{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T20:31:47Z","timestamp":1775766707736,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Heilongjiang Provincial Natural Science Foundation of China","award":["No. YQ2022E004"],"award-info":[{"award-number":["No. YQ2022E004"]}]},{"name":"Heilongjiang Provincial Natural Science Foundation of China","award":["No. LBH-TZ2211"],"award-info":[{"award-number":["No. LBH-TZ2211"]}]},{"name":"Heilongjiang Provincial Natural Science Foundation of China","award":["No. 2022YFD2001401-02"],"award-info":[{"award-number":["No. 2022YFD2001401-02"]}]},{"name":"Heilongjiang Provincial Natural Science Foundation of China","award":["No. NEAU2023QNLJ-018"],"award-info":[{"award-number":["No. NEAU2023QNLJ-018"]}]},{"name":"Hei Long Jiang Postdoctoral Foundation","award":["No. YQ2022E004"],"award-info":[{"award-number":["No. YQ2022E004"]}]},{"name":"Hei Long Jiang Postdoctoral Foundation","award":["No. LBH-TZ2211"],"award-info":[{"award-number":["No. LBH-TZ2211"]}]},{"name":"Hei Long Jiang Postdoctoral Foundation","award":["No. 2022YFD2001401-02"],"award-info":[{"award-number":["No. 2022YFD2001401-02"]}]},{"name":"Hei Long Jiang Postdoctoral Foundation","award":["No. NEAU2023QNLJ-018"],"award-info":[{"award-number":["No. NEAU2023QNLJ-018"]}]},{"name":"National Key R&amp;D Program of China","award":["No. YQ2022E004"],"award-info":[{"award-number":["No. YQ2022E004"]}]},{"name":"National Key R&amp;D Program of China","award":["No. LBH-TZ2211"],"award-info":[{"award-number":["No. LBH-TZ2211"]}]},{"name":"National Key R&amp;D Program of China","award":["No. 2022YFD2001401-02"],"award-info":[{"award-number":["No. 2022YFD2001401-02"]}]},{"name":"National Key R&amp;D Program of China","award":["No. NEAU2023QNLJ-018"],"award-info":[{"award-number":["No. NEAU2023QNLJ-018"]}]},{"name":"Northeast Agricultural University 2023 Young Leading Talent Support Program","award":["No. YQ2022E004"],"award-info":[{"award-number":["No. YQ2022E004"]}]},{"name":"Northeast Agricultural University 2023 Young Leading Talent Support Program","award":["No. LBH-TZ2211"],"award-info":[{"award-number":["No. LBH-TZ2211"]}]},{"name":"Northeast Agricultural University 2023 Young Leading Talent Support Program","award":["No. 2022YFD2001401-02"],"award-info":[{"award-number":["No. 2022YFD2001401-02"]}]},{"name":"Northeast Agricultural University 2023 Young Leading Talent Support Program","award":["No. NEAU2023QNLJ-018"],"award-info":[{"award-number":["No. NEAU2023QNLJ-018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The prediction of crop yield plays a crucial role in national economic development, encompassing grain storage, processing, and grain price trends. Employing multiple sensors to acquire remote sensing data and utilizing machine learning algorithms can enable accurate, fast, and nondestructive yield prediction for maize crops. However, current research heavily relies on single-type remote sensing data and traditional machine learning methods, resulting in the limited robustness of yield prediction models. To address these limitations, this study introduces a field-scale maize yield prediction model named the convolutional neural network\u2013attention\u2013long short-term memory network (CNN-attention-LSTM) model, which utilizes multimodal remote sensing data collected by multispectral and light detection and ranging (LIDAR) sensors mounted on unmanned aerial vehicles (UAVs). The model incorporates meteorological data throughout the crop reproductive stages and employs the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) for the initial part of the vegetative stage (initial part of the V period), the later part of the vegetative stage (later part of the V period), the reproductive stage (R period), and the maturity stage (M period), along with LIDAR data for Point75\u2013100 in the later part of the V period, Point80\u2013100 in the R period, and Point50\u2013100 in the M period, complemented by corresponding meteorological data as inputs. The resulting yield estimation demonstrates exceptional performance, with an R2 value of 0.78 and an rRMSE of 8.27%. These results surpass previous research and validate the effectiveness of multimodal data in enhancing yield prediction models. Furthermore, to assess the superiority of the proposed model, four machine learning algorithms\u2014multiple linear regression (MLR), random forest regression (RF), support vector machine (SVM), and backpropagation (BP)\u2014are compared to the CNN-attention-LSTM model through experimental analysis. The outcomes indicate that all alternative models exhibit inferior prediction accuracy compared to the CNN-attention-LSTM model. Across the test dataset within the study area, the R2 values for various nitrogen fertilizer levels consistently exceed 0.75, illustrating the robustness of the proposed model. This study introduces a novel approach for assessing maize crop yield and provides valuable insights for estimating the yield of other crops.<\/jats:p>","DOI":"10.3390\/rs15143483","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T01:01:41Z","timestamp":1689123701000},"page":"3483","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A Prediction Model of Maize Field Yield Based on the Fusion of Multitemporal and Multimodal UAV Data: A Case Study in Northeast China"],"prefix":"10.3390","volume":"15","author":[{"given":"Wenqi","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Chao","family":"Song","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Cunliang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4100-2777","authenticated-orcid":false,"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Tianhao","family":"An","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9834-8854","authenticated-orcid":false,"given":"Yijia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, LG-108, Composite Building, Pokfulam Road, Hong Kong 999077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0938-412X","authenticated-orcid":false,"given":"Xiaobo","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Nuan","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Han","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin 150030, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/s43016-021-00322-9","article-title":"A meta-analysis of projected global food demand and population at risk of hunger for the period 2010\u20132050","volume":"2","author":"Morley","year":"2021","journal-title":"Nat. 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