Mainstream multiobject tracking (MOT) algorithms employ global object detection and association methods. However, when dealing with scenarios involving crowded tiny objects in satellite videos, existing global trackers often yield numerous missed detections and unstable trajectories. To address this issue, we propose a novel joint-detection-and-tracking (JDT) framework, mask propagation and motion prediction network (MP2Net), which integrates local detection enhancements for tiny targets and bridges the gap between detection and association. Specifically, our approach incorporates a mask propagation network that enhances feature representation for tiny targets by matching frame by frame to capture local details. Additionally, we utilize an implicit motion prediction (IMP) and explicit motion prediction (EMP) strategy that merges tracking information into detection at both feature and instance levels, thereby improving tracking robustness. Experimental results on two large-scale datasets demonstrate the effectiveness and robustness of MP2Net, achieving state-of-the-art performance on typical moving objects in satellite videos, such as 66.7% multiple object tracking accuracy (MOTA) and 75.9% identity F1 score (IDF1) on the SatVideoDT challenge dataset. The code will be available at https://github.com/DonDominic/MP2Net.