Temporal Change Description (TCD) and Future Satellite Image Forecasting (FSIF) are critical, yet historically disjointed tasks in Satellite Image Time Series (SITS) analysis. Both are fundamentally limited by the common challenge of modeling long-range temporal dynamics. To explore how to improve the performance of methods on both tasks simultaneously by enhancing long-range temporal understanding capabilities, we introduce **TAMMs**, the first unified framework designed to jointly perform TCD and FSIF within a single MLLM-diffusion architecture. TAMMs introduces two key innovations: Temporal Adaptation Modules (**TAM**) enhance frozen MLLM's ability to comprehend long-range dynamics, and Semantic-Fused Control Injection (**SFCI**) mechanism translates this change understanding into fine-grained generative control. This synergistic design makes the understanding from the TCD task to directly inform and improve the consistency of the FSIF task. Extensive experiments demonstrate TAMMs significantly outperforms state-of-the-art specialist baselines on both tasks. Our dataset can be found at https://huggingface.co/datasets/IceInPot/TAMMs .

翻译：时序变化描述与未来卫星图像预测是卫星图像时间序列分析中至关重要却长期割裂的两大任务。二者均受限于建模长程时序动态这一共同挑战。为探索如何通过增强长程时序理解能力来同时提升两个任务的性能，我们提出了首个统一框架**TAMMs**，该框架在单一MLLM-扩散架构内联合执行时序变化描述与未来卫星图像预测任务。TAMMs包含两项核心创新：时序适配模块通过增强冻结MLLM的长程动态理解能力，以及语义融合控制注入机制将这种变化理解转化为细粒度生成控制。这种协同设计使得时序变化描述任务的理解能够直接指导并提升未来卫星图像预测任务的一致性。大量实验表明，TAMMs在两个任务上均显著优于当前最先进的专用基线模型。我们的数据集可在https://huggingface.co/datasets/IceInPot/TAMMs 获取。