Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods
Accurate and timely mapping of agricultural products is a crucial component in management and decision-making for promoting food security and sustainable development. The intricacy of differentiating diverse croplands due to the existence of small and winding agricultural fragments contributes to th...
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Elsevier
2025-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225003255 |
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| author | Zahra Mohammadi Mobarakeh Saeid Pourmanafi Mohsen Ahmadi |
| author_facet | Zahra Mohammadi Mobarakeh Saeid Pourmanafi Mohsen Ahmadi |
| author_sort | Zahra Mohammadi Mobarakeh |
| collection | DOAJ |
| description | Accurate and timely mapping of agricultural products is a crucial component in management and decision-making for promoting food security and sustainable development. The intricacy of differentiating diverse croplands due to the existence of small and winding agricultural fragments contributes to the complexity of crop classification in arid environments. In this study, we employed a novel hybrid approach, integrating time-series analysis, noisy data quality control, and different machine learning and deep learning models to classify croplands of complex multi-crop systems in central Iran. The classification was based on time series spectral bands of Sentinel-2 images and indices of crop growth phenology, providing valuable insights into the growth cycles of different crops in the region. Additionally, a neural network-based method was used to assess and enhance the quality of training data before modeling. For crop classification, we used four machine learning and deep learning methods including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Temporal Convolutional Neural Network (TCNN), and compared their results before and after quality control measures. The results indicated that after quality control, the overall accuracy and Kappa coefficient of the analyses were considerably improved. RF and TCNN methods demonstrated superior prediction and modeling performance compared to XGBoost and SVM models. The overall accuracy of the four methods increased from 91 %, 87 %, 83 %, and 91 % before quality control to 96 %, 94 %, 89 %, and 95 % after quality control, respectively. The results of this study highlight the effectiveness of employing time-series data and quality control procedures to enhance crop classification in complex agricultural systems. By improving the precision and accuracy of agricultural classifications our findings can contribute to optimizing resource management, food security, and sustainable development goals. |
| format | Article |
| id | doaj-art-dd11797a526f46a09c9da51dd8685c8e |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-dd11797a526f46a09c9da51dd8685c8e2025-08-20T02:57:17ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-08-0114210467810.1016/j.jag.2025.104678Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methodsZahra Mohammadi Mobarakeh0Saeid Pourmanafi1Mohsen Ahmadi2Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, IranCorresponding author.; Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, IranDepartment of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, IranAccurate and timely mapping of agricultural products is a crucial component in management and decision-making for promoting food security and sustainable development. The intricacy of differentiating diverse croplands due to the existence of small and winding agricultural fragments contributes to the complexity of crop classification in arid environments. In this study, we employed a novel hybrid approach, integrating time-series analysis, noisy data quality control, and different machine learning and deep learning models to classify croplands of complex multi-crop systems in central Iran. The classification was based on time series spectral bands of Sentinel-2 images and indices of crop growth phenology, providing valuable insights into the growth cycles of different crops in the region. Additionally, a neural network-based method was used to assess and enhance the quality of training data before modeling. For crop classification, we used four machine learning and deep learning methods including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Temporal Convolutional Neural Network (TCNN), and compared their results before and after quality control measures. The results indicated that after quality control, the overall accuracy and Kappa coefficient of the analyses were considerably improved. RF and TCNN methods demonstrated superior prediction and modeling performance compared to XGBoost and SVM models. The overall accuracy of the four methods increased from 91 %, 87 %, 83 %, and 91 % before quality control to 96 %, 94 %, 89 %, and 95 % after quality control, respectively. The results of this study highlight the effectiveness of employing time-series data and quality control procedures to enhance crop classification in complex agricultural systems. By improving the precision and accuracy of agricultural classifications our findings can contribute to optimizing resource management, food security, and sustainable development goals.http://www.sciencedirect.com/science/article/pii/S1569843225003255Agricultural systemsSentinel-2Machine learningSustainable development |
| spellingShingle | Zahra Mohammadi Mobarakeh Saeid Pourmanafi Mohsen Ahmadi Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods International Journal of Applied Earth Observations and Geoinformation Agricultural systems Sentinel-2 Machine learning Sustainable development |
| title | Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods |
| title_full | Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods |
| title_fullStr | Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods |
| title_full_unstemmed | Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods |
| title_short | Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods |
| title_sort | employing sentinel 2 time series and noisy data quality control enhance crop classification in arid environments a comparison of machine learning and deep learning methods |
| topic | Agricultural systems Sentinel-2 Machine learning Sustainable development |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225003255 |
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