example is a flood visualization example. So let's just

1:04:53 Speaker 1: look at Columbia because I know there was a period

1:04:56 Speaker 1: of high flood and low flood there. And let's select

1:05:01 Speaker 1: specifically the GKOV product, so that is the radiometrically terrain

1:05:06 Speaker 1: corrected backscattered data in different polarizations in the case of

1:05:14 Speaker 1: overland in at least in this region that I specified,

1:05:21 Speaker 1: it's just AGH and HV primarily.

1:05:25 Speaker 4: So let's.

1:05:27 Speaker 3: Oops, let's do this.

1:05:34 Speaker 1: So I'm going to draw my area of interest, and

1:05:39 Speaker 1: under filters, I'm going to select the GCOV data product.

1:05:46 Speaker 3: And I will do search.

1:05:51 Speaker 1: And so there are two hundred and thirty six nice

1:05:55 Speaker 1: art files over my area of interest. So let's just

1:05:58 Speaker 1: zoom in. And I know I've already located an area

1:06:04 Speaker 1: where there's interesting flooding. So it's this frame right here,

1:06:10 Speaker 1: and that frame is this one. Whichever frame I select,

1:06:18 Speaker 1: you can see a quick browse image right here, but

1:06:27 Speaker 1: we're specifically interested in this one. So let's just click

1:06:30 Speaker 1: on that browse image and open a separate window where

1:06:34 Speaker 1: you can actually take a closer look at that browse image.

1:06:38 Speaker 1: So I'm seeing a lot of flooding here along the river.

1:06:41 Speaker 1: This is a double bounce to all of these purple areas.

1:06:45 Speaker 1: As mentioned, these are uncalibrated data. So what you see

1:06:48 Speaker 1: up here, this bright green area here, that's a radio

1:06:55 Speaker 1: frequency interference. So that's noise that's RIFY and that is

1:07:01 Speaker 1: going to be resolved in the release of calibrated data.

1:07:05 Speaker 1: That we will be doing later in July twenty twenty six.

1:07:08 Speaker 1: All right, So this is an interesting image because of

1:07:12 Speaker 1: the flooding, and so one thing I can say is

1:07:17 Speaker 1: I want to see images more like this. So I

1:07:20 Speaker 1: want to see the time series of images over this

1:07:25 Speaker 1: my specific area of interest. Okay, And so these are

1:07:32 Speaker 1: the images the exact repeat, exact area, and let's take

1:07:41 Speaker 1: a look. So the nice thing about Vertex is you

1:07:46 Speaker 1: can actually take a look at these browse images. Take

1:07:53 Speaker 1: a quick look. And what I'm seeing here, what I'm

1:07:57 Speaker 1: most interested in is the flooding, the right purple areas

1:08:01 Speaker 1: along the river that's inundated vegetation. Okay, So I'm gonna select,

1:08:06 Speaker 1: and you see this one has a lot of that

1:08:10 Speaker 1: inundated vegetation here along the river, while this one doesn't,

1:08:15 Speaker 1: and neither does this one. So I'm just gonna select

1:08:18 Speaker 1: October twenty ninth and December fourth. And in order to

1:08:23 Speaker 1: download the data, you go to the HDF five file.

1:08:29 Speaker 1: So there are a series of files here. This is

1:08:32 Speaker 1: the actual data file, and you download the file it's

1:08:37 Speaker 1: pretty large, or you can add it to your cart.

1:08:40 Speaker 1: So I can add these two images to my cart

1:08:43 Speaker 1: and download both of them at the same time, or

1:08:46 Speaker 1: I can individually download it. So I've downloaded the images already,

1:08:50 Speaker 1: and let's just go to qgis and take a look

1:08:55 Speaker 1: at the images and create an RGB. Okay, so I

1:09:06 Speaker 1: have the latest version of QGIS three point four four,

1:09:12 Speaker 1: the stable version, and what I do is I go

1:09:16 Speaker 1: to layer at layer at raster layer and then I

1:09:23 Speaker 1: select the file of interest.

1:09:26 Speaker 3: So I need to go here.

1:09:29 Speaker 1: I actually downloaded three different dates, but we're going to

1:09:32 Speaker 1: use two dates for this example. So we're going to

1:09:35 Speaker 1: use the October twenty ninth and December fourth. You can

1:09:39 Speaker 1: see it in the name right here, twenty twenty five

1:09:41 Speaker 1: twelve O four and this one is twenty twenty five

1:09:46 Speaker 1: ten twenty nine. Okay, now let's start with the ten

1:09:54 Speaker 1: twenty nine. That's the October that's where we saw the flooding.

1:09:58 Speaker 1: So let's call that the high flood image and we'll

1:10:03 Speaker 1: open it. Now what happens is qgis does not have

1:10:08 Speaker 1: a reader to recognize this version of HDF five that

1:10:13 Speaker 1: NISAR is using. That's still in development. We're developing the

1:10:20 Speaker 1: GDL reader still. So what you need to do in

1:10:23 Speaker 1: QGIS is you need to trick it so it thinks

1:10:27 Speaker 1: it's a net CDF five net CDF file. And the

1:10:31 Speaker 1: way you do that is you go to the front,

1:10:35 Speaker 1: to the very beginning and just type net cdf colon Okay.

1:10:42 Speaker 1: The other way to do this is just to change

1:10:45 Speaker 1: the extension in the name of the file instead of

1:10:49 Speaker 1: HDF at the end, just change it to dot nc.

1:10:54 Speaker 1: So there are two ways, either specifying that it's a

1:10:58 Speaker 1: net CDF in the path beginning or changing the extension

1:11:03 Speaker 1: from HDF to dot n C. So let's just go

1:11:09 Speaker 1: this way and we'll say add and there these are

1:11:14 Speaker 1: HDF five files, so they have a bunch of different

1:11:17 Speaker 1: files within it, and the one that we want is

1:11:23 Speaker 1: this one the frequency A. There are two polarizations HH

1:11:27 Speaker 1: and HP. In this case, we're just going to work

1:11:29 Speaker 1: with the HH data frequency A. So that's about that's

1:11:33 Speaker 1: a ten meter spatial resolution.

1:11:36 Speaker 3: And we'll say at layer.

1:11:39 Speaker 2: Close.

1:11:40 Speaker 1: So this is the file and it's always good to

1:11:45 Speaker 1: view it. And what we will do is, so it's

1:11:49 Speaker 1: loading right now. Since it's a it's a very heavy file, okay,

1:11:58 Speaker 1: and this is you see that it doesn't it looks

1:12:02 Speaker 1: very dark or very bright, So that has to do

1:12:05 Speaker 1: with how we're this qgis is doing the histogram stretch

1:12:10 Speaker 1: to visualize the image. So what we do is you

1:12:13 Speaker 1: double click on the file name. You go to symbology

1:12:17 Speaker 1: and just set the maximum. So this is a cumulative

1:12:22 Speaker 1: count cut based on percentile, and just select that.

1:12:34 Speaker 3: And that's our image. Okay. So let's do the same thing.

1:12:40 Speaker 1: We'll open the this is the October Let's open the

1:12:45 Speaker 1: December file same way at raster layer and here we'll

1:12:50 Speaker 1: just select the these are very long file names, but

1:12:55 Speaker 1: this is the December fourth. Will open it again. We

1:13:03 Speaker 1: add net CDF at the beginning, and we select.

1:13:12 Speaker 3: The frequency a.

1:13:15 Speaker 1: Hh band okay, and we say at layer okay. So

1:13:23 Speaker 1: here we have our two images. It takes a little

1:13:27 Speaker 1: while to load, and it's always really good practice to

1:13:31 Speaker 1: look at your images and just make sure that everything

1:13:34 Speaker 1: looks okay. So again we'll do the accumulative count cut.

1:13:50 Speaker 3: Okay.

1:13:53 Speaker 1: So the next thing we do is we will sub.

1:14:00 Speaker 3: At these images.

1:14:01 Speaker 1: And in order to do that, you go to processing

1:14:04 Speaker 1: TOOLBOXOPS Processing toolbox and clip clip raster by extent. So

1:14:14 Speaker 1: I recently used that, but you can go to raster

1:14:17 Speaker 1: extraction and select clipped raster by extent. Okay, and so

1:14:24 Speaker 1: we want to clip. Let's see, this is the October image.

1:14:30 Speaker 3: So let's start with that.

1:14:32 Speaker 1: And let's say that the extent that we want to

1:14:35 Speaker 1: clip is what we draw.

1:14:37 Speaker 3: Will draw the extent.

1:14:39 Speaker 1: So I'm going to go here and this area this

1:14:43 Speaker 1: is where the flooding occurs along the river here. So

1:14:49 Speaker 1: we'll clip this area and we'll say run, great, close,

1:15:00 Speaker 1: will rename. So you double click on the file name

1:15:04 Speaker 1: the clipped extent and you write click and you select

1:15:08 Speaker 1: the rename the layer. Okay, so this is October twenty nine,

1:15:15 Speaker 1: twenty twenty five.

1:15:17 Speaker 3: Aheh, clipped.

1:15:22 Speaker 1: Okay. Now we want to do the same thing for

1:15:26 Speaker 1: the next image. So again we say clip raster by extent,

1:15:33 Speaker 1: and we want to clip the December file, so it's

1:15:38 Speaker 1: this one you can see at the date here, and

1:15:43 Speaker 1: then clipping extent.

1:15:44 Speaker 3: We want to.

1:15:45 Speaker 1: Clip it according to the same extent from the first image.

1:15:49 Speaker 1: So we say calculate from layer and select the image

1:15:53 Speaker 1: that we just clipped, so it will clip according to

1:15:56 Speaker 1: the extent of that extent, and we say run. Okay.

1:16:05 Speaker 3: So let's just.

1:16:09 Speaker 1: Visualize this first of all, the October twenty nine Say

1:16:14 Speaker 1: how about it symbology and max cumulative concorde. Okay, that's

1:16:41 Speaker 1: the October twenty ninth, and then let's visualize the Let's

1:16:45 Speaker 1: rename this one.

1:16:48 Speaker 3: This is.

1:16:51 Speaker 1: December fourth, so we'll say December four, twenty twenty five.

1:17:03 Speaker 2: H h.

1:17:06 Speaker 3: Clipped and we will.

1:17:11 Speaker 1: Visualize it again there. Okay, so that's my clipped image.

1:17:20 Speaker 1: So we have two clipped images from different dates. The

1:17:23 Speaker 1: next thing we want to do is we want to

1:17:25 Speaker 1: convert these These are digital numbers. We want to convert

1:17:28 Speaker 1: them to decibels. And again these images, the terrain has

1:17:33 Speaker 1: been corrected and they're radiometrically corrected, so when we convert

1:17:37 Speaker 1: them to decibel, they're actually in sigma in gamma knat sorry.

1:17:42 Speaker 1: So in order to convert them to decibels, you go

1:17:45 Speaker 1: to raster raster calculator.

1:17:48 Speaker 3: And we'll say.

1:17:52 Speaker 1: We input the expression here which is ten times log

1:17:57 Speaker 1: ten and in parenthesi you then specify the file that

1:18:03 Speaker 1: you want to convert to dB. So we'll select this

1:18:06 Speaker 1: one and we need to close the parentheses and the

1:18:10 Speaker 1: output layer. We want to say where we want to

1:18:12 Speaker 1: put this file and give it a name. So this

1:18:15 Speaker 1: is December four, twenty twenty five. Agehe clipped, and we'll

1:18:25 Speaker 1: say it's dB okay.

1:18:28 Speaker 2: So.

1:18:31 Speaker 1: We press okay, and this is our dB image. In

1:18:46 Speaker 1: this case, I'm just going to set the values for visualization.

1:18:49 Speaker 1: So usually the values go, say from minus twenty five

1:18:53 Speaker 1: to let's say minus two, or let's say is zero,

1:19:00 Speaker 1: and you can play around with that for a better visualization.

1:19:05 Speaker 1: So that's the December twenty five. We'll do the same

1:19:08 Speaker 1: thing with the October file.

1:19:12 Speaker 3: So we'll say.

1:19:13 Speaker 1: This ten times log ten and the October twenty nine

1:19:25 Speaker 1: is the one that we want to convert to dB.

1:19:28 Speaker 1: So you double click and it fills it down here,

1:19:32 Speaker 1: and then the output layer again you specify where and

1:19:36 Speaker 1: you specify the name, and in this case it's October

1:19:39 Speaker 1: twenty nine, twenty twenty five.

1:19:44 Speaker 3: HH clipped dB.

1:19:49 Speaker 1: Okay, and we say okay. Again, we will stretch it

1:20:08 Speaker 1: from minus twenty five to zero and all of these

1:20:14 Speaker 1: bright areas that you're seeing again, that's in undated vegetation.

1:20:19 Speaker 1: So one thing we want to do is let's just

1:20:21 Speaker 1: create an RGB, and in order to do that, we

1:20:29 Speaker 1: will build start. We need to build a virtual raster

1:20:32 Speaker 1: because these images are independent. So let's go ahead and

1:20:36 Speaker 1: build a virtual raster, and we say place each input

1:20:41 Speaker 1: file into a separate band. So let's just select our

1:20:45 Speaker 1: input bands. So we'll select December four and the clipped dB,

1:20:50 Speaker 1: and then we'll select October twenty nine the clipped dB.

1:20:56 Speaker 3: Okay, so we have.

1:21:01 Speaker 1: Two bands, and what we want to do now is

1:21:07 Speaker 1: visualize that as an an RGB. So you double click,

1:21:13 Speaker 1: you select multi band color band one and let's just say.

1:21:20 Speaker 3: Minus eighteen and all of them men.

1:21:24 Speaker 1: So that's how we're stretching the visualization and max.

1:21:29 Speaker 3: Let's just say it's one one.

1:21:33 Speaker 1: So in the red band we have band one, in

1:21:37 Speaker 1: the green band we have band two, and in the

1:21:39 Speaker 1: blue band will have band one. Okay, so what we're

1:21:47 Speaker 1: seeing here is that all of the green areas those

1:21:51 Speaker 1: are areas where whatever is in the green channel has

1:21:56 Speaker 1: a higher value. So the green channel has the high

1:22:00 Speaker 1: flood the inundated vegetation. So all of these green areas

1:22:05 Speaker 1: that you're seeing, all of that is flooded vegetation that's

1:22:08 Speaker 1: there in October on the October twenty nine scene and

1:22:13 Speaker 1: not in the December fourth scene. So this is just

1:22:17 Speaker 1: a simple RGB with just two images. So a couple

1:22:30 Speaker 1: of important points to remember NYSAR is designed to monitor

1:22:35 Speaker 1: continuous Earth surface changes on the order of centimeter to

1:22:39 Speaker 1: meter scale globally. It has a capability to image the

1:22:43 Speaker 1: Earth surface day and night and to almost all weather

1:22:48 Speaker 1: or cloud conditions. The key focus processes include tectonic deformation,

1:22:53 Speaker 1: iet movement, ecosystem change, and agricultural monitoring. NISAR has an

1:22:59 Speaker 1: L band and an S band radar so L band

1:23:04 Speaker 1: twenty four centimeters S band nine point four centimeters. The

1:23:09 Speaker 1: satellite has an exact twelve day repeat cycle. However, there

1:23:12 Speaker 1: are two observations within every twelve days. One is in

1:23:16 Speaker 1: the ascending orbit, the other one is in descending orbit.

1:23:20 Speaker 2: It uses a two.

1:23:21 Speaker 1: Hundred and forty kilometer imaging swath using the sweep star technique,

1:23:29 Speaker 1: and the spatial resolutions vary between three and thirty meters

1:23:33 Speaker 1: depending on the radar mode. NISAR has a repeat pass

1:23:38 Speaker 1: interferometric capability and that allows for measurements of surface deformation

1:23:44 Speaker 1: on the order of centimeters. The polarization acquisitions vary depending

1:23:50 Speaker 1: on acquisition mode, and the core science disciplines cover cryosphere, ecosystems,

1:23:56 Speaker 1: and solid earth. NYSAR can also dress different applications and

1:24:01 Speaker 1: support disaster and hazard management. NISAR products are openly and

1:24:06 Speaker 1: freely available. The L band data is available through the

1:24:09 Speaker 1: ASF DOC That's Global data. The S band data, along

1:24:13 Speaker 1: with coincident L band data, are available through israe's bounity platform.

1:24:19 Speaker 1: The s band acquisitions are over India and targeted locations

1:24:23 Speaker 1: around the world. Data processing levels span from level zero,

1:24:29 Speaker 1: which is the raw data, to level three, which are

1:24:32 Speaker 1: the geocoded derived products, with soil moisture being the only

1:24:37 Speaker 1: Level three global product being generated. Level one and level

1:24:41 Speaker 1: two products contain information about amplitude phase. There are interferometric products,

1:24:47 Speaker 1: there's coherence, and there are pixel offsets. The Level one

1:24:51 Speaker 1: products are in the satellite native coordinates range Doppler, and

1:24:55 Speaker 1: the level two products are geocoded and analysis ready. The

1:24:59 Speaker 1: data are in HDF five formats. For those interested in

1:25:05 Speaker 1: doing ecosystem type studies, probably the best data sets are

1:25:09 Speaker 1: the g COVE and the GSLC data in terms of

1:25:13 Speaker 1: data status. In July and this month twenty twenty six,

1:25:18 Speaker 1: the fully calibrated forward process data will be released with

1:25:23 Speaker 1: a data availability latency of thirty six to seventy two hours,

1:25:27 Speaker 1: and then in late twenty twenty six, the back processing

1:25:33 Speaker 1: of the archived global historical data will be released, so

1:25:37 Speaker 1: that will be the data acquired by NISAR, spanning from

1:25:42 Speaker 1: September twenty twenty five through the beginning of this forward

1:25:47 Speaker 1: processed data release and looking ahead to session two. NISAR

1:25:55 Speaker 1: Data Access and Tools. You will learn learn how to

1:26:00 Speaker 1: identify where to access NICSAR data, recognize the data format

1:26:05 Speaker 1: and tools for reading the data, and apply tools for

1:26:08 Speaker 1: visualizing and analyzing NISAR data.

1:26:14 Speaker 2: Homework and certificates.

1:26:15 Speaker 1: There is one homework assignment associated with this training. It

1:26:19 Speaker 1: opens on July sixteenth and the due date is August

1:26:22 Speaker 1: six You can access the homework through the training web page.

1:26:26 Speaker 1: A certificate of completion will be awarded to those participants

1:26:30 Speaker 1: that attend all three live webinars and complete the homework

1:26:34 Speaker 1: assignment by the deadline. You'll receive a certificate via email

1:26:38 Speaker 1: approximately two months after completion of the course. If you

1:26:44 Speaker 1: have any questions, feel free to contact me via my

1:26:48 Speaker 1: email posted here.

1:26:51 Speaker 2: And here are two great resources for those.

1:26:54 Speaker 1: That want to take a deeper dive into NISAR. One

1:26:57 Speaker 1: is the NISAR Handbook the second version of it, and

1:27:01 Speaker 1: the other one is the nice Art Data User Guide

1:27:05 Speaker 1: by the Alaska Satellite facility.

1:27:08 Speaker 2: And the QR codes.

1:27:09 Speaker 1: The one on top is for the handbook, the one

1:27:12 Speaker 1: on the bottom is for the user guide that concludes

1:27:17 Speaker 1: the first session of this training. Thank you very much

1:27:21 Speaker 1: to everyone participating. Thank you for all the questions. We

1:27:24 Speaker 1: are now ready to start the q and A session. Great. So,

1:27:34 Speaker 1: there have been a lot of really great questions coming

1:27:37 Speaker 1: in a lot of conversation on the chat. We have

1:27:42 Speaker 1: been compiling your questions on a Google doc and we

1:27:46 Speaker 1: will be sharing that in just a few moments and

1:27:49 Speaker 1: we will start answering your questions. I don't think we'll

1:27:52 Speaker 1: be able we'll have enough time to get to all

1:27:54 Speaker 1: of your questions, but we will be responding to all

1:27:59 Speaker 1: of the questions on the Google doc and we will

1:28:02 Speaker 1: be posting this document on the web page. So let's

1:28:08 Speaker 1: start with question number one. Do NASA and Israel plan

1:28:15 Speaker 1: to launch other nice our satellites in the future to

1:28:18 Speaker 1: have nice our constellation increase the temporal resolution of the acquisitions.

1:28:24 Speaker 1: That would be absolutely fantastic. I am not aware of, however,

1:28:30 Speaker 1: I will consult if there are any sort of conversations

1:28:34 Speaker 1: and I will update this, but not that I'm aware of.

1:28:40 Speaker 1: Question number two. If we're interested only on structure, should

1:28:44 Speaker 1: we try to obtain imagery during dry periods of the year.

1:28:49 Speaker 1: And the response to that is that if you are

1:28:52 Speaker 1: studying vegetation structure, you should really try to acquire imagery

1:28:58 Speaker 1: during both the dry and wet seasons, because the structural

1:29:03 Speaker 1: characteristics of the vegetation can differ significantly between these two seasons.

1:29:10 Speaker 1: So I wouldn't just try to use imagery from the

1:29:13 Speaker 1: dry season. Okay, Question number three, how deep in the

1:29:18 Speaker 1: soil can sarro or insart penetrate? This is totally dependent

1:29:23 Speaker 1: on the frequency the polarization as well as the soil wetness.

1:29:26 Speaker 1: So again, the longer the wavelength, the greater the penetration.

1:29:31 Speaker 1: So L band will penetrate further into the soil than

1:29:34 Speaker 1: s band, and the wetter the soil, the less the penetration.

1:29:40 Speaker 1: But the nice or soil moisture product that I discuss

1:29:44 Speaker 1: represents the average volumetric soil moisture of the upper five

1:29:51 Speaker 1: centimeters of the soil. Question number four, you set nicsar

1:29:58 Speaker 1: can penetrate through soil? Can it be used to detect

1:30:01 Speaker 1: and study the heavy metal contamination in the soil? So

1:30:05 Speaker 1: this is an interesting question. It likely cannot be used

1:30:08 Speaker 1: to study heavy metals in the soil because again radar

1:30:13 Speaker 1: is not sensitive to the chemical properties of the surface,

1:30:18 Speaker 1: but rather to structure and to moisture.

1:30:24 Speaker 3: Now, if there's.

1:30:27 Speaker 1: I'd say a large area that is contaminated with heavy

1:30:31 Speaker 1: metals where the dielectric is different, right, so the wetness

1:30:38 Speaker 1: is different because of those heavy metals, then you might

1:30:43 Speaker 1: be able to see differences. You can't correlate it necessarily

1:30:48 Speaker 1: why one area is wetter or drier than another area,

1:30:53 Speaker 1: but you would be able to see those differences if

1:30:56 Speaker 1: it's large enough, right, So indirectly there might be some correlations.

1:31:06 Speaker 1: Question number five, can interferometry be used to study vegetation changes?

1:31:10 Speaker 1: How can nicser contribute to mapping deforestation and tropical forests? So,

1:31:16 Speaker 1: just for your information, we will be diving deep into

1:31:21 Speaker 1: that interferometric component of NICs are in session three, So

1:31:25 Speaker 1: my colleague colleague from JPL, Eric Fueling, will be leading

1:31:29 Speaker 1: that session. He'll be discussing providing a brief background on

1:31:33 Speaker 1: InSAR and discussing the ns A interferometric products and where

1:31:38 Speaker 1: they work well and where they don't, and the type

1:31:40 Speaker 1: of corrections that you need to do. However, to answer

1:31:43 Speaker 1: your question here, you know, the best nicer product to

1:31:47 Speaker 1: study vegetation changes is the backscatter product. So that's that's

1:31:52 Speaker 1: the amplitude and that's the GKOV the GKOV product the

1:31:59 Speaker 1: level took at different polarizations. So with that, you remember

1:32:05 Speaker 1: when I was discussing the fundamentals of SAR. You know

1:32:10 Speaker 1: how you have the scattering mechanisms and within a forest

1:32:14 Speaker 1: the signal bounces from many different components. So so the

1:32:20 Speaker 1: backscatter signal can detect biomass. You can use that to

1:32:27 Speaker 1: for for doing estimates, not detect, but you can do

1:32:30 Speaker 1: estimates because you need an algorithm uh.

1:32:34 Speaker 3: OF to do estimates of biomass.

1:32:37 Speaker 1: And you can also look at forest disturbance as well

1:32:43 Speaker 1: as uh wetland inundation, agriculture, agricultural growth through time.

1:32:50 Speaker 4: So to do those.

1:32:51 Speaker 1: So for ecosystem type studies, the best product is the

1:32:55 Speaker 1: g CoV product. Okay, And and deforestation is pretty straightforward

1:33:02 Speaker 1: in the sense that the change in the signal in

1:33:05 Speaker 1: the backscatter signal is pretty significant from an area where

1:33:08 Speaker 1: you have forest, you know, where you have a high

1:33:11 Speaker 1: back scatter relatively high backscattered to an area where you

1:33:14 Speaker 1: do not have forests.

1:33:15 Speaker 3: Where you have very low backscatter.

1:33:19 Speaker 1: Okay, the next question, question number six.

1:33:24 Speaker 3: Can nights are.

1:33:25 Speaker 1: Effectively distinguished between open water emergent vegetation and invasive aquatic

1:33:31 Speaker 1: vegetation in complex wetland ecosystems. So elban can very well

1:33:38 Speaker 1: distinguish open water because smooth water surfaces produce very low backscatter. Obviously,

1:33:45 Speaker 1: sometimes there's some sources of confusion. So in the open water,

1:33:48 Speaker 1: for example, if you've got wind, it's going to look

1:33:52 Speaker 1: a little rougher, it's not going to look as dark.

1:33:58 Speaker 1: So sometimes you have some sources of confus But L

1:34:00 Speaker 1: and S band can very well. Sea band can very

1:34:03 Speaker 1: well like distinguish calm open water. Now, L band is

1:34:09 Speaker 1: less effective at detecting low emergent vegetation, especially if that

1:34:15 Speaker 1: vegetation is sparse or has a very simple structure. And

1:34:20 Speaker 1: by that I mean, you know, if it's emergent and

1:34:23 Speaker 1: it's a couple of centimeters. Remember how I said that

1:34:25 Speaker 1: the signal interaction depends on the size of the component.

1:34:30 Speaker 1: So L band is about twenty four centimeters, and if

1:34:33 Speaker 1: that emergent vegetation is just a couple of centimeters four

1:34:36 Speaker 1: or five centimeters that it's going to look like an

1:34:40 Speaker 1: open water. Like open water, it's not going to detect it.

1:34:44 Speaker 1: But S band would be better suited for detecting that

1:34:50 Speaker 1: emergent vegetation as well as sea band. And then this

1:34:54 Speaker 1: person also asks about invasive aquatic vegetation. Hard to respond

1:35:00 Speaker 1: to that, I'm not entirely sure. You know, if it's underwater,

1:35:03 Speaker 1: obviously we can't. The signal is not penetrating through the water.

1:35:08 Speaker 1: But if it's above the water, and that invasive vegetation

1:35:15 Speaker 1: has enough density and is structurally different than the surrounding vegetation,

1:35:20 Speaker 1: then you might be able to detect it with radar.

1:35:27 Speaker 1: All right, the next question number seven, I've been studying

1:35:31 Speaker 1: the principles of weather radar, specifically how we manage signal clutter.

1:35:36 Speaker 1: Given that nisar is an L and S band system,

1:35:38 Speaker 1: how do the clutter removal and ground processing algorithms in

1:35:42 Speaker 1: NICs are compared to the techniques used in traditional Doppler

1:35:46 Speaker 1: weather radar networks like Opera.

1:35:50 Speaker 3: Okay, so.

1:35:52 Speaker 1: I'll elaborate on this response. I need to consult with

1:35:57 Speaker 1: some of my colleagues. However, radars are different than nice

1:36:02 Speaker 1: er weather radars operate at much smaller wavelengths because at

1:36:08 Speaker 1: that point you're looking at the interaction of the radar

1:36:11 Speaker 1: signal with with with water droplets, so you need much

1:36:15 Speaker 1: much the much smaller wavelengths and networks radar networks like opera.

1:36:27 Speaker 4: So opera is a so opera is a.

1:36:40 Speaker 3: Platform.

1:36:41 Speaker 1: It's an observational products for end users. So these are

1:36:45 Speaker 1: products that are being derived from remote sensing data and

1:36:51 Speaker 1: these opera products, and the next session number two will

1:36:55 Speaker 1: discuss some of the opera products they use. They don't

1:37:00 Speaker 1: use the weather radar data, they use Sentinel one data,

1:37:06 Speaker 1: they use optical data, and they'll be using NICs our

1:37:09 Speaker 1: data to generate these end user products which are related

1:37:16 Speaker 1: to surface water extent, surface disturbance, displacement, vertical land motion. Okay,

1:37:28 Speaker 1: the next question, how will nisar's dual frequency L and

1:37:32 Speaker 1: S bands are observations improve earth monitoring compared to existing

1:37:37 Speaker 1: missions and what applications do you expect to benefit the most.

1:37:44 Speaker 1: I think there are many different things.

1:37:47 Speaker 4: So for.

1:37:49 Speaker 3: L band sees.

1:37:50 Speaker 1: Deeper and larger structures, S band sees shallower and finer structures,

1:37:56 Speaker 1: So combining them will provide and more thorough characteristic of

1:38:00 Speaker 1: an ecosystem so L band will work well, say, or

1:38:04 Speaker 1: works well in areas where you have dense forest. S

1:38:07 Speaker 1: band works better in areas where you have less dense forest.

1:38:10 Speaker 1: So when you combine these unique capabilities or information content

1:38:15 Speaker 1: from these different bands, then you can have a more

1:38:19 Speaker 1: thorough characterization of your ecosystem. And what applications do you expect.

1:38:27 Speaker 1: I think that that's all. It's also up to the

1:38:30 Speaker 1: end user like what they want to explore in terms

1:38:34 Speaker 1: of the use of the complementary use of L and

1:38:40 Speaker 1: S band and even cband. You know we have there's

1:38:43 Speaker 1: Sentinel one data. So how can you exploit the unique

1:38:49 Speaker 1: information content in each of these different bands to come

1:38:53 Speaker 1: up with more thorough or generate more thorough either land

1:38:57 Speaker 1: cover maps or just different sorts of products. I think

1:39:03 Speaker 1: this is super exciting, this era of multi frequency sardin.

1:39:12 Speaker 1: All right, we have a short question number nine. Swath range,

1:39:15 Speaker 1: pixel spacing and resolution per mote. Well, okay, so the

1:39:18 Speaker 1: swath is two hundred and forty kilometers for most areas.

1:39:21 Speaker 1: Some areas are half swathed. The pixel spacing that varies

1:39:26 Speaker 1: between three and ten meters for the lower level products,

1:39:32 Speaker 1: and that totally depends on the acquisition mode number ten.

1:39:39 Speaker 1: I would like to ask how NICSAR has proved to

1:39:42 Speaker 1: be better in flood mapping and climate change analysis than

1:39:46 Speaker 1: the optical remote sensing satellites, for example Lancet.

1:39:53 Speaker 4: Okay, so NICSAR, so.

1:39:55 Speaker 1: We haven't yet proved to be better. It will be better,

1:40:00 Speaker 1: you know, because we're still working on the calibrated data.

1:40:04 Speaker 1: So the data that we have generated and released is

1:40:08 Speaker 1: pre calibrated, so we really haven't started that period of

1:40:13 Speaker 1: like really rigorous science analysis with calibrated data by the community. However,

1:40:19 Speaker 1: we expect this to be huge, the data to be

1:40:23 Speaker 1: huge in terms of supporting flood mapping because of that

1:40:28 Speaker 1: unique capability that nice AR has and radar and l

1:40:33 Speaker 1: band and penetrating through the vegetation canopy and detecting standing

1:40:39 Speaker 1: water underneath the vegetation. And this is something that just

1:40:42 Speaker 1: simply cannot be done with optical data because optical is

1:40:47 Speaker 1: really just seeing the top of the canopy. And in

1:40:50 Speaker 1: areas where you have very dense vegetation, like in tropical forests, yeah,

1:40:55 Speaker 1: you're not getting a sense of the extent of flooded

1:40:59 Speaker 1: in a day inundated vegetation. So and given nicsar's high

1:41:06 Speaker 1: relatively high spatial resolution and the temple repeat exact temple

1:41:11 Speaker 1: repeat every twelve days, but really two observations within those

1:41:16 Speaker 1: twelve days.

1:41:16 Speaker 3: Every six days. I think this is going.

1:41:19 Speaker 1: To be game changing really for the study of wetland ecosystems,

1:41:23 Speaker 1: looking at wetland inundation dynamics and extent all. Right, question

1:41:33 Speaker 1: number eleven, can NICER data be downscaled with multi input

1:41:41 Speaker 1: neural networks? Yeah, that's a great question. I don't have

1:41:46 Speaker 1: an answer to that. It might be I think this

1:41:50 Speaker 1: There are many, many, many things that are worth exploring

1:41:54 Speaker 1: with the NISAR data, and this is one definitely worth

1:41:57 Speaker 1: investigating to see what sort of results you can come

1:42:00 Speaker 1: up with. Okay, so please, I didn't understand the left

1:42:11 Speaker 1: side looking and the consequences on the North Pole. So

1:42:14 Speaker 1: because of the the the look geometry and and the orbit,

1:42:21 Speaker 1: so it's in the the the it's a Sun synchronous

1:42:28 Speaker 1: polar orbit. The north pole has larger gaps with just

1:42:39 Speaker 1: the way the orbit and and the fact that it's

1:42:42 Speaker 1: left looking. And so with NISAR we've really prioritized the

1:42:49 Speaker 1: Antarctica observations in Antarctica, which in the past I think

1:42:54 Speaker 1: most radar sensors at least openly available.

1:42:56 Speaker 3: Radar data out there.

1:42:59 Speaker 1: They're right looking, so they've prioritized observations over the Arctic,

1:43:04 Speaker 1: but less so over Antarctica. In this case, it's the

1:43:07 Speaker 1: other way around, so observations from other radar sensors fill

1:43:14 Speaker 1: in the Arctic, while nisar really fills that gap in Antarctica.

1:43:24 Speaker 3: And the South Pool.

1:43:26 Speaker 1: All right, the next question when will nice our data

1:43:29 Speaker 1: be available in Google Earth Engine. That's a really good question.

1:43:33 Speaker 1: I get that question a lot, and the answer is

1:43:38 Speaker 1: that we're not sure if it's going to be available

1:43:42 Speaker 1: in Google Earth Engine. However, we are discussing this possibility,

1:43:47 Speaker 1: and yeah, we hope in sometime in the future to

1:43:53 Speaker 1: have an update on whether that will be possible or not.

1:44:00 Speaker 1: Question number fourteen, which DM is used for the radiometric correction,

1:44:06 Speaker 1: So the DM that's been used is the Copernicus Glow

1:44:10 Speaker 1: thirty meter DM. Question number fifteen, how can we use

1:44:15 Speaker 1: the dual poll HH and HV available globally to differentiate

1:44:20 Speaker 1: in speculator DOBO bounds and volume scattering phase during different

1:44:24 Speaker 1: crop growth stages? So that's really that's a great question.

1:44:31 Speaker 1: I think there are different ways, right, So HV tends

1:44:34 Speaker 1: to be much more sensitive to the presence of vegetation

1:44:37 Speaker 1: than hh And one thing you can do is you

1:44:40 Speaker 1: can look at time series of HV to look at

1:44:44 Speaker 1: crop growth.

1:44:49 Speaker 3: Another now, if you want to.

1:44:50 Speaker 1: Look at the dominant scattering mechanisms, So if you want

1:44:56 Speaker 1: to understand whether within a pixel, so the dominant scattering

1:45:01 Speaker 1: mechanism is double bounce or surface scattering or speculator scattering.

1:45:09 Speaker 1: You can do quasi polarimetric decompositions.

1:45:14 Speaker 3: And this is a great point.

1:45:15 Speaker 1: One of the things I do want to do as

1:45:17 Speaker 1: we move forward into more intermediate type nice OUR trainings

1:45:22 Speaker 1: is to have some tutorials showing how to do these

1:45:25 Speaker 1: decompositions and what sort of algorithms are out there. Okay,

1:45:31 Speaker 1: question number sixteen, this is a great question. How accurately

1:45:40 Speaker 1: can NICs our data generate a DM in a mountainous region.

1:45:45 Speaker 1: So one thing to clarify is that NICSAR has this

1:45:51 Speaker 1: interferometric capability. It's a repeat pass interferometry. And so with

1:45:56 Speaker 1: repeat pass interferometry where within one hundred meters of the

1:46:01 Speaker 1: lap of the same point, so we're very close, we're always,

1:46:07 Speaker 1: you know, whenever we have that exact repeat, we're very

1:46:10 Speaker 1: close within one hundred meters from the last exact acquisition,

1:46:16 Speaker 1: and so with repeat past interferometry, this is much much

1:46:20 Speaker 1: more more suitable for measuring small changes in the surface.

1:46:25 Speaker 1: These are changes on the order of centimeters, but not

1:46:28 Speaker 1: suitable for generating a d M. You'll have errors on

1:46:34 Speaker 1: the order of many, many, many meters. So yeah, this

1:46:40 Speaker 1: is this is just to look at small changes in

1:46:45 Speaker 1: the surface, the type of interferometry that NYSAR can do.

1:46:49 Speaker 1: And this is something that my colleague Eric Fielding will

1:46:52 Speaker 1: discuss further in session three. Okay, question number seventeen, How

1:47:03 Speaker 1: can the polarizations inform on the orientation of an object?

1:47:08 Speaker 1: So it's you know, the best way to understand the

1:47:15 Speaker 1: orientation is to do an RGB. So if you have

1:47:21 Speaker 1: two polarizations, do a false color composite where you do

1:47:26 Speaker 1: HH in the red channel, HV in the green channel,

1:47:30 Speaker 1: and then HH in the blue channel, just to start

1:47:33 Speaker 1: out with just something very simple and that way you

1:47:36 Speaker 1: can understand the unique information content in the in each polarization.

1:47:43 Speaker 1: So things that are brighter in h H that means

1:47:50 Speaker 1: you know, you have you have a large you have

1:47:57 Speaker 1: horizontal components there than in the VV, say, for example,

1:48:04 Speaker 1: if you had VV. So, yeah, the best way is

1:48:09 Speaker 1: just to do an RGB of your different polarizations. Is

1:48:16 Speaker 1: there documentation? Question eighteen? Is there documentation for the different applications? Yeah,

1:48:21 Speaker 1: there is, and I'll share a link here, But there

1:48:24 Speaker 1: was that one slide I showed of the different applications

1:48:28 Speaker 1: that NYSA can address, and there are white papers associated

1:48:33 Speaker 1: with each of those, and they're very informative and interesting

1:48:36 Speaker 1: and they're very short, but they explain the capability of

1:48:40 Speaker 1: NISAR for addressing these different applications. Question number nineteen, How

1:48:53 Speaker 1: current is the current observation plan or rather how far

1:48:57 Speaker 1: into the future is it applicable? For mighty areas of

1:49:01 Speaker 1: priority acquisitions like the US Midwest change in the next

1:49:05 Speaker 1: year so I.

1:49:10 Speaker 3: So that's another great question.

1:49:12 Speaker 1: I believe, as mentioned, this acquisition plan is going to

1:49:15 Speaker 1: be revised every six years depending on the needs of

1:49:20 Speaker 1: the UH science team and the community that the science,

1:49:27 Speaker 1: you know, the science to address the science objectives.

1:49:32 Speaker 3: And I'll update when.

1:49:34 Speaker 1: It's going to be revised again, I believe it's later, yeah,

1:49:38 Speaker 1: later this year, but there might be if there are

1:49:44 Speaker 1: priority areas that new priority areas identified, say in the

1:49:49 Speaker 1: US Midwest, where maybe four polarizations would be of great benefit.

1:49:56 Speaker 1: Then that would be something definitely to consider the next

1:50:00 Speaker 1: time it's being updated. Question number twenty, can we analyze

1:50:10 Speaker 1: images of first past and second past jointly? So I

1:50:17 Speaker 1: think this question refers to can we analyze descending and

1:50:23 Speaker 1: images from descending and ascending orbits?

1:50:26 Speaker 3: And that's that's really good. That's a really good question.

1:50:30 Speaker 1: So the viewing geometry is slightly different between a sending

1:50:35 Speaker 1: and descending, you know, because we're looking towards the side.

1:50:39 Speaker 1: And so if you have a mountainous region, you should

1:50:44 Speaker 1: never combine ascending and descending, at least, you know, with

1:50:49 Speaker 1: the backscattered data. If your area is flat, then it

1:50:53 Speaker 1: should be okay. I personally do not like to combine

1:50:58 Speaker 1: ascending and descend.

1:51:00 Speaker 3: I deal with them separately.

1:51:03 Speaker 1: I'll generate a product with you know, one stack or

1:51:06 Speaker 1: the other, and then maybe compare or combine once the

1:51:11 Speaker 1: product is generated.

1:51:15 Speaker 3: All right, which band is good for soul moisture?

1:51:19 Speaker 1: So this is an interesting one, and and part of

1:51:22 Speaker 1: the plans for our set is to have a nicer

1:51:29 Speaker 1: training on the use of the uh SO moisture data products.

1:51:36 Speaker 1: So there are actually three different algorithms as part of

1:51:40 Speaker 1: the sole moisture product, and the I think these algorithms

1:51:49 Speaker 1: are using different polarizations or combination of the polarizations, but

1:51:55 Speaker 1: the best one will be h age. HV is sensitive

1:52:04 Speaker 1: to is more sensitive to the presence of vegetation and

1:52:08 Speaker 1: will have less penetration through the soil, so hh will

1:52:13 Speaker 1: have the greatest penetration through the soil, I think, followed

1:52:18 Speaker 1: by eight by vb.

1:52:22 Speaker 3: Okay.

1:52:23 Speaker 1: The next question, all right, question twenty two. Considering nysar's

1:52:38 Speaker 1: dual frequency capability, how do we plan to combine its

1:52:42 Speaker 1: deep penetrating L band data with cannopy sensitive S band

1:52:46 Speaker 1: data to solve a specific measurement challenge for research?

1:52:50 Speaker 3: That's a great question.

1:52:51 Speaker 1: As mentioned, you know, the data is there. The only

1:52:55 Speaker 1: level three product that is being generated globally is so moture.

1:53:00 Speaker 1: So there's a lot for the community to explore, and

1:53:05 Speaker 1: these are certainly things to explore. Is that complementary nature

1:53:11 Speaker 1: of these two different bands and how they can be.

1:53:15 Speaker 3: Used to solve.

1:53:18 Speaker 1: Something very specific to your research?

1:53:30 Speaker 4: All right?

1:53:31 Speaker 1: Question number twenty three, how are slope parallel and slope

1:53:38 Speaker 1: perpendicular motion components projected into a sending NYSAR line of

1:53:43 Speaker 1: sight displacement measurements for a west facing landslide, for an

1:53:48 Speaker 1: east facing slope with similar downslope and vertical motion, how

1:53:53 Speaker 1: would the line of site display the magnitude, sign and

1:53:56 Speaker 1: sensitivity differ compared to the west facing slope. Okay, so

1:54:01 Speaker 1: that's that's a.

1:54:04 Speaker 2: That's a great question.

1:54:05 Speaker 1: So in the ascending mode, the satellite is looking left,

1:54:10 Speaker 1: so it's looking towards the west, and so this is

1:54:19 Speaker 1: so I will respond to this question in the in

1:54:24 Speaker 1: the document, but as mentioned the third session, we will

1:54:30 Speaker 1: dive will take a deep dive into the instart products

1:54:34 Speaker 1: and specifically these sorts of questions that you're asking about,

1:54:38 Speaker 1: like related to the line of sight of the observation.

1:54:43 Speaker 1: You know, what sort of movement are we actually looking

1:54:46 Speaker 1: at with the interferrograms that are generated? Number twenty four.

1:54:52 Speaker 1: What are the most promising applications of NYSAR data for

1:54:57 Speaker 1: atmospheric science, climate research, and natural have a monitoring in

1:55:01 Speaker 1: the coming years. I think there are many So atmospheric science,

1:55:10 Speaker 1: as mentioned, you know, NYSAR is not a sensor to

1:55:16 Speaker 1: do atmospheric science. However, you know when you have measurements

1:55:19 Speaker 1: like soil moisture that can feed into eve apple transpiration models,

1:55:25 Speaker 1: into weather models, and so I think you know there's

1:55:32 Speaker 1: a lot of stuff that different sorts of research that

1:55:37 Speaker 1: the products that can be generated will support natural hazard monitoring.

1:55:44 Speaker 4: There's a whole.

1:55:47 Speaker 1: Area and you know within applications that addresses disasters and

1:55:53 Speaker 1: so looking at for example, with the instart products, you

1:55:57 Speaker 1: can measure small all changes in the land surface. So

1:56:02 Speaker 1: if you see an area, for example, landslides, if you're

1:56:06 Speaker 1: looking an area where you're seeing small movements through time,

1:56:11 Speaker 1: that might be indicative of an area that might be

1:56:15 Speaker 1: at risk for a landslide, right if it's in a

1:56:17 Speaker 1: sloped area. If you're seeing, for example, you can also

1:56:25 Speaker 1: detect floods, which is as mentioned, it's ideal, so natural flooding,

1:56:34 Speaker 1: flooded ecosystems, but floods related to disasters, and usually you

1:56:39 Speaker 1: can do assessments of the extent of the flood. When

1:56:44 Speaker 1: these sorts of things happens, there's a lot of cloud covers,

1:56:46 Speaker 1: so it's very beneficial that we have a sensor, a

1:56:51 Speaker 1: radar sensor that can see through clouds and provide those assessments. Obviously,

1:56:55 Speaker 1: those assessments can only be provided as long as there's

1:56:59 Speaker 1: an observation. But yeah, there are many many natural hazard

1:57:08 Speaker 1: monitoring looking at volcano movements around volcanoes which might be

1:57:13 Speaker 1: indicative of volcanoes becoming active, movement around tectonic plates and

1:57:21 Speaker 1: faults which might indicate areas where there's a higher likelihood

1:57:26 Speaker 1: of earthquakes. So there's a lot that NYSAR can support.

1:57:41 Speaker 1: How does nisar's question number twenty five? So I will

1:57:46 Speaker 1: answer that one separately in the Google doc. Let's carry

1:57:51 Speaker 1: on to question number twenty six. Usually it's a smap

1:58:00 Speaker 1: provides meter cubic meter over cubic meter, but here you

1:58:06 Speaker 1: show it as a percent Are these comparable?

1:58:09 Speaker 3: These?

1:58:09 Speaker 1: This percentage represents the amount of soil moisture per volume.

1:58:13 Speaker 1: What is the maximum depth that nicar can reach? Okay,

1:58:16 Speaker 1: so that's a great question. It is comparable. It's volumetrics,

1:58:21 Speaker 1: so it is the volume of water within a volume.

1:58:26 Speaker 3: Of soil and.

1:58:30 Speaker 1: The maximum depth that nicar can reach. As mentioned at

1:58:36 Speaker 1: the top, nice are the nice are soul moisture kind

1:58:42 Speaker 1: of represents so moition the top five centimeters. But you know,

1:58:46 Speaker 1: actually that's going to totally vary depending on many, many, many,

1:58:51 Speaker 1: many different things. Depending on the texture of the soil,

1:58:56 Speaker 1: depending on the amount of water. So basically, the wetter

1:59:02 Speaker 1: the soil, the less the penetration. But there have been

1:59:06 Speaker 1: studies from way back showing that L band can penetrate soils.

1:59:12 Speaker 1: And this was a study like in the desert somewhere

1:59:15 Speaker 1: where there was no vegetation and super dry soils. It

1:59:19 Speaker 1: showed that the L band signal could penetrate through that

1:59:21 Speaker 1: dry soil on the order of one and a half.

1:59:25 Speaker 3: To two meters.

1:59:27 Speaker 1: So in the case of NYSA, again it just represents

1:59:32 Speaker 1: the top five centimeters.

1:59:39 Speaker 3: All right.

1:59:40 Speaker 1: The next question number twenty seven, when you mentioned interferometry

1:59:45 Speaker 1: measurements combining NISA and sent and wood data, are you

1:59:49 Speaker 1: describing bistatic collections. No, this is not bistatic at all.

1:59:56 Speaker 1: This is a combination of NICs our data which is

2:00:01 Speaker 1: more contemporaneous, you know, measurements from some pre calibrated measurements

2:00:09 Speaker 1: since middle of September twenty twenty five, and the Sentinel

2:00:13 Speaker 1: one data in that example was used to go back

2:00:17 Speaker 1: in time. So for that historical context question, how is

2:00:31 Speaker 1: it possible to combine L and S band if their

2:00:34 Speaker 1: resolutions are different? So you know, the s BAND products

2:00:42 Speaker 1: have the same they're the same level products as as

2:00:48 Speaker 1: l band. It doesn't matter if their resolutions are slightly different.

2:00:55 Speaker 1: You can interpolate or extrapolate one to the other and

2:01:00 Speaker 1: you will be learning a little more about the spand

2:01:04 Speaker 1: data and its characteristics and how to access it. Even

2:01:09 Speaker 1: though ISRAE has not yet made available the spand data,

2:01:15 Speaker 1: there will be a short video about the Bunity platform

2:01:22 Speaker 1: that's where you can access the spand data through ISRAE,

2:01:29 Speaker 1: and part of that will discuss the characteristics of the

2:01:32 Speaker 1: spand data. Okay, Question number twenty nine does each level

2:01:39 Speaker 1: have different spatial resolutions? Yes, so the different levels have

2:01:45 Speaker 1: different spatial resolutions, and within each level there are different

2:01:55 Speaker 1: acquisition modes. So for example, level two you have acquisitions.

2:02:01 Speaker 4: Over ice and over land and.

2:02:06 Speaker 1: Those acquisitions are at different spatial resolutions. Question number thirty

2:02:19 Speaker 1: what products are built off NISA right now? Are there

2:02:22 Speaker 1: partnerships with hyper scalers like Google for Google's initiatives initiatives

2:02:27 Speaker 1: using AI to improve Earth, or other AI for good

2:02:31 Speaker 1: initiatives by others? So I will have to I will

2:02:37 Speaker 1: respond to this question in the document, but right now,

2:02:41 Speaker 1: as mentioned and as you know, was the plan for

2:02:46 Speaker 1: the mission. There's the different level products. That's what we're

2:02:49 Speaker 1: concentrating on, and the only level three global product is

2:02:53 Speaker 1: the sole moisture product. I think there are plans or

2:03:00 Speaker 1: discussions about developing other products, but I will respond to

2:03:05 Speaker 1: that in terms of what those plans are. Okay, so

2:03:11 Speaker 1: we are three minutes past the hour. Let me see

2:03:17 Speaker 1: how many more questions we have.

2:03:21 Speaker 3: Okay, so we.

2:03:22 Speaker 1: Have quite quite a bit more number of questions.

2:03:30 Speaker 3: I will respond to all of the questions.

2:03:32 Speaker 1: It will take me a little bit of time, so

2:03:34 Speaker 1: give me about a week to polish and respond to

2:03:39 Speaker 1: the questions and this will be posted on the uh

2:03:43 Speaker 1: ON on the page for this training. So with that,

2:03:49 Speaker 1: I want to thank everyone really for all the enthusiasm

2:03:53 Speaker 1: and all of the great questions coming in. And remember

2:03:58 Speaker 1: that there are two more sessions. Great sessions. Data Access

2:04:04 Speaker 1: will be session number two, that will be next Thursday

2:04:07 Speaker 1: at the same time, and then session number three will

2:04:10 Speaker 1: be in two weeks and that will be on InSAR.

2:04:15 Speaker 1: So wishing everyone a great day, great weekend, and until

2:04:20 Speaker 1: next week.