Deep learning (DL) is an extremely rapidly developing field in Earth system science. However, DL systems are often black boxes, hardly contributing to process understanding. This project will develop suitably tailored DL and other machine learning techniques both to improve land-atmospheric (L-A) feedback process understanding and to derive quantitative relationships that are superior to current approaches, such as Monin-Obukhov similarity theory (MOST). The interdisciplinary collaboration of experts and unprecedented collection of data will facilitate this endeavour. We will pursue data-driven, but structurally-informed DL and other machine learning approaches, targeting (1) the determination of land-surface temperatures (LSTs) and canopy moisture with the available new generation of high-resolution satellite data; (2) the closure of the surface energy balance; (3) surface flux relationships in complex terrain as well as flux-gradient relationships in the convective boundary layer including entrainment going beyond MOST and Bulk Richardson Number (BRN) approaches; and (4) the development of a first L-A feedback foundation model to analyze the phase space structure and for downstream tasks. We will study and advance these four topics by means of four core DL techniques: (A) super-resolution approaches to approximate spatially fine-granular L-A properties; (B) physics-informed equation parameterization and approximation to discover and improve L-A feedback metrics; (C) the inclusion of L-A system-informed inductive biases in DL approaches to speed-up learning and improve generalization; (D) the application of self-supervised and contrastive pretraining for the development of an L-A state characterizing foundation model. All techniques will consider the chaotic behaviour, the uncertainty, and the limited predictability of L-A system variables. To further foster L-A system understanding, we will conduct importance weighting and generate machine learning model hierarchies. We expect to thus identify fundamental and partially novel data dependencies, process equations, process parameterizations, and computational units that are critical for the generation of accurate and generalizable relationships between atmospheric variables
