Mapping of Weed Management Methods in Orchards using Sentinel-2 and PlanetScope Data

Effective weed management is crucial for improving agricultural productivity, as weeds compete with crops for vital resources like nutrients and water. Accurate maps of weed management methods are essential for policymakers to assess farmer practices, evaluate impacts on vegetation health, biodiversity, and climate, as well as ensure compliance with policies and subsidies. However, monitoring weed management methods is challenging as commonly rely on on-ground field surveys, which are often costly, time-consuming and subject to delays. In order to tackle this problem, we leverage Earth Observation (EO) data and Machine Learning (ML). Specifically, we developed an ML approach for mapping four distinct weed management methods (Mowing, Tillage, Chemical-spraying, and No practice) in orchards using satellite image time series (SITS) data from two different sources: Sentinel-2 (S2) and PlanetScope (PS). The findings demonstrate the potential of ML-driven remote sensing to enhance the efficiency and accuracy of weed management mapping in orchards.