When it comes to taking pictures, we naturally think of cameras and shutters. It’s common to think about pointing the camera at what needs to be captured, and as soon as the shutter is pressed, you can get an image. This image will contain data with the three primary colors on 2D pixels. However, the case is quite different when it comes to earth observation (surface/atmosphere). The principle of specific imaging via satellites is a little more complex.

Scan method

Satellites can be divided into geostationary and polar orbiting satellites. This division is according to their orbits. We know that the angular speed of rotation of the Earth is fixed. If a satellite is used to continuously monitor the Earth, the height of the satellite’s orbit is fixed. It is a fixed satellite. The height of the fixed satellite is 36,000 km, and the radius of the Earth is about 6,371 km. It is conceivable that the angle of view of the earth from the satellite is only 18°. How can the effect of direct imagery be good? We want to have very high resolution images and have to use the “scan” method.

Specifically, “scanning” involves obtaining data by adjusting the angle of the viewing instrument. Then, it will have to be directed to different positions on the earth. For example, in the Fengyun 2 weather satellite, it rotates itself around its main axis. Thus, the sensor can scan the observation data in the shape of a belt on the earth. Then adjust the satellite’s attitude and tilt it up and down to scan the “parallel” data to the earth.

The red lines indicate the scanning path and the direction of rotation of the satellite

Of course, different satellites may use different attitude adjustment methods to improve observation utilization. In the Fengyun 2, there is a 360° rotation of the satellite itself. However, only a few tens of degrees face the earth. In addition, its utilization rate is obviously not high. Nevertheless, for the Fengyun 4, a three-axis stabilization method is adopted. It actively controls the orientation of the satellite, so that the detector always faces the earth during the rotation process. With this design, the observation efficiency is obviously much better.

“Fengyun-4 Weather Satellite”

Why not the three-axis stabilization method?

So some people can’t help but ask, why didn’t the three-axis stabilization method control the orientation of the satellite earlier? Because the three-axis stabilization method is not so simple. The spin stabilization method can easily stabilize its own attitude as the satellite moves around the earth. The key point is that the satellite itself is axisymmetric with respect to its main axis. In contrast, the three-axis stabilization method wants to keep the probe always facing the ground. The attitude of the satellite and how to stabilize is a big problem.

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Second, the solar radiation from the spin-stabilized satellite is uniform. However, the stable three-axis option has one side always facing the ground. The difference in the amount of solar radiation on both sides of the satellite will cause it to deform. The development and use of materials and the arrangement of instruments all require careful attention. Obtaining images of the Earth at such distances is by no means simple.

polar orbit

Because the geostationary option is far from the earth, and the observation accuracy will definitely be limited. If we want to obtain higher precision observational data, we need polar-orbiting satellites. Polar-orbiting satellites (also called sun-synchronous satellites) have a lower orbit than geostationary satellites. They measure about 840 km and orbit around the north and south poles of the Earth. Its observation method can be imagined as scanning during CT. Wherever the instrument/satellite sweeps, it picks up the spot it sweeps. Therefore, the satellite orbit and the longitude and latitude of the observation data can be directly matched. Planetary scientists often call this “tracking data”. As an example, the CloudSat satellite scanned the Earth in one day at one station is shown below


We can see that the coverage is relatively low. Of course, there are also traveling satellites. This will add the detection instrument to swing perpendicular to the orbital direction. This will help the equipment detect data from a surface. For example, the image on the left represents surface precipitation measured by the GPM satellite. If you draw a profile on the red line, you can get the precipitation rate profile on the red line (right picture).

Time-lapse problem

As simple as it sounds, the process is quite complex. Obviously, there is a time difference in the observed data from each orbit. The period of the satellite in orbit around the earth is about 84 minutes. When we select a track for data analysis, it’s actually hard to tell that those 84 minutes of data are at the same time. Time difference should also be taken into account when selecting a certain period of data analysis.

A global or regional data analysis will consider multiple days of datasets. This data will be plotted on a graph and it could represent the long-term average state. “Average state” data is not the average of a long period of continuous observation. This is actually the point observed again after a long interval of time. This is because the observation gets less “concurrent data”. The average of different points on the map is actually the average of different times. It’s a bit confusing. Anyway, in addition to spatial resolution, improving temporal accuracy is also a very important thing.