<p>Sea&#8211;ice concentration, the surface fraction of ice in a given area, is a key component of the Arctic climate system, governing for example the ocean&#8211;atmosphere heat exchange. Satellite&#8211;based remote sensing offers the possibility for large&#8211;scale monitoring of the sea&#8211;ice concentration. Using passive microwave measurements, it is possible to observe the sea&#8211;ice concentration all year long, almost independently of cloud coverage. The spatial resolution of these measurements is limited to 5 km and coarser. Data from the visible and thermal infrared spectrum offer finer resolutions of 250 m&#8211;1 km, but need clear&#8211;sky scenes and, in case of visible data, sunlight. In previous work, we developed and analysed a merged dataset of passive microwave and thermal infrared data, combining AMSR2 and MODIS satellite data at 1 km spatial resolution. It has benefits over passive microwave data in terms of the finer spatial resolution and an enhanced potential for lead detection. At the same time, it outperforms thermal infrared data due to its spatially continuous coverage and the statistical consistency with the extensively evaluated passive microwave data. Due to higher surface temperatures in summer, the thermal&#8211;infrared based retrieval is limited to winter and spring months. In this contribution, we present first results of extending the existing dataset to summer by using visible data instead of thermal infrared data. The reflectance contrast between ice and water is used for the sea&#8211;ice concentration retrieval and results of merging visible and microwave data at 1 km spatial resolution are presented. Difficulties for both, the microwave and visual, data are surface melt processes during summer, which make sea&#8211;ice concentration retrieval more challenging. The merged microwave, infrared and visual dataset opens the possibility for a year&#8211;long, spatially continuous sea ice concentration dataset at a spatial resolution of 1 km.</p>
Estimation of energy fluxes and photosynthesis from thermal infrared, spectral reflectances, microwave data and SVAT modeling