Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies

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.

An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites

“…We adopted the majorization-minimization (MM) strategy to solve the OGSTV problem in Formula (8). In each iteration of the MM algorithm, a convex quadratic problem is minimized, yielding a convergent solution to Formula (8).…”

“…The GF-4 satellite has a high temporal resolution, high spatial resolution, and large imaging width. It has a ground spatial resolution of 50 m, an image width of 500 × 500 km, an orbital height of 36,000 km, and allows high-frequency, all-weather, continuous, long-term observation of large areas [8,44].…”

“…Therefore, geostationary orbit remote sensing satellites are now producing an unprecedented amount of earth observation data. For example, recently, the new generations of the GOES-R satellite series in North America [3,4], the Himawari-8/9 satellite 2 of 23 in Japan [4,5], and the Fengyun-4A (FY-4A) satellite [6,7] and the GF-4 satellite in China [8,9] have been successfully launched, and the MTG-I satellite in Europe is scheduled for launch in 2021 [10]. Images from geostationary orbit remote sensing satellites offer an opportunity to use super-resolution reconstruction (SRR) technology to further improve image spatial resolution and mapping.…”

Evaluation of the Relationship Between Snow Cover Index and Land Surface Temperature for Sulaymaniyah City, Iraq

“…Territorial temperature difference, solar energy flux and precipitation are the primary factors that influence snow"s mass balance it is forecast that snow will continue to decline in the next; it has been explore that temperature increases have a strong influence on both snow melt and snowfall [5]. Remote sensing technology offers practical more opportunity to observe snow cover than using in situ data, "the normalized difference snow indicator (NDSI)"is the more popular manner, which incorporates the reflective in the "green" and "SWIR" bands to improve the spectrogram sign of snow [6]. In the field of snow observation other problem found which "mixed pixels" contain both snow and vegetation due snow-covered areas can overlap forests [7].…”

Assessment the Impact of Climate Changes on Snow Cover Area Using Landsat Data in Sulaymaniyah City, Iraq

“…Geographical Information System (GIS) and (RS) techniques have grown in popularity due to their unique and broad utility in performing accurate, mapping, and shrivelling of distant regions [7]. This technology allows for the quantitative measurement of the physical characteristics of snow and water in remote and difficult-to-reach locations where earth surveys would be costly and risky, in basins where there is no good info on snow cover, this approach may be utilized to obtain snow cover [8] For snow cover area mapping, the Normalized Difference Snow Index (NDSI) in conjunction with the threshold classification approach is commonly utilized (snow-covered or snow free) [9] can be more significant in differentiating the snow pixel from clouds and other class categories [10]. "Normalized Difference Water Index" (NDWI) deduced from remote sensing data is extremely sensitive to changes in hydrological conditions and is known to be highly related to water content [11].…”