Particles generated from spacecraft surfaces will interfere with the remote sensing of emissions from objects in space, the earth, and its upper atmosphere. At Optical System Contamination-II we reviewed the sources, sizes, and composition of particles observed in local spacecraft environments and presented predictions of the optical signatures these particles would generate. In this paper we present predictions of the signatures of these nearfield particles as detected by the MSX spacecraft optical systems. Particles leaving spacecraft surfaces will be accelerated by atmospheric drag (and magnetic forces if charged). Our simulations map out the particle trajectories. Both velocities and accelerations relative to the spacecraft x,y,z coordinate system allow the particle to move through the optical sensors' fields-of-view after they leave the spacecraft surfaces. The particle's trajectory during the optical system integration time gives rise to a particle track in the detected image. Predictions of tracks for both staring and scanning systems are presented. Particles can be remotely detected across the UV-IR spectral region by their thermal emission, scattered sunlight, and earthshine. The theoretical, spectra-bandpass-integrated signatures of these particles (as a function of size and composition) is then mapped back onto the UV, visible, and IR sensor systems. At distances less than kilometers, these particles are out of focus, and their image is blurred over several pixels. The absolute irradiances from single particles in the 1 to 100 micrometers size range are added to detector array noise levels to determine detection thresholds for a variety of bandpasses. We show examples of accurate particle position and trajectory retrieval using mouse-based algorithms operating on sensor array images. Particle image blurring over several pixels affects detection sensitivity. Although the eye is excellent at feature recognition, we have developed a variety of automated algorithms for particle track enhancement and extraction to facilitate the analysis of large databases. We discuss and provide examples of the operations that successfully detected particle signatures.