The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing
文献类型:期刊论文
作者 | Yang, Bin3,4,6; Zhu, Wanxue1,2,5,8; Rezaei, Ehsan Eyshi1; Li, Jing5; Sun, Zhigang2,3,5,6; Zhang, Junqiang4,6,7 |
刊名 | REMOTE SENSING |
出版日期 | 2022-04-01 |
卷号 | 14期号:7页码:18 |
关键词 | unmanned aerial vehicle remote sensing maize yield multispectral |
DOI | 10.3390/rs14071559 |
通讯作者 | Sun, Zhigang(zhigang.sun@igsnrr.ac.cn) |
英文摘要 | Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R-2 increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R-2 = 0.93, RMSE = 0.77 t center dot ha(-1)). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction. |
WOS关键词 | RICE GRAIN-YIELD ; ABOVEGROUND BIOMASS ; VEGETATION INDEXES ; CHLOROPHYLL CONTENT ; SPECTRAL INDEXES ; NEURAL-NETWORKS ; CORN YIELD ; RGB IMAGES ; WHEAT ; NITROGEN |
资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23050102] ; Key Projects of the Chinese Academy of Sciences[KJZDSW-113] ; National Natural Science Foundation of China[31870421] ; National Natural Science Foundation of China[6187030909] ; Program of Yellow River Delta Scholars |
WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:000790654900001 |
资助机构 | Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Projects of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Program of Yellow River Delta Scholars |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/175936] |
专题 | 中国科学院地理科学与资源研究所 |
通讯作者 | Sun, Zhigang |
作者单位 | 1.Leibniz Ctr Agr Landscape Res ZALF, D-15374 Muncheberg, Germany 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Shandong Dongying Inst Geog Sci, Dongying 257000, Peoples R China 4.Yusense Informat Technol & Equipment Qingdao Inc, Qingdao 266000, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, CAS Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China 7.Chinese Acad Sci, Changchun Inst Opt, Fine Mech & Phys, Changchun 130033, Peoples R China 8.Univ Gottingen, Dept Crop Sci, D-37075 Gottingen, Germany |
推荐引用方式 GB/T 7714 | Yang, Bin,Zhu, Wanxue,Rezaei, Ehsan Eyshi,et al. The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing[J]. REMOTE SENSING,2022,14(7):18. |
APA | Yang, Bin,Zhu, Wanxue,Rezaei, Ehsan Eyshi,Li, Jing,Sun, Zhigang,&Zhang, Junqiang.(2022).The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing.REMOTE SENSING,14(7),18. |
MLA | Yang, Bin,et al."The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing".REMOTE SENSING 14.7(2022):18. |
入库方式: OAI收割
来源:地理科学与资源研究所
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