文献类型： 外文期刊

作者： Fan, Yiguang ¹ ; Liu, Yang ¹ ; Yue, Jibo ³ ; Jin, Xiuliang ⁴ ; Chen, Riqiang ¹ ; Bian, Mingbo ¹ ; Ma, Yanpeng ¹ ; Yang, Guijun ¹ ; Feng, Haikuan ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Shaanxi, Peoples R China

3.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China

4.Chinese Acad Agr Sci, Key Lab Crop Physiol & Ecol, Minist Agr, Inst Crop Sci, Beijing 100081, Peoples R China

5.Nanjing Agr Univ, Coll Agr, Nanjing 210095, Jiangsu, Peoples R China

关键词： Potato; Yield; Remote sensing; Environmental variables

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 223 卷

页码：

收录情况： SCI

摘要： Timely monitoring of potato yield status is crucial for guiding field management and ensuring the sustainability of food trade. In recent years, non -destructive remote sensing techniques have emerged as one of the most promising methods for crop yield monitoring. However, most existing potato yield estimation models have primarily been tested at specific growth stages within a single ecosystem, highlighting the urgent need for a model that can be applied across different ecological environments. In this study, we developed a semi-physical model based on hierarchical linear modeling (HLM) to automatically correct yield estimation based on remotely sensed vegetation indices using environmentally relevant variables such as net solar radiation at the surface (ssr), air temperature at 2 m above the surface (t2m), and soil water content in the root zone (swvl). The accuracy and transferability of the model were further evaluated by comparing it with aboveground destructive sampling methods, conventional empirical models, and machine learning approaches. The results demonstrated that sitespecific relationships exist between aboveground agronomic traits parameters like biomass and leaf area index (LAI) with respect to yield estimation. Conventional linear statistical and machine learning methods faced challenges in transferring their constructed yield estimation models across regions and years. However, the HLM approach exhibited excellent generalization ability across all trial samples. In the middle and later stages of potato growth, the standardized LAI determining index proved to be the most effective vegetation for predicting yield, while ssr, skt max , and shallow swvl were identified as key environmental variables affecting yield. By integrating these variables using HLM modeling techniques, we achieved optimal generalization performance (R 2 = 0.57, RMSE = 7.65 t/ha, NRMSE = 25.02 % during tuber growth stage; R 2 = 0.60, RMSE = 7.37 t/ha, NRMSE = 24.13 % during starch accumulation stage). This study underscores the importance of considering environmental factors when constructing a reliable model for estimating potato yield at different spatial and temporal scales using remote sensing technology.