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This study derives regression models for above-ground biomass (AGB)estimation in miombo woodlands of Tanzania that utilise the high availabilityand low cost of Sentinel-1 data. The limited forest canopy penetration ofC-band SAR sensors along with the sparseness of available ground truth restricttheir usefulness in traditional AGB regression models. Therefore, we propose touse AGB predictions based on airborne laser scanning (ALS) data as a surrogateresponse variable for SAR data. This dramatically increases the availabletraining data and opens for flexible regression models that capture fine-scaleAGB dynamics. This becomes a sequential modelling approach, where the firstregression stage has linked in situ data to ALS data and produced the AGBprediction map; We perform the subsequent stage, where this map is related toSentinel-1 data. We develop a traditional, parametric regression model andalternative non-parametric models for this stage. The latter uses a conditionalgenerative adversarial network (cGAN) to translate Sentinel-1 images intoALS-based AGB prediction maps. The convolution filters in the neural networksmake them contextual. We compare the sequential models to traditional,non-sequential regression models, all trained on limited AGB ground referencedata. Results show that our newly proposed non-sequential Sentinel-1-basedregression model performs better quantitatively than the sequential models, butachieves less sensitivity to fine-scale AGB dynamics. The contextual cGAN-basedsequential models best reproduce the distribution of ALS-based AGB predictions.They also reach a lower RMSE against in situ AGB data than the parametricsequential model, indicating a potential for further development.

On the potential of sequential and non-sequential regression models for Sentinel-1-based biomass prediction in Tanzanian miombo forests