Hey, everybody.
Welcome to Remote Sensing Foundations.
This is the first lecture of the class,
and I'm super excited
to dive into remote sensing with you all.
Remote sensing is a skill that I learned in grad school
and has sensed dictated a lot of my research
and professional career.
It's super fun and super powerful,
and I hope that at the end of this class,
you too will be amazed
at all the possibilities of remote sensing.
So in this first lecture,
I'm gonna introduce you to what remote sensing is,
including explaining how particle physics relates to it.
I know that sounds a bit scary,
but hopefully we'll break it down a bit
so it's not so scary.
Upon completion of this module,
my hope is that you'll be able to define remote sensing,
define how and where it's used,
name and define the components
of the electromagnetic spectrum,
and understand how energy and matter interact
with the surface terrain.
Great, so let's get started. What is remote sensing?
Perhaps the most simplified definition
would be the collection of information about an object,
but without making physical contact with it.
For the purpose of this course,
I'm gonna add some words to that definition.
So I think of remote sensing as the science
of identifying, observing, collecting, and measuring objects
without coming into direct contact with them,
so without touching them, without smelling them,
without tasting them.
I know usually we think of remote sensing
just as a visual thing,
but in terms of the comprehensive definition of it,
really all of the senses can be remotely sensed.
So in that sense, remote sensing is carried out by humans
and animals with the aid of our eyes and other senses,
like smell and hearing every day.
Another important point that I wanna make is that satellites
don't actually take pictures of our planet.
Instead, what they do is record electromagnetic energy
that is reflected or omitted from objects on Earth.
And that point will become more clear
in the next couple of slides.
So before I jump into how physics is related
to remote sensing, I just wanna demonstrate
how broadly used remote sensing is.
So I won't read this entire slide to you,
but you can see that remote sensing analysis
is used in many sectors,
and also where it is used is growing each year,
from commercial applications to non-profit
to science, engineering, medicine.
There's a whole host of different career options
for people who do remote sensing analysis.
So it's not just an academic pursuit.
It's used in a lot of different jobs.
Okay, so let's now dive deeper into some of the science
behind what we are measuring in remote sensing.
Great, so now we're gonna go back to physics
to understand light or energy.
And this may be background for some of you
if you recently took physics in college or high school.
A photon is one of the fundamental particles in physics
and it's a light particle.
By fundamental I mean that it cannot be split,
it doesn't have a substructure.
And photons are really interesting
because they exhibit a wave particle duality.
So in experiments when scientists such as Albert Einstein
were trying to figure out whether photons were,
or whether light behaved as particle or waves,
depending on the experiment
and whether you were trying to detect photons or not,
the signal shows up as a wave or a particle,
so, very interesting.
In its wave form,
light, it can be seen as electromagnetic waves,
which is basically the combined movement
of magnetic and electric fields.
And these waves can be defined
by their wavelength and frequency,
and that determines how much energy is emitted.
I wanna make the point that frequency
and wavelength are inversely related.
So as frequency goes up, wavelength goes down.
As wavelength gets longer, frequency gets shorter.
So wavelength is basically the sort of,
it's the width of your sine wave.
And then the frequency is the time that it takes
for one wave oscillation to happen.
You can see in the equations at the bottom that frequency
and wavelength are related by the speed of light.
So frequency is equal to the speed of light over wavelength
and the speed of light is a constant.
And then if you're trying to calculate energy,
energy is equal to Planck's constant times frequency.
And so of course because frequency
and wavelength are related,
you could also calculate energy by your wavelength.
So the electromagnetic spectrum is the entire range
of electromagnetic radiation or energy based off of
all of the different frequencies or wavelengths.
So it's everything from the shortest wavelength,
which would be super high frequency,
and that corresponds to high energy,
all the way to a super long wavelength
and a short frequency,
which would correspond to a low energy state.
This figure shows some of the most common sources
of electromagnetic energy.
As you can see, there's a lot of different sources
of electromagnetic energy
that we come into contact with every single day.
And these have a variety of wavelengths and frequencies.
So on the left side of this figure, you can see sources
that have long wavelengths and low frequencies,
so such as subway systems, AM/FM radios, cell phones.
And then on the right side of this figure
you can see sources that have short wavelengths
and high frequencies, such as medical X-rays.
In terms of this class,
one of the most important sources
of electromagnetic radiation that we will study is the Sun.
And I wanna point out that there are a variety
of different kinds of radiation that the Sun emits.
So we have the visible light,
so red, green, blue light that we can see with our eyes,
but there's also radiation
that we cannot see with our eyes at the sentiment.
So infrared or ultraviolet radiation, again, we can't see,
but can be very important sources of radiation
in remote sensing.
All of the energy from the Sun
that reaches the Earth is called solar radiation.
A very simplified view of remote sensing
would be to say that remote sensing
is measuring the reflection of the Sun's radiation
as it comes into contact
with different surfaces on the Earth.
However, solar radiation does not take a straight path
from the Sun to the Earth and then back from the Earth
to whatever sensor is trying to measure that radiation.
You can see in this graph that around 6% of solar radiation
is reflected by the atmosphere,
16% is absorbed by the atmosphere,
20% is reflected by the clouds,
and 3% is absorbed by the clouds.
Then when the remaining radiation reaches the Earth,
some of that energy or radiation is absorbed
into the Earth's surface and some is reflected back.
And then that radiation
that's reflected back from the Earth, it can be absorbed
or scattered by the clouds and the atmosphere as well.
So spend a minute studying this graphic,
and you can start to appreciate how complex it can be
to study and understand the path light takes
from the Sun to the Earth and then back to a sensor
that is trying to remotely sense the Earth's surface.
Here's another figure that shows electromagnetic radiation
going from the Sun to the Earth to the sensor.
I just wanted to add this figure
because I think it shows in a slightly different way
all of the areas where light can be scattered
or absorbed, transmitted, emitted.
Again, the important point here
is that electromagnetic radiation from the Sun is absorbed
or scattered through the atmosphere
both before and after it reaches Earth.
Now, we'll jump into spectral signatures.
Each surface on Earth, because of its makeup and texture,
has what is called a spectral signature.
This basically is the unique distribution
of reflected, emitted, and absorbed radiation of an object.
In this graph you can see the percent reflectance
of some common features on the Earth's surface.
So snow and ice are very highly reflective,
as well as clouds, whereas clear water
and turbid water have very low percent reflectance.
You can also see that vegetation
has a sort of unique spectral signature,
and we'll talk about that,
how that spectral signature can help us identify them
on the landscape on the following slide.
You can also imagine
that different satellites are constructed
to measure different ranges of spectral reflectance
with high accuracy and precision
in different wavelength ranges
depending on what that satellite is designed to measure.
So for example, some satellites are constructed
to measure terrestrial surfaces,
so they might be better equipped to measure
and differentiate between different kinds of vegetation.
So as I noted in the previous slide,
vegetation can have very unique spectral signatures,
and that's really helpful because it helps us differentiate
between different kinds of vegetation,
so for example, between grasslands and forests.
It can also help us differentiate
between different plant species.
But it can also help us tell the difference
between plants of the same species that are healthy
and unhealthy or that are undergoing seasonal changes.
So for example, in the graph that I'm showing here,
this is from a study that looked
at leaves from the sweetgum tree at different phases
of going from green to brown
and then falling off in the winter.
And you can see that the spectral signatures
of these different colored leaves are very, very different.
And you can imagine that if you had spectral information
about these different leaves,
you could learn to tell from the light signatures,
the spectral signatures
which of these pixels had more yellow leaves,
which of them had more brown leaves,
which of them had more green leaves.
We'll explore this topic a lot more in future lectures.