The presence of melt ponds on Arctic summer sea ice strongly alters the absorption of solar radiation by the sea ice-ocean system and thereby the Arctic energy budget. Therefore, melt ponds are key to the positive sea ice-albedo feedback, which is one of the main drivers of the amplified Arctic warming observed in recent decades, and even affects the global climate. To analyze the mechanisms of melt pond evolution and their implications on the sea ice state, and to improve their representation in climate models, comprehensive observational data are needed. This dissertation presents a new approach to retrieve melt pond, sea ice and open ocean fractions at pan-Arctic scales from Sentinel-3 optical satellite data. The newly developed Melt Pond Detection 2 (MPD2) algorithm is the first fully physical retrieval that can distinguish these three surface types at the spatial resolution of 1.2 km. Because multiple combinations of surface type fractions result in similar observations at this coarse resolution, prior information are required for retrieval. As part of the development process, a reference data set of 33 local melt pond fraction maps with a spatial resolution of 10 m has been created from Sentinel-2 satellite data. Parts of these data were then used to calibrate an empirical pre-retrieval to provide preliminary estimates of surface type fractions. In addition, the correlation between sea ice optical properties and air temperature history has been investigated using measurement data from field campaigns. This correlation and the results of the pre-retrieval are used to initialize and constrain the physical retrieval. The results are validated against the full extent of the reference data set, leading to an uncertainty estimate of 7.8 % and 9 % for the melt pond and open ocean fractions, respectively. The MPD2 algorithm has been applied to seven years of Sentinel-3 observations from 2017 to 2023. This data set can be continued for future years and expanded by the application to previous satellite sensors. Finally, the newly produced data set has been used to study regional differences in melt pond evolution: the lowest melt pond fractions are found in the Central Arctic with low seasonal variability, and the highest fractions are observed in the landfast ice-dominated Canadian Archipelago; the highest seasonal and interannual variability are observed in the Beaufort Sea. Additionally, a pan-Arctic analysis correlating the melt pond fraction product with sea ice surface roughness data has been carried out: this showed that flat sea ice features higher melt pond fractions at the beginning of the melt season, while later in the season melt pond fractions tend to be higher on deformed sea ice.