Knowledge of winter sea ice production in Arctic polynyas is an important prerequisite for estimating the dense water formation that drives vertical mixing in the upper ocean. Satellite techniques using relatively high-resolution thermal infrared data from MODIS in combination with atmospheric reanalysis data have proved to be a powerful tool for monitoring large and regularly forming polynyas and for resolving narrow thin ice areas (i.e. leads) along shelf breaks and across the Arctic Ocean. However, the selection of atmospheric data sets has a strong influence on the derived polynya characteristics, as it affects the calculation of heat loss to the atmosphere, which is determined by the local thin-ice thickness. To overcome this methodological ambiguity, we present a temperature adjustment algorithm that provides corrections to the 2-m air temperature through MODIS ice surface temperatures. It thus reduces the differences in calculated surface heat fluxes that can result from the use of varying atmospheric input data sets. The adjustment algorithm itself is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies of the developed algorithm and present time series of polynya characteristics in the winter season of 2019/20, which in general was characterized by a particularly strong polar vortex and inherent effects on sea ice dynamics. It becomes apparent that the application of the empirically derived correction significantly reduces the difference between the different atmospheric products used from 49% to 23%. We apply additional filtering strategies that aim to increase the ability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites.