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Research Article

Mapping rapeseed in China during 2017-2021 using Sentinel data: an automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC)

Yunze Zanga State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China;b Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaView further author information
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Yuean Qiua State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China;b Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaView further author information
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Xuehong Chena State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China;b Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaCorrespondence[email protected]
View further author information
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Jin Chena State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China;b Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaView further author information
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Wei Yangc Center for Environmental Remote Sensing, Chiba University, Chiba, JapanView further author information
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Yifei Liua State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China;b Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaView further author information
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Longkang Penga State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China;b Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaView further author information
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Miaogen Shend Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaView further author information
&
Xin Caoa State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing, China;b Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, ChinaView further author information
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Article: 2163576 | Received 09 Jun 2022, Accepted 24 Dec 2022, Published online: 10 Jan 2023

References

  • Anhui Provincial Department of Agriculture. 2019. “Office of the Anhui Provincial Department of Agriculture and Rural Affairs on the Issuance of Technical Guidance for Oilseed Rape Production in 2019.” Anhui Provincial Department of Agriculture. Accessed 3 September 2022. http://nync.ah.gov.cn/public/7021/11243741.html.
  • Ashourloo, D., H. S. Shahrabi, M. Azadbakht, H. Aghighi, H. Nematollahi, A. Alimohammadi, and A. A. Matkan. 2019. “Automatic Canola Mapping Using Time Series of Sentinel 2 Images.” Isprs Journal of Photogrammetry and Remote Sensing 156: 63–24. doi:10.1016/j.isprsjprs.2019.08.007.
  • Bonjean, A. P., C. Dequidt, and T. Sang. 2016. “Rapeseed in China.” Ocl 23 (6): D605. doi:10.1051/ocl/2016045.
  • Boschetti, L., S. P. Flasse, and P. A. Brivio. 2004. “Analysis of the Conflict Between Omission and Commission in Low Spatial Resolution Dichotomic Thematic Products: The Pareto Boundary.” Remote Sensing of Environment 91 (3–4): 280–292. doi:10.1016/j.rse.2004.02.015.
  • Carré, P., and A. Pouzet. 2014. “Rapeseed Market, Worldwide and in Europe.” Ocl 21 (1): D102. doi:10.1051/ocl/2013054.
  • Chandrasekar, K., M. V. R. S. Sai, P. S. Roy, and R. S. Dwevedi. 2010. “Land Surface Water Index (LSWI) Response to Rainfall and NDVI Using the MODIS Vegetation Index Product.” International Journal of Remote Sensing 31 (15): 3987–4005. doi:10.1080/01431160802575653.
  • Chen, J., J. Chen, A. P. Liao, X. Cao, L. J. Chen, X. H. Chen, C. Y. He, et al. 2015. “Global Land Cover Mapping at 30 M Resolution: A POK-Based Operational Approach.” Isprs Journal of Photogrammetry and Remote Sensing 103: 7–27. doi:10.1016/j.isprsjprs.2014.09.002.
  • Chen, X. H., D. M. Yin, J. Chen, and X. Cao. 2016. “Effect of Training Strategy for Positive and Unlabelled Learning Classification: Test on Landsat Imagery.” Remote Sensing Letters 7 (11): 1063–1072. doi:10.1080/2150704x.2016.1217437.
  • Chuine, I., and J. Régnière. 2017. “Process-Based Models of Phenology for Plants and Animals.” Annual Review of Ecology, Evolution, and Systematics 48 (1): 159–182. doi:10.1146/annurev-ecolsys-110316-022706.
  • Czaplewski, R. L., and G. P. Catts. 1992. “Calibrtion of Remotely Sensed Proprotion or are Estimates for Misclassification.” Remote Sensing of Environment 39 (1): 29–43. doi:10.1016/0034-4257(92)90138-A.
  • D’Andrimont, R., M. Taymans, G. Lemoine, A. Ceglar, M. Yordanov, and M. van der Velde. 2020. “Detecting Flowering Phenology in Oil Seed Rape Parcels with Sentinel-1 and -2 Time Series.” Remote Sensing of Environment 239: 111660. doi:10.1016/j.rse.2020.111660.
  • Dash, T., S. Chitlangia, A. Ahuja, and A. Srinivasan. 2022. “A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks.” Scientific Reports 12 (1): 1. doi:10.1038/s41598-021-04590-0.
  • Dong, J., X. Xiao, M. A. Menarguez, G. Zhang, Y. Qin, D. Thau, C. Biradar, and B. Moore. 2016. “Mapping Paddy Rice Planting Area in Northeastern Asia with Landsat 8 Images, Phenology-Based Algorithm and Google Earth Engine.” Remote Sensing of Environment 185: 142–154. doi:10.1016/j.rse.2016.02.016.
  • Drusch, M., U. Del Bello, S. Carlier, O. Colin, V. Fernandez, F. Gascon, B. Hoersch, et al. 2012. “Sentinel-2: Esa’s Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment 120: 25–36. doi:10.1016/j.rse.2011.11.026.
  • Elkan, C., and K. Noto. 2008. “Learning Classifiers from Only Positive and Unlabeled Data.” Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining 213–220. doi: 10.1145/1401890.1401920
  • ESA. 2020. “Sentinel-2: Cloud Probability.” Accessed 3 September 2022. https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY.
  • Fang, S. H., W. C. Tang, Y. Peng, Y. Gong, C. Dai, R. H. Chai, and K. Liu. 2016. “Remote Estimation of Vegetation Fraction and Flower Fraction in Oilseed Rape with Unmanned Aerial Vehicle Data.” Remote Sensing 8 (5): 416. doi:10.3390/rs8050416.
  • Firrisa, M. T., I. van Duren, and A. Voinov. 2013. “Energy Efficiency for Rapeseed Biodiesel Production in Different Farming Systems.” Energy Efficiency 7 (1): 79–95. doi:10.1007/s12053-013-9201-2.
  • Food and Agriculture Organization of the United Nations (FAO). 2021. “Crops and Livestock Products.” Food and Agriculture Organization of the United Nations. Accessed 3 September 2022. https://www.fao.org/3/cb4477en/online/cb4477en.html#chapter-2_1.
  • Food and Agriculture Organization of the United Nations (FAO). 2022. “FAOSTAT.” Accessed 3 September 2022. https://www.fao.org/faostat/en/#data/QCL.
  • Foody, G. M., and A. Mathur. 2004. “A Relative Evaluation of Multiclass Image Classification by Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42 (6): 1335–1343. doi:10.1109/Tgrs.2004.827257.
  • Frantz, D., E. Hass, A. Uhl, J. Stoffels, and J. Hill. 2018. “Improvement of the Fmask Algorithm for Sentinel-2 Images: Separating Clouds from Bright Surfaces Based on Parallax Effects.” Remote sensing of environment 215: 471–481. doi:10.1016/j.rse.2018.04.046.
  • Friedl, M. A., D. K. McIver, J. C. F. Hodges, X. Y. Zhang, D. Muchoney, A. H. Strahler, C. E. Woodcock, et al. 2002. “Global Land Cover Mapping from MODIS: Algorithms and Early Results.” Remote Sensing of Environment 83 (1–2): 287–302. doi:10.1016/S0034-4257(02)00078-0.
  • Fu, D. H., L. Y. Jiang, A. S. Masons, M. L. Xiao, L. R. Zhu, L. Z. Li, Q. H. Zhou, C. J. Shen, and C. H. Huang. 2016. “Research Progress and Strategies for Multifunctional Rapeseed: A Case Study of China.” Journal of Integrative Agriculture 15 (8): 1673–1684. doi:10.1016/S2095-3119(16)61384-9.
  • Gallego, F. J. 2004. “Remote Sensing and Land Cover Area Estimation.” International Journal of Remote Sensing 25 (15): 3019–3047. doi:10.1080/01431160310001619607.
  • General Office of Zhejiang Provincial Government. 2020. “The General Office of the Zhejiang Provincial People’s Government on the Enhancement of Oil Supply Security Capacity to Promote the Implementation of High-Quality Industrial Development Views.” Accessed 3 September 3. https://www.zj.gov.cn/art/2020/10/28/art_1229017139_2037686.html.
  • Ge, S., J. S. Zhang, Y. Z. Pan, Z. Yang, and S. Zhu. 2021. “Transferable Deep Learning Model Based on the Phenological Matching Principle for Mapping Crop Extent.” International Journal of Applied Earth Observation and Geoinformation 102: 102451. doi:10.1016/j.jag.2021.102451.
  • Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote sensing of environment 202: 18–27. doi:10.1016/j.rse.2017.06.031.
  • Gumma, M. K., P. S. Thenkabail, K. Charyulu Deevi, I. A. Mohammed, P. Teluguntla, A. Oliphant, J. Xiong, T. Aye, and A. M. Whitbread. 2018. “Mapping Cropland Fallow Areas in Myanmar to Scale Up Sustainable Intensification of Pulse Crops in the Farming System.” GIScience & Remote Sensing 55 (6): 926–949. Taylor & Francis. doi:10.1080/15481603.2018.1482855.
  • Han, Y. 2015. “National Storage Cancellation of the Main Production Areas Prices Fell Chongqing Rapeseed Supply and Demand Stable Prices Firm.” Accessed 3 September 2022. https://www.moa.gov.cn/xw/qg/201905/t20190523_6314442.htm.
  • Han, J. C., Z. Zhang, and J. Cao. 2021. “Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2.” Remote Sensing 13 (1): 1. doi. doi:10.3390/rs13010105.
  • Han, J. C., Z. Zhang, Y. C. Luo, J. Cao, L. L. Zhang, J. Zhang, and Z. Y. Li. 2021. “The RapeseedMap10 Database: Annual Maps of Rapeseed at a Spatial Resolution of 10m Based on Multi-Source Data.” Earth System Science Data 13 (6): 2857–2874. doi:10.5194/essd-13-2857-2021.
  • Huang, X., J. Huang, L. Xuecao, Q. Shen, and Z. Chen. 2022. “Early Mapping of Winter Wheat in Henan Province of China Using Time Series of Sentinel-2 Data.” GIScience & Remote Sensing 59 (1): 1534–1549. doi:10.1080/15481603.2022.2104999.
  • Hu, Q., W. Hua, Y. Yin, X. K. Zhang, L. J. Liu, J. Q. Shi, Y. G. Zhao, L. Qin, C. Chen, and H. Z. Wang. 2017. “Rapeseed Research and Production in China.” Crop Journal 5 (2): 127–135. doi:10.1016/j.cj.2016.06.005.
  • Jiangsu Rural Statistics Division. 2019. “The Province’s Rapeseed Production This Year to Stop the Decline to Increase.” Accessed 3 September 2022. http://tj.jiangsu.gov.cn/art/2019/12/4/art_4027_8834637.html.
  • Jin, Z. N., G. Azzari, C. You, S. Di Tommaso, S. Aston, M. Burke, and D. B. Lobell. 2019. “Smallholder Maize Area and Yield Mapping at National Scales with Google Earth Engine.” Remote Sensing of Environment 228: 115–128. doi:10.1016/j.rse.2019.04.016.
  • Karra, K., C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis, and S. P. Brumby. 2021. “Global Land Use/Land Cover with Sentinel 2 and Deep Learning.” In 2021 IEEE international geoscience and remote sensing symposium IGARSS 4704-4707. doi: 10.1109/IGARSS47720.2021.9553499.
  • Konduri, V. S., J. Kumar, W. W. Hargrove, F. M. Hoffman, and A. R. Ganguly. 2020. “Mapping Crops Within the Growing Season Across the United States.” Remote Sensing of Environment 251: 112048. doi:10.1016/j.rse.2020.112048.
  • Lei, L., X. Y. Wang, Y. F. Zhong, H. W. Zhao, X. Hu, and C. Luo. 2021. “DOCC: Deep One-Class Crop Classification via Positive and Unlabeled Learning for Multi-Modal Satellite Imagery.” International Journal of Applied Earth Observation and Geoinformation 105: 102598. doi:10.1016/j.jag.2021.102598.
  • Li, W., Q. Guo, and C. Elkan. 2010. “A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data.” IEEE Transactions on Geoscience and Remote Sensing 49 (2): 717–725. doi:10.1109/TGRS.2010.2058578.
  • Liu, G., X. Wang, G. Baiocchi, M. Casazza, F. Meng, Y. Cai, Y. Hao, W. Feng, and Z. Yang. 2020. “On the Accuracy of Official Chinese Crop Production Data: Evidence from Biophysical Indexes of Net Primary Production.” Proceedings of the National Academy of Sciences 117 (41): 25434–25444. doi:10.1073/pnas.1919850117.
  • Li, S., W. Zhang, L. Zhao, and X. Wang. 2021. “Phenological Period Identification of Oilseed Rape Based on Time-Series PolSar Image and Decision Tree Model.” Acta Agriculturae Zhejiangensis 11 (33): 2116–2127. doi:10.3969/j.issn.1004-1524.2021.11.14.
  • Lu, Y., and L. Wang. 2021. “How to Automate Timely Large-Scale Mangrove Mapping with Remote Sensing.” Remote Sensing of Environment 264: 112584. doi:10.1016/j.rse.2021.112584.
  • Mandal, D., V. Kumar, D. Ratha, S. Dey, A. Bhattacharya, J. M. Lopez-Sanchez, H. McNairn, and Y. S. Rao. 2020. “Dual Polarimetric Radar Vegetation Index for Crop Growth Monitoring Using Sentinel-1 SAR Data.” Remote Sensing of Environment 247: 111954. doi:10.1016/j.rse.2020.111954.
  • Mansaray, L. R., L. Yang, V. T. S. Kabba, A. S. Kanu, J. Huang, and F. Wang. 2019. “Optimising Rice Mapping in Cloud-Prone Environments by Combining Quad-Source Optical with Sentinel-1A Microwave Satellite Imagery.” GIScience & Remote Sensing 56 (8): 1333–1354. Taylor & Francis. doi:10.1080/15481603.2019.1646978.
  • Maxwell, A. E., T. A. Warner, and F. Fang. 2018. “Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review.” International Journal of Remote Sensing 39 (9): 2784–2817. doi:10.1080/01431161.2018.1433343.
  • Meng, S. Y., Y. F. Zhong, C. Luo, X. Hu, X. Y. Wang, and S. X. Huang. 2020. “Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China.” Remote Sensing 12 (2): 226. doi:10.3390/rs12020226.
  • Mercier, A., J. Betbeder, S. Rapinel, N. Jegou, J. Baudry, and L. Hubert-Moy. 2020. “Evaluation of Sentinel-1 And-2 Time Series for Estimating LAI and Biomass of Wheat and Rapeseed Crop Types.” Journal of Applied Remote Sensing 14 (2): 1. doi:10.1117/1.JRS.14.024512.
  • Ozdogan, M., and C. E. Woodcock. 2006. “Resolution Dependent Errors in Remote Sensing of Cultivated Areas.” Remote Sensing of Environment 103 (2): 203–217. doi:10.1016/j.rse.2006.04.004.
  • Phan, T. N., V. Kuch, and L. W. Lehnert. 2020. “Land Cover Classification Using Google Earth Engine and Random Forest Classifier—the Role of Image Composition.” Remote Sensing 12 (15): 2411. doi:10.3390/rs12152411.
  • Pontius, R. G., and M. Millones. 2011. “Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment.” International Journal of Remote Sensing 32 (15): 4407–4429. doi:10.1080/01431161.2011.552923.
  • Pott, L. P., T. J. C. Amado, R. A. Schwalbert, G. M. Corassa, and I. A. Ciampitti. 2021. “Satellite-Based Data Fusion Crop Type Classification and Mapping in Rio Grande Do Sul, Brazil.” Isprs Journal of Photogrammetry and Remote Sensing 176: 196–210. doi:10.1016/j.isprsjprs.2021.04.015.
  • Qian, W., J. Meng, M. Li, M. Frauen, O. Sass, J. Noack, and C. Jung. 2006. “Introgression of Genomic Components from Chinese Brassica Rapa Contributes to Widening the Genetic Diversity in Rapeseed (B. Napus L.), with Emphasis on the Evolution of Chinese Rapeseed.” Theoretical and Applied Genetics 113 (1): 49–54. doi:10.1007/s00122-006-0269-3.
  • Qiu, B., Y. Huang, C. Chen, Z. Tang, and F. Zou. 2018. “Mapping Spatiotemporal Dynamics of Maize in China from 2005 to 2017 Through Designing Leaf Moisture Based Indicator from Normalized Multi-Band Drought Index.” Computers and Electronics in Agriculture 153: 82–93. doi:10.1016/j.compag.2018.07.039.
  • Qiu, B., F. Jiang, C. Chen, Z. Tang, W. Wenbin, and J. Berry. 2021. “Phenology-Pigment Based Automated Peanut Mapping Using Sentinel-2 Images.” GIScience & Remote Sensing 58 (8): 1335–1351. Taylor & Francis. doi:10.1080/15481603.2021.1987005.
  • Qiu, B. W., Y. H. Luo, Z. H. Tang, C. C. Chen, D. F. Lu, H. Y. Huang, Y. Z. Chen, N. Chen, and W. M. Xu. 2017. “Winter Wheat Mapping Combining Variations Before and After Estimated Heading Dates.” Isprs Journal of Photogrammetry and Remote Sensing 123: 35–46. doi:10.1016/j.isprsjprs.2016.09.016.
  • Raboanatahiry, N., L. Huaixin, Y. Longjiang, and L. Maoteng. 2021. “Rapeseed (Brassica Napus): Processing, Utilization, and Genetic Improvement.” Agronomy 11 (9): 1776. doi:10.3390/agronomy11091776.
  • Shen, M., J. Chen, X. Zhu, and Y. Tang. 2014. “Yellow Flowers Can Decrease NDVI and EVI Values: Evidence from a Field Experiment in an Alpine Meadow.” Canadian Journal of Remote Sensing 35 (2): 99–106. doi:10.5589/m09-003.
  • Shen, M., J. Chen, X. Zhu, Y. Tang, and X. Chen. 2010. “Do Flowers Affect Biomass Estimate Accuracy from NDVI and EVI?” International Journal of Remote Sensing 31 (8): 2139–2149. doi:10.1080/01431160903578812.
  • Sulik, J. J., and D. S. Long. 2015. “Spectral Indices for Yellow Canola Flowers.” International Journal of Remote Sensing 36 (10): 2751–2765. doi:10.1080/01431161.2015.1047994.
  • Sulik, J. J., and D. S. Long. 2016. “Spectral Considerations for Modeling Yield of Canola.” Remote Sensing of Environment 184: 161–174. doi:10.1016/j.rse.2016.06.016.
  • Sulik, J. J., and D. S. Long. 2020. “Automated Detection of Phenological Transitions for Yellow Flowering Plants Such as Brassica Oilseeds.” Agrosystems, Geosciences & Environment 3 (1): e20125. doi:10.1002/agg2.20125.
  • Tao, J. B., W. B. Liu, W. X. Tan, X. B. Kong, and M. Xu. 2019. “Fusing Multi-Source Data to Map Spatio-Temporal Dynamics of Winter Rape on the Jianghan Plain and Dongting Lake Plain, China.” Journal of Integrative Agriculture 18 (10): 2393–2407. doi:10.1016/S2095-3119(19)62577-3.
  • Thorp, K. R., and D. Drajat. 2021. “Deep Machine Learning with Sentinel Satellite Data to Map Paddy Rice Production Stages Across West Java, Indonesia.” Remote Sensing of Environment 265: 112679. doi:10.1016/j.rse.2021.112679.
  • Tian, Z., Y. H. Ji, L. X. Sun, X. L. Xu, D. L. Fan, H. L. Zhong, Z. R. Liang, and G. Ficsher. 2018. “Changes in Production Potentials of Rapeseed in the Yangtze River Basin of China Under Climate Change: A Multi-Model Ensemble Approach.” Journal of Geographical Sciences 28 (11): 1700–1714. doi:10.1007/s11442-018-1538-1.
  • Tian, Z., Y. H. Ji, H. Q. Xu, H. G. Qiu, L. X. Sun, H. L. Zhong, and J. G. Liu. 2021. “The Potential Contribution of Growing Rapeseed in Winter Fallow Fields Across Yangtze River Basin to Energy and Food Security in China.” Resources Conservation and Recycling 164: 105159. doi:10.1016/j.resconrec.2020.105159.
  • Tian, J. Y., L. Wang, D. M. Yin, X. J. Li, C. Y. Diao, H. L. Gong, C. Shi, et al. 2020. “Development of Spectral-Phenological Features for Deep Learning to Understand Spartina Alterniflora Invasion.” Remote sensing of environment 242: 111745. doi:10.1016/j.rse.2021.112679.
  • Torres, R., P. Snoeij, D. Geudtner, D. Bibby, M. Davidson, E. Attema, P. Potin, et al. 2012. “GMES Sentinel-1 Mission.” Remote sensing of environment 120 (May): 9–24. doi:10.1016/j.rse.2011.05.028.
  • USDA. 2022. “Oilseeds: World Markets and Trade.” USDA Foreign Agricultural Service. Accessed 3 September 2022. https://www.fas.usda.gov/data/oilseeds-world-markets-and-trade.
  • van Duren, Iris, A. Voinov, O. Arodudu, and M. Tesfaye Firrisa. 2015. “Where to Produce Rapeseed Biodiesel and Why? Mapping European Rapeseed Energy Efficiency.” Renewable Energy 74: 49–59. doi:10.1016/j.renene.2014.07.016.
  • Veloso, A., S. Mermoz, A. Bouvet, T. L. Toan, M. Planells, J. F. Dejoux, and E. Ceschia. 2017. “Understanding the Temporal Behavior of Crops Using Sentinel-1 and Sentinel-2-Like Data for Agricultural Applications.” Remote Sensing of Environment 199: 415–426. doi:10.1016/j.rse.2017.07.015.
  • Waldner, F., and P. Defourny. 2017. “Where Can Pixel Counting Area Estimates Meet User-Defined Accuracy Requirements?” International Journal of Applied Earth Observation and Geoinformation 60: 1–10. doi:10.1016/j.jag.2017.03.014.
  • Wang, H. Z., C. Y. Guan, and C. L. Zhang. 2007. Studies on rapeseed production and cultivation science and technology in China. In The 12th International Rapeseed Congress Proceeding Science, edited by Fu, T.D., and Guan, C.Y., 2–7. Wuhan, China: Press USA Inc.
  • Weiss, M., F. Jacob, and G. Duveiller. 2020. “Remote Sensing for Agricultural Applications: A Meta-Review.” Remote Sensing of Environment 236: 111402. doi:10.1016/j.rse.2019.111402.
  • Xie, X., J. Niu, X. Liu, Z. Chen, S. Tang, and S. Yu. 2021. “A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis.” Medical Image Analysis 69: 69. doi:10.1016/j.media.2021.101985.
  • Xiong, J., P. S. Thenkabail, M. K. Gumma, P. Teluguntla, J. Poehnelt, R. G. Congalton, K. Yadav, and D. Thau. 2017. “Automated Cropland Mapping of Continental Africa Using Google Earth Engine Cloud Computing.” Isprs Journal of Photogrammetry and Remote Sensing 126: 225–244. doi:10.1016/j.isprsjprs.2017.01.019.
  • Xu, J., J. Yang, X. Xiong, L. Haifeng, J. Huang, K. C. Ting, Y. Ying, and T. Lin. 2021. “Towards Interpreting Multi-Temporal Deep Learning Models in Crop Mapping.” Remote Sensing of Environment 264: 264. doi:10.1016/j.rse.2021.112599.
  • Yang, L., R. Huang, J. Huang, T. Lin, L. Wang, R. Mijiti, P. Wei, et al. 2022. “Semantic Segmentation Based on Temporal Features: Learning of Temporal-Spatial Information from Time-Series SAR Images for Paddy Rice Mapping.” IEEE Transactions on Geoscience and Remote Sensing 60. doi:10.1109/TGRS.2021.3099522.
  • Yin, Y., and H. Wang. 2012. “Achievement, Problem and Scientific Policy of Rapeseed Industry Development in China.” Journal of Agricultural Science and Technology 14 (4): 1–7. doi:10.3969/j.issn.1008-0864.2012.04.01.
  • Zanaga, D., R. van de Kerchove, W. de Keersmaecker, N. Souverijns, C. Brockmann, R. Quast, A. G. Jan Wevers, A. Paccini, and S. Vergnaud. 2021. “ESA WorldCover 10 M 2020 V100.” doi:10.1038/s41467-021-24227-0.
  • Zang, Y. Z., X. H. Chen, J. Chen, Y. G. Tian, Y. S. Shi, X. Cao, and X. H. Cui. 2020. “Remote Sensing Index for Mapping Canola Flowers Using MODIS Data.” Remote Sensing 12 (23): 3912. doi:10.3390/rs12233912.
  • Zhang, X. L., and Y. He. 2013. “Rapid Estimation of Seed Yield Using Hyperspectral Images of Oilseed Rape Leaves.” Industrial Crops and Products 42: 416–420. doi:10.1016/j.indcrop.2012.06.021.
  • Zhang, H. Y., W. B. Liu, and L. P. Zhang. 2022. “Seamless and Automated Rapeseed Mapping for Large Cloudy Regions Using Time-Series Optical Satellite Imagery.” Isprs Journal of Photogrammetry and Remote Sensing 184: 45–62. doi:10.1016/j.isprsjprs.2021.12.001.
  • Zhang, G., X. Xiao, C. M. Biradar, J. Dong, Y. Qin, M. A. Menarguez, Y. Zhou, et al. 2017. “Spatiotemporal Patterns of Paddy Rice Croplands in China and India from 2000 to 2015.” The Science of the Total Environment 579: 82–92. doi:10.1016/j.scitotenv.2016.10.223.
  • Zhang, Y. X., W. Zhang, K. Xu, and J. Li. 2022. “Phenological Phase Identification of Oilseed Rape (Brassica Napus L.) Using Typical Stokes Parameters.” Geomatics and Information Science of Wuhan University. doi:10.13203/j.whugis20210394.
  • Zhan, P., W. Q. Zhu, and N. Li. 2021. “An Automated Rice Mapping Method Based on Flooding Signals in Synthetic Aperture Radar Time Series.” Remote Sensing of Environment 252: 112112. doi:10.1016/j.rse.2020.112112.
  • Zhong, L. H., P. Gong, and G. S. Biging. 2014. “Efficient Corn and Soybean Mapping with Temporal Extendability: A Multi-Year Experiment Using Landsat Imagery.” Remote Sensing of Environment 140: 1–13. doi:10.1016/j.rse.2013.08.023.
  • Zhou, Y., J. Luo, L. Feng, Y. Yang, Y. Chen, and W. Wei. 2019. “Long-Short-Term-Memory-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data.” GIScience & Remote Sensing 56 (8): 1170–1191. Taylor & Francis. doi:10.1080/15481603.2019.1628412.

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