Remote sensing super-resolution aims to enhance the spatial details of satellite images by introducing meaningful high-frequency features while avoiding hallucinations and spectral distortions. High-resolution imagery is usually not publicly available, whereas low-resolution imagery is freely available with a much higher revisit rate, such as the Sentinel-2 multispectral imaging mission. Cross-sensor super-resolution has the potential to bridge this gap, providing high spatial and temporal resolution imagery which are otherwise unavailable for many remote sensing users and applications. With the recent advancements in diffusion models, many methodologies have emerged which take advantage of their generative power to perform super-resolution. We propose an adapted latent diffusion approach, since image diffusion is computationally prohibitive to be applied to large Earth observation datasets. Contrary to standard latent diffusion, we encode the low-resolution image to condition the diffusion process, forcing better spectral consistency with the input imagery. The model includes visible and near-infrared bands. To ensure trustworthy results, we utilize the probabilistic nature of diffusion models to generate pixel-level uncertainty maps. This confidence metric is crucial for real-world applications, such as environmental monitoring, land cover classification, and change detection, where accurate surface feature reconstruction and spectral consistency are essential. The uncertainty map allows users to evaluate the reliability of the product for these tasks. The proposed model super-resolves Sentinel-2 imagery at 10 to 2.5 m and is the first multispectral remote sensing (RS) super-resolution diffusion model efficient enough to process large-scale RS datasets, as well as the only model providing a pixelwise uncertainty metric.

In recent years, there has been a growing interest in using image super-resolution (SR) techniques in remote sensing. These techniques aim to reconstruct high-resolution (HR) imagery from low-resolution (LR) sources. Despite the development of sophisticated SR methodologies, determining what constitutes “good” SR is still a matter of debate. Present-day literature often presents SR models through a strong computer vision perspective, heavily relying on synthetic datasets. Moreover, commonly used metrics often prioritize attributes that do not necessarily correspond to improvements in spatial resolution. To address this challenge, we present OpenSR-test, a comprehensive benchmark designed exclusively for evaluating SR of remote sensing images. Our framework incorporates specific quality metrics and curated cross-sensor datasets, each spanning various scale factors with consistent metadata. Utilizing OpenSR-test, we evaluate the state-of-the-art SR algorithms from a remote sensing perspective. The OpenSR-test framework and datasets are publicly available at https://esaopensr.github.io/opensr-test/ .

The acquisition of near-infrared (NIR) imagery is crucial for various remote sensing (RS) applications but is often unavailable in remote sensing datasets, providing only the Red-Green-Blue (RGB) bands. Most classical RS workflows rely on NIR information, such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), for vegetation and soil monitoring or land cover classification workflows. To overcome this limitation, we introduce NIR-GAN, a conditional generative adversarial network designed to synthesize the NIR band directly from RGB imagery through an image-to-image translation framework. Crucially, the proposed model integrates geographical and climatic context using location embeddings, and incorporates application-specific index-derived loss functions to optimize synthesized outputs for downstream tasks. NIR-GAN enables the generation of partly synthetic datasets, facilitating the training and evaluation of multispectral machine learning models. The developed model demonstrates the capability to generate realistic NIR images from various sources of RS data, regardless of the spatial resolution and spectral characteristics of the sensor, reaching up to 26.6 dB PSNR on Sentinel-2 images. Although synthetic data will never be able to replace the acquisition of real NIR information, this approach can fill the gap in situations where this information would otherwise be absent, including but not limited to the assembly of large-scale training datasets.

The super-resolution (SR) of Sentinel-2 (S2) imagery has been constrained by the absence of reliable high-resolution reference data for its 20- and 60-meter spectral bands. In this letter, we introduce SEN2NEON, a cross-sensor benchmark dataset that rigorously harmonizes high-resolution AVIRIS-NG hyperspectral imagery with real Sentinel-2 Level-2A reflectances through spectral response convolution and radiometric correction. SEN2NEON provides physically aligned low- and high-resolution (LR–HR) pairs for all Sentinel-2 bands (except the cirrus band) at the original S2 and 2.5m reference resolution, respectively. Compared to existing cross-sensor datasets, it offers a larger number of spectrally aligned bands, a finer effective reference resolution, and stronger LR–HR consistency. We quantify harmonization quality via a cross-dataset alignment analysis based on LR–HR resampling consistency metrics. By preserving the original Sentinel-2 observations as the LR input and deriving the HR reference via sensor-aware harmonization rather than synthetic degradation, SEN2NEON establishes a reproducible and physically grounded benchmark for multispectral Sentinel-2 super-resolution research. The sensor-aware harmonization strategy underlying SEN2NEON further provides a transferable and physically grounded framework for super-resolution benchmarking that can be extended to a broader range of multispectral satellite sensors.