Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation
The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/283 |
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| author | Reza Maleki Falin Wu Guoxin Qu Amel Oubara Loghman Fathollahi Gongliu Yang |
| author_facet | Reza Maleki Falin Wu Guoxin Qu Amel Oubara Loghman Fathollahi Gongliu Yang |
| author_sort | Reza Maleki |
| collection | DOAJ |
| description | The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation. |
| format | Article |
| id | doaj-art-94af160e92194be48b4317f804967fc1 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-94af160e92194be48b4317f804967fc12025-01-24T13:47:59ZengMDPI AGRemote Sensing2072-42922025-01-0117228310.3390/rs17020283Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland SegmentationReza Maleki0Falin Wu1Guoxin Qu2Amel Oubara3Loghman Fathollahi4Gongliu Yang5SNARS Laboratory, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaSNARS Laboratory, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaBeijing System Design Institute of Electro-Mechanic Engineering, Beijing 100854, ChinaSNARS Laboratory, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaMeteorological Department of West Azerbaijan Province, Iran Meteorological Organization (IRIMO), Orumiyeh 670056, IranSchool of Mechanical Engineering, Zhejiang University, Hangzhou 310030, ChinaThe increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation.https://www.mdpi.com/2072-4292/17/2/283cropland segmentationcrop phenological alignmenttransfer deep learningSentinel-2time series |
| spellingShingle | Reza Maleki Falin Wu Guoxin Qu Amel Oubara Loghman Fathollahi Gongliu Yang Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation Remote Sensing cropland segmentation crop phenological alignment transfer deep learning Sentinel-2 time series |
| title | Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation |
| title_full | Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation |
| title_fullStr | Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation |
| title_full_unstemmed | Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation |
| title_short | Adaptive Month Matching: A Phenological Alignment Method for Transfer Learning in Cropland Segmentation |
| title_sort | adaptive month matching a phenological alignment method for transfer learning in cropland segmentation |
| topic | cropland segmentation crop phenological alignment transfer deep learning Sentinel-2 time series |
| url | https://www.mdpi.com/2072-4292/17/2/283 |
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