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Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method

文献类型：期刊论文

作者Liu, Yuanyuan1,2; Wang, Shaoqiang1,2,3; Chen, Jinghua1,2; Chen, Bin1,2; Wang, Xiaobo1,2; Hao, Dongze2,4; Sun, Leigang5,6
刊名REMOTE SENSING
出版日期2022-10-01
卷号14期号:19页码:21
关键词crop yield prediction remote sensing deep learning feature importance attention
DOI10.3390/rs14195045
通讯作者Wang, Shaoqiang(sqwang@igsnrr.ac.cn)
英文摘要As the second largest rice producer, India contributes about 20% of the world's 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 (R-2 = 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 (R-2 approximate to 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.
WOS关键词CROPPING SYSTEM ; WHEAT YIELD ; ASSIMILATION ; GROWTH
资助项目National Natural Science Foundation of China[31861143015] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23100202] ; Science and Technology Planning Project of Hebei Academy of Sciences of China[22A03]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000867043500001
出版者MDPI
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Science and Technology Planning Project of Hebei Academy of Sciences of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/185828]  
专题中国科学院地理科学与资源研究所
通讯作者Wang, Shaoqiang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Univ Geosci, Sch Geog & Informat Engn, Lab Reginal Ecol Proc & Environm Change, Wuhan 430074, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
5.Hebei Acad Sci, Inst Geog Sci, Shijiazhuang 050011, Hebei, Peoples R China
6.Hebei Technol Innovat Ctr Geog Informat Applicat, Shijiazhuang 050011, Hebei, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Yuanyuan,Wang, Shaoqiang,Chen, Jinghua,et al. Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method[J]. REMOTE SENSING,2022,14(19):21.
APA Liu, Yuanyuan.,Wang, Shaoqiang.,Chen, Jinghua.,Chen, Bin.,Wang, Xiaobo.,...&Sun, Leigang.(2022).Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method.REMOTE SENSING,14(19),21.
MLA Liu, Yuanyuan,et al."Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method".REMOTE SENSING 14.19(2022):21.

入库方式： OAI收割

来源：地理科学与资源研究所

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