Generating Sentinel-2 Additional Bands from Landsat 8/9 for HLS Dataset with Deep Convolutional Networks

The Harmonized LandSat-Sentinel dataset marks a significant stride in merging two esteemed Earth Observation satellite platforms, LandSat 8/9 and Sentinel-2. Despite the notable convergence, there’s a partial mismatch in features between their S30 and L30 products due to differing band specifications. To bridge this gap, our study employs cutting-edge generative neural networks. We utilize an Encoder-Decoder architecture, leveraging advanced SSL-pretrained backbones as feature encoders alongside a versatile CNN decoder. Training our model on the S30 HLS product, we capitalize on the precise harmonization of common bands, allowing direct application of our model to infer missing bands in the L30 product. We assess the accuracy of the four predicted bands using two evaluation approaches: comparing them to S30 products taken on the same day and analyzing their performance across a S30 product timeseries. Our findings demonstrate promising results, signaling a pathway towards achieving full feature parity between these two HLS products.