Tree species classification from Moderate Resolution Imaging Spectroradiometer (MODIS) time series is critical for environmental monitoring, yet challenging due to subtle inter-species signatures, strong spatial-spectral-temporal coupling, and the difficulty of modeling large-scale geographical correlations. This letter presents a Graph-regulated Disentangled Sparse Mamba model (Geo-GDS-Mamba) with three contributions. First, a mini-batch graph-regulated stream models geographic correlations among samples via a Radial-Basis-Function kernel over their physical coordinates, independent of spectral content, and fuses this context through a graph convolutional network without full-batch memory costs. Second, a disentangling Mamba architecture separates the spatial, spectral, and temporal information coupled within each sample into three independent processing streams. Third, sparse token modules adaptively retain the top 30\% most informative tokens per branch via learned attention or similarity scores, mitigating Mamba's correlation-decay limitation while reducing computational cost. Experiments on annual MOD13Q1 data across two Canadian provinces show Geo-GDS-Mamba achieves 95.35\% OA (94.73\% Kappa) in Alberta, outperforming eleven state-of-the-art baselines, and generalizes without fine-tuning to unseen Saskatchewan data at 87.63\% OA, using only 1.74M parameters and 0.18 GFLOPs. Ablation studies confirm each component's contribution, led by a 4.15-point OA gain from the geographic graph stream alone.

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