Subpixel Snow Mapping Using Daily AVHRR/2 Data over Qinghai–Tibet Plateau

Based on a linear spectral mixture model and multiple endmember spectral mixture analysis, using daily advanced very-high-resolution radiometer (AVHRR/2) data of the Qinghai–Tibet Plateau, a subpixel snow mapping algorithm was proposed in this paper, for prolonging the historical time series of the...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-06, Vol.14 (12), p.2899
Main Authors: Zhu, Ji, Cao, Shuqin, Shang, Guofei, Shi, Jiancheng, Wang, Xinyun, Zheng, Zhaojun, Liu, Chenzou, Yang, Huicai, Xie, Baoni
Format: Article
Language:English
Subjects:
Algorithms
AVHRR/2 data
cloud removal
Clouds
fractional snow cover
Hydrologic cycle
Hydrology
Ice
Lookup tables
Mapping
Mixtures
Mountain regions
multiple end-members
Normalized difference vegetative index
Pixels
Precipitation
Qinghai–Tibet Plateau
Remote sensing
Sensors
Snow
Snow cover
Time series
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Summary:Based on a linear spectral mixture model and multiple endmember spectral mixture analysis, using daily advanced very-high-resolution radiometer (AVHRR/2) data of the Qinghai–Tibet Plateau, a subpixel snow mapping algorithm was proposed in this paper, for prolonging the historical time series of the fractional snow cover data to 40 years. In particular, the normalized difference vegetation index (NDVI), and channels 1 and 2 of AVHRR/2 data were used to automatically select the end-members directly, from a certain AVHRR/2 image. A look-up table of sample spectra of mixed pixels and their respective snow cover percentages was introduced for one AVHRR/2 image. According to the established look-up tables, the fractional snow cover of each mixed pixel can then be extracted from the AVHRR/2 images. Before the subpixel snow mapping, the cloud pollution of the AVHRR/2 images was mitigated, with both the thick and thin clouds almost removed from the AVHRR/2 images. It turns out that the processing speed of the subpixel snow mapping is three times faster than the process not using the look-up table. The mapping algorithm was validated against the snow-covered area from Thematic Mapper (TM) data, with the root-mean-square errors (RMSEs) well below 0.12. Results show that the proposed algorithm for subpixel snow mapping is both effective and efficient, especially in such a mountainous region as the Qinghai–Tibet Plateau.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14122899