From Spectra to Biophysical Insights: End-to-End Learning with a Biased
  Radiative Transfer Model
Yihang She Clement Atzberger Andrew Blake Srinivasan Keshav
Advances in machine learning have boosted the use of Earth observation data
for climate change research. Yet, the interpretability of machine-learned
representations remains a challenge, particularly in understanding forests'
biophysical reactions to climate change. Traditional methods in remote sensing
that invert radiative transfer models (RTMs) to retrieve biophysical variables
from spectral data often fail to account for biases inherent in the RTM,
especially for complex forests. We propose to integrate RTMs into an
auto-encoder architecture, creating an end-to-end learning approach. Our method
not only corrects biases in RTMs but also outperforms traditional techniques
for variable retrieval like neural network regression. Furthermore, our
framework has potential generally for inverting biased physical models. The
code is available on https://github.com/yihshe/ai-refined-rtm.git.