GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance
Journal:
arXiv
Published Date:
Apr 2, 2025
Abstract
Accurate Above-Ground Biomass (AGB) mapping at both large scale and high
spatio-temporal resolution is essential for applications ranging from climate
modeling to biodiversity assessment, and sustainable supply chain monitoring.
At present, fine-grained AGB mapping relies on costly airborne laser scanning
acquisition campaigns usually limited to regional scales. Initiatives such as
the ESA CCI map attempt to generate global biomass products from diverse
spaceborne sensors but at a coarser resolution. To enable global,
high-resolution (HR) mapping, several works propose to regress AGB from HR
satellite observations such as ESA Sentinel-1/2 images. We propose a novel way
to address HR AGB estimation, by leveraging both HR satellite observations and
existing low-resolution (LR) biomass products. We cast this problem as Guided
Super-Resolution (GSR), aiming at upsampling LR biomass maps (sources) from
$100$ to $10$ m resolution, using auxiliary HR co-registered satellite images
(guides). We compare super-resolving AGB maps with and without guidance,
against direct regression from satellite images, on the public BioMassters
dataset. We observe that Multi-Scale Guidance (MSG) outperforms direct
regression both for regression ($-780$ t/ha RMSE) and perception ($+2.0$ dB
PSNR) metrics, and better captures high-biomass values, without significant
computational overhead. Interestingly, unlike the RGB+Depth setting they were
originally designed for, our best-performing AGB GSR approaches are those that
most preserve the guide image texture. Our results make a strong case for
adopting the GSR framework for accurate HR biomass mapping at scale. Our code
and model weights are made publicly available
(https://github.com/kaankaramanofficial/GSR4B).
