Exploring Cropmarks: Leveraging Spectral Physically-based Reflectance Models for Enhanced Archaeological Detection - Agapiou, Athos; Gravanis, Elias (My University, 2025)
The detection of buried archaeological remains through optical remote sensing imagery has become a widely adopted technique. Researchers have examined the use of satellite, airborne, and low-altitude platforms, which capture the spectral responses of vegetation growing over shallowly buried archaeological sites [1-2]. Findings indicate that such approaches are effective for mapping and detecting archaeological proxies (cropmarks) across extensive areas. However, limitations persist, largely due to phenological variations in crop growth and specific environmental and soil conditions. To address these challenges, this study aims to develop regional physically based reflectance models that provide insights into soil and plant properties closely associated with crop mark formation.
Keywords: Cropmarks; Buried archaeological remains; Remote sensing; PROSAIL; Machine learning