中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
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, Wanxue1,2,7,8; Rezaei, Ehsan Eyshi2; Li, Jing7; Sun, Zhigang1,3,4,7; Zhang, Junqiang3,5,6
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
DOI10.3390/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.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.Leibniz Ctr Agr Landscape Res ZALF, D-15374 Muncheberg, Germany
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, CAS Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China
4.Shandong Dongying Inst Geog Sci, Dongying 257000, Peoples R China
5.Chinese Acad Sci, Changchun Inst Opt, Fine Mech & Phys, Changchun 130033, Peoples R China
6.Yusense Informat Technol & Equipment Qingdao Inc, Qingdao 266000, Peoples R China
7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, 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

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。