Gap-filled snow cover fraction from Sentinel-1 and Sentinel-2 constellations&#160;
Authors: Tanis Cemal Melih , Luojus Kari , Kosmale Miriam , Gascoin Simon , Schwaizer Gabriele , Hetzenecker Markus , Zschenderlein Lina , Ablain Michäel , Dorandeu Joel 
Published: March 2022

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&lt;p&gt;&lt;span&gt;Copernicus Land Monitoring Service has recently launched a group of high-resolution snow cover products which are derived from Sentinel-1 and Sentinel-2 constellations. High-&amp;#160;Resolution Snow and Ice Monitoring (HRSI) products include Fractional Snow Cover (FSC)&amp;#160;from the&amp;#160;Sentinel-2 constellation and Wet and Dry Snow (WDS)&amp;#160;covering Europe&amp;#160;and SAR Wet Snow (SWS) products&amp;#160;for selected mountain regions&amp;#160;derived from&amp;#160;the&amp;#160;Sentinel-1 constellation.&amp;#160;The&amp;#160;FSC&amp;#160;and WDS&amp;#160;products&amp;#160;have&amp;#160;gaps in the snow cover data due to cloud&amp;#160;presence and&amp;#160;the&amp;#160;SWS&amp;#160;product&amp;#160;provides&amp;#160;only information on&amp;#160;the melting snow extent, but dry snow areas and snow-free areas cannot be&amp;#160;discriminated&amp;#160;by means of&amp;#160;SAR data.&amp;#160;In the same portfolio, we&amp;#160;provide&amp;#160;the&amp;#160;daily&amp;#160;cumulative&amp;#160;Gap-filled&amp;#160;Fractional&amp;#160;Snow&amp;#160;Cover (GFSC) product, which is a fusion of those three products. In this product, we gap-fill&amp;#160;the&amp;#160;FSC product&amp;#160;using the&amp;#160;wet snow&amp;#160;presence detected&amp;#160;by the&amp;#160;SWS in&amp;#160;the&amp;#160;spatial domain. In&amp;#160;the&amp;#160;temporal domain, all recent data in&amp;#160;the&amp;#160;last 7 days&amp;#160;are&amp;#160;used for gap-filling&amp;#160;by temporal composition.&amp;#160;The product aims to have a complete snow cover map of&amp;#160;Europe.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;The quality of the product is assessed using in-situ data and&amp;#160;&lt;/span&gt;&lt;em&gt;&lt;span&gt;gap simulation,&amp;#160;&lt;/span&gt;&lt;/em&gt;&lt;span&gt;for the period of 09.2017 - 08.2018&amp;#160;for&amp;#160;mountain&amp;#160;ranges&amp;#160;in&amp;#160;the&amp;#160;Pyrenees, Alps, Scandinavia&amp;#160;,&amp;#160;East Turkey&amp;#160;and Corsica, covered by 34 Sentinel-2 tiles.&amp;#160;In-situ&amp;#160;snow depth information&amp;#160;is converted to binary snow cover information using a&amp;#160;snow depth&amp;#160;threshold. For the gap simulation method,&amp;#160;as first step,&amp;#160;FSC&amp;#160;products with observed snow information are selected.&amp;#160;Then,&amp;#160;an artificial cloud mask is overlaid&amp;#160;on these products, and&amp;#160;the gap-filling method is run to generate&amp;#160;GFSC products.&amp;#160;The&amp;#160;resulting&amp;#160;GFSC products are compared with the corresponding&amp;#160;observed&amp;#160;FSC products, considering them as reference data.&amp;#160;This&amp;#160;comparison&amp;#160;shows the agreement between the FSC product and the gap-filling methods.&amp;#160;For both comparison methods, FSC values in GFSC and FSC products are converted to binary snow cover information using an&amp;#160;FSC threshold.&amp;#160;Resulting&amp;#160;binary snow cover information is used in contingency tables and performance metrics are calculated for&amp;#160;the&amp;#160;product&amp;#160;and for different gap-filling methods.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;We have found&amp;#160;that&amp;#160;the gap-filling&amp;#160;provides&amp;#160;5 times&amp;#160;more pixels with snow cover information&amp;#160;and&amp;#160;the&amp;#160;quality is&amp;#160;fairly&amp;#160;good.&amp;#160;The comparison with in-situ data&amp;#160;shows an&amp;#160;accuracy&amp;#160;over 88%&amp;#160;in temporal gap-filling and&amp;#160;precision&amp;#160;over 87% in spatial gap-filling.&amp;#160;The comparison of the gap simulated GFSC and the FSC products shows&amp;#160;an&amp;#160;accuracy over 97% in temporal gap-filling and precision over 83% in spatial gap-filling.&amp;#160;Temporal gap-filling performance is consistent throughout the seasons,&amp;#160;although it&amp;#160;is&amp;#160;less accurate&amp;#160;in the accumulation season for the spatial gap-filling, which is expected as the wet snow algorithm is developed for&amp;#160;the&amp;#160;melting season&amp;#160;conditions.&amp;#160;The assessment shows that the methods are working well and 7 days old FSC, WDS and SWS data are still valid to fill the gaps in the data.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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