{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T10:10:52Z","timestamp":1772964652063,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"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":["62201063"],"award-info":[{"award-number":["62201063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42192580"],"award-info":[{"award-number":["42192580"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42192584"],"award-info":[{"award-number":["42192584"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China Major Program","award":["62201063"],"award-info":[{"award-number":["62201063"]}]},{"name":"National Natural Science Foundation of China Major Program","award":["42192580"],"award-info":[{"award-number":["42192580"]}]},{"name":"National Natural Science Foundation of China Major Program","award":["42192584"],"award-info":[{"award-number":["42192584"]}]},{"name":"Alibaba Innovative Research (AIR) project","award":["62201063"],"award-info":[{"award-number":["62201063"]}]},{"name":"Alibaba Innovative Research (AIR) project","award":["42192580"],"award-info":[{"award-number":["42192580"]}]},{"name":"Alibaba Innovative Research (AIR) project","award":["42192584"],"award-info":[{"award-number":["42192584"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The timely and accurate mapping of crops over large areas is essential for alleviating food crises and formulating agricultural policies. However, most existing classical crop mapping methods usually require the whole-year historical time-series data that cannot respond quickly to the current planting information, let alone for future prediction. To address this issue, we propose a novel spatial\u2013temporal feature and deep integration strategy for crop growth pattern prediction and early mapping (STPM). Specifically, the STPM first learns crop spatial\u2013temporal evolving patterns from historical data to generate future remote sensing images based on the current observations. Then, a robust crop type recognition model is applied by combining the current early data with the predicted images for early crop mapping. Compared to existing spatial\u2013temporal prediction models, our proposed model integrates local, global, and temporal multi-modal features comprehensively. Not only does it achieve the capability to predict longer sequence lengths (exceeding 100 days), but it also demonstrates a significant improvement in prediction accuracy for each time step. In addition, this paper analyses the impact of feature dimensionality and initial data length on prediction and early crop mapping accuracy, demonstrating the necessity of multi-modal feature fusion for spatial\u2013temporal prediction of high-resolution remote sensing data and the benefits of longer initial time-series (i.e., longer crop planting time) for crop identification. In general, our method has the potential to carry out early crop mapping on a large scale and provide information to formulate changes in agricultural conditions promptly.<\/jats:p>","DOI":"10.3390\/rs15133285","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T01:49:21Z","timestamp":1687830561000},"page":"3285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Crop Growth Patterns with Spatial\u2013Temporal Deep Feature Exploration for Early Mapping"],"prefix":"10.3390","volume":"15","author":[{"given":"Kaiyuan","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"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 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3125-2310","authenticated-orcid":false,"given":"Wenzhi","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"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 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4988-3484","authenticated-orcid":false,"given":"Jiage","family":"Chen","sequence":"additional","affiliation":[{"name":"National Geomatics Center of China, Beijing 100830, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4175-7590","authenticated-orcid":false,"given":"Liqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"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 100875, China"}]},{"given":"Duoduo","family":"Hu","sequence":"additional","affiliation":[{"name":"Alibaba DAMO Academy, Hangzhou 311121, China"}]},{"given":"Qiao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"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 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2209478119","DOI":"10.1073\/pnas.2209478119","article-title":"Population trends and the transition to agriculture: Global processes as seen from North America","volume":"120","author":"Milner","year":"2023","journal-title":"Proc. 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