National scale sub-meter time series mangrove mapping using Landsat imagery and deep transfer learning

Current mangrove time-series products are constrained to 25 m resolution, hindering precise delineation of boundaries, small patches, and internal structures, thus compromising area estimates and ecological assessments. Key barriers are the paucity of historical high-resolution imagery and high-quality labeled samples. To this end, we developed the Sub-meter Mangrove Transfer Learning Mapping (SMTLM) framework, combining a pre-trained model from 2020 existing sub-meter products with transfer fine-tuning using few-shot samples from target historical years. We resampled time-series Landsat imagery to sub-meter resolution and selected proven spectral bands and vegetation indices. To improve temporal transferability, we designed and evaluated three strategies: direct transfer learning (DTL), partial fine-tuning (PFT), and layer-wise fine-tuning (LFT). SMTLM generated China's first national Sub-meter Time-series Mangrove Map (STMM) for 1990-2020, achieving overall accuracy of 93.4%-97.2% and F1-scores of 0.93-0.97. STMM indicates a 105% mangrove area increase from 2000 (14,316 ha) to 2020 (29,413 ha), nearly five times the 23.3% from existing product. When benchmarked against STMM, existing product exhibit commission and omission errors totaling 9,496–19,089 ha, and STMM detecting 2-4 times more mangrove patches. SMTLM provides a scalable solution for sub-meter mapping under sample-limited conditions, enhancing temporal transferability of high-resolution methods. STMM offers a long-term, high-resolution dataset for precise mangrove dynamics assessment, revealing far greater 21st-century recovery in China than previously recognized. Optimized 30 m Landsat imagery via SMTLM retrieves fine spatial details comparable to sub-meter results, highlighting sub-meter mapping's improvement over existing coarse-resolution products for reliable monitoring.