文献类型： 外文期刊
作者： Xue, Hanyu 1 ; Xu, Xingang 1 ; Zhu, Qingzhen 1 ; Meng, Yang 2 ; Long, Huiling 2 ; Li, Heli 2 ; Song, Xiaoyu 2 ; Yang, Guijun 2 ; Yang, Min 2 ; Li, Yafeng 1 ; Jiang, Xiangtai 2 ;
作者机构： 1.Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Minist Agr & Rural Affairs, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China
关键词： Hierarchical linear model; Rice yield and quality; Estimation model
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）
ISSN： 0168-1699
年卷期： 2024 年 218 卷
页码：
收录情况： SCI
摘要： Timely and accurate estimation of crop yield and quality can provide a practical and effective basis for the formulation of national food policies. Based on remote sensing data of rice multi-growth stages from 2021 to 2022 in Ninghe District of Tianjin, the methods coupling hierarchical linear model (HLM) with typical vegetation indices and meteorological data were used to estimate yield and quality in rice. The results showed that: (1) Compared with the multiple linear regression model, the methods of estimating rice yield and quality based on HLM had higher stability in interannual expansion; (2) For the three growth stages of rice, namely, jointing, booting and filling stage, the hierarchical linear model of yield and quality estimation at booting stage showed the highest performance. The R<^>2 of the GRVI rice yield estimation model, MSR rice amylose content estimation model, and RDVI rice protein content estimation model were 0.52, 0.70, and 0.88, and RMSE were 1.17 t/ha, 0.85 %, and 0.33 %, respectively. The nRMSE was 11.06 %, 4.46 %, and 3.37 %, respectively. The HLM rice yield and quality estimation models based on multi-growth period data were further established. The GCI rice yield estimation model, MSR amylose content estimation model, and DVI protein content estimation model R<^>2 were 0.67, 0.75, and 0.91, and RMSE were 1.01 t/ha, 0.78 %, and 0.30 %. The nRMSE was 9.48 %, 4.07 %, and 3.05 %. It indicates that the method using HLM has a good potential for accessing yield and quality in rice and shows better scalability at interannual and regional scales.
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