Rapeseed mapping using machine learning methods and Sentinel-1 time series coupled with growing degree-days information
In light of recent escalations of geopolitical conflicts around the world, mapping rapeseed areas has garnered great interest given its importance to food security. Sentinel-1 (S1) SAR data was used for timely and regular rapeseed mapping. By coupling S1 data with GDD (Growing Degree Days) informati...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Science of Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000501 |
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| Summary: | In light of recent escalations of geopolitical conflicts around the world, mapping rapeseed areas has garnered great interest given its importance to food security. Sentinel-1 (S1) SAR data was used for timely and regular rapeseed mapping. By coupling S1 data with GDD (Growing Degree Days) information, S1 GDD series were also created and assessed. Rapeseed classification was realized using random forest (RF) and inception time (IT). An alignment method based on detected flowering dates was proposed with the aim of alleviating the possible shifts in he growth cycle between the different sites and years. The spatial (cross-regional) transferability of the models was tested accordingly, before and after alignment. The results showed that using the S1 time series before alignment, the overall F1-score achieved by RF was 81.3 % ± 16.5 %, and the overall F1-score of IT was 89.2 % ± 5.7 %. After alignment, RF achieved an overall F1-score of 90.3 % ± 8.2 %, while the overall F1-score of IT was 91.7 % ± 3.8 %. Using the S1 GDD series, before alignment, the overall score of RF was 58.2 % ± 36.8 %, while the overall F1-score achieved by IT was 86.6 % ± 11.7 %. After the alignment of the S1 GDD series, the F1-score achieved by RF was 73.9 % ± 29.0 %, and the F1-score of IT was 86.8 % ± 10.3. The best configuration for rapeseed mapping was using IT with S1 time series after alignment, as it gave the highest overall F1-score and the best consistency with the lowest standard deviation. Overall, the S1 time series provided better results than the S1 GDD series, meaning that employing thermal time does not enhance the classification performance. The results indicate that the proposed method enables reliable, timely and continuous rapeseed monitoring, Paving the way for more effective food stock management and planning by policymakers and stakeholders. |
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| ISSN: | 2666-0172 |