<div> <p><span>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-&#160;Resolution Snow and Ice Monitoring (HRSI) products include Fractional Snow Cover (FSC)&#160;from the&#160;Sentinel-2 constellation and Wet and Dry Snow (WDS)&#160;covering Europe&#160;and SAR Wet Snow (SWS) products&#160;for selected mountain regions&#160;derived from&#160;the&#160;Sentinel-1 constellation.&#160;The&#160;FSC&#160;and WDS&#160;products&#160;have&#160;gaps in the snow cover data due to cloud&#160;presence and&#160;the&#160;SWS&#160;product&#160;provides&#160;only information on&#160;the melting snow extent, but dry snow areas and snow-free areas cannot be&#160;discriminated&#160;by means of&#160;SAR data.&#160;In the same portfolio, we&#160;provide&#160;the&#160;daily&#160;cumulative&#160;Gap-filled&#160;Fractional&#160;Snow&#160;Cover (GFSC) product, which is a fusion of those three products. In this product, we gap-fill&#160;the&#160;FSC product&#160;using the&#160;wet snow&#160;presence detected&#160;by the&#160;SWS in&#160;the&#160;spatial domain. In&#160;the&#160;temporal domain, all recent data in&#160;the&#160;last 7 days&#160;are&#160;used for gap-filling&#160;by temporal composition.&#160;The product aims to have a complete snow cover map of&#160;Europe.</span><span>&#160;</span></p> </div><div> <p><span>The quality of the product is assessed using in-situ data and&#160;</span><em><span>gap simulation,&#160;</span></em><span>for the period of 09.2017 - 08.2018&#160;for&#160;mountain&#160;ranges&#160;in&#160;the&#160;Pyrenees, Alps, Scandinavia&#160;,&#160;East Turkey&#160;and Corsica, covered by 34 Sentinel-2 tiles.&#160;In-situ&#160;snow depth information&#160;is converted to binary snow cover information using a&#160;snow depth&#160;threshold. For the gap simulation method,&#160;as first step,&#160;FSC&#160;products with observed snow information are selected.&#160;Then,&#160;an artificial cloud mask is overlaid&#160;on these products, and&#160;the gap-filling method is run to generate&#160;GFSC products.&#160;The&#160;resulting&#160;GFSC products are compared with the corresponding&#160;observed&#160;FSC products, considering them as reference data.&#160;This&#160;comparison&#160;shows the agreement between the FSC product and the gap-filling methods.&#160;For both comparison methods, FSC values in GFSC and FSC products are converted to binary snow cover information using an&#160;FSC threshold.&#160;Resulting&#160;binary snow cover information is used in contingency tables and performance metrics are calculated for&#160;the&#160;product&#160;and for different gap-filling methods.</span><span>&#160;</span></p> </div><div> <p><span>We have found&#160;that&#160;the gap-filling&#160;provides&#160;5 times&#160;more pixels with snow cover information&#160;and&#160;the&#160;quality is&#160;fairly&#160;good.&#160;The comparison with in-situ data&#160;shows an&#160;accuracy&#160;over 88%&#160;in temporal gap-filling and&#160;precision&#160;over 87% in spatial gap-filling.&#160;The comparison of the gap simulated GFSC and the FSC products shows&#160;an&#160;accuracy over 97% in temporal gap-filling and precision over 83% in spatial gap-filling.&#160;Temporal gap-filling performance is consistent throughout the seasons,&#160;although it&#160;is&#160;less accurate&#160;in the accumulation season for the spatial gap-filling, which is expected as the wet snow algorithm is developed for&#160;the&#160;melting season&#160;conditions.&#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.</span><span>&#160;</span></p> </div>
<p>Sufficient groundwater resources are unarguably essential for human populations all over the world. With a contribution of over 30% to freshwater reserves in the global hydrological cycle, it is important to increase the capacity of the currently sparse groundwater monitoring network. Spatially and temporally continuous monitoring of groundwater as an Essential Climate Variable (ECV) can be realized with remote sensing techniques. Within the &#8220;Global Gravity-Based Groundwater Product&#8221; G3P-project (www.g3p.eu), gravimetric satellite missions GRACE and GRACE-FO are applied for global groundwater monitoring. Groundwater derived from gravimetric measurements require detailed knowledge of all continental water compartments, which are contributing to the total water storage variations.</p><p>Within the G3P project the Finnish Meteorological Institute is producing a global gap-filled Snow Water Equivalent (SWE) product that describes the snow compartment for global groundwater estimation. The product complements remote sensing-based information with model-based data for regions where remote sensing can&#8217;t observe SWE on global scale.</p><p>The production of SWE from long-term satellite observations covering the full GRACE and GRACE-FO mission period from 2002 to 2021 are investigated. The Finnish Meteorological Institute efforts within the Copernicus Land monitoring service and ESA frameworks ensure operational Near-Real-Time information on SWE for the Northern hemisphere. Microwave and optical remote sensing sensor techniques are the basis for the SWE monitoring services. Validation with in-situ reference data is important in understanding product accuracy. Pixel-level uncertainties provided with the snow product support efforts on groundwater estimation. Model- and remote sensing-based SWE are evaluated on various regional scales.&#160; As part of the new Gravity-Based Groundwater Product G3P, global Snow Water Equivalent products will be presented and discussed.</p>
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