159 lines
6.3 KiB
Markdown
159 lines
6.3 KiB
Markdown
---
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title: "Collocation (remote sensing)"
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chunk: 1/2
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source: "https://en.wikipedia.org/wiki/Collocation_(remote_sensing)"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T09:53:37.734675+00:00"
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instance: "kb-cron"
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---
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Collocation is a procedure used in remote sensing
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to match measurements from two or more different instruments.
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This is done for two main reasons:
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for validation purposes when comparing measurements of the same variable,
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and to relate measurements of two different variables
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either for performing retrievals or for prediction.
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In the second case the data is later fed into some type of statistical
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inverse method
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such as an artificial neural network, statistical classification algorithm,
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kernel estimator or a linear least squares.
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In principle, most collocation problems can be solved by a nearest neighbor search,
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but in practice there are many other considerations involved and the best method is
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highly specific to the particular matching of instruments.
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Here we deal with some of the most important considerations along with specific examples.
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There are at least two main considerations when performing collocations.
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The first is the sampling pattern of the instrument.
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Measurements may be dense and regular, such as those from a cross-track
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scanning satellite instrument. In this case, some form of interpolation
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may be appropriate. On the other hand, the measurements may be
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sparse, such as a one-off field campaign designed for some
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particular validation exercise.
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The second consideration is the instrument footprint, which
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can range from something approaching a point measurement
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such as that of a radiosonde, or it might be several
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kilometers in diameter such as that of a satellite-mounted,
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microwave radiometer. In the latter case, it is appropriate
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to take into account the instrument antenna pattern when
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making comparisons with another instrument having both a smaller
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footprint and a denser sampling, that is, several measurements
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from the one instrument will fit into the footprint of the other.
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Just as the instrument has a spatial footprint, it will also have
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a temporal footprint, often called the integration time.
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While the integration time is usually less than a second,
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which for meteorological applications is essentially instantaneous,
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there are many instances where some form of time averaging can considerably
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ease the collocation process.
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The collocations will need to be screened based on both the time
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and length scales of the phenomenon of interest.
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This will further facilitate the collocation process since
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remote sensing and other measurement data is almost always
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binned in some way.
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Certain atmospheric phenomena such as clouds or convection are quite transient
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so that we need not consider collocations with a time error of more than an hour or so.
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Sea ice, on the other hand, moves and evolves quite slowly, so that
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measurements separated by as much as a day or more might still be useful.
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== Satellites ==
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The satellites that most concern us are those with a low-Earth, polar orbit since geostationary satellites view the same point throughout their lifetime.
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The diagram shows measurements from AMSU-B
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instruments mounted on three satellites over a period of 12 hours.
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This illustrates both the orbit path and the scan pattern which runs crosswise.
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Since the orbit of a satellite is deterministic,
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barring orbit maneuvers, we can predict the location of the
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satellite at a given time and, by extension, the location of
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the measurement pixels.
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In theory, collocations can be performed by inverting the
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determining equations starting from the desired time period.
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In practice, partially processed data (usually referred to as
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level 1b, 1c or level 2) contain the coordinates of each of
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the measurement pixels and
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it is common to simply feed these coordinates to a nearest neighbor search.
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As mentioned previously, the satellite data is always binned
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in some manner.
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At minimum, the data will be arranged in
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swaths extending from pole to pole.
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The swaths will be labelled by time period and the
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approximate location known.
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== Radiosondes ==
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Radiosondes are particularly important for collocation studies
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because they measure atmospheric variables more accurately and more
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directly than satellite or other remote-sensing instruments.
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In addition, radiosonde samples are effectively instantaneous point measurements.
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One issue with radiosondes carried aloft by weather balloons is
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balloon drift. In,
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this is handled by averaging all the satellite pixels within a 50 km radius
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of the balloon launch.
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If high-resolution sonde data, which normally has a constant
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sampling rate or includes the measurement time, is used,
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then the lateral motion can be traced from the wind data.
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Even with low-resolution data, the motion can still
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be approximated by assuming a constant ascent rate.
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Excepting a short bit towards the end,
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the linear ascent can be clearly seen in the figure above.
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We can show that the ascent rate of a balloon is given
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by the following equation
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v
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=
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g
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k
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h
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(
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1
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−
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R
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a
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/
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R
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s
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)
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c
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D
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{\displaystyle v={\sqrt {\frac {gkh(1-R_{a}/R_{s})}{c_{D}}}}}
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where g is gravitational acceleration,
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k relates the height, h, and surface area, A,
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of the balloon to its volume: V = khA;
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Rs is the equivalent "gas constant" of the balloon,
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Ra is the gas constant of the air
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and cD is the drag coefficient of the balloon.
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Substituting some sensible values for each of the constants,
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k=1. (the balloon is a perfect cylinder), h=2. m, cD = 1.
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and Ra is the gas constant of helium,
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returns an ascent rate of 4.1 m/s. Compare this with the
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values shown in the histogram which compiles all of the
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radiosonde launches from the Polarstern research vessel
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over a period of eleven years between 1992 and 2003. |