{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:41:33Z","timestamp":1767847293255,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T00:00:00Z","timestamp":1663027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Open Research Foundation of CMA\/Henan Key Laboratory of Agrometeorological Support and Applied Technique","award":["AMF202204"],"award-info":[{"award-number":["AMF202204"]}]},{"name":"the Open Research Foundation of CMA\/Henan Key Laboratory of Agrometeorological Support and Applied Technique","award":["No. 2021041"],"award-info":[{"award-number":["No. 2021041"]}]},{"name":"the Hefei Municipal Natural Science Foundation","award":["AMF202204"],"award-info":[{"award-number":["AMF202204"]}]},{"name":"the Hefei Municipal Natural Science Foundation","award":["No. 2021041"],"award-info":[{"award-number":["No. 2021041"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rice is one of the most important food crops for human beings. The timely and accurate understanding of the distribution of rice can provide an important scientific basis for food security, agricultural policy formulation, and regional development planning. As an active remote sensing system, polarimetric synthetic aperture radar (PolSAR) has the advantage of working both day and night and in all weather conditions and hence plays an important role in rice growing area identification. This paper focuses on the topic of rice planting area identification using multi-temporal PolSAR images and a deep learning method. A rice planting area identification attention U-Net (RIAU-Net) model is proposed, which is trained by multi-temporal Sentinel-1 dual-polarimetric images acquired in different periods of rice growth. In addition, considering the diversity of the rice growth period in different years caused by the different climatic conditions and other factors, a transfer mechanism is investigated to apply the well-trained model to monitor the rice planting areas in different years. The experimental results show that the proposed method can significantly improve the classification accuracy, with 11\u201314% F1-score improvement compared with the traditional methods and a pleasing generalization ability in different years. Moreover, the classified rice planting regions are continuous. For reproducibility, the source codes of the well-trained RIAU-Net model are provided.<\/jats:p>","DOI":"10.3390\/rs14184573","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T22:37:28Z","timestamp":1663108648000},"page":"4573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Rice Planting Area Identification Based on Multi-Temporal Sentinel-1 SAR Images and an Attention U-Net Model"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1354-8035","authenticated-orcid":false,"given":"Xiaoshuang","family":"Ma","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"CMA Henan Agrometeorological Support and Applied Technique Key Laboratory, Zhengzhou 450000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3883-6431","authenticated-orcid":false,"given":"Zunyi","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"CMA Henan Agrometeorological Support and Applied Technique Key Laboratory, Zhengzhou 450000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"China JIKAN Research Institute of Engineering Investigations and Design, Co., Ltd., Xi\u2019an 710000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Fang","sequence":"additional","affiliation":[{"name":"China JIKAN Research Institute of Engineering Investigations and Design, Co., Ltd., Xi\u2019an 710000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinglei","family":"Wu","sequence":"additional","affiliation":[{"name":"China JIKAN Research Institute of Engineering Investigations and Design, Co., Ltd., Xi\u2019an 710000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.rse.2013.09.001","article-title":"Capability of C-band backscattering coefficients from high-resolution satellite SAR sensors to assess biophysical variables in paddy rice","volume":"140","author":"Inoue","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, T., Wang, F., Yi, Q., Xie, L., and Yao, X. (2022). A bibliometric and visualized analysis of research progress and trends in rice remote densing over the past 42 years (1980\u20132021). Remote Sens., 14.","DOI":"10.3390\/rs14153607"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Phan, A., Ha, D.N., Man, C.D., Nguyen, T.T., Bui, H.Q., and Nguyen, T.T.N. (2019). Rapid Assessment of flood inundation and damaged rice drea in red river delta from Sentinel 1A imagery. Remote Sens., 11.","DOI":"10.3390\/rs11172034"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"H\u00fctt, C., Koppe, W., Miao, Y., and Bareth, G. (2016). Best accuracy Land Use\/Land Cover (LULC) classification to derive crop types using multitemporal, multisensor, and multi-polarization SAR satellite images. Remote Sens., 8.","DOI":"10.3390\/rs8080684"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8807","DOI":"10.1109\/TGRS.2020.2990978","article-title":"SAR Image despeckling by noisy reference-based deep learning method","volume":"58","author":"Ma","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., El Hajj, M., Zribi, M., Minh, D.H.T., Ndikumana, E., Courault, D., and Belhouchette, H. (2019). Mapping paddy rice using Sentinel-1 SAR time series in Camargue, France. Remote Sens., 11.","DOI":"10.3390\/rs11070887"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4090","DOI":"10.3390\/rs6054090","article-title":"Monitoring changes in rice cultivated area from SAR and optical satellite Images in Ben Tre and Tra Vinh Provinces in Mekong Delta, Vietnam","volume":"6","author":"Karila","year":"2014","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/LGRS.2010.2055830","article-title":"Rice Crop monitoring in south China with RADARSAT-2 quad-polarimetric SAR data","volume":"8","author":"Wu","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Phan, H., Le Toan, T., and Bouvet, A. (2021). Understanding dense time series of Sentinel-1 backscatter from rice fields: Case study in a province of the Mekong Delta, Vietnam. Remote Sens., 13.","DOI":"10.3390\/rs13050921"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sun, C., Zhang, H., Ge, J., Wang, C., Li, L., and Xu, L. (2022). Rice Mapping in a subtropical hilly region based on Sentinel-1 time deries feature snalysis and the dual Branch BiLSTM model. Remote Sens., 14.","DOI":"10.3390\/rs14133213"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/JSTARS.2017.2784784","article-title":"Mapping double and single crop paddy rice with Sentinel-1A at varying spatial scales and polarizations in Hanoi, Vietnam","volume":"11","author":"Lasko","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kobayashi, S., and Ide, H. (2022). Rice crop monitoring using Sentinel-1 SAR data: A case study in Saku, Japan. Remote Sens., 14.","DOI":"10.3390\/rs14143254"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3812","DOI":"10.1109\/JSTARS.2014.2387214","article-title":"Capability of rice mapping using hybrid polarimetric SAR data","volume":"8","author":"Xie","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3082","DOI":"10.1109\/JSTARS.2016.2586102","article-title":"Rice mapping using RADARSAT-2 dual- and quad-Pol data in a complex land-use watershed: Cau river basin (Vietnam)","volume":"9","author":"Hoang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111234","DOI":"10.1016\/j.rse.2019.111234","article-title":"Crop phenology retrieval via polarimetric SAR decomposition and random forest algorithm","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chang, L., Chen, Y., Wang, J., and Chang, Y. (2021). Rice-field mapping with Sentinel-1A SAR time-series data. Remote Sens., 13.","DOI":"10.3390\/rs13010103"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, H., Fu, D., Huang, C., Su, F., Liu, Q., Liu, G., and Wu, S. (2020). An approach to high-resolution rice paddy mapping using time-series Sentinel-1 SAR data in the mun river basin, Thailand. Remote Sens., 12.","DOI":"10.3390\/rs12233959"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Ho Tong Minh, D., Dang Nguyen, H.T., Baghdadi, N., Courault, D., Hossard, L., and El Moussawi, I. (2018). Estimation of rice height and biomass using multitemporal SAR Sentinel-1 for camargue, Southern France. Remote Sens., 10.","DOI":"10.1117\/12.2325174"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1109\/TGRS.2013.2268319","article-title":"Polarimetric response of rice fields at C-Band: Analysis and phenology retrieval","volume":"52","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1109\/JSTARS.2016.2547843","article-title":"Paddy-rice phenology classification based on machine-Learning methods using multitemporal co-polar X-Band SAR images","volume":"9","author":"Erten","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep learning based multi-temporal crop classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_22","first-page":"102551","article-title":"Pixel-level rice planting information monitoring in Fujin City based on time-series SAR imagery","volume":"104","author":"Pang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112599","DOI":"10.1016\/j.rse.2021.112599","article-title":"Towards interpreting multi-temporal deep learning models in crop mapping","volume":"264","author":"Xu","year":"2021","journal-title":"Remote Sens Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Paul, S., Kumari, M., and Murthy, C. (2022). Generating pre-harvest crop maps by applying convolutional neural network on multi-temporal Sentinel-1 data. Int. J. Remote Sens., 1\u201324.","DOI":"10.1080\/01431161.2022.2030072"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, L., Zhang, H., Wang, C., Wei, S., Zhang, B., Wu, F., and Tang, Y. (2021). Paddy Rice Mapping in thailand using time-series Sentinel-1 data and deep learning model. Remote Sens., 13.","DOI":"10.3390\/rs13193994"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, Z., Zhong, R., Xiong, X., Guo, C., Xu, J., Zhu, Y., Xu, J., Ying, Y., Ting, K.C., and Huang, J. (2022). Large-scale rice mapping using multi-task spatiotemporal deep learning and Sentinel-1 SAR time series. Remote Sens., 14.","DOI":"10.3390\/rs14030699"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.isprsjprs.2021.02.011","article-title":"Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model","volume":"174","author":"Wei","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","first-page":"1","article-title":"Oil spill detection based on deep convolutional neural networks using polarimetric scattering information from Sentinel-1 SAR images","volume":"60","author":"Ma","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sun, W., Li, P., Du, B., Yang, J., Tian, L., Li, M., and Zhao, L. (2020). Scatter matrix based domain adaptation for bi-temporal polarimetric SAR images. Remote Sens., 12.","DOI":"10.3390\/rs12040658"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.1109\/TGRS.2017.2780195","article-title":"Assessment of the potential of H\/A\/Alpha decomposition for polarimetric interferometric SAR data","volume":"56","author":"Salehi","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2332","DOI":"10.1109\/36.964969","article-title":"Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H\/A\/Alpha-Wishart classifier","volume":"39","author":"Pottier","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","unstructured":"Lopez-Martinez, C., and Pottier, E. (2004, January 20\u201324). Statistical assessment of eigenvector-based target decomposition theorems in radar polarimetry. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2004), Anchorage, AK, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3039","DOI":"10.1109\/TGRS.2008.922033","article-title":"Evaluation and bias removal of multilook effect on entropy\/alpha\/anisotropy in polarimetric SAR decomposition","volume":"46","author":"Lee","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","unstructured":"Oktay, O., Schlemper, J., and Folgoc, L. (2018). Attention U-Net: Learning where to look for the pancreas. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_37","first-page":"1","article-title":"Development of a dual-Attention U-Net model for sea ice and open water classification on SAR images","volume":"19","author":"Ren","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nava, L., Bhuyan, K., Meena, S.R., Monserrat, O., and Catani, F. (2022). Rapid mapping of landslides on SAR data by Attention U-Net. Remote Sens., 14.","DOI":"10.3390\/rs14061449"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TGRS.2002.808066","article-title":"A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data","volume":"41","author":"Conradsen","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1918","DOI":"10.1109\/TGRS.2018.2870188","article-title":"A nonlinear guided filter for polarimetric SAR image despeckling","volume":"57","author":"Ma","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4573\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:30:27Z","timestamp":1760142627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4573"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,13]]},"references-count":41,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184573"],"URL":"https:\/\/doi.org\/10.3390\/rs14184573","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,13]]}}}