<p>In the Central Andes, the Spanish invasion of the sixteenth century was marked by the large-scale displacement of communities, forced resettlement, and, as a consequence, the abandonment of agricultural infrastructure. However, preexisting agricultural stone-walled terraces and fields remained a central concern as communities actively reshaped colonial landscapes. In this study, I present a systematic methodological approach for detecting pre-Hispanic and colonial agricultural fields and terraces in the Andean highlands using multispectral satellite remote sensing (MSRS) analysis. My results show that MSRS provides a promising method for large-scale analysis of agricultural infrastructure that offsets the limitations of pedestrian survey and can be built in a progressive manner that makes it more accessible than other forms of image analysis and AI-based identification. Through the sequential targeting (ST) workflow, I systematically reduced the area of interest through a four-step process that combines elevation and slope data and vegetation index analysis. I applied this workflow to a case study in Huarochirí (Lima, Peru), analyzing an area of almost 255 km² around the community of San Andrés de Tupicocha, and identifying agricultural terraces and fields that were impacted by Spanish colonial resettlement policies. I integrated high-resolution multispectral imagery from PeruSAT-1, archaeological, geographic, and historical information, and observations gathered through field visits and conversations with community members. Approximately 22% of the analyzed area contains agricultural infrastructure that can be detected through associated vegetation patterns. This approach offers a replicable method for identifying agricultural infrastructure in challenging Andean terrain while reducing computational requirements and technical complexity.</p>