A benchmark dataset for Landsat-to-Sentinel image generation using deep learning-driven super-resolution techniques

High-resolution satellite imagery plays a vital role in accurately analyzing surface changes, vegetation dynamics, and land cover transitions for environmental monitoring and Earth science applications. While the Landsat satellite series provides long-term, high-coverage time-series data—essential for studying large-scale phenomena such as deforestation, urban expansion, and agricultural transformation—its 30-meter spatial resolution often falls short in applications requiring finer detail. To address this limitation, this study introduces Land2Sent, a novel remote sensing super-resolution dataset specifically designed for the Landsat 8/9 to Sentinel-2A/B image enhancement task. The Land2Sent dataset aims to upscale Landsat imagery from 30 m to 10 m by utilizing the higher-resolution Sentinel-2 images as reference. Both normalized 4-band (R, G, B, NIR) images and original 16-bit 4-band images are included to assess the impact of bit depth on model performance. Using this dataset, ten state-of-the-art deep learning models are evaluated for their ability to reconstruct super-resolved images from low-resolution Landsat inputs. The performance of these models is assessed using quantitative metrics across the full dataset, as well as through visual inspection and Normalized Difference Vegetation Index (NDVI) analysis of selected image patches.