In this era of global warming, the regular and accurate mapping of vegetation conditions is essential for monitoring ecosystems, climate sustainability and biodiversity. In this context, this work proposes a physics-guided data-driven methodology to invert radiative transfer models (RTM) for the retrieval of vegetation biophysical variables. A hybrid paradigm is proposed by incorporating the physical model to be inverted into the design of a neural network architecture, which is trained by exploiting unlabeled satellite images. In this study, we show how the proposed strategy allows the simultaneous probabilistic inversion of all input PROSAIL model parameters by exploiting Sentinel-2 images. The interest of the proposed self-supervised learning strategy is corroborated by showing the limitations of existing simulation-trained machine learning algorithms. Results are assessed on leaf area index (LAI) and canopy chlorophyll content (CCC) in-situ measurements collected on four different field campaigns over three European tests sites. Prediction accuracies are compared with performances reached by the well-established Biophysical Processor (BP) of the Sentinel Application Platform (SNAP). Obtained overall accuracies corroborate that the proposed methodology achieves performances equivalent to or better than the state-of-the-art methods.

中文翻译：

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用于从 Sentinel-2 图像检索生物物理参数的物理约束深度学习：PROSAIL 模型的反演


在这个全球变暖的时代，定期准确地绘制植被状况图对于监测生态系统、气候可持续性和生物多样性至关重要。在这种背景下，这项工作提出了一种物理引导的数据驱动方法来反演辐射传输模型（RTM）以检索植被生物物理变量。通过将要反转的物理模型合并到神经网络架构的设计中，提出了一种混合范例，该神经网络架构是通过利用未标记的卫星图像进行训练的。在本研究中，我们展示了所提出的策略如何通过利用 Sentinel-2 图像来同时对所有输入 PROSAIL 模型参数进行概率反演。通过显示现有模拟训练机器学习算法的局限性，证实了所提出的自监督学习策略的兴趣。结果根据叶面积指数 (LAI) 和冠层叶绿素含量 (CCC) 现场测量值进行评估，这些测量值是在三个欧洲测试地点的四次不同田间活动中收集的。将预测精度与 Sentinel 应用平台 (SNAP) 成熟的生物物理处理器 (BP) 所达到的性能进行比较。获得的总体精度证实所提出的方法实现了相当于或优于最先进方法的性能。