###### Abstract  
Satellite sensors like Landsat 8 OLI (L8) and Sentinel-2 MSI (S2) provide valuable multispectral Earth observations that differ in spatial resolution and spectral bands, limiting synergistic use. L8 has a 30 m resolution and a lower revisit frequency, while S2 offers up to a 10 m resolution and more spectral bands, such as red edge bands. Translating observations from L8 to S2 can increase data availability by combining their images to leverage the unique strengths of each product. In this study, a conditional generative adversarial network (CGAN) is developed to perform sensor-specific domain translation focused on green, near-infrared (NIR), and red edge bands. The models were trained on the pairs of co-located L8-S2 imagery from multiple locations. The CGAN aims to downscale 30 m L8 bands to 10 m S2-like green and 20 m S2-like NIR and red edge bands. Two translation methodologies are employed--direct single-step translation from L8 to S2 and indirect multistep translation. The direct approach involves predicting the S2-like bands in a single step from L8 bands. The multistep approach uses two steps--the initial model predicts the corresponding S2-like band that is available in L8, and then the final model predicts the unavailable S2-like red edge bands from the S2-like band predicted in the first step. Quantitative evaluation reveals that both approaches result in lower spectral distortion and higher spatial correlation compared to native L8 bands. Qualitative analysis supports the superior fidelity and robustness achieved through multistep translation. By translating L8 bands to higher spatial and spectral S2-like imagery, this work increases data availability for improved earth monitoring. The results validate CGANs for cross-sensor domain adaptation and provide a reusable computational framework for satellite image translation. S 20231550202023155021550215502155  
Several studies have investigated the benefits of the combined use of Landsat 8 OLI (L8) with S2 [7; 8; 9; 10; 11]. [PERSON] et al. [12] took advantage of the greater number of cloud-free pixels available of combined L8 and S2 products to improve crop intensity mapping. [PERSON] et al. [13] leveraged the denser frequency of combined L8 and S2 to capture temporally dynamic ephemeral floods in drylands. In addition to enhancing temporal frequency, another added value of combining L8 and S2 is enhanced spectral information. S2 contains three red-edge spectral bands between the wavelengths of red and near-infrared that are not available in L8. The red-edge spectral bands increase land-use and land-cover mapping accuracy [14] and are beneficial for measuring leaf area index [15], estimating chlorophyll content [16], improving the mapping accuracy of wetlands [17], and land-use and land-cover change [18]. Despite the spatial and spectral similarity between L8 and S2, there are differences in spatial resolution and spectral bands between the two sensors that lead to uncertainty when directly combined without adequate pre-processing. To harmonize the two satellite sensors, [PERSON] et al. [19] developed Harmonized Landsat and Sentinel-2 (HLS) products by combining seven common L8 and S2 spectral bands to generate 30 m spatially co-registered Landsat-like bands at 5-day intervals. In the HLS products, L8 and S2 are spatially harmonized by resampling the S2 data into 30 m grids that overlap with L8 and spectrally harmonized through spectral bandpass adjustment using 150 globally distributed hyperspectral Hyperion images. Since the release of HLS products, they have been used in various remote sensing applications because of their increased data availability [12; 20; 21; 22]. In addition, several methods have been developed to improve harmonization between L8 and S2 [23; 24; 25; 26; 27]. [PERSON] and [PERSON] [24] proposed an improved algorithm to harmonize the two datasets through a time series-based reflectance adjustment (TRA) approach to minimize the difference in surface reflectance.