A Weakly Supervised Deep Learning Framework for Estimating Above-Ground Biomass for Non-Forest Landscapes From Optical Images

Abstract. Above-ground biomass (AGB) maps are essential for carbon accounting and sustainable land management, yet AGB for non-forest landscapes remains poorly accounted for in global datasets. Here, we make use of deep learning and high-resolution PlanetScope imagery to introduce the concept of AGB contribution maps, which are high-resolution AGB predictions that capture local patterns. These maps can be predicted at any resolution from 1 to 100 m, providing insights into the spatial features included in the coarser resolution AGB maps, being essential for mapping trees outside forests. Our method employs a weakly supervised hybrid framework that transfers information from an existing 100 m global AGB map to high‑resolution optical satellite imagery, enabling the interpretation of detailed spatial patterns. We demonstrate that our map achieves detailed and spatially consistent patterns of woody vegetation in African savanna landscapes comparable to UAV-based LiDAR. Aggregated AGB values are well aligned with independent in-situ measurements (r2 = 0.71, bias 1 %), which is contrary to the original coarse AGB map used for training (r2 = 0.17, bias 48 %), indicating the capability of our approach to refine the existing map towards a higher accuracy for estimating tree biomass outside forests. This suggests that our model has learned tree-level information that is not present in the original AGB training data, providing a framework to refine existing coarse-resolution AGB maps. The granular and multi-resolution results provide no contribution to global efforts in sustainable land management of non-forest landscapes at any preferred scale and resolution.