光学遥感影像对地球观测不可或缺，但持续存在的云层遮挡严重限制了其下游应用价值。现有云去除（CR）方法大多针对低层保真度进行优化，易过度平滑对分析就绪数据（ARD）至关重要的纹理与边界，导致视觉上合理的重建结果与其语义实用性之间存在不匹配。为弥合这一差距，我们提出TDP-CR——一种任务驱动的多模态框架，可联合执行云去除与地表覆盖分割。该框架的核心是提示引导融合（PGF）机制，其利用可学习的退化提示来编码云层厚度与空间不确定性；通过融合全局通道上下文与局部提示条件化的空间偏差，PGF自适应地仅在光学数据受云污染区域引入合成孔径雷达（SAR）信息。此外，我们设计了一种参数高效的两阶段训练策略，将重建学习与语义表征学习解耦。在LuojiaSET-OSFCR数据集上的实验表明，本框架性能优越：TDP-CR在PSNR指标上以仅15%的参数量超越当前重型先进基线0.18 dB，并在mIoU指标上相较其他多任务方法稳定提升1.4%，有效生成分析就绪数据。

Optical remote sensing imagery is indispensable for Earth observation, yet persistent cloud occlusion limits its downstream utility. Most cloud removal (CR) methods are optimized for low-level fidelity and can over-smooth textures and boundaries that are critical for analysis-ready data (ARD), leading to a mismatch between visually plausible restoration and semantic utility. To bridge this gap, we propose TDP-CR, a task-driven multimodal framework that jointly performs cloud removal and land-cover segmentation. Central to our approach is a Prompt-Guided Fusion (PGF) mechanism, which utilizes a learnable degradation prompt to encode cloud thickness and spatial uncertainty. By combining global channel context with local prompt-conditioned spatial bias, PGF adaptively integrates Synthetic Aperture Radar (SAR) information only where optical data is corrupted. We further introduce a parameter-efficient two-phase training strategy that decouples reconstruction and semantic representation learning. Experiments on the LuojiaSET-OSFCR dataset demonstrate the superiority of our framework: TDP-CR surpasses heavy state-of-the-art baselines by 0.18 dB in PSNR while using only 15\% of the parameters, and achieves a 1.4\% improvement in mIoU consistently against multi-task competitors, effectively delivering analysis-ready data.