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.

翻译：机器学习的发展推动了地球观测数据在气候变化研究中的应用。然而，机器学习表示的可解释性仍是一个挑战，尤其是在理解森林对气候变化的生物物理响应方面。传统遥感方法通过反演辐射传输模型（RTM）从光谱数据中提取生物物理变量，但这些方法往往无法处理RTM固有的偏倚，尤其是在复杂森林场景中。我们提出将RTM集成到自编码器架构中，构建一种端到端学习方法。该方法不仅能校正RTM的偏倚，还在变量反演任务中优于传统技术（如神经网络回归）。此外，该框架具有普遍适用于反演偏倚物理模型的潜力。代码已开源发布于https://github.com/yihshe/ai-refined-rtm.git。