NASA ARSET Foundational Concepts

Speaker 1: Welcome everyone to our RSET training series. Introduction to Thermal

Speaker 1: Remote Sensing and Applications in urban heat Island Mapping Today

Speaker 1: is part one of our training series, Foundational Concepts of

Speaker 1: Thermal Remote Sensing. My name is Savannah Cooley. I'm a

Speaker 1: researcher at NASA, AIMES research center with the Bay Area

Speaker 1: Environmental Research Institute in Mountain View, California, and I'm a

Speaker 1: trainer with the RSET Ecological Conservation Team. I'll start with

Speaker 1: a few quick words about the RSET program. The NASA

Speaker 1: Applied Remote Sensing Training or RSET program provides cost free

Speaker 1: training on the use of remote sensing observations, analysis methods,

Speaker 1: and tools. We provide training in several thematic areas, including agriculture,

Speaker 1: climate and resilience disasters, ecological conservation, health and air quality,

Speaker 1: water resources, and wildland fires. Our SET provides trainings both

Speaker 1: online or in person. Our online trainings are delivered in

Speaker 1: two formats, live an instructor led like Today's training, or

Speaker 1: asynchronous in self paste like NASA's free and accessible data.

Speaker 1: All of our trainings are offered at no cost. We

Speaker 1: try to offer trainings in more than one language whenever

Speaker 1: we can, and we only use no costs in open

Speaker 1: source software and data. We offer our trainings at a

Speaker 1: range of levels, so you can find a training series

Speaker 1: that fits your level of experience and need. Please visit

Speaker 1: us at our website to learn more. We'll start today

Speaker 1: by giving an overview of this training series introduction to

Speaker 1: thermal remote sensing, foundational concepts and applications. Here we are

Speaker 1: looking at a thermal image of the oxets in Toulouse, France.

Speaker 1: In the thermal infrared, we measure both the effect of

Speaker 1: temperature and emissivity. Emissivity relates to the composition of the material.

Speaker 1: Because the cross is made of bronze and copper, it

Speaker 1: has a different emissivity and temperature and therefore a different

Speaker 1: thermal emission than the surrounding material of the pavement. Thermal

Speaker 1: infrared radiometer instruments such as NASA JPL's Airborne High Test

Speaker 1: Instrument and NASA JPL's spaceborne Ecostress Mission measure land surface temperature.

Speaker 1: You'll learn to use some of this data in part

Speaker 1: two of this training. First, and foremost, for part one

Speaker 1: of the training, you will gain foundational knowledge about the

Speaker 1: principles and concepts that underpin all thermal remote sensing instruments

Speaker 1: as well as learn about some key applications. This training

Speaker 1: has seven learning objectives. Learning objectives one through five focus

Speaker 1: on the underlying science of sensor capabilities and general applications,

Speaker 1: and will be covered in Part one today. Learning objectives

Speaker 1: six and seven are specific to workflows for the use

Speaker 1: of ecostress data for urban heat islands and will be

Speaker 1: the focus of Part two. We have fundamentals of remote

Speaker 1: sensing as the only prerequisite for this training. However, we

Speaker 1: also have several suggested trainings that we highly encourage participants

Speaker 1: to take. The first is satellite remote sensing for measuring

Speaker 1: urban heat islands and constructing heat vulnerability indices. The next

Speaker 1: is a brief introduction to the Ecostress mission, which is

Speaker 1: another our set training on the ecostres Us Mission website.

Speaker 1: And then finally, there's a number of tutorials available on

Speaker 1: the Land Processes doc that also provide more details on

Speaker 1: ecostress and how to access ecostress data. There are two

Speaker 1: parts in this training. In Part one today will introduce

Speaker 1: the theoretical background of thermal remote sensing. Part two will

Speaker 1: cover hands on exercises focused on urban heat island applications.

Speaker 1: The homework opens on the last day of the training

Speaker 1: on June second. It is due two weeks later on

Speaker 1: June sixteenth, twenty twenty six. A certificate of completion will

Speaker 1: be awarded to participants who attend all live sessions and

Speaker 1: complete the homework assignment before the given due date. We'll

Speaker 1: start today by giving an overview of this training series

Speaker 1: introduction to thermal remote sensing, foundational concepts and applications. We

Speaker 1: have two instructors for Part one. I'm a Savannah Cooley

Speaker 1: and I'm a research scientist in the Biospheric Sciences Branch

Speaker 1: within the Earth's Science Division at NASA Ames Research Center

Speaker 1: with the Bay Area Environmental Research Institute. Our other instructor

Speaker 1: is Glenn Holly, who is a research scientist in the

Speaker 1: Earth Science Section of NASA Jet Propulsion Laboratory, California Institute

Speaker 1: of Technology. By the end of Part one, participants will

Speaker 1: be able to identify the fundamental concepts and physical principles

Speaker 1: of thermal infrared remote sensing, define the role of emissivity

Speaker 1: retrievals and ensuring the accuracy of satellite derived land surface

Speaker 1: temperature products, distinguish key differences between thermal and optical remote

Speaker 1: sensing approaches, including emission versus reflection, day versus night capability,

Speaker 1: and atmospheric window considerations. Identify applications of thermal remote sensing

Speaker 1: data for ecosystem stewardship, agricultural management, climate adaptation, and urban planning. Finally,

Speaker 1: compare the characteristics of current and upcoming thermal missions in

Speaker 1: the context of their suitability to different application use cases.

Speaker 1: Let's take a moment to consider this knowledge check. This

Speaker 1: training builds on the concepts from the prerequisite for this training,

Speaker 1: fundamentals of remote sensing, so a basic knowledge of remote

Speaker 1: sensing and satellite and sensor characteristics is assumed. I'll pause

Speaker 1: for a few moments to give everyone a chance to

Speaker 1: think for yourself about which of the options A through

Speaker 1: D listed below best completes the sentence. Thermal remote sensing

Speaker 1: data can be used for dot dot dot As you

Speaker 1: can see, options A, C, and D all describe real

Speaker 1: remote sensing applications, but they use fundamentally different parts of

Speaker 1: the electromagnetic spectrum. Thermal infrared remote sensing, which is the

Speaker 1: focus of today's training, works by measuring energy that is

Speaker 1: emitted from the surface itself, not reflected from the sun

Speaker 1: therm more remote sensing, in particular, detects emitted radiation at

Speaker 1: wavelengths in the thermal infrared bands between eight and fourteen microns,

Speaker 1: which we will discuss more in depth in the next section.

Speaker 1: B is the closest to what we'll be focusing on.

Speaker 1: By the end of today, you'll be able to explain

Speaker 1: exactly why B is correct and how it differs from

Speaker 1: the others. If you have questions during today's training, you

Speaker 1: can put them in the Q and A box within

Speaker 1: WebEx at any time. We encourage you to put your

Speaker 1: questions into the Q and A chat as they arise.

Speaker 1: The earlier you share your questions in the chat, the

Speaker 1: more likely it is that we will be able to

Speaker 1: respond to them in real time during the Q and

Speaker 1: A session. If there are questions we do not have

Speaker 1: time to address during the live Q and A session,

Speaker 1: we will make sure to collect these and post the

Speaker 1: written answers to the training page within a week after

Speaker 1: the training. In other words, we will try to address

Speaker 1: as many questions as we can today. All of these

Speaker 1: questions and answers will be posted, along with the additional

Speaker 1: questions and written answers to the training page within a

Speaker 1: week after the training. Let's dive into section one, the

Speaker 1: theoretical basis of temperature measurements. This diagram shows the full

Speaker 1: electromagnetic spectrum from long wavelength radio waves on the left

Speaker 1: to short wavelength gamma rays on the right. The visible

Speaker 1: light that our eyes can see occupies just a small

Speaker 1: sliver of this spectrum, roughly point four too point seven micrometers.

Speaker 1: Most optical remote sensing works in the visible, near infrared,

Speaker 1: and short wave infrared regions, spanning abouto point four to

Speaker 1: two point five micrometers. The thermal infrared region extends from

Speaker 1: approximately three to fourteen micrometers. Within this we distinguish two

Speaker 1: main atmospheric windows. Mid wave infrared or MWR, which is

Speaker 1: the window that spans roughly three to five micrometers. This

Speaker 1: region has complications from mixed solar reflection and thermal emission

Speaker 1: during daytime. The thermal infrared or TIR window spans roughly

Speaker 1: eight to fourteen micrometers. This is our primary working range

Speaker 1: for land surface temperature retrieval because it contains purely emitted

Speaker 1: thermal radiation with no solar contamination. Notice the atmosphere absorption

Speaker 1: features shown here. Water vapor absorbs strongly throughout much of

Speaker 1: the infrared. Carbon dioxide has absorption bands around four point

Speaker 1: three and fifteen micrometers. Ozone absorbs around nine point six micrometers,

Speaker 1: right in the middle of our thermal infrared window, which

Speaker 1: is why some sensors avoid that specific wavelength. A key

Speaker 1: point is that thermal infrared remote sensing measures emitted energy

Speaker 1: radiation that originates from the surface itself based on its temperature,

Speaker 1: rather than reflected energy from an external source like the sun.

Speaker 1: This fundamental difference has major implications for how we acquire, process,

Speaker 1: and interpret thermal data. Before we discuss how we measure

Speaker 1: temperature remotely, let's establish what temperature actually means from a

Speaker 1: physics perspective. Is fundamentally a measure of the average kinetic

Speaker 1: energy of molecular motion within a substance. Every atom in

Speaker 1: molecule in matter is in constant motion, vibrating, rotating, and translating.

Speaker 1: The faster these particles move on average, the higher the

Speaker 1: temperature for an ideal gas, The average kinetic energy of

Speaker 1: molecules is directly proportional to the absolute temperature. The average

Speaker 1: kinetic energy equals three halves times Boltzmann's constant times temperature.

Speaker 1: So here in this equation k E represents the average

Speaker 1: kinetic energy, so the mean energy of motion per molecule

Speaker 1: in jewels, measured as one half m. The squared the

Speaker 1: three halves factor represents the three degrees of freedom corresponding

Speaker 1: to the spatial dimensions for movement in a three D

Speaker 1: space where it's there's also a contributing half sigma T

Speaker 1: to each to energy for each dimension. K represents the

Speaker 1: Boltzmann constant, which relates temperature to energy, holding a constant

Speaker 1: value of one point three eight times ten to the

Speaker 1: minus twenty three jewels per Calvin T is absolute temperature,

Speaker 1: the temperature in kelvin. The equation twells us that energy

Speaker 1: is directly proportional to the temperature in kelvin, meaning that

Speaker 1: zero kelvin, which is absolute zero, corresponds to zero kinetic energy.

Speaker 1: This relationship tells us that temperature is not some abstract property.

Speaker 1: It's a direct measure of how much energy is stored

Speaker 1: in the random motions of atoms and molecules. We measure

Speaker 1: temperature and several scales. Kelvin is the absolute scale, starting

Speaker 1: at absolute zero, where all molecular motion would theoretically cease

Speaker 1: zero Calvin equals negative two hundred and seventy three point

Speaker 1: one five degrees celsius. In remote sensing, we typically work

Speaker 1: in kelvin because the radiation laws require absolute temperature. The

Speaker 1: key insight here is that temperature reflects the internal thermal

Speaker 1: energy of matter. An object at three hundred Calvin has

Speaker 1: molecules moving at a certain average speed. An object at

Speaker 1: three hundred and ten Calvin has molecules moving faster with

Speaker 1: more kinetic energy. This kinetic energy is what ultimately gets

Speaker 1: converted into the thermal radiation we measure from space. Now

Speaker 1: we arrive at a crucial connection. How does the kinetic

Speaker 1: energy of molecular motion become electromagnetic radiation that we can

Speaker 1: detect from space. The answer lies in the behavior of

Speaker 1: charged particles. Atoms in molecules contain electrons and protrons, charged particles.

Speaker 1: When molecules vibrate, rotate, or collide with each other, these

Speaker 1: charges are accelerated. And here's the physics. According to Maxwell's

Speaker 1: equations of electromagnetism, any accelerating charge emits electromagnetic radiation, So

Speaker 1: the process works like this. First, molecules at any temperature

Speaker 1: above absolute zero are in constant random motion. Vibrating, rotating, colliding.

Speaker 1: These motions cause the charge particles within atoms to accelerate.

Speaker 1: Accelerating charges radiate electromagnetic energy. The hotter the object, the

Speaker 1: more vigorous the molecular motion, and therefore the more radiation emitted.

Speaker 1: This is the fundamental bridge between the thermodynamic definition of

Speaker 1: temperature kinetic energy of molecules, and what we measure remotely

Speaker 1: electromagnetic radiation. You can think of it then, thermal radiation

Speaker 1: is the electromagnetic exhaust, if you will, of molecular kinetic energy.

Speaker 1: When we point a thermal sensor at the Earth's surface,

Speaker 1: we're detecting the electromagnetic signature of all that molecular motion

Speaker 1: happening at the surface. The intensity and spectral distribution of

Speaker 1: this radiation follow precise physical laws, the Plank function and

Speaker 1: Stefan Boltzmann law, which we'll discuss next. These laws quantify

Speaker 1: exactly how much kinetic energy converts to radiated energy. This

Speaker 1: connection is why thermal remote sensing works. By measuring the

Speaker 1: electromagnetic radiation emitted by a surface, we can infer its

Speaker 1: temperature because that radiation is a direct consequence of the

Speaker 1: surface's internal thermal energy. To understand thermal radiation quantitatively, we

Speaker 1: need to introduce a concept of a black body. A

Speaker 1: black body is a theoretical ideal, a perfect absorber and

Speaker 1: perfect emitter of radiation. A black body has two defining properties. First,

Speaker 1: it absorbs all incident radiation at all wavelengths. Nothing is

Speaker 1: reflected or transmitted. We express this as absorptivity alpha equals one. Second,

Speaker 1: black bodies emit the maximum possible radiation for its temperature.

Speaker 1: We express this as emissivity epsilon equals one. The term

Speaker 1: black body comes from the fact that an object that

Speaker 1: absorbs all visible light would appear perfectly black. But don't

Speaker 1: let the name confuse you. A black body at high

Speaker 1: temperature actually glows brightly. Think of the heating element on

Speaker 1: an electric stove. As it heats up, it goes from

Speaker 1: black to red, to orange to yellow. In the real world,

Speaker 1: no material is a per black body. Real materials are

Speaker 1: sometimes called gray bodies. They have amissivity less than one,

Speaker 1: meaning they emit less radiation than a black body at

Speaker 1: the same temperature. The closest approximation to a black body

Speaker 1: is a cavity with a small opening. As the diagram shows,

Speaker 1: radiation entering the opening, bounces around inside and is almost

Speaker 1: completely absorbed. This is why laboratory black body calibration sources

Speaker 1: use cavity designs. Why does the black body concept matter

Speaker 1: and remote sensing because the radiation laws we use Plank's law,

Speaker 1: Stefens Stephen Boltzmann's law describe black body radiation. To apply

Speaker 1: these laws to real surfaces, we must account for their emissivity,

Speaker 1: which tells us how efficiently they emit compared to a

Speaker 1: black body. Water, for example, has a massivity of around

Speaker 1: onero point nine to nine in the thirdsmal infrared, very

Speaker 1: close to a black body, but dietation is similar around

Speaker 1: point ninety seven to point nine nine, But bear soil

Speaker 1: might be point nine two to point ninety six, and

Speaker 1: dry sand can be as low as point nine. These

Speaker 1: differences matter for accurate temperature retrieval. Now we come to

Speaker 1: the fundamental equation of thermal radiation Plank's radiation law. This equation,

Speaker 1: developed by Max Plank in nineteen hundred, describes exactly how

Speaker 1: much energy a black body emits at each wavelength for

Speaker 1: a given temperature. The equation is b of lambda and

Speaker 1: t equals two hc squared over lambdra to the fifth

Speaker 1: power times one over the quantity e to the power

Speaker 1: of HC over lambda ktnus one. Let's break this down

Speaker 1: and define each term so B as a function of

Speaker 1: lambda and T is the spectral radiance the power emitted

Speaker 1: per unit area per unit solid angle per unit wavelength.

Speaker 1: Its units are watts per meter squared per stridian per micromemeter.

Speaker 1: Lambda is the wavelength in micrometers. T, like we've seen before,

Speaker 1: is the absolute temperature in kelvin. H is Plank's constant

Speaker 1: equal to six point two six times ten to the

Speaker 1: negative thirty four duel seconds. This is the fundamental quantum

Speaker 1: of action. C is the speed of light approximately three

Speaker 1: times ten to the eight meters per second. K like

Speaker 1: we saw before, is Boltzmann's constant, and this constant links

Speaker 1: temperature to energy at the molecular level. If you remember

Speaker 1: a few slides back, and E is just the base

Speaker 1: of the natural logarithm approximately two point seven one eight.

Speaker 1: The graph on this slide shows Plank curves for three

Speaker 1: different temperatures relevant to Earth's surfaces. So three hundred calvin,

Speaker 1: three hundred and ton Calvin, and three hundred and twenty Calvin.

Speaker 1: Roughly twenty seven, thirty seven, and forty seven degrees celsius.

Speaker 1: Notice several important features. First, higher temperatures produce more radiation

Speaker 1: at all wavelengths, the curve shifts upward. Second, higher temperatures

Speaker 1: shift the peak towards shorter wavelengths. Will quantify this with

Speaker 1: Wine's law. Next. Third, in the eight to twelve micrometer range,

Speaker 1: the curves are well separated. A ten Calvin time difference

Speaker 1: produces a clearly measurable radiance difference. This is why this

Speaker 1: wavelength region is so useful for temperature sensing. Plank's law

Speaker 1: is the foundation for all thermal remote sensing. When we

Speaker 1: measure radiance at a specific wavelength, we can invert this

Speaker 1: equation to solve for temperature. If we know the emissivity.

Speaker 1: Willen's displacement law tells us where a black body's emission

Speaker 1: spectrum peaks at what wavelength the most energy is emitted.

Speaker 1: The equation is elegantly simple. Where we have lambda max

Speaker 1: so the maximum wavelength of the peak emission in micrometers,

Speaker 1: which is equal to the term b divided by t.

Speaker 1: Where b is Wien's displacement constant equal to two ninety

Speaker 1: eight My michrometer calvin. This is a derived constant that

Speaker 1: comes from finding the maximum of planks function. The inverse

Speaker 1: relationship means that hotter objects emit most strongly at shorter wavelengths.

Speaker 1: This graph visualizes Ween's displacement law by comparing the emission

Speaker 1: spectra of the Sun versus the Earth. First notice the

Speaker 1: logarithmic skills on both axes. This allows us to compare

Speaker 1: the massive energy output of the Sun with the relatively

Speaker 1: tiny output of the Earth on a single plot. The

Speaker 1: core principle here is the equation lambda max equals b

Speaker 1: divided by TEA. Where the Sun at around six thousand

Speaker 1: calvin is incredibly hot, so its peak emission is pushed

Speaker 1: to the left at zero point four eight micrometers. This

Speaker 1: falls squarely the visible light spectrum, which is why we

Speaker 1: see sunlight. The Earth at around three hundred ish Telvin

Speaker 1: is much cooler, so its peak shifts to the right

Speaker 1: at about nine point sixty six micrometers. This is the

Speaker 1: thermal infrared. This shift explains why we design satellite sensors

Speaker 1: differently based on the target. We use the eight to

Speaker 1: fourteen micrometer range for Earth observation because that's where terrestrial

Speaker 1: surfaces emit the most signal. While Plank's law showed us

Speaker 1: the shape of the emission curve, the Stephen Boltzmann law

Speaker 1: tells us the total area under that curve the total

Speaker 1: energy emitted. The relationship is defined by this equation M

Speaker 1: equals epsilon sigma T to the fourth. Let's break that down.

Speaker 1: M is the radiant exitance, which is the total power

Speaker 1: emitted per unit area measured in watts per square meter.

Speaker 1: Epsilon is emissivity, which, as we discussed before, is the

Speaker 1: efficiency factor ranging from zero to one. For a perfect

Speaker 1: black body, that value would be one, and like we said,

Speaker 1: real materials are usually less efficient, so we will be

Speaker 1: diving into this more in the next section. And sigma

Speaker 1: is the Stephen Boltzmann constant, So this is a derived

Speaker 1: constant that roughly equals five point sixty seven times ten

Speaker 1: to the minus eight. And then again T is the

Speaker 1: absolute temperature in Calvin. The power of T to the

Speaker 1: fourth is the most critical feature here where you know.

Speaker 1: This basically shows a massive nonlinearity, meaning that energy output

Speaker 1: doesn't grow with temperature, it grows really really fast. Look

Speaker 1: at the graph on the slide. Here we are comparing

Speaker 1: a standard Earth surface temperature of three hundred kelvin to

Speaker 1: a slightly warmer three hundred and ton kelvin, this is

Speaker 1: only a three point three percent increase in temperature. However,

Speaker 1: if you look at the energy jump from on the

Speaker 1: y axis, the output rises from roughly four hundred and

Speaker 1: fifty nine to five hundred and twenty three watts per

Speaker 1: square meter. This is a fourteen percent increase in radiated energy.

Speaker 1: So why does this matter? This extreme sensitivity has multiple

Speaker 1: important implications for remote sensing in general. We can see

Speaker 1: this as great news in the sense that it means

Speaker 1: that our thermal sensors are incredibly sensitive. We can detect

Speaker 1: very subtle differences in surface temperature because the signal changes

Speaker 1: so dramatically. For climate physics, this fourth dependence relationship with

Speaker 1: temperature acts as a stabilizing feedback valve. As the Earth worms,

Speaker 1: it sheds heat much more rapidly into space, which helps

Speaker 1: prevent runaway heating. For data analysis, it does complicate things

Speaker 1: in a certain sense because the relationship isn't linear. So

Speaker 1: if you have a pixel that contains both hot and

Speaker 1: cold objects, the hot objects will dominate the signal disproportionately.

Speaker 1: This is a problem we call subpixel mixing. We apply

Speaker 1: the stuff in Boltzmann law to reconcile the difference between

Speaker 1: a real surface and a perfect black body. Kinetic temperature

Speaker 1: t KIN is what we can think of as the

Speaker 1: ground truth, the actual thermal state of the surface determined

Speaker 1: by molecular motion, but satellite sensors don't measure this directly. Instead,

Speaker 1: they measure radiance and convert it to brightness temperature t

Speaker 1: B by inverting Plank's law. Brightness temperature is defined as

Speaker 1: the temperature a perfect black body would have to be

Speaker 1: to emit the radiance we observe. We call the radiometric

Speaker 1: temperature t RAD, which is the temperature that integrates brightness

Speaker 1: temperature across all wavelengths. Via the stuff in Boltzmann constant,

Speaker 1: t RAD can be related directly back to kinnetic temperature

Speaker 1: with the equation Here, t RAD equals the emissivity to

Speaker 1: the one fourth power multiplied by kinetic temperature. Since emissivity

Speaker 1: is less than one for all real surfaces, t RAD

Speaker 1: is less than t kinetic t kin. The dryer and

Speaker 1: sandier the surface, the larger the gap between them. Let's

Speaker 1: go into a review question to convert measured radiance to

Speaker 1: kinetic temperature. What additional information is needed. I'll pause here

Speaker 1: and let folks think a little bit on your own.

Speaker 1: The correct answer is b surface and massivity. Remember, satellite

Speaker 1: sensors measure radiance and convert it to brightness temperature, which

Speaker 1: is defined as the temperature a perfect black body would

Speaker 1: have to be to emit the radiance we observe. The

Speaker 1: problem is that real surfaces aren't perfect black bodies. They

Speaker 1: have emissivities less than one. So if we assume everything

Speaker 1: has a massivity of one, will systematically underestimate surface temperature.

Speaker 1: The key takeaway is that the lower the emissivity, the

Speaker 1: larger the gap between what a sensor measures and the surface.

Speaker 1: The surface is true temperature. This is why accurate emissivity

Speaker 1: is so critical and why we will spend time delving

Speaker 1: into the topic in the next section. In section two,

Speaker 1: we will focus on emissivity. Here you can see radiance

Speaker 1: curves of surface emission, surface emission and reflection, and at

Speaker 1: sensor radiance for a quartz amissivity spectrum. Atmospheric corrections are

Speaker 1: necessary to isolate surface features which are obscured by atmospheric attenuation.

Speaker 1: The red curve shows what the surface is actually emitting.

Speaker 1: You can see the characteristic quartz and massivity feature as

Speaker 1: a dip near eight point five to nine micrometers. The

Speaker 1: blue nerve adds in the reflected downwelling component and atmospheric attenuation.

Speaker 1: By the time we get to the black curve what

Speaker 1: the sensor actually sees. The Coretz spectral feature is still visible,

Speaker 1: but it is shifted under atmospheric structure. The ozone absorption

Speaker 1: feature near nine point six micrometers is cutting right through

Speaker 1: the middle of our tir window. Water, vapor and CO

Speaker 1: two are carving out additional structure throughout. This is a

Speaker 1: core challenge. We have one measurement at sensor radiance, but

Speaker 1: we are trying to solve for two unknown simultaneously, temperature

Speaker 1: and emissivity. The rest of the signal is atmospheric noise

Speaker 1: we have to characterize and remove before we can even

Speaker 1: get to the surface. This is precisely why atmospheric correction

Speaker 1: is not optional and why am acivit retrieval algorithms exist.

Speaker 1: We need to mathematically untangle all of these contributions to

Speaker 1: get back to the surface. Temperature emissivity is defined as

Speaker 1: the ratio of the actual emission to black body. Emission

Speaker 1: emissivity varies with material roughness, angle, and wavelength. The top

Speaker 1: graph illustrates the differences in radiances between a black body,

Speaker 1: which is shown in the solid black line, versus quartz material,

Speaker 1: which has lower emissivity than a black body. As a result,

Speaker 1: we can see that emissivity of quartz on the lower

Speaker 1: graph is lower than one across all wavelengths, especially in

Speaker 1: the eight to ten micron window. Emissivity for most natural

Speaker 1: surfaces varies from point five to five to point nine

Speaker 1: to nine. Here are some examples from different surfaces, including soils, rocks, sands,

Speaker 1: and a gray body, which, unlike a black body, is

Speaker 1: an imperfect emitter, though as you can see, compared to

Speaker 1: these other surfaces, is closer to one across all wavelengths.

Speaker 1: There are two main approaches for estimating amissivity. First is

Speaker 1: split window and the second is temperature emissivity separation or

Speaker 1: tests algorithm retrieval. The split window method used in Modus

Speaker 1: data products, for instance, uses the brightness temperature difference between

Speaker 1: two adjacent thermal channels typically around eleven and twelve micrometers

Speaker 1: to simultaneously correct for atmospheric water vapor and estimate surface temperature.

Speaker 1: It works by assuming a fixed or land cover base

Speaker 1: a massivity assigned by vegetation class lookup tables. This approach

Speaker 1: is computationally efficient and works well over vegetated surfaces and

Speaker 1: water where amissivity is high and relatively stable between around

Speaker 1: point nine seven to point nine nine. Typically. However, it

Speaker 1: is sensitive to errors when the assumed emissivity is not correct,

Speaker 1: particularly particularly over arid, bare, and geologically complex surfaces. The

Speaker 1: temperature emissivity separation or TESTS method, which is used in

Speaker 1: aster and ecostress data sets, simultaneously retrieves both temperature and

Speaker 1: spectral emmissivity from multiple thermal bands. Rather than assuming amissivity

Speaker 1: from a lookup table. Tests uses a semi empirical relationship

Speaker 1: based on the minimum and messivity and the spectral contrast

Speaker 1: across bands, the MMD, which is the minimum maximumum difference

Speaker 1: in order to constrain the solution. This yields both a

Speaker 1: temperature estimate and a full emissivity spectrum, providing much richer

Speaker 1: information about surface composition and substantially reducing errors over bare

Speaker 1: and heterogeneous surfaces. Split window errors will occur in a

Speaker 1: systematic fashion anytime that emissivity is incorrectly assigned, leading to

Speaker 1: systematic biases. The Mona Loa caldera example here provides a

Speaker 1: clear illustration. Basaltic lava flows have strongly variable and spectrally

Speaker 1: complex emissivity in the eight to twelve micrometer range, dropping

Speaker 1: below point nine in some channels. The ten percent emissivity

Speaker 1: difference shown in the graph arises in the eleven micron channel.

Speaker 1: When comparing the standard level to modus split window approach

Speaker 1: to the tests algorithm retrieval from the same modus input,

Speaker 1: the split window algorithm retrieves the temperature of three one

Speaker 1: hundred and ten calvin, approximately twelve to fourteen calvin cooler

Speaker 1: than the aster tests, which was at three hundred and

Speaker 1: twenty two on average, and the modus tests at three

Speaker 1: hundred and twenty four calvin, both of which explicitly retrieve amissivity.

Speaker 1: This is not a subtle calibration issue. It's a physically

Speaker 1: meaningful error driven entirely by the wrong emissivity assumption. The

Speaker 1: rule of thumb shown here is that a one point

Speaker 1: five percent emissivity error propagates to roughly one calvin of

Speaker 1: LST error. This means that over rocky, sandy, and geologically

Speaker 1: diverse surfaces. Split window errors can reach three to ten

Speaker 1: calvin for applications such as urban heat island analysis and

Speaker 1: ecosystem thermal stress, where one or two Calvin differences even

Speaker 1: are scientifically meaningful. Here are more comparisons between the LST

Speaker 1: retrievals based on split window emissivity values versus the tests

Speaker 1: algorithm emissivity. There is similar accuracy over vegetation and water

Speaker 1: of about one calvin. However, we see a split window

Speaker 1: cold bias over bear regions in this case, ranging from

Speaker 1: three to five calvin. This is especially important for thermal

Speaker 1: sensing applications of urban heat islands. Cities are highly heterogeneous,

Speaker 1: with asphalt next to grass and other surfaces in small areas.

Speaker 1: Split window isssumes a massivity based on land cover, but

Speaker 1: tests calculates it. If the urban land cover map is outdated,

Speaker 1: the LST will be incorrect. Tests is more robust for

Speaker 1: rapidly changing urban environments. Now that we've covered the how

Speaker 1: the physics of missivity and temperature, let's look at the what,

Speaker 1: where and when. In section three, we survey the landscape

Speaker 1: of current and upcoming thermal infrared missions and highlight the

Speaker 1: key application areas that drive this science. This will help

Speaker 1: you connect those physical equations to the actual data products

Speaker 1: you'll use in Part two. Thermal remote sensing is a

Speaker 1: diverse field with sensors tailored for different scales. Mode is

Speaker 1: an aster on the Terra platform have provided a global

Speaker 1: record since two thousand. Lansat offers an incredible historical archive,

Speaker 1: though it has fewer thermal bands compared to multi spectral

Speaker 1: TIR sensors like ast Ecostress. Our focus for this training

Speaker 1: is unique because it's on the International Space Station. This

Speaker 1: allows us to sample the diurnal cycle, measuring heat at

Speaker 1: different times of day rather than just once in the morning.

Speaker 1: The ability to observe temperature at different times of day

Speaker 1: is advantageous for many applications, including the urban heat island

Speaker 1: focus that our training will delve into in Part two. Finally,

Speaker 1: high Test is an airborne instrument used for high resolution campaigns,

Speaker 1: providing one to thirty meter resolution that is invaluable for

Speaker 1: validating spaceborne data. We are entering a golden age of

Speaker 1: thermal remote sensing. While ast led the way in nineteen

Speaker 1: ninety nine, we now have Trishna, which is scheduled to

Speaker 1: launch in twenty twenty six, Eagle Ter which was formally

Speaker 1: called Speech and that this mission has been in reformulation

Speaker 1: and does not currently have a new launch date, and

Speaker 1: then LSTM, which is scheduled to launch in twenty twenty eight.

Speaker 1: The trend is moving toward higher spatial resolution around fifty

Speaker 1: to sixty meters and more frequent repeat cycles, which will

Speaker 1: revolutionize how we monitor urban heat and water use. This

Speaker 1: figure illustrates why sensor band placement matters for emissivity retrieval.

Speaker 1: Each panel compares how well three different sensor configurations can

Speaker 1: capture the missivity spectral shape for two geologically distinct surface

Speaker 1: types felsic minerals like gypsum on the left and mapic

Speaker 1: minerals like pyroxene on the right. The blue shaded region

Speaker 1: represents the uncertainty envelope for tr A, TRB, and tr

Speaker 1: C band configurations, while the redline shows laboratory measured and massivity.

Speaker 1: This is what we would refer to as the ground

Speaker 1: truth that we're trying to match. Notice that felsic minerals

Speaker 1: like gypsum have a pronounced emissivity minimum near eight point

Speaker 1: five to nine micrometers. This feature falls at a wavelength

Speaker 1: where some sensor configurations have poor band coverage, leading to

Speaker 1: mist or smooth spectral structure. Mathic minerals like pyroxene show

Speaker 1: a different, more subdued spectral shape, but the same principle applies.

Speaker 1: If your sensor bands don't straddle key spectral features, you

Speaker 1: lose compositional information. The takeaway here is that aster ecostress

Speaker 1: and speary SBG now EGO TAR those configurations with their

Speaker 1: strategic band placement across the eight through twelve micrometer window,

Speaker 1: do a much better job of capturing these spectral features

Speaker 1: than coursercuit figurations. This has direct implications not just for geology,

Speaker 1: but for urban surface mapping. Pavement, concrete, and roofing materials

Speaker 1: all have distinctive spectral and massivity signatures that better placed

Speaker 1: bands can resolve. Now, let's take a step back and

Speaker 1: compare thermal remote sensing to optical remote sensing, which is

Speaker 1: what many of you may be more familiar with. If

Speaker 1: you are coming from a traditional optical remote sensing background

Speaker 1: and are most familiar with sensors like sentinel to or

Speaker 1: lansat visible neurinfrared, there are three key differences to remember.

Speaker 1: This table captures an important conceptual distinction in Earth observation.

Speaker 1: Optical sensors are fundamentally passive receivers of reflected sunlight. They

Speaker 1: have no signal at night and are limited to daytime observations.

Speaker 1: Thermal sensors, by contrast, detect energy that the surface itself

Speaker 1: is generating twenty four hours a day, driven purely by

Speaker 1: the surfaces temperature. This is why Ecostress can observe nighttime

Speaker 1: land surface temperature from the International Space Station. The surface

Speaker 1: is always emitting. Notice also the difference in what each

Speaker 1: type tells us about the surface. Optical data is rich

Speaker 1: in biochemical information, for example, plant pigments, water content, soil organics,

Speaker 1: while thermal data tells us directly about thermodynamic state, energy balance,

Speaker 1: evaporative cooling, heat stress, and surface composition through a massivity.

Speaker 1: A key practical trade off is that achieving fine spatial

Speaker 1: resolution is fundamentally harder in the thermal compared to the optical,

Speaker 1: because the emitted photon flux is much lower than reflected

Speaker 1: solar This is why even state of the art missions

Speaker 1: like Ecostress have seventy meter pixels while sentinel to achieves

Speaker 1: ten meters. This resolution gap is one of the major

Speaker 1: motivations for the downscaling exercise we'll do in part two

Speaker 1: of this training. There are many different applications of LST

Speaker 1: and emissivity in earth science. For hydrology, we use thermal

Speaker 1: data to estimate of appotranspiration or ET, which is very

Speaker 1: useful for agricultural water management. LST is a key input

Speaker 1: to land surface models for drought monitoring, soil moisture and

Speaker 1: ET estimation. LST exerts control over the partitioning of energy

Speaker 1: into latent and sensible heat flexes and provides an indicator

Speaker 1: of surface warming trends from anthropogenic climate change. Smaller massivity

Speaker 1: errors can lead to massive errors in energy balance models,

Speaker 1: making accurate tir retrievals essential for climate projection. For example,

Speaker 1: an error of point one in the emissivity will result

Speaker 1: in climate models having errors of up to seven watts

Speaker 1: per meter squared in their upward long wave radiation ITS estimates.

Speaker 1: This is a much larger term than the surface radiative

Speaker 1: forcing of about two to three watts permeter squared. Due

Speaker 1: to an increase in greenhouse gases. In the field of ecology,

Speaker 1: we can identify ecosystem thermal stress before plants show visible

Speaker 1: signs of wilting using satellite based LST. In tropical forests,

Speaker 1: existing studies have documented the increased thermal stress of burned

Speaker 1: forest relative to logged and intact forests, even over a

Speaker 1: decade after the forest fire occurred. For urban heat applications,

Speaker 1: of course, we use these maps to identify heat vulnerable neighborhoods,

Speaker 1: which is the key focus of Part two. This slide

Speaker 1: illustrates two compelling applications of long term LST records for

Speaker 1: detecting climate signals that would be invisible to shorter observational windows.

Speaker 1: The map on the left shows Modus Land Service temperature

Speaker 1: across the northern Hemisphere for August two thousand and four.

Speaker 1: This is a snapshot of what the global thermal landscape

Speaker 1: looks like at the peak of northern summer. This kind

Speaker 1: of global coverage, updated every one to two days, is

Speaker 1: what makes motives such a powerful climate modern monitoring tool.

Speaker 1: The upper right panel shows a result from Schneider and

Speaker 1: Hook twenty ten. Surface water temperature at Lake Tahoe increased

Speaker 1: at a rate of point one two degrees celsius per

Speaker 1: year over a roughly two decade period. This is a

Speaker 1: lake that sits at over six thousand feet elevation in

Speaker 1: the Sierra Nevada, a system that we might expect to

Speaker 1: be buffered from rapid warming. The thermal record tells a

Speaker 1: different story, and it's a story we can only read

Speaker 1: because we have a consistent, well calibrated spaceborne thermal record

Speaker 1: going back to the early nineteen nineties. The lower right

Speaker 1: panel shows a different type of trend analysis from Holy

Speaker 1: and Do set twenty twenty the number of annual average

Speaker 1: nighttime days exceeding twenty degrees celsius in Anaheim, California. The

Speaker 1: trend of ero point six additional days per year with

Speaker 1: n our square to point nine three is remarkably strong.

Speaker 1: Nights that stay hot and are a key public health indicator.

Speaker 1: Nighttime heat prevents the body from recovering from daytime heat stress,

Speaker 1: and this is one of the primary drivers of heat

Speaker 1: related mortality during extreme events. Together, these two examples show

Speaker 1: that LST isn't just a snapshot measurement. It can also

Speaker 1: provide a long term climate variable. The consistency of the

Speaker 1: Modus record across more than two decades is what makes

Speaker 1: these trend analyzes possible, and it's exactly why maintaining continuity

Speaker 1: in thermal missions matters. LST can be used for deriving

Speaker 1: many other biophysical properties of the land surface, including of

Speaker 1: appo transporration ET. As mentioned before, ET is the total

Speaker 1: amount of water going into the atmosphere from the surface

Speaker 1: of aporation plus transpiration from leaves and canopy interception. Here

Speaker 1: we see average of apple transpiration at local time for

Speaker 1: June through August twenty nineteen. One thing to emphasize again

Speaker 1: is the importance of overpastime. Due to its sun synchronous orbit,

Speaker 1: Landsat observations always occur at ten thirty am before p KEAT.

Speaker 1: In contrast, ecostress can observe a location at three PM

Speaker 1: when the heat island is most intense. This justifies why

Speaker 1: we use ecostress specifically for this training, So stay tuned

Speaker 1: uned for Part two. Now we will go into a

Speaker 1: summary for Part one, Foundational concepts of thermal remote sensing.

Speaker 1: Let's take a moment to consolidate what we've covered in

Speaker 1: Part one before moving into the Q and A. We've

Speaker 1: built up from first principles from what temperature means at

Speaker 1: the molecular level through the radiation laws that govern how

Speaker 1: the internal energy becomes measurable electromagnetic radiation, through the practical

Speaker 1: complications of emissivity and atmospheric effects, and finally to the

Speaker 1: missions and applications that put all of this to work.

Speaker 1: The through line in all of this is that thermal

Speaker 1: remote sensing gives us a direct window into the thermal

Speaker 1: dynamic state of Earth's surface in a way that optical

Speaker 1: data simply cannot. When we point a thermal sensor at

Speaker 1: the Los Angeles Basin, we're not measuring reflected sunlight. We're

Speaker 1: measuring the electromagnetic signature of every parking lot, every park,

Speaker 1: every neighborhood's thermal energy. Part two will take you from

Speaker 1: this conceptual foundation into hands on analysis of exactly that.

Speaker 1: Part two will go from theory to data accessing real

Speaker 1: ecostress observations, working through quality screening, and running a machine

Speaker 1: learning workflow to downscale thermal data for fine scale urban analysis.

Speaker 1: The case study we'll use is write in the Los

Speaker 1: Angeles Basin, which you may recognize from the header image

Speaker 1: in our slides. Remember that the homework opens on the

Speaker 1: last day of the training on June second. It is

Speaker 1: due two weeks later on June sixteenth. I would like

Speaker 1: to acknowledge my fellow colleagues in the Arset Ecological Conservation

Speaker 1: Team who have supported the creation of this training Sativa Cruz,

Speaker 1: Juan Torres, Berres and and Fain. I would also like

Speaker 1: to acknowledge the RSET program staff who made this training possible,

Speaker 1: including Selwyn Hudson, Odoy, Maria Maravito, Suzanne Monty, and Melanie Follette.

Speaker 1: We encourage you to check out our website as well

Speaker 1: as the RSET YouTube channel linked here in this slide.

Speaker 1: You can also join our quarterly newsletter to stay up

Speaker 1: to date on our latest trainings by sending an email

Speaker 1: with no subject to our set dash join at lists

Speaker 1: dot NASA dot gov, and then follow the instructions sent

Speaker 1: in the response. The contact information for myself and our

Speaker 1: other guest instructor, Glenn Holly, is also here if you'd

Speaker 1: like to email us directly with specific questions about this training. Right,

Speaker 1: we will now transition to Q and A session, So

Speaker 1: let's start with question one. We have. Question one states

Speaker 1: something I've always been a bit confused about is what

Speaker 1: we mean when we talk about light being absorbed? Where

Speaker 1: does it go? Thank you for this question, So light

Speaker 1: that is not transmitted or reflected is absorbed by the

Speaker 1: material and converted into thermal energy the objects that gets

Speaker 1: and so and as a result, object gets hotter as

Speaker 1: light is absorbed, but the light's energy doesn't go away. So,

Speaker 1: in other words, light that is absorbed occurs when the

Speaker 1: electromagnetic energy is transferred directly into the atoms or molecules

Speaker 1: of the material itself that it hits. Question two, which

Speaker 1: satellite platform collection and spectral bands are being used for

Speaker 1: deriving LST measurements? Are there LST values sourced directly from

Speaker 1: a standard product eg. Modus Landset or derived through a

Speaker 1: custom retrieval methodology, And perhaps, Glenn, I'll maybe let you

Speaker 1: speak to this question since you were your answer was

Speaker 1: the one that we're showing here.

Speaker 2: Sure.

Speaker 3: Yeah, So there are several NASA satellites flying right now

Speaker 3: that actually have both thermal bands and also visible shortwaven

Speaker 3: for air bands, and probably the most common or most

Speaker 3: well known are the Motus series of satellites, so Modus

Speaker 3: Terra and Modus Aqua, and more recently we have the

Speaker 3: Beers follow on instruments which are also have very similar

Speaker 3: thermal bands. And so as soon as we have more

Speaker 3: than if we have more more than three or more

Speaker 3: thermal bands on one of these sensors, then we can

Speaker 3: directly apply the temperature emissivity separation algorithm that Savannah was

Speaker 3: going over ino slides. So for example, with the three

Speaker 3: Motus bands, we can apply tests and then directly retrieve

Speaker 3: the land service temperature and emissivity from those three thermal bands.

Speaker 3: So we don't technically measure you know, the land service

Speaker 3: temperature directly, we measure more the thermal radiance and from

Speaker 3: that we derive what the temperature should have been to

Speaker 3: produce that thermal radiance.

Speaker 2: And then another.

Speaker 3: Example yeah I had was lance at eight and nine,

Speaker 3: which in fact have two thermal bands on bands ten

Speaker 3: and eleven, and so we can't use the test algorithm

Speaker 3: in that case to produce the temperature emissivity. Instead we

Speaker 3: use a split window approach and also direct single channel

Speaker 3: approach to derive the temperature, and we use an external

Speaker 3: emissivity database. And then of course e can stress was

Speaker 3: mentioned by Savannah as well in our slides, and that

Speaker 3: in fact us five thermal bands and actually gives us

Speaker 3: the highest temperature accuracy because in general, the more thermal

Speaker 3: bands we have, the more accurately we can retreat themessivity

Speaker 3: and then also the surface temperature.

Speaker 1: Great, thank you Glenn. Let's go into question three, which

Speaker 1: asks about eco if is the ecostress data currently accessible?

Speaker 1: And the short answer is yes. There are a number

Speaker 1: of different ways to access ecostress data. So we'll list

Speaker 1: a few of the most common ways here, the biggest ways,

Speaker 1: and then we will in part two, you know, provide

Speaker 1: some some examples of of of accessing the data. So

Speaker 1: the Land Processes Distributed Active Archive Center or doc lp

Speaker 1: DOC is the the where the data of the Ecostress

Speaker 1: mission are stored. And this link here provides some UH

Speaker 1: tutorials that are public by the lp DAC and that

Speaker 1: they are a great one great place to start for

Speaker 1: for learning to work with ecostress data. Another is the

Speaker 1: NASA's Earth Data Search, which has you know a number

Speaker 1: of different filters, filtering options and kind of ways of

Speaker 1: refining the location and temporal bounds of of the data

Speaker 1: that you're interested in using. And so you can this

Speaker 1: this UH provides, you know, the link here will link

Speaker 1: directly to that Earth Earth Data Search where you can

Speaker 1: just type in ecostress and a number of different ecostress

Speaker 1: products will become available so include not including lands surface

Speaker 1: temperature and amissivity, as well as other of the products

Speaker 1: that are derived like I mentioned, such as evappo transpiration.

Speaker 1: And then the subset tool known as appeers is also

Speaker 1: one way to obtain ecostress data both over areas as

Speaker 1: well as in point sample locations, and there are also

Speaker 1: a video available demonstrating how to access eco data with

Speaker 1: the peers. So in Part two, I did use appeers

Speaker 1: for the first demo and we went over very briefly

Speaker 1: because there's a more extensive video already available that we

Speaker 1: will link to that provides kind of a step by

Speaker 1: step demonstration of how to use a peers for obtaining

Speaker 1: ecostress data. And then finally Google Earth Engine, which will

Speaker 1: be the primary focus of Part two, the second demo

Speaker 1: of Part two, which Glenn, I don't know if you

Speaker 1: want to say any words about that right now, but

Speaker 1: you know, stay tuned for all participants for next for

Speaker 1: next time to to do a deeper dive there. But yeah, Glenn,

Speaker 1: I'll let you maybe just say a few words now

Speaker 1: just to let folks know how that will work, you mean.

Speaker 2: Part two right out via the tutorial training.

Speaker 1: Yeah, yeah, the next week yeah.

Speaker 3: So we'll be going through a tutorial on how to

Speaker 3: what we call thermal sharp sharpen the data, which means

Speaker 3: we take our observed land surface temperature at our native

Speaker 3: resolution and then actually sharpen it or downscale it to

Speaker 3: higher spatial resolutions that are more similar to what we

Speaker 3: get from visible shortwave infrared data and products, which assreciate

Speaker 3: about the ten to thirty meter scale. So we'll be

Speaker 3: going through a demo of actually how to do that

Speaker 3: within Google Earth Engine, and then we'll also go through

Speaker 3: a demo on how to actually visualize the data within

Speaker 3: a GIS software software platform called GIS. So we'll be

Speaker 3: going through some of the physics of how we do that,

Speaker 3: the modeling aspects, and actually step you through step by

Speaker 3: step one on how we can do that.

Speaker 2: Within Google and Change.

Speaker 1: Great, So for now we'll leave. We'll leave you know,

Speaker 1: question three as is we are. We will provide you know,

Speaker 1: we will publish this question and answer with document with

Speaker 1: the links available, and you know, after this session we

Speaker 1: will kind of also flesh out some of our answers

Speaker 1: for them to be a bit more complete and comprehensive,

Speaker 1: so look out for that, and then also stay tuned

Speaker 1: for Part two, where we will provide some live demonstrations

Speaker 1: of accessing ecosource data. All right, Question four, how are

Speaker 1: land service temperature data products validated? So this is a

Speaker 1: great question, and we'll start by talking about ground based

Speaker 1: valalidation approaches and then we'll talk about airborne campaigns which

Speaker 1: are also used. So from the ground, we use thermal

Speaker 1: radiometers that measure surface leaving radiance, so and then that's

Speaker 1: converted to surface temperature, and then we compare those temperatures

Speaker 1: measured on the ground to the satellite value at the

Speaker 1: exact same time and location. So one example of a

Speaker 1: validation site that is routinely used for land surface satellite

Speaker 1: based land surface temperature validations is at Lake Tahoe in California,

Speaker 1: Nevada and the United States. And that is a site

Speaker 1: that's been in operation for uh several decades now and

Speaker 1: provides and and and the focus on water bodies and

Speaker 1: partainicular for validation is useful because water bodies are reliable

Speaker 1: in that their emissivity is very stable and known a

Speaker 1: priori isolating atmospheric correction accuracy. So that is one example

Speaker 1: of one such site we will link to in the

Speaker 1: published Q and a document to a website hosted at

Speaker 1: NASA Jet Propulsion Lab which provides an overview of some

Speaker 1: of the most commonly used sites that NASA uses for

Speaker 1: validating satellite based LANDSERFCE temperature. And so that can be

Speaker 1: a place for folks to look further into if they

Speaker 1: want more information about the validation network that is ground based.

Speaker 1: And then the other kind of major way that we

Speaker 1: validate and compare satellite based lancer force temperature is through

Speaker 1: high resolution airborne campaigns. So one instrument which we mentioned

Speaker 1: today in in the slides is high tests, which Glenn

Speaker 1: has actually been recently I think working this year on

Speaker 1: on on a a new the newest validation campaign I believe,

Speaker 1: and Glynn, if you'd like to speak to I know

Speaker 1: you said that that work is in the process of publication,

Speaker 1: but maybe at some point we can link that study

Speaker 1: as well as other re existing studies that have been

Speaker 1: done with specifically with high tests, for folks to learn

Speaker 1: more about that instrument and see how how those kinds

Speaker 1: of validations are are done.

Speaker 2: Yeah.

Speaker 3: So the we have this hyperspectral airborne sensey called high

Speaker 3: tests that we fly out of JPL and it can

Speaker 3: be also used indirectly to validate and to evaluate the

Speaker 3: satellide sensors. So, for example, if we fly high tests

Speaker 3: during or near an overpass of Ecostress, for example over

Speaker 3: Los Angeles, we can directly match up the high test

Speaker 3: pixels to the Ecostress pixels and upscale them and then

Speaker 3: use that indirectly as a way to validate, to we

Speaker 3: even calibrate the sensor that is flying over us at

Speaker 3: that time. In general with high tests, actually we use

Speaker 3: it mostly just for algorithm analysis, and it's a hyperspectral sensors,

Speaker 3: so we can convolve the wavelengths to any other sensors

Speaker 3: spectral resolution, which is a really powerful way to test

Speaker 3: and validate various algorithms. So it has a multi use

Speaker 3: type of instrument, and it's also used for actually science

Speaker 3: on the ground as well, so it has multiple uses.

Speaker 1: Thank you. Let's go to question five here, I understand

Speaker 1: since thermal sensing relies on naturally emitted weak thermal radiation,

Speaker 1: the sensors have a coarser resolution to capture thermal energy effectively.

Speaker 1: Is NASA or the European Space Agency working towards making

Speaker 1: thermal imagery sensor resolution even higher? Than the existing ones.

Speaker 1: What level of resolutions achievable in the near future since

Speaker 1: urban heat island and local heat hotspot studies require much

Speaker 1: higher resolution, so this is an exciting question, and yes,

Speaker 1: the short answers that both NASA and and ISSA are

Speaker 1: actively pushing towards finer resolution thermal observations, with the next

Speaker 1: generation of agency missions delivering you know, roughly fifty sixty

Speaker 1: meter thermal infrared data within day to three day revisits,

Speaker 1: and as Glenn mentioned, airborne thermal instruments provide even finer

Speaker 1: spatial resolution data as well as higher spectral resolution, which

Speaker 1: can be very useful for certain applications if you know

Speaker 1: the fifty ish meter range is not meeting the needs

Speaker 1: of a given application. And the second part to this

Speaker 1: answer is that longer wavelengths in the thermal infrared require

Speaker 1: larger optical detector elements to collect enough photons and cryo

Speaker 1: cooling adds mass, power and cost, so this has historically

Speaker 1: kept thermal infrared sensors or satellites at sixty two hundred meters,

Speaker 1: with the example of aster Ecostress landst versus ten to

Speaker 1: thirty meters, which is the visible Nearonfred sensors. Is there

Speaker 1: anything you'd like to add to that, Glenn.

Speaker 2: Yeah.

Speaker 3: In fact, the ISA future censor called LSTM, they've actually

Speaker 3: pushed that down to fifty meter resolution, so that should

Speaker 3: probably be the highest thermal resolution data that we'll ever

Speaker 3: acquire in space by the end of this decade. So

Speaker 3: that's pretty exciting, But of course it's highly cost constraints,

Speaker 3: so going any higher than that just really ramps up

Speaker 3: the cost in terms of the size of the telescope needed,

Speaker 3: the fry cooling, et cetera, et cetera. So it's really

Speaker 3: cost cost driven.

Speaker 1: Yeah. Yeah, So fifty meters is what we have, that

Speaker 1: what we can is in the foreseeable future of this

Speaker 1: decade in terms of the spatial resolution, which kind of

Speaker 1: emisity retrieval algorithm will these new mission and use. So

Speaker 1: for my understanding, and I you know, I'm not I'm

Speaker 1: not as familiar with with TRISHNA and LSTM and Glenn,

Speaker 1: maybe maybe you have been to some of these meetings

Speaker 1: and can speak to that, But for my understanding, the

Speaker 1: temperature emissivity separation retrievals, which you know, as Glenn mentioned,

Speaker 1: requires multiple multiple thermal bands to be able to really

Speaker 1: work and the more bands we have, the better the

Speaker 1: estimates are. And so I from my understanding, all of

Speaker 1: these new missions are going to be focused on able

Speaker 1: to use tes retrievals or test retrievals rather than relying

Speaker 1: on the split window approach. Okay, and then Glenn, I

Speaker 1: actually didn't say that you added this part. So do

Speaker 1: you want to speak to the LSTM and other missions?

Speaker 2: Sure? So, yeah, I'm going to go back up to

Speaker 2: the question.

Speaker 1: Yeah, question six. Yeah, maybe justin actually at this point,

Speaker 1: but yeah, go ahead.

Speaker 3: Yes, there are other algorithms being explored that we actually

Speaker 3: have been working with directly with those teams on LSTM

Speaker 3: and Trishna is the other French Indian mission that will

Speaker 3: actually be.

Speaker 2: Going up within a year, I believe.

Speaker 3: But we are exploring other algorithms that are becoming more modernized,

Speaker 3: if you will. One is called optimal estimation, and it's

Speaker 3: a basin theory algorithm where given our existing climatology, if

Speaker 3: you will, or existing measurements of emissivities and temperature over

Speaker 3: the past twenty years that we've already acquired, we can

Speaker 3: utilize that knowledge, that existing knowledge that we have of

Speaker 3: the surface and use that in a more of a

Speaker 3: constrained retrieval where we can start off with our first

Speaker 3: gas and then pivot off of that first gas based

Speaker 3: on what our measurement is telling us as well. And

Speaker 3: so some of these are being explored and they're a

Speaker 3: little bit more stable than the existing algorithms, but the

Speaker 3: word is still at out and whether they will actually

Speaker 3: be more accurate because they haven't actually been implemented operationally

Speaker 3: yet with any current algorithms.

Speaker 1: Great, thank you. Question seven, how do these data sets

Speaker 1: differ from landstuh data sets which are already available from USGS,

Speaker 1: so by these days that I'm guessing maybe these questions

Speaker 1: referring to ecostress data and how they how it differs

Speaker 1: from LANDSAT. So both the spatial resolution and the temporal

Speaker 1: resolution and the overpast time are the key differences between

Speaker 1: Ecostress in LANDSAT, And so I'll just highlight that Slide

Speaker 1: thirty six of the presentation that was given today provides

Speaker 1: a you know, a table where different instruments including LANCEAT

Speaker 1: and ecostress are compared. But you know, the brief The

Speaker 1: brief answer is that LANSAT has both larger spatial resolution

Speaker 1: of one hundred meter native resolution per pixel and then

Speaker 1: also a longer repeat time and then observation location time

Speaker 1: of day in the morning ten thirty am, whereas Ecostress

Speaker 1: has a seventy meter pixel resolution has a varying temporal

Speaker 1: repeat pattern based on the orbit of the International Space

Speaker 1: Station that is mounted on and so for in any

Speaker 1: given location once every three to five days, and then

Speaker 1: also the time of day of an Ecostress overpass also differs.

Speaker 1: And so those are I think the key differences. Question eight,

Speaker 1: how can we validate the thermal infrared remote sensing drive

Speaker 1: lands Service temperature data if we don't have in situ data.

Speaker 1: So one of the things that we mentioned so if

Speaker 1: you if you don't have ground based thermal radiometer available

Speaker 1: for validation, there are ways to compare other land surface

Speaker 1: temperature results over the same time and place. And if

Speaker 1: that and if those sensors are then validated to other

Speaker 1: ground based networks and others, then that is a way

Speaker 1: of doing a validation right, So for example, you could

Speaker 1: look at ecostress versus landsat and how they compare and

Speaker 1: then indirectly validate those Lancet LST from previously validated Ecostress measurements.

Speaker 1: Is there anything you'd like to add, Glenn to that.

Speaker 3: That's pretty much the any way I can think that

Speaker 3: you could do it indirectly, but you really need direct,

Speaker 3: ground based, independent set of measurements to validate the space

Speaker 3: of one data.

Speaker 1: Question nine asks. In part two, we are going to

Speaker 1: learn to downscale native seventy meter ecostress LST to ten

Speaker 1: meter using a random force model for a neighborhood level

Speaker 1: urban heat vulnerability analysis in London. Do you recommend use

Speaker 1: the ten meter downscaled product or is it safer to

Speaker 1: stick to the native seventy meter resolution to avoid introducing

Speaker 1: model artifacts when aggregating to neighborhood polygons and Glenn, I'll

Speaker 1: let you maybe speak to that if I see you.

Speaker 1: You've written on an answer here, but maybe yeah, I'll

Speaker 1: let you speak to that.

Speaker 3: Yeah. Well, this will really be eliminated in the tutorial

Speaker 3: that I'm going to give on the sharpening tutorial, because

Speaker 3: what you can see at ten meters versus seventy meters

Speaker 3: is just an incredible a lot amount of detail. For example,

Speaker 3: you can see what we call street level temperatures, so

Speaker 3: you can see you down through the scale of a

Speaker 3: rooftop or a small street. You can see differences in

Speaker 3: temperatures of a stand of trees on a street, for example.

Speaker 3: At seventy meters, it's a lot harder to discriminate.

Speaker 2: You can kind of.

Speaker 3: See blobs of cool and warm temperatures in the urban environment.

Speaker 3: But again, you know that the one thing to keep

Speaker 3: in mind again is that we're modeling that ten meters.

Speaker 3: We're not directly observing it from space or from able data,

Speaker 3: so there is an inherent uncertainty associated with that, you know,

Speaker 3: ten meter downscaling or thermal sharpening. So it really depends

Speaker 3: on your application.

Speaker 2: If you want, if you need temperatures at a certain.

Speaker 3: Level of accuracy, I would probably stick with the seventy

Speaker 3: meter data. If you want to, you know, better discern

Speaker 3: at the temperature of one rooftop from another, you know,

Speaker 3: then I'll go with a ten meter scale resolution. But

Speaker 3: we do provide in the thermal sharpening. We actually do

Speaker 3: provide an output of the accuracy estimate of the thermal

Speaker 3: sharpened data, and that's included within the Google Earth package.

Speaker 3: And oh yeah, the other thing is that we're actually

Speaker 3: not technically making up temperatures because if we apply this

Speaker 3: radiometric normalization that we call it. Whether if you averaged

Speaker 3: up the individual ten meter or twenty and thirty meter

Speaker 3: components to the original seventy meter data, you would still

Speaker 3: end up with the same temperature, So we're not modifying

Speaker 3: that original, original retrieved temperature.

Speaker 1: Thank you. Yeah, So let's and you know, after part two,

Speaker 1: I think there might there will be opportunity for more discussion,

Speaker 1: you know, along along these lines of questions about you know,

Speaker 1: particular applications and then thinking about, yeah, considerations of accuracy

Speaker 1: and suitability for particular applications. So thanks for that question,

Speaker 1: and we'll move to question ten. How can we select

Speaker 1: the appropriate remote sensing data for our LST based studies

Speaker 1: considering the trade off between temporal and spatial resolution within

Speaker 1: complex heterogeneous urban cities, And yeah, I think that really

Speaker 1: does come down to what your application requires in terms

Speaker 1: of spatial resolution versus temporal resolution. And then also, as

Speaker 1: I mentioned, you know, time of day, right, so if

Speaker 1: you want observations at night, for example, ecostress, you know

Speaker 1: as of now, really is the sensor that will allow

Speaker 1: for delivering that for various times of day? Right? Yeah?

Speaker 1: And then modus on the other hand, which provides a

Speaker 1: daily repeat, which is more a higher repeat than ecostress,

Speaker 1: but has comes at a cost of lower spatial resolution

Speaker 1: of one kilometer, So it varies on what depending on

Speaker 1: what you want to prioritize. Question eleven. Can land service

Speaker 1: temperature be used on a small scale to see underground structures?

Speaker 1: For example, due to the differences in temperature between normal

Speaker 1: soil and soil that has stone or marble under it

Speaker 1: near the surface. Right, So if there are different temperatures

Speaker 1: that you know are due to the different materials a

Speaker 1: different substrate, then that will be detected by the sensor.

Speaker 1: But right, you won't be able to necessarily determine that

Speaker 1: there's a structure there. It won't tell you what you know,

Speaker 1: what is causing that difference in temperature, But if there

Speaker 1: is a gradient in temperature, then that gradient will be

Speaker 1: detected within you know, the accuracy that we have of

Speaker 1: the sensor. So that's the short answer to that, I

Speaker 1: think Question twelve, Oh, yeah.

Speaker 3: Did you want to add Yeah, Just just a quick

Speaker 3: example would be if you have a thicker slab of

Speaker 3: concrete or asphalt on a road, we could tell the

Speaker 3: difference in the temperature because the thickert slab would have

Speaker 3: a higher thermal mass and higher heat storage and stare

Speaker 3: the temperatures store that energy.

Speaker 2: Over the longer time periods.

Speaker 3: And so for example, at the nighttime data, we'd be

Speaker 3: able to see that as an increased temperature of that

Speaker 3: thicker slab. And so, you know, while we wouldn't know exactly,

Speaker 3: you know what the reason is, we could infer that, well,

Speaker 3: this area of the concrete or asphalt is lovelock likely

Speaker 3: thicker than than as for.

Speaker 4: Example, Yeah, great, let's go to question twelve, where.

Speaker 1: The participant asks could you repeat the example he mentioned

Speaker 1: regarding the extent to which incorrect emmissivity values can effect

Speaker 1: of resulting LST value. So the example we gave was

Speaker 1: the Mona Loa Caldera, which in which features basaltic lava

Speaker 1: flows that have variable and spectrally complex emissivities in the

Speaker 1: eight to twelve window. So and in some of these

Speaker 1: bands that emissivity goes below point nine. The graph which

Speaker 1: we will we will refer to the specific slide number

Speaker 1: that showed this, but there was a ten percent emissivity

Speaker 1: difference in that graph in the eleven micron band. When

Speaker 1: comparing the standard level to modus split window approach to

Speaker 1: the temperature and emissivity the test algorithm approach from that

Speaker 1: same modus input. So the split window algorithm or treated

Speaker 1: a temperature of three hundred and ten calvin, and that

Speaker 1: was approximately fourteen degrees for fourteen calvin cooler than the

Speaker 1: tests algorithm. And so the apples to apples comparison here

Speaker 1: is just is the modus input with the test algorithm

Speaker 1: versus the modus input with this slit window algorithm. The

Speaker 1: graph also showed the aster tests algorithm. But if you're

Speaker 1: really if you're wanting to think about kind of the

Speaker 1: comparison between the two algorithms, that those are the two

Speaker 1: that you would want to compare. So the rule of

Speaker 1: thumb also on the following side that we showed was

Speaker 1: basically just saying that that there's about one point five

Speaker 1: percent amissivity error that propagates to roughly one calvin of

Speaker 1: LST error. So that means that over rocky, sandy and

Speaker 1: geologically diverse surfaces, split window errors can reach three to

Speaker 1: ten calvin. For applications such as urban heat island analysis

Speaker 1: and ecosystem thermal stress, where you know even one to

Speaker 1: two calvin differences are meaningful. This you know is a

Speaker 1: really important consideration is what is the massivity retrieval algorithm

Speaker 1: that's being used and what does that mean in terms

Speaker 1: of the error that's propagated into the land surface temperature product. Great,

Speaker 1: let's move into question thirteen. How well does the ecostress

Speaker 1: temperature and massivity separation algorithm perform over highly hydrogen as

Speaker 1: complex urban materials. So, Glenn, would you like to get

Speaker 1: started with that.

Speaker 3: Sure, it's actually a good question because it's something that

Speaker 3: we're still working on to improve and it gets into

Speaker 3: a little bit of the nitty gritty of what the

Speaker 3: algorithm actually does. So it's a little tough to explain

Speaker 3: in words without actually looking at the ated or other documents.

Speaker 3: But the test algorithm works on using what we call

Speaker 3: an emissivity calibration curve that is based on a set

Speaker 3: of lab spectra that we measure in the lab of

Speaker 3: various types of natural.

Speaker 2: And also some man made surfaces.

Speaker 3: And based on that calibration curve, we can use that

Speaker 3: as a constraint to separate the temperature and emssility components

Speaker 3: from the thermal measurement. Now, the problem is that over

Speaker 3: urban areas that consist of mostly man made materials, think

Speaker 3: of metals, different types of brick, asphole, et cetera. And

Speaker 3: so the calibration curve is not directly optimized for those

Speaker 3: man made materials, and so we've been talking in the

Speaker 3: future of maybe deriving and calibration curve that is optimized

Speaker 3: only for urban materials. And we've applied only that calibration

Speaker 3: curve over cities and apply the regular calibration curve over

Speaker 3: natural materials. But the issue that arises there is that

Speaker 3: you start developing artifacts where you have to classify an

Speaker 3: area as urban FIR and then apply that part of

Speaker 3: the cove and then appy.

Speaker 2: Different codes to national materials.

Speaker 3: So you could end up with some strange artifacts coming

Speaker 3: through in the in the retrials.

Speaker 2: But it's definitely something that we're working on to prove.

Speaker 1: Thank you question fourteen is. And actually before we move on,

Speaker 1: I will also just mention that we will link to

Speaker 1: what Glenn. Glenn mentioned the ATBD. That's the Algorithm Theoretical

Speaker 1: Basis document which describes the algorithms that are used, and

Speaker 1: so that that will be linked to in the question

Speaker 1: for further reading, and it's it's also linked in our

Speaker 1: resources slide for this training Question fourteen asks is there

Speaker 1: a straightforward way to determine the smallest temperature change that

Speaker 1: can be detected across an urban environment with known emissivity.

Speaker 1: So I'll start and then maybe Glenn, if there's anything

Speaker 1: you want to add, we can you can work work

Speaker 1: at that question that way, because I think this is

Speaker 1: a really interesting question, and from my understanding, the kind

Speaker 1: of floor of this is set by the sensors noise

Speaker 1: equivalent delta temperature, So for ecostress, that's under point three

Speaker 1: kelvin per pixel with emissivity known in atmospheric correction handled,

Speaker 1: we can you know, detect single pixel pixel temperature differences

Speaker 1: of you know, about zero point three or point five

Speaker 1: calvin and I'll have to verify the exact number of that,

Speaker 1: but it's you know, less than one Calvin and then

Speaker 1: you can do meaningfully better through spatial or temporal averaging.

Speaker 1: So that's Glenn. I don't know if there's more that

Speaker 1: you'd want to add to that.

Speaker 3: Yeah, you're pretty much got it right. It all depends

Speaker 3: on the actual accuracy of the measurement itself, right, which

Speaker 3: is constrained by both the radiometric noise of the sensor,

Speaker 3: and then the actual retrieval algorithm error, which is the

Speaker 3: model that takes us from the thermal measurement down to

Speaker 3: the lands of temperature. So if you combine all those

Speaker 3: errors together, that is your precision of your measurements. So

Speaker 3: typically we strive for an accuracy of one degree or less.

Speaker 3: Often it's much higher than that. You know, we can

Speaker 3: get tense of a degree accuracy, but on average, you know,

Speaker 3: in general we want a degree or less. And so

Speaker 3: if you have two pixels next to each other, maybe

Speaker 3: with a very similar type of material, and they vary

Speaker 3: within that one degree level, then it would be very difficult.

Speaker 2: For us to determine those differences.

Speaker 3: So it really all depends on the actual accuracy of

Speaker 3: a measurement at any given time.

Speaker 2: And we actually do provide an.

Speaker 3: Uncertainty estimate, and this is a pixel by pixel based

Speaker 3: a certainty estimate of our retrievals for the ecostress products

Speaker 3: or the landside products. And given that uncertainty estimate, you

Speaker 3: can you know, convince yourself either way of whether you believe.

Speaker 2: The accuracy of of any given pixel.

Speaker 1: Yeah, thank you. Question fifteen asks would LST be kinetic

Speaker 1: temperature or radiometric temperature? And so let's go back to

Speaker 1: you know, the earlier slides of this of this training

Speaker 1: where we learned that true kinetic temperature is the you know,

Speaker 1: is the actual temperature of the surface, right, and so

Speaker 1: thermal instruments that we use don't measure this directly, but

Speaker 1: LST is intended to recover kinetic temperature by applying the

Speaker 1: missivity correction that converts the radiometric measurement into the physical temperature.

Speaker 1: So conceptually that retrieval pipeline would be the measured radiance

Speaker 1: to the brightness temperature, to the radiometric temperature which is

Speaker 1: the integrated you know, multi band measurement, and then the

Speaker 1: kinetic temperature, which is the emissivity corrected LST product that

Speaker 1: we know and that we will be working with in

Speaker 1: part two of this training. Yeah, so I think I'll

Speaker 1: just from the interest of time, move on, but we

Speaker 1: will we will flesh out some of these answers in

Speaker 1: more detail and kind of link back to specific slides

Speaker 1: where we're where we went over this material. Question sixteen

Speaker 1: asks is there a way to distinguish between land surface

Speaker 1: temperature and the temperature of the air near the land surface? Great? So, yes,

Speaker 1: we in fact in the Lake Tahoe site that I mentioned.

Speaker 1: We actually have instruments, you know, designed designed to do this,

Speaker 1: where we have a radiometer measuring the surface, the surface

Speaker 1: of the of the water the skin temperature, whereas we

Speaker 1: also have temperature probes at various depths of the water

Speaker 1: measuring bulk temperature. And then there's also an air temperature

Speaker 1: measure that we that we use two that we include

Speaker 1: in the in the validation site. And is there a

Speaker 1: way to distinguish Glenn, maybe do you want to maybe

Speaker 1: add that, I guess I'm not really sure about the

Speaker 1: distinguishing part between the answer temperature.

Speaker 3: We can measure that the differences between the two and

Speaker 3: they are linked, but they can also differ because various

Speaker 3: different types of circumstances. For example, if there's wind blowing

Speaker 3: or urine diirect sunlight versus in the shade, the two

Speaker 3: types of temperatures can be quite different. Also, think of

Speaker 3: on a really hot day, the surface is going to

Speaker 3: heat up a lot quicker than the air temperature above it,

Speaker 3: and that's mostly due to the different thermal mass of

Speaker 3: air versus the ground. And this is actually one of

Speaker 3: the problems that we deal with a lot is how

Speaker 3: do we derive the air temperature, which is, you know,

Speaker 3: typically what we feel as humans walking around, and which

Speaker 3: is what city planners and decision makers really want to know.

Speaker 3: That that value more than just the skin surface temperature.

Speaker 3: And so we actually spend a lot of time modeling

Speaker 3: the air temperature from the land surface temperature. And we

Speaker 3: can do that using empirical models or land surface models,

Speaker 3: and it's typically has a pretty high incertainty because it's

Speaker 3: it's a difficult it's it's a difficult modeling exercise.

Speaker 2: Think of in an urban environ.

Speaker 3: There's a lot of different things going on, but that,

Speaker 3: in a sense is the temperature that we really want

Speaker 3: to get to in terms of burden studies.

Speaker 2: Is the air temperature.

Speaker 1: Yeah, and maybe we could actually just think about an

Speaker 1: example here in an urban setting, for instance, a parking

Speaker 1: lot that might show for instance, sixty degrees celsius LST,

Speaker 1: while the air temperature you know, two meters above might

Speaker 1: be as low as thirty five degrees celsius. And you know,

Speaker 1: both of these are correct, and they're just measuring different things.

Speaker 1: Where the LST is the you know, dependent on the

Speaker 1: surface property itself, the albedo, the heat capacity, the moisture

Speaker 1: and will vary you know, a lot across land cover

Speaker 1: types within a city, and that's something that we'll look into,

Speaker 1: you know, in part two and the demos, and we'll

Speaker 1: at green spaces, specifically green spaces that have access to

Speaker 1: water or you know, one of them is actually around

Speaker 1: a reservoir area and the other is a park a

Speaker 1: nature preserve that has access to a sufficient water that

Speaker 1: the vegetation is able to basically stay cool with evaporative cooling,

Speaker 1: whereas you know, the air temperature would integrate heat flexes

Speaker 1: from the surface over the surrounding boundary layer, and then

Speaker 1: that's going to be kind of a more spatially smooth

Speaker 1: by atmospheric mixing than the LST itself. Hopefully that's that helps,

Speaker 1: And I think maybe after this session we can also

Speaker 1: kind of maybe provide a few examples from the literature

Speaker 1: that look into kind of that problem that Glenn was

Speaker 1: mentioning about how to kind of model air temperature. I

Speaker 1: think that might be nice for us to provide a

Speaker 1: few a couple examples of that to give some military illustration.

Speaker 1: All right, but yeah, great question. Let's go into question seventeen.

Speaker 1: Given the known uncertainties in retrieving near surface temperature, humidity,

Speaker 1: and radiative fluxes from satellite data, especially due to surface emissivity,

Speaker 1: selective gas absorption, and cloud radiation interactions. What approaches are

Speaker 1: NAS emissions exploring to better capture the combined human relevant

Speaker 1: exposure burden in dense environments. So I would say this

Speaker 1: is sounds like kind of an active, large area of

Speaker 1: multiple maybe sub questions are problems within this bigger question.

Speaker 1: And so NASA's strategy combines multimission earth observations, right, so

Speaker 1: there's an integration with reanalysis and institute networks and physiologically

Speaker 1: relevant indices that go beyond temperature alone. So I would

Speaker 1: say the general direction is towards actionable heat exposure products

Speaker 1: that integrate temperature, humidity, radiation, air quality, you know, all

Speaker 1: of these various factors at urban relevant spatial scales. And

Speaker 1: I would say that integration and consideration of these different

Speaker 1: these different factors is something that is really important and

Speaker 1: something that we, I would say, have a lot of

Speaker 1: opportunity to hone and refine and perhaps in future trainings

Speaker 1: could could go more into because certainly for this training

Speaker 1: we're really focused on land service temperature itself as kind

Speaker 1: of the core measurement that we're considering. But I appreciate

Speaker 1: the you know, the the inquiry in the invitation, and

Speaker 1: I'm not sure, Glenn, if you might have anything to

Speaker 1: add at this time, but this is a question that

Speaker 1: I'd like to maybe circle back to also and maybe yeah,

Speaker 1: think think more about and once we publish the Q

Speaker 1: and a document, we can update what we provide here.

Speaker 1: But Glenn, do you have anything off off the cuff

Speaker 1: to say about that?

Speaker 3: Yeah, I mean it's a pretty loaded question, but a

Speaker 3: good one. There are no single mass emissions that are directly,

Speaker 3: you know, aimed at trying to answer, you know, what

Speaker 3: is the human relevant exposure about and in urban environments

Speaker 3: but specifically, but there are different groups and different research

Speaker 3: groups that are coming up with the diferent kinds of

Speaker 3: models that are able to explain what that human relevant

Speaker 3: temperature is, you know, in urban environments for example.

Speaker 2: And one of those.

Speaker 3: Is the UTCI or the Urban Thermal Comfort Index, which

Speaker 3: has been published in the liverature in several different research

Speaker 3: groups right now. But it combines different variables that explain

Speaker 3: the physiological temperature that we experience as humans. So the

Speaker 3: air temperature, combines, humidity of course, direct thermal radiation, wind speed,

Speaker 3: and various other other factors. And you know all of

Speaker 3: those inputs are you know a lot of those are

Speaker 3: provided by by NASA through different missions, And so I

Speaker 3: guess the NASA's mission here would be to provide the

Speaker 3: highest resolution input data that we possibly can that are

Speaker 3: used in these models.

Speaker 1: Yeah, thank you. Question eighteen asks could you please explain

Speaker 1: the physics behind the difference between split window and the

Speaker 1: tests algorithm? So maybe I'll provide a brief summary in Glenn.

Speaker 1: If you want to do a deeper dive, that's great.

Speaker 1: But basically, the fundamental difference is that the differences in

Speaker 1: what each algorithm is assuming on the onset. So the

Speaker 1: split window algorithm assumes an emissivity is known based on

Speaker 1: a land cover classification lookup table, and it uses the

Speaker 1: brightness temperature difference between two channels to correct for atmospheric

Speaker 1: water vapor. Meanwhile, the test algorithm assumes nothing about amissivity

Speaker 1: to start with, and uses at least three or more

Speaker 1: channels thermal channels plus an empirical spectral construt to solve

Speaker 1: for the emissivity and temperature at the same time. And Glenn,

Speaker 1: if you want to provide a more in depth explanation,

Speaker 1: that would be great. We can also circle back to

Speaker 1: it and in our write up of this final document

Speaker 1: before we publish it to the website, we can do

Speaker 1: we can add in the details. So up to you

Speaker 1: on how in depth you want to go for now.

Speaker 3: Yeah, I mean you could have an entire seminar just

Speaker 3: on this very topic, so I won't go into too

Speaker 3: much more depth then you just explained. But split window

Speaker 3: algorithms generally are employed with the sensor that has two

Speaker 3: thermal bands, and so the two bands are used two

Speaker 3: different parts of the thermal window to account for the

Speaker 3: atmospheric absorption between those two bands, and then use an

Speaker 3: assumed emissivity to derive the temperature. Whereas the test algorithm,

Speaker 3: as you said, is able to do that all in

Speaker 3: one go. We're able to to both retrieve the temperature

Speaker 3: and the emissivity. That it requires three or more bands,

Speaker 3: and that is the constraint that is needed to apply

Speaker 3: the test algorithm.

Speaker 1: Great. Thanks, and we'll link to again the algorithm theoretical

Speaker 1: basis document and other documents that are relevant for kind

Speaker 1: of a deeper dive into understanding how these algorithms work.

Speaker 1: Question nineteen, Will we need qtas, Google Earth Engine or

Speaker 1: are slash our studio for part two? The answer is

Speaker 1: yes if you if you would like to follow along

Speaker 1: with the with the two demos that will be that

Speaker 1: will happen in part two, participants will need qjas, Google

Speaker 1: Earth Engine and are our studio for part two. So

Speaker 1: all of these are available and between now and next

Speaker 1: week in part two, I highly encourage participants who maybe

Speaker 1: had don't have those set up to get those set

Speaker 1: up if you would like to follow along with the demos.

Speaker 1: Question twenty. Given that diseases such as epola, hendivirus and

Speaker 1: these COVID nineteen and NIPA are strongly influenced by near

Speaker 1: surface temperature, humidity, aerosol load, and air quality interactions, what

Speaker 1: steps are NAS emissions taking to improve retrievals of these

Speaker 1: human relevant ground level exposure parameters, especially where uncertainties and emissivity,

Speaker 1: selective gas absorption, and cloud radiation interactions limit current satellite products. Yeah,

Speaker 1: so I think this is maybe a similar kind of

Speaker 1: big picture question that we were that we spoke to earlier,

Speaker 1: where you know, all of these I would say, uh,

Speaker 1: you know, retrievals of these different variables that that we know,

Speaker 1: you know, matter in disease outbreaks, and you know that

Speaker 1: is one very important application of NASA data. So so

Speaker 1: this this training in particular is focused on surface temperature. Right,

Speaker 1: there are other trainings that our set provides that look

Speaker 1: into other data products that you know. For instance, our

Speaker 1: set has an entire thematic area focused on air quality,

Speaker 1: and so I would suggest to the participant who asked

Speaker 1: this question to start by looking at these different thematic

Speaker 1: areas and that's how you know, this information is currently

Speaker 1: organized where we systematically go through these different thematic areas

Speaker 1: and provide trainings to address these yeah, different different factors

Speaker 1: that are that are that together influence complex systems such

Speaker 1: as disease outbreak. So thank you for the work that

Speaker 1: you're doing on this important application. And I'm glad to

Speaker 1: hear that that land service temperature you know, is one

Speaker 1: of the data products that you're currently using in your

Speaker 1: work and that you're invent that you're invested in wanting

Speaker 1: to continually improve those those estimates. So that's that's great

Speaker 1: to hear, and thank you for emphasizing that. Glenn, is

Speaker 1: there anything that you'd like to add to this question.

Speaker 2: No, that that was a good answer.

Speaker 3: I mean, the one thing to keep in mind is

Speaker 3: that NASA cannot produce high resolution near surface temperature and

Speaker 3: humidity right now directly from satellite data.

Speaker 2: These are all sort.

Speaker 3: Of model derived variables that are usually provided from numerical way,

Speaker 3: the models and so forth, and it's really difficult to

Speaker 3: get those values, you know, down at the sub kilometer

Speaker 3: scale right now, just because of the difficulty in retrieving

Speaker 3: nia surface said temperature and you know, humanity measurements.

Speaker 2: So I would.

Speaker 3: Say that it's more more of the modeling communities, either

Speaker 3: for the NASA, GMAO or UK me MET office that

Speaker 3: are striving to get to get us down to that

Speaker 3: sort of very fine scale surface temperature, humility measurements to

Speaker 3: be able to answer some of those questions.

Speaker 1: Yeah, thank you. So question twenty one will be the

Speaker 1: last question that we will answer live. The remaining questions

Speaker 1: that we didn't have time to get to live will

Speaker 1: be answered offline and we'll share We will share this

Speaker 1: Q and a document on our training page within one

Speaker 1: week of the training. So question twenty one asks is

Speaker 1: ecostress data available globally, how do we access as data?

Speaker 1: So for the data access question, I've pasted you know

Speaker 1: some of the links that we included in the earlier

Speaker 1: question and the ECO and for the first part of

Speaker 1: the question of coverage, Ecostress is near global, but due

Speaker 1: to the orbit of the International Space Station, it includes

Speaker 1: plus or minus fifty one point six degrees latitude. So

Speaker 1: basically the polls are not included are not covered by Ecostress.

Speaker 1: So with that we will conclude today's live Q and

Speaker 1: A session. Thank you all so much for your participation

Speaker 1: and engagement, and we look forward to seeing you next

Speaker 1: week at part two of our training series. Thank you

Speaker 1: all and we will see you next week

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