Minimize Cloud Identification

The cloud identification processing is a series of tests aimed at identifying different types of cloud (or snow) present on output images acquired by SLSTR.

The identification of cloud-affected pixels is accomplished by applying a series of tests in turn to the brightness temperature data in the 12, 11 and 3.7 µm channels, and to the reflectance data in the 1.6 µm, 1.375 µm channel, and in the visible channels. The table below summarises the cloud clearing tests to be applied, including one test flagging snow-covered surfaces.

Tests name Views Day/night time Land/sea
Gross cloud test Nadir and oblique views separately both both
Thin cirrus test Nadir and oblique views separately both both
Medium/high level cloud test Nadir and oblique views separately night only both
Fog/low stratus test Nadir and oblique views separately night only both
11 micron spatial coherence test Nadir and oblique views separately both both
1.6 micron histogram test Nadir and oblique views separately day only sea only
11/12 micron nadir/along-track test both views both sea only
11/3.7 micron nadir/along-track test both views night only sea only
Visible channel cloud test Nadir and oblique views separately day only land or both
Infra-red histogram test Nadir and oblique views separately both sea only
2.25 micron histogram test Nadir and oblique views separately day only sea only
1.375 micron threshold test Nadir and oblique views separately day only both
Snow-covered surface test Nadir and oblique views separately day only both

Table: Basic Cloud identification test list

 

Some of the tests depend on results from the tests performed previously and hence the order in which they are applied is important.

The infra-red histogram test is applied after the other tests, and only uses those pixels that have not been flagged as cloudy by any of the preceding tests.

The 1.6 micron histogram test operates only on pixels not previously flagged as cloudy by the gross cloud test or the thin cirrus and 11 micron spatial coherence tests, and must therefore follow these tests.

Each test makes use of a look-up table of parameters with which the brightness temperature or reflectance data is compared. Where tests are applied to oblique and nadir view images separately, the parameters may be defined separately for the two cases. More generally, the comparison parameters may depend on the air mass in the line of sight, and this is implemented by allowing the tabular parameters to depend on the across track position.

Some tests are only applied under certain conditions (during day or night time only, over land or sea pixels only), as described in the table above.

A Bayesian and Probabilistic method of cloud screening is also used. As these cloud screening methods use the Meteorological data, they occur after the meteorological annotations processing stage.