Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

Abstract. Turbulent heat flux (THF) over leads is an important variable used for monitoring climate change in the Arctic. Presently, THF over leads is often calculated from satellite imagery. The accuracy of the estimated THF is low for mixed pixels that consist of ice and leads, because the mixed pixels along lead boundaries will lower the accuracy of the surface temperature measured over leads and the corresponding lead map. To address this problem, a deep residual convolutional neural network (CNN)-based framework is proposed to estimate THF over leads at the subpixel scale (DeepSTHF) with remotely sensed imagery. The DeepSTHF allows the production of a sea surface temperature (SST) image and a corresponding lead map with a finer spatial resolution than the input SST image using two CNNs, so that the subpixel scale THF can be estimated from them. The proposed approach is assessed using simulated and real MODIS imagery and compared against the conventional bicubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine spatial resolution SST images and lead maps, thereby allowing researchers to obtain more accurate and reliable THF over leads.