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of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["42101386"],"award-info":[{"award-number":["42101386"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["22A520021"],"award-info":[{"award-number":["22A520021"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["222102110439"],"award-info":[{"award-number":["222102110439"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["232102211043"],"award-info":[{"award-number":["232102211043"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["NRMSSHR2022Z01"],"award-info":[{"award-number":["NRMSSHR2022Z01"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["2022-08"],"award-info":[{"award-number":["2022-08"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People\u2019s Republic of China","award":["KLSMNR-202302"],"award-info":[{"award-number":["KLSMNR-202302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rice is one of the most important staple foods in the world, feeding more than 50% of the global population. However, rice is also a significant emitter of greenhouse gases and plays a role in global climate change. As a result, quickly and accurately obtaining the rice mapping is crucial for ensuring global food security and mitigating global warming. In this study, we proposed an automated rice mapping method called automated rice mapping using V-shaped phenological features of rice (Auto-RMVPF) based on the time-series Sentinel-1A images, which are composed of four main steps. First, the dynamic threshold method automatically extracts abundant rice samples by flooding signals. Second, the second-order difference method automatically extracts the phenological period of rice based on the scattering feature of rice samples. Then, the key \u201cV\u201d feature of the VH backscatter time series, which rises before and after rice transplanting due to flooding, is used for rice mapping. Finally, the farmland mask is extracted to avoid interference from non-farmland features on the rice map, and the median filter is applied to remove noise from the rice map and obtain the final spatial distribution of rice. The results show that the Auto-RMVPF method not only can automatically obtain abundant rice samples but also can extract the accurate phenological period of rice. At the same time, the accuracy of rice mapping is also satisfactory, with an overall accuracy is more than 95% and an F1 score of over 0.91. The overall accuracy of the Auto-RMVPF method is improved by 2.8\u201312.2% compared with support vector machine (SVM) with an overall accuracy of 89.9% (25 training samples) and 92.2% (124 training samples), random forest (RF) with an overall accuracy of 82.8% (25 training samples) and 88.3% (124 training samples), and automated rice mapping using synthetic aperture radar flooding signals (ARM-SARFS) with an overall accuracy of 89.9%. Altogether, these experimental results suggest that the Auto-RMVPF method has broad prospects for automatic rice mapping, especially for mountainous regions where ground samples are often not easily accessible.<\/jats:p>","DOI":"10.3390\/rs15112785","type":"journal-article","created":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T16:17:33Z","timestamp":1685204253000},"page":"2785","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Guixiang","family":"Tian","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Jiang","sequence":"additional","affiliation":[{"name":"The Second Surveying and Mapping Institute of Hunan Province, Changsha 430103, China"},{"name":"Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Changsha 430103, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baojun","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4358-6449","authenticated-orcid":false,"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengwei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3027-9837","authenticated-orcid":false,"given":"Jianhui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huijin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Province Engineering Research Center of Spatial Information Processing, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"De Bernardis, C.G., Vicente-Guijalba, F., Martinez-Marin, T., and Lopez-Sanchez, J.M. 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