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Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products
| Content Provider | MDPI |
|---|---|
| Author | Esteban, Alonso-González Guillermo, Rodríguez-López Juan, Ignacio López-Moreno Revuelto, Jesús Gascoin, Simon |
| Copyright Year | 2021 |
| Abstract | Understanding those processes in which snow dynamics has a significant influence requires long-term and high spatio-temporal resolution observations. While new optical space-borne sensors overcome many previous snow cover monitoring limitations, their short temporal length limits their application in climatological studies. This work describes and evaluates a probabilistic spatial downscaling of MODIS snow cover observations in mountain areas. The approach takes advantage of the already available high spatial resolution Sentinel-2 snow observations to obtain a snow probability occurrence, which is then used to determine the snow-covered areas inside partially snow-covered MODIS pixels. The methodology is supported by one main hypothesis: the snow distribution is strongly controlled by the topographic characteristics and this control has a high interannual persistence. Two approaches are proposed to increase the 500 m resolution MODIS snow cover observations to the 20 m grid resolution of Sentinel-2. The first of these computes the probability inside partially snow-covered MODIS pixels by determining the snow occurrence frequency for the 20 m Sentinel-2 pixels when clear-sky conditions occurred for both platforms. The second approach determines the snow probability occurrence for each Sentinel-2 pixel by computing the number of days in which snow was observed on each grid cell and then dividing it by the total number of clear-sky days per grid cell. The methodology was evaluated in three mountain areas in the Iberian Peninsula from 2015 to 2021. The 20 m resolution snow cover maps derived from the two probabilistic methods provide better results than those obtained with MODIS images downscaled to 20 m with a nearest-neighbor method in the three test sites, but the first provides superior performance. The evaluation showed that mean kappa values were at least 10% better for the two probabilistic methods, improving the scores in one of these sites by 25%. In addition, as the Sentinel-2 dataset becomes longer in time, the probabilistic approaches will become more robust, especially in areas where frequent cloud cover resulted in lower accuracy estimates. |
| Starting Page | 4513 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13224513 |
| Journal | Remote Sensing |
| Issue Number | 22 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-10 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Snow Distribution Mountain Areas Optical Satellite Sensors High Resolution Downscaling Snow Cover Area Modis Sentinel-2 |
| Content Type | Text |
| Resource Type | Article |
