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A Cloud masking algorithm for the XBAER aerosol retrieval using MERIS data

Author:
Mei, Linlu, Vountas, Marco, Gómez-Chova, Luis, Rozanov, Vladimir, Jäger, Malte, Lotz, Wolfhardt, Burrows, John P., Hollmann, Rainer
Source:
Remote sensing of environment 2017 v.197 pp. 141-160
ISSN:
0034-4257
Subject:
aerosols, algorithms, altitude, biomass, burning, color, dust, experts, geophysics, ice, image analysis, instrumentation, lidar, models, radiative transfer, reflectance, remote sensing, snow, spectrometers, vegetation
Abstract:
To determine aerosol optical thickness, AOT, and other geophysical parameters describing conditions in the atmosphere and at the earth's surface by inversion of remote sensing measurements from space based instrumentation, it is necessary to separate ground scenes into cloud free and cloudy or cloud contaminated. Identifying the presence of cloud in a ground scene and establishing an accurate and adequate cloud mask is a challenging task.In this study, measurements by the European Space Agency (ESA) MEdium Resolution Imaging Spectrometer (MERIS) have been used to develop a cloud identification and cloud mask algorithm for preprocessing prior to application of the new algorithm called eXtensible Bremen AErosol Retrieval (XBAER), which retrieves AOT. The new XBAER cloud identification and cloud mask algorithm is called XBAER-CM. This uses thresholds of the reflectance and reflectance ratios measured by MERIS at Top Of Atmosphere (TOA).In this study the parameters used to determine the presence of cloud in ground scenes are i) the brightness of the scenes, ii) the homogeneity or variability of the radiance and iii) cloud height or altitude information. The threshold values used to identify the presence of cloud are selected by using accurate radiative transfer modeling with different surface and atmospheric scenarios. A histogram analysis has been used for different cloud (thin, thick, two-layers, aerosol contaminated cloud), aerosol (dust and biomass burning) and surface scenarios (vegetation, urban, desert and water). Additionally, a snow/ice detection algorithm has been adapted from MerIs Cloud fRation fOr Sciamachy (MICROS) algorithm.A validation for the resulting cloud mask data products has been undertaken. This comprised i) comparison of regions scenes, which have been manually generated by experts and ii) more global comparison with cloud identification data products from surface synoptic observations (SYNOP) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). As a part of verification and validation, the XBAER-CM results have been shown to be in good agreement with the “manually”-created masks, considered to be the true reference for a set of challenging scenarios. The overall accuracy compared with SYNOP and CALIOP are 84.4% and 83.2%, respectively. The XBAER-CM data product is a standalone data product but valuable for use with algorithms, which retrieve other cloud, aerosol and surface parameters from the measurements of MERIS and the follow on instruments such as Sentinel 3 Ocean and Land Color Instrument (OLCI) now in space.
Agid:
5729431