The research, led by Aran Dezhban and Paul Mario Koola of the Ocean Engineering Department at Texas A&M University at Galveston, introduces HARBOR, which stands for Harmonization-based Adaptive Resolution Boosting for Optical Remote sensing. Rather than proposing a new network architecture, the framework integrates four established components into a single end-to-end pipeline: a same-day, close-time data preparation strategy for temporally consistent cross-sensor learning; statistical radiometric harmonization to reduce cross-sensor reflectance differences; synthetic generation of Sentinel-2-like imagery to expand limited supervised datasets; and a customized Efficient Sub-Pixel Convolutional Neural Network, or ESPCN, adapted for multispectral satellite imagery using a fused loss formulation. The authors’ central claim is that satellite super-resolution research has long over-weighted network optimization while under-weighting the cross-sensor calibration that determines whether the enhanced product remains scientifically meaningful.

The case study focused on Galveston Island, Texas, a barrier island whose mix of water, sand, vegetation, and urban surfaces makes it a demanding testbed for coastal mapping. PlanetScope imagery, resampled from its native 3 metres to 5 metres, served as the high-resolution reference for reconstructing Sentinel-2 inputs from 10 metres to 5 metres, an exact 2× scale factor chosen because sub-pixel convolution requires an integer relationship between input and target grids. The 3-metre-to-10-metre ratio of roughly 3.33 cannot be realized by a PixelShuffle operation, so retargeting the reference to 5 metres was a deliberate methodological construction rather than a compromise. Bicubic interpolation performed that grid alignment only; the authors explicitly note it does not reproduce the Sentinel-2 modulation transfer function or act as a physically faithful sensor degradation model.

Assembling a training set proved to be the study’s most stubborn data problem. Coastal surface conditions change within minutes: tides shift, waves reshape wet sand, sediment moves, and illumination varies. The team therefore prioritized same-day acquisitions, identifying 44 same-day Sentinel-2 and PlanetScope pairs before filtering and preprocessing reduced the usable set to just 18. Sentinel-2 overpasses were stable near 17:05 UTC, while PlanetScope acquisition times varied more; most pairs were separated by less than 30 minutes, with a minimum gap of 15 seconds and a maximum of just over one hour. Geometric compatibility was quantified using eight ground control points on stable features visible in both sensors, yielding a residual RMSE of 0.58 metres, equivalent to 5.8 percent of a 10-metre Sentinel-2 pixel and well inside the sub-pixel tolerance normally applied to cross-sensor super-resolution.

Eighteen pairs are far too few to train even a compact network robustly, and this scarcity is where the harmonization stage earns its keep twice over. The workflow estimates a gamma-based power-law relationship between the two sensors from the same-day pairs, applied per band with a multivariate model whose covariance matrix contains substantial off-diagonal terms, indicating that the bands do not behave independently. The fitted mean gamma increased from roughly 0.611 in the Blue band to about 0.754 in the near-infrared, consistent with known differences between the sensors’ spectral response functions, which are broadest and most wavelength-shifted in the Red and NIR bands. The power-law form was chosen because the dominant cross-sensor discrepancy is a monotonic, non-linear compression of the reflectance range rather than a constant offset, and it preserves pixel ordering and interval endpoints in ways a linear gain-and-offset fit cannot. Once calibrated, this transfer function was applied to PlanetScope scenes to generate synthetic Sentinel-2-like imagery at 10 metres, expanding the supervised training set from 18 real pairs to 191 temporally consistent pairs, each built from a single PlanetScope acquisition so that the LR input and HR reference share the same moment.

The network at the heart of the pipeline is deliberately lightweight. The ESPCN performs feature extraction in low-resolution space and applies learned upsampling only at the final PixelShuffle stage, so the expensive convolutions run before the spatial dimensions grow, sharply reducing computational cost compared with approaches that first interpolate into high-resolution space. The adapted architecture uses four input channels for Blue, Green, Red, and NIR, three convolutional layers with tanh activations, PixelShuffle, and a final sigmoid, totaling just 29,552 trainable parameters. Training was optimized with a fused loss combining a dominant mean-squared-error term with structural-similarity and weak perceptual terms, the latter computed only on the three visible bands by a frozen ImageNet-pretrained VGG19 that never observes the NIR channel. In the loss-fusion experiments, the full-fusion scenario lowered MSE by 71.1 percent, raised PSNR by 5.37 dB, and improved SSIM and R² relative to a pure MSE baseline, while keeping pixel fidelity as the anchor to avoid the instability and hallucination risks associated with adversarial training.

A component ablation delivered the study’s most striking quantitative finding. With the training-set size and every hyperparameter held fixed, removing radiometric harmonization reduced PSNR from 49.96 to 43.57 dB, a loss of 6.39 dB, while replacing the fused loss with pure MSE supervision cost only 1.63 dB. Harmonization is therefore the single largest contributor to reconstruction quality, roughly four times the contribution of the loss design, while synthetic expansion is what makes supervised training feasible at all: restricting training to the 18 real harmonized pairs dropped PSNR to 38.26 dB and R² to 0.634, and disabling all three components yielded only 32.03 dB. In a like-for-like benchmark against bicubic interpolation, SRCNN, ESRGAN, and the recent satellite-focused MBGPIN, all trained from scratch under an identical 20-epoch protocol on the same harmonized 191-pair dataset, the proposed ESPCN recorded the best value on all four metrics with roughly three orders of magnitude fewer parameters than the two largest competitors. Scene-level Friedman and Holm-corrected Wilcoxon tests confirmed significant PSNR improvements over all four methods, though the SSIM difference against MBGPIN was not statistically significant, which the authors present as structural comparability rather than demonstrated superiority.

Practical performance figures reinforce the efficiency argument. The final model reached a PSNR of 49.87 dB, an SSIM of 0.992, and an R² of 0.984 on the held-out synthetic test split, with five-fold scene-level cross-validation and three seed-repeated runs confirming stability. Applied to native Sentinel-2 imagery and scored against real PlanetScope acquisitions, the same model reached 41.18 dB, a lower figure the authors identify as the more representative guide to operational cross-sensor performance. Stratified evaluation showed reconstruction was most reliable on homogeneous water patches at 52.79 dB and least reliable on mixed sand-vegetation surfaces at 45.29 dB, the expected consequence of reconstructing boundaries without spectral unmixing. Inference is fast enough for archive-scale use: a single scene required milliseconds for the forward pass on a consumer MacBook Air with an Apple M5 processor, extrapolating to an estimated 14.5 seconds for a full Sentinel-2 tile, and radiometric agreement between the pooled super-resolved product and its native Sentinel-2 input exceeded 0.999 correlation in all four bands.

The authors are careful about what transfers. The harmonization parameters and trained weights are calibrated to a single study site and encode the surface types and radiometry of Galveston; they require re-estimation on local same-day pairs before use elsewhere, and what transfers is the end-to-end workflow itself rather than the Galveston-calibrated model. The workflow was developed on essentially cloud-free imagery and was not trained to handle cloud, cloud shadow, or thin cirrus, so operational use on partially cloudy scenes would require an upstream mask. No downstream task was demonstrated: applications to shoreline extraction, coastal change detection, or the monitoring of Nature-Based Solutions such as dunes, salt marshes, and mangroves for coastal flood resilience remain potential future directions rather than measured outcomes, and the authors note that boundary and mixed-cover surfaces, where many such features occur, are precisely where reconstruction is least reliable. Future work includes applying the framework to other freely available satellites such as Landsat, training for higher scale factors, adding more spectral bands, and quantifying downstream value through task-level validation.

What makes HARBOR scientifically notable is its reframing of a familiar engineering problem. Super-resolution of satellite imagery has been dominated by architectural innovation, from residual networks and attention mechanisms to transformers, diffusion models, and generative priors, yet the enhanced image must also retain physically meaningful spectral behavior to be useful for science. By explicitly separating resolution enhancement from cross-sensor calibration, and by demonstrating with a controlled ablation that harmonization outweighs loss design by a factor of four, the study offers a re-implementable template for anyone seeking to sharpen open satellite archives without sacrificing the radiometric fidelity that makes them valuable. For coastal engineers and environmental monitors watching shorelines retreat and natural defenses erode, the message is that the sharpest view of the coast may come not from a new satellite but from a better-assembled pipeline.

Subject of Research: Cross-sensor super-resolution of Sentinel-2 satellite imagery using radiometric harmonization for coastal applications

Article Title: A harmonization-based framework for cross-sensor super-resolution of Sentinel-2 imagery for coastal applications

Article References: Dezhban, A., & Koola, P. M. (2026). A harmonization-based framework for cross-sensor super-resolution of Sentinel-2 imagery for coastal applications. Frontiers in Marine Science, 13, Article 1950325. https://doi.org/10.3389/fmars.2026.1950325

Image Credits: AI Generated

DOI: 10.3389/fmars.2026.1950325

Keywords: Sentinel-2, PlanetScope, super-resolution, radiometric harmonization, ESPCN, coastal monitoring, remote sensing, deep learning, Galveston Island, satellite imagery, sub-pixel convolution, Nature-Based Solutions