Abstract
Snow cover is an essential climate variable of the Global Climate Observing System. Gaofen-4 (GF-4) is the first Chinese geostationary satellite to obtain optical imagery with high spatial and temporal resolution, which presents unique advantages in snow cover monitoring. However, the panchromatic and multispectral sensor (PMS) onboard GF-4 lacks the shortwave infrared (SWIR) band, which is crucial for snow cover detection. To reach the potential of GF-4 PMS in snow cover monitoring, this study developed a novel method termed the restored snow index (RSI). The SWIR reflectance of snow cover is restored firstly, and then the RSI is calculated with the restored reflectance. The distribution of snow cover can be mapped with a threshold, which should be adjusted according to actual situations. The RSI was validated using two pairs of GF-4 PMS and Landsat-8 Operational Land Imager images. The validation results show that the RSI can effectively map the distribution of snow cover in these cases, and all of the classification accuracies are above 95%. Signal saturation slightly affects PMS images, but cloud contamination is an important limiting factor. Therefore, we propose that the RSI is an efficient method for monitoring snow cover from GF-4 PMS imagery without requiring the SWIR reflectance.
Highlights
Snow is an important type of land cover
We proposed a novel method termed the restored snow index (RSI) for snow cover monitoring from GF-4 panchromatic and multispectral sensor (PMS) imagery
Our study introduced a new method, termed the RSI, to monitor snow cover from GF-4 PMS imagery without the shortwave infrared (SWIR) band
Summary
Snow is an important type of land cover. During the northern hemisphere winter, approximately one-third of the global land surface can be covered by snow [1,2]. Seasonal variation characteristics owing to extensive snowfall in winter and rapid snowmelt in spring make snow a significantly dynamic component of the ecosystem [2,3]. Snow cover has been selected as one of the essential climate variables of the Global Climate Observing System [6]. The long duration of snowfall in winter can lead to snow disasters, whereas the rapid snowmelt in spring can result in flooding hazards [14,15]. Monitoring snow cover is essential for climate change study, water resource management, and disaster prevention and mitigation
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