{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:53:32Z","timestamp":1762325612356,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T00:00:00Z","timestamp":1686355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971395","41930110","42001278"],"award-info":[{"award-number":["41971395","41930110","42001278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, synthetic aperture radar (SAR) has been a widely used data source in the remote sensing field due to its ability to work all day and in all weather conditions. Among SAR satellites, Sentinel-1 is frequently used to monitor large-scale ground objects. The Mekong Delta is a major agricultural region in Southeast Asia, so monitoring its cropland is of great importance. However, it is a challenge to distinguish cropland from other ground objects, such as aquaculture and wetland, in this region. To address this problem, the study proposes a statistical feature combination from the Sentinel-1 dual-polarimetric (dual-pol) data time series based on the m\/\u03c7 decomposition method. Then the feature combination is put into the proposed Omni-dimensional Dynamic Convolution Residual Segmentation Model (ODCRS Model) of high fitting speed and classification accuracy to realize the cropland extraction of the Mekong Delta region. Experiments show that the ODCRS model achieves an overall accuracy of 93.85%, a MIoU of 88.04%, and a MPA of 93.70%. The extraction results show that our method can effectively distinguish cropland from aquaculture areas and wetlands.<\/jats:p>","DOI":"10.3390\/rs15123050","type":"journal-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T01:59:07Z","timestamp":1686535147000},"page":"3050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Cropland Data Extraction in Mekong Delta Based on Time Series Sentinel-1 Dual-Polarized Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Jingling","family":"Jiang","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0088-8148","authenticated-orcid":false,"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Ji","family":"Ge","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China"}]},{"given":"Chunling","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2508-6800","authenticated-orcid":false,"given":"Lu","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4887-923X","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100049, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"151918","DOI":"10.1016\/j.scitotenv.2021.151918","article-title":"Land use change in the Vietnamese Mekong Delta: New evidence from remote sensing","volume":"813","author":"Vu","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"148816","DOI":"10.1016\/j.scitotenv.2021.148816","article-title":"Production of solid biofuels from organic waste in developing countries: A review from sustainability and economic feasibility perspectives","volume":"795","author":"Cattaneo","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_3","unstructured":"Lilao, B., and Karlyn, E. (2023, January 10). Food Security and Vulnerability in the Lower Mekong River Basin. Available online: http:\/\/www.jstor.org\/stable\/wateresoimpa.14.6.000."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/s13280-021-01577-z","article-title":"The worst 2020 saline water intrusion disaster of the past century in the Mekong Delta: Impacts, causes, and management implications","volume":"51","author":"Park","year":"2022","journal-title":"Ambio"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"140596","DOI":"10.1016\/j.scitotenv.2020.140596","article-title":"Future projections of flood dynamics in the Vietnamese Mekong Delta","volume":"742","author":"Triet","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s00704-018-2617-z","article-title":"Future changes in rice yields over the Mekong River Delta due to climate change\u2014Alarming or alerting?","volume":"137","author":"Jiang","year":"2018","journal-title":"Theor. Appl. Clim."},{"key":"ref_7","unstructured":"Le, H.-M., and Ludwig, M. (2023, June 03). The Salinization of Agricultural Hubs: Impacts and Adjustments to Intensifying Saltwater Intrusion in the Mekong Delta. Available online: http:\/\/hdl.handle.net\/10419\/264102."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-023-02193-0","article-title":"A synthesis of hydroclimatic, ecological, and socioeconomic data for transdisciplinary research in the Mekong","volume":"10","author":"Tiwari","year":"2023","journal-title":"Sci. Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104081","DOI":"10.1016\/j.infrared.2022.104081","article-title":"Impacts of inter-annual cropland changes on land surface temperature based on multi-time series thermal infrared images","volume":"122","author":"Chen","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Q., Guo, P., Dong, S., Liu, Y., Pan, Y., and Li, C. (2023). Extraction of Cropland Spatial Distribution Information Using Multi-Seasonal Fractal Features: A Case Study of Black Soil in Lishu County, China. Agriculture, 13.","DOI":"10.3390\/agriculture13020486"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lu, R., Wang, N., Zhang, Y., Lin, Y., Wu, W., and Shi, Z. (2022). Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-scale Feature Fusion in South Xinjiang, China. Remote Sens., 14.","DOI":"10.3390\/rs14092253"},{"key":"ref_13","first-page":"1","article-title":"Graph Neural Networks Extract High-Resolution Cultivated Land Maps From Sentinel-2 Image Series","volume":"19","author":"Tulczyjew","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, S., Shao, H., Xian, W., Yin, Z., You, M., Zhong, J., and Qi, J. (2022). Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries. Remote Sens., 14.","DOI":"10.3390\/rs14153806"},{"key":"ref_15","first-page":"300","article-title":"Flooded cropland mapping based on GF-3 and Mapbox imagery using semantic segmentation: A case study of Typhoon Siamba in western Guangdong in July 2022","volume":"12552","author":"Ku","year":"2023","journal-title":"SPIE"},{"key":"ref_16","first-page":"103006","article-title":"From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2","volume":"113","author":"Qiu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yao, C., and Zhang, J. (2022, January 20\u201322). A method for segmentation and extraction of cultivated land plots from high-resolution remote sensing images. Proceedings of the Second International Conference on Optics and Image Processing (ICOIP 2022), Taian, China.","DOI":"10.1117\/12.2644189"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, S., Shao, H., Xian, W., Zhang, S., Zhong, J., and Qi, J. (2021). Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Time series Fusion of Multi-Source Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13193956"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"84518","DOI":"10.1109\/ACCESS.2022.3197650","article-title":"Monitoring the Spatio-Time series Changes of Non-Cultivated Land via Long-Time Series Remote Sensing Images in Xinghua","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wen, C., Lu, M., Bi, Y., Zhang, S., Xue, B., Zhang, M., Zhou, Q., and Wu, W. (2022). An Object-Based Genetic Programming Approach for Cropland Field Extraction. Remote Sens., 14.","DOI":"10.3390\/rs14051275"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, Z., Chen, S., Meng, X., Zhu, R., Lu, J., Cao, L., and Lu, P. (2022). Full Convolution Neural Network Combined with Contextual Feature Representation for Cropland Extraction from High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14092157"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, W., Deng, X., Guo, S., Chen, J., Sun, L., Zheng, X., Xiong, Y., Shen, Y., and Wang, X. (2020). High-Resolution U-Net: Preserving Image Details for Cultivated Land Extraction. Sensors, 20.","DOI":"10.3390\/s20154064"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102819","DOI":"10.1016\/j.apgeog.2022.102819","article-title":"Spatiotime series variations in the eco-health condition of China\u2019s long-term stable cultivated land using Google Earth Engine from 2001 to 2019","volume":"149","author":"Li","year":"2022","journal-title":"Appl. Geogr."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Raney, R.K., Cahill, J.T., Patterson, G.W., and Bussey, D.B.J. (2012). The m-chi decomposition of hybrid dual-polarimetric radar data with application to lunar craters. J. Geophys. Res. Planets, 117.","DOI":"10.1029\/2011JE003986"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_26","unstructured":"Li, C., Zhou, A., and Yao, A. (2022). Omni-dimensional dynamic convolution. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/01431161.2010.532826","article-title":"Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis","volume":"33","author":"Nguyen","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","first-page":"100272","article-title":"Land cover mapping of the Mekong Delta to support natural resource management with multi-time series Sentinel-1A synthetic aperture radar imagery","volume":"17","author":"Ngo","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_29","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2023, January 03). ESA WorldCover 10 m 2020 v100. Available online: https:\/\/zenodo.org\/record\/5571936."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TGRS.2009.2026052","article-title":"The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification","volume":"47","author":"McNairn","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"102052","article-title":"Crop characterization using an improved scattering power decomposition technique for compact polarimetric SAR data","volume":"88","author":"Kumar","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"405","DOI":"10.5194\/isprs-annals-V-3-2022-405-2022","article-title":"Soybean Yield Forecast Using Dual-Polarimetric C-Band Synthetic Aperture Radar","volume":"3","author":"Hosseini","year":"2022","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3712","DOI":"10.1109\/JSTARS.2019.2947088","article-title":"Evaluation of Hybrid Polarimetric Decomposition Techniques for Forest Biomass Estimation","volume":"12","author":"Tomar","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"113381","DOI":"10.1016\/j.rse.2022.113381","article-title":"Retrieval performances of different crop growth descriptors from full- and compact-polarimetric SAR decompositions","volume":"285","author":"Wang","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"77","article-title":"Explicit scale estimators with high breakdown point","volume":"1","author":"Rousseeuw","year":"1992","journal-title":"L1-Stat. Anal. Relat. Methods"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Cris\u00f3stomo de Castro Filho, H., Ab\u00edlio de Carvalho, O., Ferreira de Carvalho, O.L., Pozzobon de Bem, P., dos Santos de Moura, R., Olino de Albuquerque, A., Silva, C.R., Ferreira, P.H.G., Guimare, R.F., and Gomes, R.A.T. (2020). Rice crop detection using LSTM, Bi-LSTM, and machine learning models from Sentinel-1 time series. Remote Sens., 12.","DOI":"10.3390\/rs12162655"},{"key":"ref_38","unstructured":"Ghosh, S., Wellington, M., and Holmatov, B. (2022). Mekong River Delta Crop Mapping Using a Machine Learning Approach, International Water Management Institute (IWMI). CGIAR Initiative on LowEmission Food Systems (Mitigate+)."},{"key":"ref_39","first-page":"102451","article-title":"Transferable deep learning model based on the phenological matching principle for mapping crop extent","volume":"102","author":"Ge","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.5194\/essd-15-1501-2023","article-title":"Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data","volume":"15","author":"Sun","year":"2023","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xiao, X., Lian, S., Luo, Z., and Li, S. (2018, January 19\u201321). Weighted res-unet for high-quality retina vessel segmentation. Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China.","DOI":"10.1109\/ITME.2018.00080"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cloude, S. (2009). Polarisation: Applications in Remote Sensing, Oxford University.","DOI":"10.1093\/acprof:oso\/9780199569731.001.0001"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3050\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:52:28Z","timestamp":1760125948000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3050"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,10]]},"references-count":42,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123050"],"URL":"https:\/\/doi.org\/10.3390\/rs15123050","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,6,10]]}}}