文献类型： 外文期刊

作者： Chen, Wenqian ¹ ; Huang, Yurong ¹ ; Tan, Wei ¹ ; Deng, Yujia ¹ ; Yang, Cuihong ² ; Zhu, Xiguang ² ; Shen, Jian ³ ; Liu, Nanfeng ¹ ;

作者机构： 1.Sun Yat Sen Univ, Sch Geog & Planning, Guangdong Prov Key Lab Urbanizat & Geosimulat, Carbon Water Res Stn Karst Reg Northern, Guangzhou 510275, Peoples R China

2.Huizhou Inst Agr Sci, Huizhou 516023, Peoples R China

3.Guangdong Acad Agr Sci, Key Lab Plant Nutr & Fertilizer South Reg, Minist Agr & Rural Affairs, Guangdong Key Lab Nutrient Cycling & Farmland Cons, Guangzhou 510640, Peoples R China

关键词： crop yield traits; hyperspectral remote sensing; structural equation modeling; indirect inversion

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2025 年 17 卷 12 期

页码：

收录情况： SCI

摘要： Potatoes, as the world's fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = -0.57), yield (-0.37), and starch content (-0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (-0.27), and dry weight (-0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R2 = 0.58-0.84) than indirect approaches (R2 = 0.16-0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral-yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction.