干旱区生态环境知识资源中心(Arid): Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model

Arid

DOI	10.1016/j.ecolmodel.2021.109651
	Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model
	Ji, Zhonglin; Pan, Yaozhong; Li, Nan
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, 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	ECOLOGICAL MODELLING
ISSN	0304-3800
EISSN	1872-7026
出版年	2021
卷号	455
英文摘要	This paper describes integrating remote sensing and meteorological data for dynamic winter wheat yield prediction at fixed date intervals with an integral regression model. In most cases, both rainfall and irrigation supply the water needs of crops. We postulated that meteorological precipitation could not fully represent the actual amount of water available to crops, whereas the temperature vegetation dryness index (TVDI) can represent actual water, which monitors soil moisture. According to this, meteorological and remote-sensing data based on sunshine hours, temperature, and TVDI formed a light-temperature-water data combination, which are the three most fundamental and critical factors related to crop growth. The new data combination was used as the input variables of the integral regression model, which could predict the crop yield at a fixed date interval of any length. The dynamic results were then compared with that of the meteorology data combination (sunshine hours, temperature, and precipitation). We selected a total of seven county-level areas in semi-humid and arid regions in China. The proposed dynamic prediction approach achieved good results at the county level from March 21 to maturity at 16-day intervals with predicted R2 > 0.94 and root mean squared error range of 15.54-121.01 kg ha-1 between predicted and actual yield with leave-one-year-out cross-validation for each prediction date. The trend of dynamic prediction accuracy had a relatively stable state. In addition, TVDI contained more crop water information, and prediction results using TVDI and meteorology were generally superior to that using only meteorology for semi-humid and arid areas, with the best results for arid regions. Thus, integrating meteorology and TVDI could effectively dynamically predict yield at county level for most agroclimatic regions, and particularly for arid regions. The work has implications for dynamic yield prediction and improving prediction accuracy and can be extended to other areas.
英文关键词	Yield prediction Winter wheat MODIS TVDI Precipitation County level
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000670630000008
WOS关键词	REMOTE-SENSING INFORMATION ; PATH COEFFICIENT ANALYSIS ; WINTER-WHEAT YIELD ; SOIL-MOISTURE ; SURFACE-TEMPERATURE ; MAIZE YIELD ; TIME-SERIES ; WATER-STRESS ; CLIMATE DATA ; RADIATION
WOS类目	Ecology
WOS研究方向	Environmental Sciences & Ecology
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/2XILL650/350026
作者单位	[Ji, Zhonglin; Pan, Yaozhong; Li, Nan] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China; [Ji, Zhonglin; Pan, Yaozhong; Li, Nan] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China; [Pan, Yaozhong] Qinghai Normal Univ, Acad Plateau Sci & Sustainabil, Xining 810016, Peoples R China; [Ji, Zhonglin; Li, Nan] Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing 100875, Peoples R China
推荐引用方式 GB/T 7714	Ji, Zhonglin,Pan, Yaozhong,Li, Nan. Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model[J]. 北京师范大学,2021,455.
APA	Ji, Zhonglin,Pan, Yaozhong,&Li, Nan.(2021).Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model.ECOLOGICAL MODELLING,455.
MLA	Ji, Zhonglin,et al."Integrating the temperature vegetation dryness index and meteorology parameters to dynamically predict crop yield with fixed date intervals using an integral regression model".ECOLOGICAL MODELLING 455(2021).