Modern agricultural production necessitates real-time, precise monitoring of crop growth status to optimize management decisions. While remote sensing technologies offer multi-scale observational capabilities, conventional crop monitoring models face two critical limitations: (1) the independent retrieval of individual physiological traits, which overlooks the dynamic coupling between structural and physiological traits, and (2) inadequate cross-platform model transferability (e.g., from UAV images to satellite images), hindering the scaling of field-level precision to regional applications. To address these challenges, we proposed a deep learning-based framework, Cross-Task Growth Neural Network (CTGNN). This framework employed a dual-stream architecture to process spectral features for Leaf Area Index (LAI) and Soil Plant Analysis Development (SPAD), while using cross-trait attention mechanisms to capture their interactions. We further assessed the knowledge transfer capabilities of the model by comparing two transfer learning strategies-Transfer Component Analysis (TCA) and Domain-Adversarial Neural Networks (DANN)-in facilitating the adaptation of UAV-derived (1.3 cm/pixel) data to satellite-scale (3 m/pixel) monitoring. Validation using UAV-satellite synergetic datasets from extensively field-tested oat cultivars in China's Bashang Plateau demonstrates that CTGNN significantly reduces the prediction errors for LAI and SPAD compared with independent trait models, with RMSE reductions of 6.4-14.4 % and 10.5-15.6 %, respectively. In a cross-domain transfer learning scenario, the CTGNN model with the DANN strategy requires only 5 % of satellite-labeled data for fine-tuning to achieve regional-scale monitoring (LAI: R2 = 0.769; SPAD: R2 = 0.714). This framework provides a novel approach for the collaborative inversion of multiple crop growth traits, while its UAV-satellite cross-scale transfer capability facilitates optimal decision-making in oat variety breeding and cultivation technique dissemination, particularly in arid and semi-arid regions. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).