Anthropogenic Marine Debris (AMD) is one of the most important pollutants in the oceans. Millions of tons of debris across oceans have created a critical environmental problem. This study presents a novel method aimed to improve the identification of macroplastics through remote sensing over beaches, combining AMD hyperspectral laboratory characterization and digital supervised classification in high spatial resolution imagery. Several samples were collected from the Chiloé Island beaches, Chile. Spectral signature samples and physical properties were assessed through laboratory work. HyLogger3® (CSIRO), PS-300 Apogee and ASD Field Spec hyperspectral systems were used to characterize each sample. Using those measurements, a spectral library was generated by processing, filtering and sorting the spectral data gathered, determining distinctive spectral bands for digital classification. By using this spectral library, a digital classification method was implemented over World-View 3 imagery, covering the three beaches selected as test sites. Distinct classification methods and geospatial analyses were applied to determine land cover composition, aimed for the detection of Styrofoam and the rest of anthropogenic marine debris. Four field campaigns were carried out to validate the AMD classification and mass retrievals, performed on >300 ground based points. The AMD hyperspectral library was successfully applied for an AMD digital classification in satellite imagery. Support Vector Machine method showed the best performance, resulting in an overall accuracy equivalent to 88% and over 50?tons of debris estimated on the pilot beaches. These results prove the feasibility of quantifying macro-AMD through the integration of hyperspectral laboratory measurements and remote sensing imagery, allowing to estimate anthropogenic influence on natural ecosystems and providing valuable information for further development of the methodology and sustainable AMD management.