Time Series approach to map areas of Agricultural Plastic Waste generation

Abstract

The escalating presence of plastics in agriculture has raised concerns regarding Agricultural Plastic Waste (APW). Hitherto, a lack of comprehensive plasticulture data impedes effective waste management strategies, potentially resulting in plastic pollution and contributing to microplastic formation. APW locations and quantities are pivotal for territorial planning and the formulation of public policies on waste management and land use. Remote detection of agri-plastics has garnered increased consideration, particularly in mapping plastic-mulched farmlands (PMFs) dispersed in extensive regions. This study investigates whether the use of satellite image time series can map PMFs accurately. We assessed pixel-based classification using a 16-day composite time series of Sentinel-2 imagery (S2-16D) obtained from the Brazil Data Cube project. The Plastic-Mulched Landcover Index (PMLI) was joined into S2-16D bands because it is an index focused on PMF detection. Four classifiers (RF, MLP, L-TAE, and TCNN) were compared through agreed classification metrics. The most promising outcome showed an overall accuracy of 100% employing L-TAE but with visible noise in the map. The time series enhanced accuracy while minimizing background confusion, offering a viable solution for PMF detection. The PMF map presented herein represents an initial stride toward fostering circularity in plasticulture throughout South America.