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MODIS-Based AVHRR Cloud and Snow Separation Algorithm
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DOI：10.1109/TGRS.2021.3059428.png)
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The long-term data record (LTDR) has the goal of developing a quality and consistent Advanced Very High Resolution Radiometer (AVHRR) surface reflectance and albedo products dating back to 1982 at 0.05x00B0; spatial resolution. Distinguishing between cloud and snow is of critical importance when analyzing global albedo trends, for they influence the Earthx2019;s energy balance. However, this task is specially challenging when working with AVHRR given its limited spectral bands. Therefore, the current version of the LTDR does not distinguish between snow and clouds. To this end, we propose the Moderate Resolution Imaging Spectroradiometer (MODIS)-based AVHRR Class Separation Algorithm (MACSSA), whose goal is to identify clear land and snow pixels using AVHRR data. We make use of a combination of optical and thermal information from satellite and reanalysis data, along with monthly climatology information. These are used as inputs for two different support vector machine (SVM) models, which are then applied to AVHRR data to retrieve the MACSSA predicted tags. These are compared first against reference tags retrieved from the MYD10C1 product over pixels with less than 2-min overpass time difference between MODIS Aqua and NOAA16x2013;19, distributed all around the world, and second against the Climate Change Initiative Cloud (Cloudx005F;cci AVHRR) project. We found the product to be highly accurate in identifying clear land pixels, with a probability of detection of clear pixels (PODclear) of 97x0025;. The discrimination of snow and clouds shows a PODsnow of 89x0025;, which is encouraging given the spectral limitations of the AVHRR sensor.
Keyword:
Snow
Cloud computing
MODIS
Clouds
Spatial resolution
Satellites
Optical sensors
Advanced Very High Resolution Radiometer (AVHRR)
cloud
long-term data record (LTDR)
Moderate Resolution Imaging Spectroradiometer (MODIS)-based AVHRR Class Separation Algorithm (MACSSA)
MODIS
snow
support vector machine (SVM)
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8.6
论文数:
2.1W
被引数:
10.7W
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