@article {aAlbors25,
	title = {BiomSHARP: Biomass Super-Resolution for High Accuracy Prediction},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {63},
	year = {2025},
	month = {11/2025},
	pages = {1-18},
	chapter = {1},
	abstract = {<p><span style="color: $\#$333333; font-family: {\textquoteright}HelveticaNeue Regular{\textquoteright}, sans-serif; font-size: 18px;">Accurate estimation of above-ground biomass (AGB) is essential to understanding carbon stocks and flows, monitoring forest health, assessing biodiversity, and tracking ecological disturbances, which together help to inform climate policies. Imminent global satellite biomass missions (such as ESA{\textquoteright}s BIOMASS and NASA-ISRO{\textquoteright}s NISAR satellites) will offer valuable environmental monitoring, but their low spatial resolution limits their application in detailed local assessments. In this study, we present biomass super-resolution for high accuracy prediction (BiomSHARP), a deep learning (DL) model that extends the hierarchical attention Transformer (HAT) architecture, adapting it to enhance coarse-resolution biomass maps by fusing them with high-resolution (HR) multispectral data from sensors such as Sentinel-2 or Landsat. BiomSHARP achieves 25-m biomass predictions{\textemdash}four times the spatial resolution of the input{\textemdash}bridging the gap between global-scale monitoring and local-scale applications. In a first set of experiments, conducted in a local area in Europe, we demonstrate that BiomSHARP outperforms both traditional interpolation methods and state-of-the-art (SOTA) DL interpolation and prediction approaches for HR AGB estimation across all evaluated metrics [mean absolute error (MAE), mean squared error (mse), root mse (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM)], while using a comparable/lower number of parameters. Moreover, the model exhibits strong global-scale generalization, as demonstrated by its ability to accurately estimate biomass across diverse climatic regions despite being trained on a limited subset of data. Furthermore, the model presents strong temporal generalization, achieving improved performance in estimating AGB from 2020 data even when trained solely on 2010 data. We also analyze the impact of different combinations of spectral bands on biomass estimation, identifying optimal subsets that reduce redundancy and improve computational efficiency. BiomSHARP represents a promising approach to advance global environmental assessments and support improved climate strategies. The code and models are publicly available at: https://github.com/laiaalbors/biomsharp</span></p>},
	keywords = {Above-Ground Biomass (AGB), Biomass super-resolution for high accuracy prediction (BiomSHARP), Climate monitoring, Deep Learning (DL), Guided biomass super-resolution (SR), Multispectral imagery, Remote Sensing (RS), Satellite biomass estimation},
	issn = {1558-0644},
	doi = {10.1109/TGRS.2025.3636434},
	url = {https://ieeexplore.ieee.org/abstract/document/11265727},
	author = {Laia Albors and Javier Marcello and Ferran Marques}
}
