Bridging spatio-temporal gaps in ALS data using Landsat time series and forest disturbance-recovery metrics via multi-task neural networks

European forests contribute to climate change mitigation by sequestering carbon, conserving biodiversity, and enhancing water retention. However, climate-induced disturbances such as fires, windthrows, droughts, and pest outbreaks underscore the need for stronger forest monitoring systems. National Forest Inventories (NFIs) serve as the primary source of forest data and information in Europe. Yet, inconsistencies in timing, coverage, methodologies, and data quality highlight the need for a more harmonized and spatially detailed approach. Critically, predicting forest variables directly from satellite data remains challenging, mainly due to the difficulties in aligning remote sensing with ground data. Meanwhile, the operational use of airborne laser scanning (ALS) data is limited by high costs, infrequent updates, and inconsistent coverage from different sensors and flight conditions. This study presents a novel approach relying on fully connected neural networks to integrate Landsat satellite time series and forest disturbance and recovery metrics with ALS data to predict forest height metrics, which can then be used to accurately predict critical forest variables, such as growing stock volume (GSV) and stand basal area (BA). The method was tested across five ecologically and geographically diverse European forest regions: Tuscany (Italy), the Netherlands, the Canton of Grisons (Switzerland), Białowieża Forest (Poland), and the Vindelälven-Juhttátahkka Biosphere Reserve (Sweden). ALS forest height metrics were predicted with R 2 values ranging from 0.47 to 0.68. Then, based on field data, forest height metrics were used to predict GSV (R 2 = 0.78) and BA (R 2 = 0.69). Our method addresses the issue of limited spatial and temporal availability of ALS data by predicting ALS-derived height metrics using Landsat time series. This study examines the challenges of combining satellite and NFI data, building on the premise that satellite data can be effectively used to predict forest height metrics derived from ALS, which in turn can be used to accurately quantify several forest variables. The methods presented here support scalable and cost-effective forest monitoring by providing the spatially and temporally detailed information needed to implement climate-smart forestry. • Methodological approach to map 3D forest structure using optical data. • Spatio-temporal limits of ALS and NFI data were addressed using neural networks. • Synthetic ALS Forest height metrics predicted with an average R 2 of 0.62 • Estimated GSV (R 2 = 0.78) and BA (R 2 = 0.69) using predicted height metrics. • Novel method employed to predict multiple variables at once using deep learning.