干旱区生态环境知识资源中心(Arid): Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Arid

DOI	10.3390/s21041406
	Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index
	Ji, Zhonglin; Pan, Yaozhong; Zhu, Xiufang; Wang, Jinyun; Li, Qiannan
通讯作者	Pan, YZ (corresponding author), Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China. ; Pan, YZ (corresponding author), Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China. ; Pan, YZ (corresponding author), Qinghai Normal Univ, Acad Plateau Sci & Sustainabil, Xining 810016, Peoples R China.
来源期刊	SENSORS
EISSN	1424-8220
出版年	2021
卷号	21 期号:4
英文摘要	Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R-2). Duration and rate represent crop growth days and rate, respectively. Max-R-2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R-2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R-2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R-2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R-2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R-2 models, the accuracy of models built with Max-R-2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.
英文关键词	yield prediction corn MODIS NDVI time series crop phenology growth phase length growth rate
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000624674700001
WOS关键词	NDVI TIME-SERIES ; FORECASTING WHEAT ; WINTER-WHEAT ; CORN ; MODEL ; MAIZE ; QUANTIFICATION ; PRODUCTIVITY ; IRRIGATION ; SENESCENCE
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
来源机构	北京师范大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351765
作者单位	[Ji, Zhonglin; Pan, Yaozhong; Zhu, Xiufang; Wang, Jinyun; Li, Qiannan] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China; [Ji, Zhonglin; Pan, Yaozhong; Zhu, Xiufang; Wang, Jinyun; Li, Qiannan] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China; [Ji, Zhonglin; Zhu, Xiufang; Wang, Jinyun; Li, Qiannan] Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing 100875, Peoples R China; [Pan, Yaozhong] Qinghai Normal Univ, Acad Plateau Sci & Sustainabil, Xining 810016, Peoples R China
推荐引用方式 GB/T 7714	Ji, Zhonglin,Pan, Yaozhong,Zhu, Xiufang,et al. Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index[J]. 北京师范大学,2021,21(4).
APA	Ji, Zhonglin,Pan, Yaozhong,Zhu, Xiufang,Wang, Jinyun,&Li, Qiannan.(2021).Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index.SENSORS,21(4).
MLA	Ji, Zhonglin,et al."Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index".SENSORS 21.4(2021).