A model suitable for estimating above-ground biomass of potatoes at different regional levels

文献类型： 外文期刊

第一作者： Liu, Yang

作者： Liu, Yang;Fan, Yiguang;Ma, Yanpeng;Chen, Riqiang;Bian, Mingbo;Yang, Guijun;Feng, Haikuan;Yue, Jibo;Jin, Xiuliang

作者机构：

关键词： Potato; Hierarchical linear model; Hyperspectral; Meteorological data; Biomass

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：7.7； 五年影响因子：8.4 ）

ISSN： 0168-1699

年卷期： 2024 年 222 卷

页码：

收录情况： SCI

摘要： Above-ground biomass (AGB) is an important agronomic indicator that reflects crop growth and estimates yield. The AGB estimation using remote sensing becomes a non-destructive, rapid, and alternative method to postharvest laboratory measurements. However, most of the AGB estimation models constructed based on remote sensing data are difficult to expand regionally, which limits the applicability of the models. This study combined ground-based hyperspectral and meteorological data by using a hierarchical linear modeling (HLM) to construct an AGB estimation model that was generalized across different regions. Experimental data from both regions were acquired and validated, namely from Xiaotangshan Experimental Base, Beijing, 2019 (North China) and Keshan Farm, Qiqihar Branch, Heilongjiang General Bureau of Reclamation, 2022 (Northeast China). Compared to OLS, RFR and GRU, the HLM method was better for estimating potato AGB in different regions with R 2 = 0.54, RMSE = 429.62 kg/hm 2 , NRMSE = 28.20 %. The results of this study demonstrated that HLM could be used as a powerful method to improve the transferability of AGB estimation at different regions.

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