For micro-satellite constellations, frequent Earth observation alone does not guarantee archive usability; the archive is operationally useful only when the spatial image quality remains adequate for downstream exploitation. This study presents an integrated framework for assessing spatial image quality using NEONSAT-1 imagery by linking optical design analysis, image simulation, GIQE-based NIIRS estimation, and YOLOv8-based object detection within a single workflow. NEONSAT-1 panchromatic (PAN), pan-sharpened (PS), and multispectral (MS) products were analyzed together with controlled simulations of system MTF, altitude-dependent GSD variation, and super-resolution processing. Among the native products, PS imagery showed the highest NIIRS and overall detection performance. In the controlled experiments, higher system MTF increased RER and NIIRS, while lower simulated altitude generally produced finer GSD and higher NIIRS for both PS and PAN products. However, detection performance varied by scene, product type, and target class and did not increase in direct proportion to NIIRS. In the super-resolution case study, ×2 SR provided the most consistent NIIRS improvement, whereas detection responses at higher SR scales were target class dependent. These results suggest that spatial image quality should be evaluated not only through interpretability metrics such as NIIRS but also in relation to practical downstream performance. The proposed framework provides a baseline for future constellation-scale image quality assessment.