{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T03:27:50Z","timestamp":1775618870052,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"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":["31861143015"],"award-info":[{"award-number":["31861143015"]}],"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":["XDA23100202"],"award-info":[{"award-number":["XDA23100202"]}],"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":["22A03"],"award-info":[{"award-number":["22A03"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["31861143015"],"award-info":[{"award-number":["31861143015"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA23100202"],"award-info":[{"award-number":["XDA23100202"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["22A03"],"award-info":[{"award-number":["22A03"]}]},{"name":"Science and Technology Planning Project of Hebei Academy of Sciences of China","award":["31861143015"],"award-info":[{"award-number":["31861143015"]}]},{"name":"Science and Technology Planning Project of Hebei Academy of Sciences of China","award":["XDA23100202"],"award-info":[{"award-number":["XDA23100202"]}]},{"name":"Science and Technology Planning Project of Hebei Academy of Sciences of China","award":["22A03"],"award-info":[{"award-number":["22A03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As the second largest rice producer, India contributes about 20% of the world\u2019s rice production. Timely, accurate, and reliable rice yield prediction in India is crucial for global food security and health issues. Deep learning models have achieved excellent performances in predicting crop yield. However, the interpretation of deep learning models is still rare. In this study, we proposed a transformer-based model, Informer, to predict rice yield across the Indian Indo-Gangetic Plains by integrating time-series satellite data, environmental variables, and rice yield records from 2001 to 2016. The results showed that Informer had better performance (R2 = 0.81, RMSE = 0.41 t\/ha) than four other machine learning and deep learning models for end-of-season prediction. For within-season prediction, the Informer model could achieve stable performances (R2 \u2248 0.78) after late September, which indicated that the optimal prediction could be achieved 2 months before rice maturity. In addition, we interpreted the prediction models by evaluating the input feature importance and analyzing hidden features. The evaluation of feature importance indicated that NIRV was the most critical factor, while intervals 6 (mid-August) and 12 (mid-November) were the key periods for rice yield prediction. The hidden feature analysis demonstrated that the attention-based long short-term memory (AtLSTM) model accumulated the information of each growth period, while the Informer model focused on the information around intervals 5 to 6 (August) and 11 to 12 (November). Our findings provided a reliable and simple framework for crop yield prediction and a new perspective for explaining the internal mechanism of deep learning models.<\/jats:p>","DOI":"10.3390\/rs14195045","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T05:12:21Z","timestamp":1665378741000},"page":"5045","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuanyuan","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Shaoqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Lab of Reginal Ecological Processes and Environmental Change, School of Geography and Information Engineering, Chinese University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4891-5264","authenticated-orcid":false,"given":"Jinghua","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1042-5649","authenticated-orcid":false,"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiaobo","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Dongze","family":"Hao","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Leigang","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang 050011, China"},{"name":"Hebei Technology Innovation Center for Geographic Information Application, Shijiazhuang 050011, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41597-019-0036-3","article-title":"High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data","volume":"6","author":"Singha","year":"2019","journal-title":"Sci. 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