@inproceedings{shadaydeh2024physics,
    type = {inproceedings},
    key = {shadaydeh2024physics},
    title = {Physics Informed Modeling of Ecosystem Respiration  via Dynamic Mode Decomposition with Control Input},
    author = {Maha Shadaydeh and Joachim Denzler and Mirco Migliavacca},
    booktitle = {ICLR Workshop on Machine Learning for Remote Sensing (ICLR-WS)},
    year = {2024},
    abstract = {Ecosystem respiration (Reco) represents a significant component of the global carbon cycle, and accurate characterization of its dynamics is essential for a comprehensive understanding of ecosystem-climate interactions and the impacts of climate extremes on the ecosystem. In this paper, we present a novel data-driven and physics-aware method for estimating Reco dynamics using the dynamic mode decomposition with control input (DMDc), an emerging tool for analyzing nonlinear dynamical systems. The proposed model represents Reco as a state space model with an autonomous component and an exogenous control input. The control input can be any ecosystem driver(s), such as air temperature, soil temperature, or soil water content. This unique modeling approach allows controlled intervention to study the effects of different inputs on the system. We further discuss using time delay embedding of both components to characterize  Reco dynamics.  Experimental results using Fluxnet2015  data show that the prediction accuracy of Reco dynamics achieved with DMDc is comparable to state-of-the-art methods, making it a promising tool for analyzing the dynamic behavior of different vegetation ecosystems on multi-temporal scales in response to different climatic drivers.  },
    url = {https://ml-for-rs.github.io/iclr2024/camera_ready/papers/72.pdf},
    pages = {},
    note = {},
}