###### Abstract
However, both HLS and TRA generate images at 30 m that do not take advantage of the higher spatial resolution S2 imagery. Hence, to incorporate the higher spatial details provided by S2, [PERSON] et al. [23] and [PERSON] and [PERSON] [27] used a deep learning-based fusion method to learn the non-linear relationship and generate images at a higher spatial resolution of 10 m. [PERSON] et al. [23] used a convolutional neural network (CNN) to predict 10 m L8 images with the help of S2 data during training, while [PERSON] and [PERSON] [27] used a Generative Adversarial Network (GAN). However, both studies excluded the S2 red edge bands in their analysis. Conversely, [PERSON] et al. [25] simulated L8-like and S2-like images from airborne hyperspectral images and then applied regression and machine learning algorithms to predict S2-like red edge bands from L8 at 30 m, which shares the same limitations as HLS and TRA regarding spatial details. Only one harmonization study targeted 10 m resolution and included red edge bands [26]. [PERSON] et al. [26] trained a super-resolution CNN model to predict the S2-like red edge bands at 10 m from L8. However, CNNs are limited by their user-defined loss function and do not learn from real data, unlike GANs. Recently, [PERSON] et al. [28] developed a feature-level data fusion framework using a GAN to reconstruct 10 m Sentinel-2-like imagery from 30 m historical Landsat archives. Their GAN-based super-resolution method demonstrated effective reconstruction of synthetic Landsat data to Sentinel-2 observations, thus suggesting potential for improving both spatial and spectral resolutions of harmonized products between L8 and S2. It is evident that existing harmonization methods and products have incentivized and improved the synergistic use of L8 and S2, but more research is needed to enhance both the spatial and spectral resolutions of harmonized products between L8 and S2. Translating L8 to S2-like images can be defined as a domain transfer problem, where the two sensors, L8 and S2 (the two domains), capture the same land surface area. Image translation algorithms can transfer domain-specific information from one domain to another, for example, [PERSON] et al. [29] proposed a deep learning-based framework for similar domain adaptation tasks using adversarial networks. Subsequently, their GAN framework has been effectively used for several image-processing tasks, such as image super-resolution [30], registration [31], classification [32], and translation [33]. An example of the efficacy of GANs in enhancing spatial resolution is provided by [PERSON] et al. [34],who used a dual GAN model to super-resolve historical Landsat imagery for long-term vegetation monitoring. [PERSON] et al. (2018) developed a conditional GAN (CGAN) for translating images across multiple domains, for example, translating satellite imagery to maps. Their network learned the representations between the two domains to predict an image from the desired distribution (e.g., maps) given an image from the source distribution (e.g., satellite imagery). Following the advancements in generative modeling using GANs, remote sensing researchers have addressed the issue of satellite data availability by translating multi-sensor images. Recent work has explored the generation of synthetic bands for existing satellites as a way to synchronize multispectral data across different data products, utilizing a combination of variational autoencoders and generative adversarial networks ([PERSON] et al., 2017). [PERSON] et al. (2017) explored the potential of GANs for optical and SAR image matching. [PERSON] et al. (2018) translated publicly available low-resolution Sentinel-1 data to commercial high-resolution TerraSAR-X using a dialectical generative adversarial network (DiGAN). [PERSON] et al. (2018) synthesized optical and SAR imagery for cloud removal using GAN and [PERSON] et al. (2018) generated alternative representations of SAR by synthesizing SAR and optical imagery to improve visual interpretability. [PERSON] et al.