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Collocation is a procedure used in remote sensing to match measurements from two or more different instruments. This is done for two main reasons: for validation purposes when comparing measurements of the same variable, and to relate measurements of two different variables either for performing retrievals or for prediction. In the second case the data is later fed into some type of statistical inverse method such as a neural network, statistical classification algorithm, kernel estimator or a linear least squares. In principle, most collocation problems can be solved by a nearest neighbor search, but in practice there are many other considerations involved and the best method is highly specific to the particular matching of instruments. Here we deal with some of the most important considerations along with specific examples. There are at least two main considerations when performing collocations. The first is the sampling pattern of the instrument. Measurements may be dense and regular, such as those from a cross-track scanning satellite instrument. In this case, some form of interpolation may be appropriate. On the other hand, the measurements may be sparse, such as a one-off field campaign designed for some particular validation exercise. The second consideration is the instrument footprint, which can range from something approaching a point measurement such as that of a radiosonde, or it might be several kilometers in diameter such as that of a satellite-mounted, microwave radiometer. In the latter case, it is appropriate to take into account the instrument antenna pattern when making comparisons with another instrument having both a smaller footprint and a denser sampling, that is, several measurements from the one instrument will fit into the footprint of the other. Just as the instrument has a spatial footprint, it will also have a temporal footprint, often called the integration time. While the integration time is usually less than a second, which for meteorological applications is essentially instantaneous, there are many instances where some form of time averaging can considerably ease the collocation process. The collocations will need to be screened based on both the time and length scales of the phenomenon of interest. This will further facilitate the collocation process since remote sensing and other measurement data is almost always binned in some way. Certain atmospheric phenomena such as clouds or convection are quite transient so that we need not consider collocations with a time error of more than an hour or so. Sea ice, on the other hand, moves and evolves quite slowly, so that measurements separated by as much as a day or more might still be useful. ==Satellites== The satellites that most concern us are those with a low-Earth, polar orbit since geostationary satellites view the same point throughout their lifetime. The diagram shows measurements from AMSU-B instruments mounted on three satellites over a period of 12 hours. This illustrates both the orbit path and the scan pattern which runs crosswise. Since the orbit of a satellite is deterministic, barring orbit maneuvers, we can predict the location of the satellite at a given time and, by extension, the location of the measurement pixels. In theory, collocations can be performed by inverting the determining equations starting from the desired time period. In practice, partially processed data (usually referred to as level 1b, 1c or level 2) contain the coordinates of each of the measurement pixels and it is common to simply feed these coordinates to a nearest neighbor search. As mentioned previously, the satellite data is always binned in some manner. At minimum, the data will be arranged in swaths extending from pole to pole. The swaths will be labelled by time period and the approximate location known. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Collocation (remote sensing)」の詳細全文を読む スポンサード リンク
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