The rapid expansion of rice-crayfish farming in China has triggered significant land-use transformations, yet long-term mapping of these patterns remains challenging due to sample limitations and spectral complexities. This study developed a robust classification framework integrating synergistic sample generation and hierarchical classification to address this gap. We first proposed a sample generation method integrating temporal migration with feature-based enlargement strategy, then designed a two-layer stratified classification approach combining machine learning (Random Forest) with phenology-based techniques. Applied to the Jianghan Plain (2013-2022), our framework achieved high accuracy, with overall accuracy higher than 87 % annually and correlation around 0.90 with statistical data. Critical land use dynamics were noticed as follows: (1) Land-use transitions accelerated during 2016-2022, with rice-crayfish expanding predominantly at the expense of traditional rice cultivation (77 % f 4.76 %) of rice-crayfish fields originated from rice-based cropping). (2) Single-rice areas declined by 24 % f 3.02 %, while rapeseed-rice and wheat-rice systems decreased by 21 % f 5.41 % and 26 % f 5.32 %, respectively. (3) Conversions from dryland and water bodies to rice-crayfish emerged during 2019-2022, a later phase of expansion when the conversion to rice-crayfish became widespread. Overall, this study proposed a robust land use type classification framework for complex regions with limited samples in longterm, providing a transferable solution for monitoring land-system changes under rapid transitions. By revealing the transformative impact of rice-crayfish system expansion on traditional land use patterns, this study highlights its substantial effects on conventional rice cultivation and offers valuable insights for formulating adaptive land management strategies that support ecological sustainability and regional food security.