The accurate identification of the presence of cloud in the
ground scenes observed by remote-sensing satellites is an end in itself. The
lack of knowledge of cloud at high latitudes increases the error and
uncertainty in the evaluation and assessment of the changing impact of
aerosol and cloud in a warming climate. A prerequisite for the accurate
retrieval of aerosol optical thickness (AOT) is the knowledge of the presence
of cloud in a ground scene.
In our study, observations of the upwelling radiance in the visible (VIS),
near infrared (NIR), shortwave infrared (SWIR) and the thermal infrared
(TIR), coupled with solar extraterrestrial irradiance, are used to determine
the reflectance. We have developed a new cloud identification algorithm for
application to the reflectance observations of the Advanced Along-Track Scanning
Radiometer (AATSR) on European Space Agency (ESA)-Envisat and Sea and Land
Surface Temperature Radiometer (SLSTR) on board the ESA Copernicus Sentinel-3A
and -3B. The resultant AATSR–SLSTR cloud identification algorithm (ASCIA)
addresses the requirements for the study AOT at high latitudes and utilizes
time-series measurements. It is assumed that cloud-free surfaces have
unchanged or little changed patterns for a given sampling period, whereas
cloudy or partly cloudy scenes show much higher variability in space and
time. In this method, the Pearson correlation coefficient (PCC) parameter is
used to measure the “stability” of the atmosphere–surface system observed
by satellites. The cloud-free surface is classified by analysing the PCC
values on the block scale 25×25 km2. Subsequently, the
reflection at 3.7 µm is used for accurate cloud identification at
scene level: with areas of either 1×1 or 0.5×0.5 km2.
The ASCIA data product has been validated by comparison with independent
observations, e.g. surface synoptic observations (SYNOP), the data from
AErosol RObotic NETwork (AERONET) and the following satellite products:
(i) the ESA standard cloud product from AATSR L2 nadir cloud flag; (ii) the
product from a method based on a clear-snow spectral shape developed at IUP
Bremen (Istomina et al., 2010), which we call ISTO; and (iii) the Moderate
Resolution Imaging Spectroradiometer (MODIS) products. In comparison to
ground-based SYNOP measurements, we achieved a promising agreement better
than 95 % and 83 % within ±2 and ±1 okta respectively. In
general, ASCIA shows an improved performance in comparison to other
algorithms applied to AATSR measurements for the identification of clouds in
a ground scene observed at high latitudes.</p