Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics - Laura Elena Cué La Rosa; Dario Augusto Borges Oliveira; Raul Queiroz Feitosa (International Journal of Applied Earth Observations and Geoinformation)
Remote sensing has revolutionized crop mapping and monitoring, providing valuable insights for sustainable agricultural practices. However, successfully implementing remote sensing-based crop type identification in tropical regions remains challenging. Unlike temperate regions, tropical areas benefit from favorable weather conditions that support diverse land management practices, resulting in complex crop dynamics that are difficult to model. Additionally, frequent cloud cover in tropical regions limits the use of optical data during extended periods of the year, making SAR (Synthetic Aperture Radar) a more attractive alternative. Traditional models like Conditional Random Fields (CRFs) have been useful in classifying crop types using spatial and temporal contexts. However, these models often use fixed inputs, optimizing them towards a specific task, and fail to consider the CRFs’ feedback for end-to-end learning features and the spatiotemporal dependencies. This work addresses this gap by introducing an end-to-end hybrid model that combines deep learning with CRFs to incorporate prior knowledge-based modeling of crop dynamics. The proposed framework integrates a backbone encoder–decoder to capture spatial and temporal contexts, with a CRF block that models temporal dynamics by accounting for label dependencies between adjacent epochs. This block facilitates the integration of both data-driven and expert-domain temporal constraints, allowing the framework to adapt to local agricultural practices and enhance crop dynamics modeling. We evaluate the framework using multi-temporal SAR image sequences from two municipalities in Brazil, comparing the effectiveness of learned versus prior-knowledge temporal constraints. The results show improvements of up to 30% in per-class F1 score and 12% in average F1 score compared to a baseline model that excludes temporal dependencies. These findings highlight the value of incorporating prior knowledge-driven temporal constraints into crop mapping models.