| Summary: | Snow and glacier research is important in Iceland for a variety of reasons. Water resource forecasting for hydro-power production is important and monitoring of long-term changes and trends provide guidance for adoption strategies due to climate change. Activity in glacier-covered volcanoes can cause volcanic ash and tephra deposits leading to enhanced melt or in some cases glacier surface isolation reducing melt significantly. The high natural climate variability can pose a risk to the reliability of the energy production and delivery systems as drought conditions, low-flow periods, and years with low summer melt are challenging to predict. Many Earth observing satellites provide data that can improve estimations of physical processes that can be challenging to model accurately, such as snow cover and surface albedo of snow and ice-covered surfaces. In this research, satellite data were used to create gap-filled products of daily snow cover and surface albedo in Iceland from 2000 to 2021 at a 500 m horizontal resolution. The products relied on data collected from the two satellites carrying the MODIS sensor, Aqua and Terra, providing sub-daily overpass. A process pipeline was developed to merge the daily data and apply temporal aggregation to reduce the high number of cloud-obscured pixels. Due to high cloud cover, yielding many pixels obscured by clouds even after merging and temporal aggregation, machine learning models were further developed to fully reclassify the remaining unclassified data. The output from this process was a spatio-temporal product with capabilities for further analysis and extraction of various statistical parameters describing snow cover and albedo properties in Iceland. To better understand the seasonal and inter-annual variability and possible trends, a surface energy balance model was developed utilizing remotely sensed snow cover and albedo as prognostic variables. Surface albedo was used to constrain net short wave radiation forced at a snow-covered surface and fractional snow ...
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