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Because most existing crop mapping models based on remote sensing data have poor generalizability, they cannot be rapidly deployed for crop identification tasks in different regions. Based on a priori knowledge of phenology, we designed an off-center Bayesian deep learning remote sensing crop classification method that can highlight phenological features, combined with an attention mechanism and residual connectivity. In this paper, we first optimize the input image and input features based on a phenology analysis. Then, a convolutional neural network (CNN), recurrent neural network (RNN), and random forest classifier (RFC) were built based on farm data in northeastern Inner Mongolia and applied to perform comparisons with the method proposed here. Then, classification tests were performed on soybean, maize, and rice from four measurement areas in northeastern China to verify the accuracy of the above methods. To further explore the reliability of the method proposed in this paper, an uncertainty analysis was conducted by Bayesian deep learning to analyze the model\u2019s learning process and model structure for interpretability. Finally, statistical data collected in Suibin County, Heilongjiang Province, over many years, and Shandong Province in 2020 were used as reference data to verify the applicability of the methods. The experimental results show that the classification accuracy of the three crops reached 90.73% overall and the average F1 and IOU were 89.57% and 81.48%, respectively. Furthermore, the proposed method can be directly applied to crop area estimations in different years in other regions based on its good correlation with official statistics.<\/jats:p>","DOI":"10.3390\/rs15030674","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T05:46:11Z","timestamp":1674452771000},"page":"674","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Remote Sensing Crop Recognition by Coupling Phenological Features and Off-Center Bayesian Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5867-5356","authenticated-orcid":false,"given":"Yongchuang","family":"Wu","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1983-5978","authenticated-orcid":false,"given":"Penghai","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China"},{"name":"Engineering Center for Geographic Information of Anhui Province, Hefei 230601, China"}]},{"given":"Yanlan","family":"Wu","sequence":"additional","affiliation":[{"name":"Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China"},{"name":"Engineering Center for Geographic Information of Anhui Province, Hefei 230601, China"},{"name":"School of Artificial Intelligence, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei 230601, China"}]},{"given":"Hui","family":"Yang","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-7953","authenticated-orcid":false,"given":"Biao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China"},{"name":"Engineering Center for Geographic Information of Anhui Province, Hefei 230601, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/nature10452","article-title":"Solutions for a Cultivated Planet","volume":"478","author":"Foley","year":"2011","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1126\/science.1185383","article-title":"Food Security: The Challenge of Feeding 9 Billion People","volume":"327","author":"Godfray","year":"2010","journal-title":"Science (80-)"},{"key":"ref_3","first-page":"114","article-title":"A Global Map of Rainfed Cropland Areas (GMRCA) at the End of Last Millennium Using Remote Sensing","volume":"11","author":"Biradar","year":"2009","journal-title":"Int. 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