A Machine Learning Approach to Generate High-Resolution Maps of Irrigated Olive Groves
The increasing severity of water scarcity in southern Europe, caused by climate change, requires advanced and more efficient approaches to agricultural water management. In particular, in this paper, we address this problem for olive groves—a cornerstone of the region’s economy. We propose a novel f...
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MDPI AG
2025-05-01
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| author | Rosa Gutiérrez-Cabrera Ana M. Tarquis Javier Borondo |
| author_facet | Rosa Gutiérrez-Cabrera Ana M. Tarquis Javier Borondo |
| author_sort | Rosa Gutiérrez-Cabrera |
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| description | The increasing severity of water scarcity in southern Europe, caused by climate change, requires advanced and more efficient approaches to agricultural water management. In particular, in this paper, we address this problem for olive groves—a cornerstone of the region’s economy. We propose a novel framework for generating high-resolution maps of irrigated olive groves that integrates remote sensing imagery and machine learning. Our approach leverages multi-temporal Sentinel-2 data, specifically the Normalized Difference Vegetation Index (NDVI), to capture seasonal vegetation dynamics. For classification, we explore two distinct models: (1) A Dynamic Time Warping (DTW)-based approach (with and without the Sakoe–Chiba Band constraints), where DTW aligns temporal NDVI sequences to enable robust comparisons of irrigation regimes, followed by a K-Nearest Neighbor classifier (KNN) that classifies plots as irrigated or rainfed. (2) An eXtreme Gradient Boosting (XGBoost) model that directly uses temporal NDVI profiles. Additionally, we compare the dependence of model performance on the length of the NDVI time series (ranging from one to seven seasons), finding that XGBoost requires a shorter time series to achieve optimal results, while KNN with DTW can benefit from longer historical records. Indeed, XGBoost nearly reaches its maximum accuracy using only data based on three seasons, achieving 0.79 compared to its peak performance of 0.80. Hence, our results indicate that this approach can accurately differentiate between irrigated and rainfed plots, enabling the generation of high-resolution irrigation maps for southern Spain. Finally, we argue that the results of this paper go beyond mere mapping: they lay the foundation for a comprehensive management guide that can optimize water use, with broad implications. Such implications range from empowering precision agriculture to providing a roadmap for land management, ensuring both the sustainability and productivity of olive groves in drought-affected regions. |
| format | Article |
| id | doaj-art-13ff05979daa4ffe84d96f05cff0ca96 |
| institution | OA Journals |
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| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Land |
| spelling | doaj-art-13ff05979daa4ffe84d96f05cff0ca962025-08-20T01:56:19ZengMDPI AGLand2073-445X2025-05-01145100110.3390/land14051001A Machine Learning Approach to Generate High-Resolution Maps of Irrigated Olive GrovesRosa Gutiérrez-Cabrera0Ana M. Tarquis1Javier Borondo2Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, 28040 Madrid, SpainGrupo de Sistemas Complejos, Universidad Politécnica de Madrid, 28040 Madrid, SpainAgrowingData, 04001 Almería, SpainThe increasing severity of water scarcity in southern Europe, caused by climate change, requires advanced and more efficient approaches to agricultural water management. In particular, in this paper, we address this problem for olive groves—a cornerstone of the region’s economy. We propose a novel framework for generating high-resolution maps of irrigated olive groves that integrates remote sensing imagery and machine learning. Our approach leverages multi-temporal Sentinel-2 data, specifically the Normalized Difference Vegetation Index (NDVI), to capture seasonal vegetation dynamics. For classification, we explore two distinct models: (1) A Dynamic Time Warping (DTW)-based approach (with and without the Sakoe–Chiba Band constraints), where DTW aligns temporal NDVI sequences to enable robust comparisons of irrigation regimes, followed by a K-Nearest Neighbor classifier (KNN) that classifies plots as irrigated or rainfed. (2) An eXtreme Gradient Boosting (XGBoost) model that directly uses temporal NDVI profiles. Additionally, we compare the dependence of model performance on the length of the NDVI time series (ranging from one to seven seasons), finding that XGBoost requires a shorter time series to achieve optimal results, while KNN with DTW can benefit from longer historical records. Indeed, XGBoost nearly reaches its maximum accuracy using only data based on three seasons, achieving 0.79 compared to its peak performance of 0.80. Hence, our results indicate that this approach can accurately differentiate between irrigated and rainfed plots, enabling the generation of high-resolution irrigation maps for southern Spain. Finally, we argue that the results of this paper go beyond mere mapping: they lay the foundation for a comprehensive management guide that can optimize water use, with broad implications. Such implications range from empowering precision agriculture to providing a roadmap for land management, ensuring both the sustainability and productivity of olive groves in drought-affected regions.https://www.mdpi.com/2073-445X/14/5/1001remote sensingNDVImachine learningDTWKNNXGBoost |
| spellingShingle | Rosa Gutiérrez-Cabrera Ana M. Tarquis Javier Borondo A Machine Learning Approach to Generate High-Resolution Maps of Irrigated Olive Groves Land remote sensing NDVI machine learning DTW KNN XGBoost |
| title | A Machine Learning Approach to Generate High-Resolution Maps of Irrigated Olive Groves |
| title_full | A Machine Learning Approach to Generate High-Resolution Maps of Irrigated Olive Groves |
| title_fullStr | A Machine Learning Approach to Generate High-Resolution Maps of Irrigated Olive Groves |
| title_full_unstemmed | A Machine Learning Approach to Generate High-Resolution Maps of Irrigated Olive Groves |
| title_short | A Machine Learning Approach to Generate High-Resolution Maps of Irrigated Olive Groves |
| title_sort | machine learning approach to generate high resolution maps of irrigated olive groves |
| topic | remote sensing NDVI machine learning DTW KNN XGBoost |
| url | https://www.mdpi.com/2073-445X/14/5/1001 |
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