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UID:pretalx-global-workshop-2026-LEYYXN@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261009T140000
DTEND;TZID=Europe/Amsterdam:20261009T141500
DESCRIPTION:Accurate snow monitoring requires high spatial and temporal res
 olution to capture rapid processes such as melt and accumulation. However\
 , current satellite missions present inherent trade-offs: optical sensors 
 such as Sentinel-2 provide high spatial resolution (tens of meters) but li
 mited revisit times\, while sensors like MODIS offer daily observations at
  coarser spatial resolution (∼500 m). In addition\, different sensors re
 trieve complementary snow properties\, including snow cover extent from op
 tical data and wet/dry snow conditions from SAR observations. \n\nTo overc
 ome these limitations\, multi-mission data integration is essential. Furth
 ermore\, robust estimation of Snow Water Equivalent (SWE) requires the cou
 pling of remote sensing observations with physically-based or conceptual s
 now models driven by meteorological forcing. The increasing volume and com
 plexity of such datasets demand scalable\, cloud-based processing solution
 s\, particularly for large-scale applications. \n\nIn this contribution\, 
 we present a scalable workflow for large-scale snow water equivalent (SWE)
  estimation\, aimed at generating daily high-resolution (50 m) SWE data ac
 ross extensive regions\, such as for example the extratropical Andes withi
 n the SNOWCOP project and South Tyrol within the Open-Earth-Monitor projec
 t. The workflow explores alternative cloud-based processing strategies\, i
 ncluding (i) data access through Copernicus Data Space Ecosystem or other 
 STAC APIs combined with containerized processing environments (Docker)\, e
 nabling flexible and reproducible workflows without systematic local data 
 download\, and (ii) data-proximate processing using openEO. These compleme
 ntary approaches allow us to evaluate trade-offs between flexibility\, sca
 lability\, and computational efficiency for multi-source data fusion and l
 arge-scale snow monitoring applications.
DTSTAMP:20260624T071616Z
LOCATION:Room 18
SUMMARY:Large-scale snow monitoring: multi-mission data integration and sca
 lable processing strategies - Valentina Premier
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/LEYYXN/
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