中文标题#
果园中杂草管理方法的映射使用 Sentinel-2 和 PlanetScope 数据
英文标题#
Mapping of Weed Management Methods in Orchards using Sentinel-2 and PlanetScope Data
中文摘要#
有效的杂草管理对于提高农业生产力至关重要，因为杂草与作物竞争营养和水等关键资源。 准确的杂草管理方法图谱对于政策制定者评估农民做法、评估对植被健康、生物多样性和气候的影响，以及确保政策和补贴的合规性至关重要。 然而，监测杂草管理方法具有挑战性，因为它们通常依赖于基于地面的田间调查，这往往成本高、耗时且容易延误。 为了应对这一问题，我们利用了地球观测数据和机器学习（ML）。 具体而言，我们分别使用 Sentinel-2 和 PlanetScope 卫星时间序列数据开发了单独的机器学习模型，以在果园中对四种不同的杂草管理方法（修剪、耕作、化学喷洒和无措施）进行分类。 研究结果表明，基于机器学习的遥感技术有潜力提高果园杂草管理制图的效率和准确性。
英文摘要#
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 they commonly rely on ground-based field surveys, which are often costly, time-consuming and subject to delays. In order to tackle this problem, we leverage earth observation data and Machine Learning (ML). Specifically, we developed separate ML models using Sentinel-2 and PlanetScope satellite time series data, respectively, to classify four distinct weed management methods (Mowing, Tillage, Chemical-spraying, and No practice) in orchards. The findings demonstrate the potential of ML-driven remote sensing to enhance the efficiency and accuracy of weed management mapping in orchards.
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