Abstract
Reliable cloud masks in Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products have a high potential to improve the retrieval of snow properties. However, cloud–snow confusion is a popular problem in MODIS snow cover products, especially in boreal forest areas. A large amount of forest snow is misclassified as clouds because of the low normalized difference snow index (NDSI), and excessive cloud masks limit the application of snow products. In addition, ice clouds are easily misclassified as snow due to their similar spectral characteristics, which leads to snow commission errors. In this paper, we quantitatively evaluated the cloud–snow confusion in Northeast China and found that snow-covered forests and transition zones from snow-covered to snow-free areas are prone to being misclassified as clouds, while clouds are less likely to be misclassified as snow. A temporal-sequence cloud–snow-distinguishing algorithm based on the high-frequency observation characteristics of the Himawarri-8 geostationary meteorological satellite is proposed. In the temporal-sequence images acquired from that satellite, the NDSI variance in cloud pixels should be greater than that of snow because clouds vary over time, while snow is relatively stable. In the MODIS snow cover products, the cloud pixels with NDSI variance lower than a threshold are identified as cloud-free areas and attributed their raw NDSI value, while the snow pixels with NDSI variance greater than the threshold are marked as clouds. We applied this method to MOD10A1 C6 in Northeast China. The results showed that the excessive cloud masks were greatly eliminated, and the new cloud mask was in good agreement with the real cloud distribution. At the same time, some possible ice clouds which had been misclassified as snow for their spectral characteristics similar to those of snow were identified correctly.
Highlights
Compared with the limited amount of in situ measurements provided by sparse meteorological stations, remote sensing technology can produce spatiotemporal patterns of snow cover on a larger scale [6,7]
When the solar zenith angles >70, the corresponding illumination conditions are poor, and snow detection is challenging. These screens are significant for global snow production but will result in great snow omission errors in high-altitude boreal forest areas
Moderate Resolution Imaging Spectroradiometer (MODIS) snow product C6 improved the identification of clouds and snow; in addition, a series of screens were adopted to reduce the misclassification of snow, including lowNDSI screens, low-reflectance screens, and low-solar-elevation screens
Summary
The study area is located in Northeast China, and the sites comprise a seasonally snow-covered boreal forest. Northeast China is one of the three primary snow-covered regions in China [51]. In this area, the seasonal snow cover usually lasts from November to March of the following year. The latitude of the study area is from 38◦ 420 N to 53◦ 360 N, and boreal forests are widespread in the region, especially in the Daxing’an Mountains, Xiaoxing’an Mountains, and Changbai Mountains [52]. The high latitude corresponds to the low solar elevation in winter. In the MODIS snow cover products of this region, the high forest coverage and low solar elevation lead to serious cloud–snow confusion
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