Evaluating multi-seasonal SAR and optical imagery for above-ground biomass estimation using the national forest inventory of Zambia

• Evaluated potential of seasonal SAR-optical remotely sensed data for AGB mapping. • Extensive and high-quality National Forest Inventory data was used. • SAR-Annual images gave greater accuracy than individual seasonal images. • Optical-Dry season images gave greater accuracy than hot season and annual images. • A multi-seasonal SAR-optical image combination is recommended for AGB estimation. Mapping forest above-ground biomass (AGB) is crucial for monitoring forest ecosystems and assessing the success of conservation initiatives such as the REDD + carbon projects. Traditional field-based approaches to measuring AGB, however, face significant challenges, due to high financial costs and logistical constraints. Remote sensing, including both active and passive sensors, presents a promising and cost-effective alternative, yet its practical utility and accuracy for capturing forest AGB in diverse and complex ecosystems remains largely unexplored. This research used an extensive national forest inventory (NFI) dataset to evaluate the ability to map the AGB of the Miombo woodlands in Zambia across four agro-ecological zones using both multi-seasonal SAR (Sentinel-1A) and optical (Landsat-8 OLI) imagery. A multi-level experiment was designed to (i) compare the accuracy of AGB estimation using SAR and optical data when used independently, and in combination, using a Random Forest regression model, (ii) assess the effect of seasonality on the accuracy of AGB estimation when using SAR and optical datasets, and (iii) evaluate the effect of variation in climatic and environmental conditions on AGB estimation. Experimental results show that multi-seasonal images (across the rainy, hot and dry seasons) outperformed single-season and annual images. Combining SAR backscatter in the hot season, optical bands in the dry season, and vegetation indices in the hot season produced the most accurate AGB model ( R = 0.69, MAE = 14.01 Mg ha −1 and RMSE = 18.23 Mg ha −1 ). The models performed distinctly across different agro-ecological zones ( R = 0.44 – 0.79), suggesting that fitting local models could be beneficial. These results based on the extensive NFI of Zambia demonstrate that seasonal effects and fitting local models can lead to more accurate AGB estimation within the Miombo woodlands, which is of significance for ongoing REDD + carbon projects in Zambia and other African countries.