On the potential of sequential and non-sequential regression models for Sentinel-1-based biomass prediction in Tanzanian miombo forests. (arXiv:2106.15020v2 [cs.LG] UPDATED)

This study derives regression models for above-ground biomass (AGB)
estimation in miombo woodlands of Tanzania that utilise the high availability
and low cost of Sentinel-1 data. The limited forest canopy penetration of
C-band SAR sensors along with the sparseness of available ground truth restrict
their usefulness in traditional AGB regression models. Therefore, we propose to
use AGB predictions based on airborne laser scanning (ALS) data as a surrogate
response variable for SAR data. This dramatically increases the available
training data …

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