 |
| Titel |
Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery |
| VerfasserIn |
J. P. Musial, F. Hüsler, M. Sütterlin, C. Neuhaus, S. Wunderle |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1867-1381
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 3 ; Nr. 7, no. 3 (2014-03-28), S.799-822 |
| Datensatznummer |
250115649
|
| Publikation (Nr.) |
 copernicus.org/amt-7-799-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| Derivation of probability estimates complementary to geophysical data
sets has gained special attention over the last years. Information about
a confidence level of provided physical quantities is required to construct
an error budget of higher-level products and to correctly interpret final
results of a particular analysis. Regarding the generation of products based
on satellite data a common input consists of a cloud mask which allows
discrimination between surface and cloud signals. Further the surface
information is divided between snow and snow-free components. At any step of
this discrimination process a misclassification in a cloud/snow mask
propagates to higher-level products and may alter their usability. Within
this scope a novel probabilistic cloud mask (PCM) algorithm suited for the
1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data
is proposed which provides three types of probability estimates between:
cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to
the majority of available techniques which are usually based on the
decision-tree approach in the PCM algorithm all spectral, angular and
ancillary information is used in a single step to retrieve probability
estimates from the precomputed look-up tables (LUTs). Moreover, the issue of
derivation of a single threshold value for a spectral test was overcome by
the concept of multidimensional information space which is divided into small
bins by an extensive set of intervals. The discrimination between snow and
ice clouds and detection of broken, thin clouds was enhanced by means of the
invariant coordinate system (ICS) transformation. The study area covers a
wide range of environmental conditions spanning from Iceland through central
Europe to northern parts of Africa which exhibit diverse difficulties for
cloud/snow masking algorithms. The retrieved PCM cloud classification was
compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks,
SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical
feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of
conducted analyses proved fine detection skills of the PCM method with
results comparable to or better than the reference PPS algorithm. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|