中文标题#
果園中雜草管理方法的映射使用 Sentinel-2 和 PlanetScope 數據
英文标题#
Mapping of Weed Management Methods in Orchards using Sentinel-2 and PlanetScope Data
中文摘要#
有效的雜草管理對於提高農業生產力至關重要，因為雜草與作物競爭營養和水等關鍵資源。準確的雜草管理方法圖譜對於政策制定者評估農民做法、評估對植被健康、生物多樣性和氣候的影響，以及確保政策和補貼的合規性至關重要。然而，監測雜草管理方法具有挑戰性，因為它們通常依賴於基於地面的田間調查，這往往成本高、耗時且容易延誤。為了應對這一問題，我們利用了地球觀測數據和機器學習（ML）。具體而言，我們分別使用 Sentinel-2 和 PlanetScope 衛星時間序列數據開發了單獨的機器學習模型，以在果園中對四種不同的雜草管理方法（修剪、耕作、化學噴灑和無措施）進行分類。研究結果表明，基於機器學習的遙感技術有潛力提高果園雜草管理制圖的效率和準確性。
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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