Remote sensing, Question from an old exam

In summary, a satellite infrared radiometer with a wavelength of 10 micrometers and a 5x5 km footprint is observing a region of mixed open water and ice covered sea surface. The open water has a temperature of -1 degree celsius and an emissivity of 0.8, while the sea ice can be assumed as a blackbody. The brightness temperature for pure open water is 261.049 K and for pure sea ice is 266.0000846 K. However, taking the weighted average of these temperatures is not valid as the detector measures total power received at a certain wavelength. Instead, the intensities of the two sources should be weighted and then converted back to a temperature. Using this method, the
  • #1
Firben
145
0

Homework Statement


A satellite infrared radiometer (λ = 10 micrometer, footprint size 5x5 km) is observing a region of mixed open water and ice covered sea surface, where the temperature of the open water is -1 degree celsius and that of the sea ice -7 degree celsius. In the infrared, sea ice can be assumed as blackbody, while open water has an emissivity of about 0.8. Neglect any interference of the atmosphere

What is the brightness temperature if the pixel contains 30 % pure open water and 70 % sea ice ?

Homework Equations



Tb^-1 = k/(h*v)*ln[1+e^((h)(v)/((k)(T))-1)/ε]

ε = 1 for a black body


The Attempt at a Solution



i got the observed brighness temperature for pure water to be 261.049 K and 266.0000846 K for pure sea ice
(Expected value)
E(ξ) = 0.3*261.049 + 0.7*266.0000846 = 264.514 K

This is not valied since it is Rayleigh-Jeans , Why ?

It should by Planck but i don't know the intensity ?
 
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  • #2
I know nothing about this subject, but it seems wrong to me to take the weighted average of the temperatures. The detector does not directly measure temperature, it measures total power received at a certain wavelength. Shouldn't you take the weighted average of those powers for the two sources, then infer the apparent temperature?
 
  • #3
Why must i use the Planck function ?
 
  • #4
If i start by calculating the brightness temperature of open water (grey body) by using this equation:

https://wikimedia.org/api/rest_v1/media/math/render/svg/c993a06e73c39413e249b260d33368395a75b5d4
(Tb^-1 = k/(h*v)*ln[1+e^((h)(v)/((k)(T))-1)/ε])

and plug in my values, i get the following temperature for open water T ~ 262.89 K

Then when i calculate the brightness temperature for sea water (which is a black body). I used Planck's law:

https://wikimedia.org/api/rest_v1/media/math/render/svg/5a1df6cc98f89d1f11d7eb5425cc67b4dd9bd8a2
(Planck's law)

for the intensity. And for the brightness temperature i used the equation:

https://wikimedia.org/api/rest_v1/media/math/render/svg/0ffad7df33ab06c82841a0030ed0c67abaefcbff

Tb = (hc/kλ)ln^-1(1+ 2hc^2/(I*λ^5))

(For some reason my equation's can't be seen here, they are from wikipedia: url:https://en.wikipedia.org/wiki/Brightness_temperature#Calculating_by_wavelength)

Now when the pixel contain 70 % sea ice, I multiplied the Planck's law with 0.7 and plugged in my values in the equation below and got the brightness temperature to be equal to T ~ 57 K. Which looks way to cold. If i average the sea water and the open water that the pixel contain: (57 K + 262.89 K)/2 i get the following value T(avg) ~ 160 K. I think i have missed something here ? its look's to cold.
 
  • #5
Is this the right method ?
 
  • #6
Firben said:
Is this the right method ?
As I wrote, it does not seem right to me to be averaging temperatures. The sensor does not directly sense those.
For each of the water bodies, you can calculate the intensity at the given wavelength. The sensor will sense the weighted average of these. You can then turn that intensity back into a temperature.

The equation at the Wikipedia site doesn't make sense to me. The -1 exponent on the ln() looks like an error. If I derive it from the preceding equation (Iλ=) I get just plain ln().
 
  • #7
If i start by calculate the intensity of the sea ice and the open water, and then take the weighted average of the two intensities. I then got a intensity value of 2.155*10^10 w/m^2, in which i put into the brightness temperature equation. And from there i got a brightness temperature value of 267.403 K.
 
  • #8
Firben said:
If i start by calculate the intensity of the sea ice and the open water, and then take the weighted average of the two intensities. I then got a intensity value of 2.155*10^10 w/m^2, in which i put into the brightness temperature equation. And from there i got a brightness temperature value of 267.403 K.
Those intensities are at the sensors wavelength, right?
Without checking your arithmetic, that seems a reasonable answer.
 

1. What is remote sensing?

Remote sensing is the process of gathering information about an object or area without physically being in contact with it. This is typically done using specialized sensors and technologies, such as satellites or aircraft, to collect data from a distance.

2. How does remote sensing work?

Remote sensing works by using sensors to detect and measure electromagnetic radiation, such as visible light, infrared, and microwave, that is reflected or emitted from objects on the Earth's surface. This data is then processed and analyzed to create images and maps of the area being observed.

3. What are the applications of remote sensing?

Remote sensing has a wide range of applications, including environmental monitoring, natural resource management, urban planning, agriculture, and disaster response. It is also used in various industries, such as forestry, mining, and oil and gas exploration.

4. What are the advantages of remote sensing?

Remote sensing has several advantages, including the ability to gather data over large areas quickly and efficiently, the ability to access remote or inaccessible areas, and the ability to collect data over time to track changes in an area. It also allows for non-invasive and non-destructive data collection.

5. What are the limitations of remote sensing?

While remote sensing has many advantages, it also has limitations. These include the potential for errors and inaccuracies in the data, limitations in the types of data that can be collected, and the need for specialized equipment and expertise. Additionally, weather conditions and atmospheric interference can affect the quality of the data collected.

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