Detecting Soil Tillage in Portugal: Challenges and Insights from Rules-Based and Machine Learning Approaches Using Sentinel-1 and Sentinel-2 Data

Tiago G. Morais, Tiago Domingos, João Falcão, Manuel Camacho, Ana Marques, Inês Neves, Hugo Lopes and Ricardo F. M. Teixeira ()
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Tiago G. Morais: MARETEC—Marine, Environment and Technology Centre, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal
Tiago Domingos: MARETEC—Marine, Environment and Technology Centre, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal
João Falcão: Instituto de Financiamento da Agricultura e Pescas (IFAP), 1649-034 Lisbon, Portugal
Manuel Camacho: Instituto de Financiamento da Agricultura e Pescas (IFAP), 1649-034 Lisbon, Portugal
Ana Marques: Instituto de Financiamento da Agricultura e Pescas (IFAP), 1649-034 Lisbon, Portugal
Inês Neves: Instituto de Financiamento da Agricultura e Pescas (IFAP), 1649-034 Lisbon, Portugal
Hugo Lopes: Instituto de Financiamento da Agricultura e Pescas (IFAP), 1649-034 Lisbon, Portugal
Ricardo F. M. Teixeira: MARETEC—Marine, Environment and Technology Centre, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal

Sustainability, 2024, vol. 16, issue 23, 1-15

Abstract: Monitoring soil tillage activities, such as plowing and cultivating, is essential for aligning agricultural practices with environmental standards for soil health. Detecting these activities presents significant challenges, especially when relying on remotely sensed data. This paper addresses these challenges within the framework of the Common Agricultural Policy (CAP), which requires EU countries to enhance their environmental monitoring and climate action efforts. We used remote sensing data from Sentinel-1 and Sentinel-2 missions to detect soil tillage practices in 73 test farms in Portugal. Three approaches were explored: a rule-based method and two machine learning techniques based on XGBoost (XGB). One machine learning approach utilized the original imbalanced dataset, while the other employed a SMOTE (Synthetic Minority Oversampling Technique) approach to balance underrepresented soil tillage operations within the training set. Our findings highlight the inherent difficulty in detecting soil tillage operations across all methods, though the XGB-SMOTE approach demonstrated the most promising results, achieving a recall of 67% and an AUC-ROC (area under the receiver operating characteristic curve) of 74%. These results underscore the need for further research to develop a fully automated detection model. This work has potential applications for monitoring compliance with CAP mandates and informing environmental policy to better support sustainable agricultural practices.

Keywords: Common Agricultural Policy; change detection; remote sensing; XGBoost; environmental monitoring (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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