Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index
文献类型： 外文期刊
作者： Qi, Yaqin 1 ; Chen, Xi 1 ; Chen, Zhengchao 1 ; Zhang, Xin 2 ; Shen, Congju 2 ; Chen, Yan 3 ; Peng, Yuanying 4 ; Chen, Bing 2 ; Wang, Qiong 2 ; Liu, Taijie 2 ; Zhang, Hao 1 ;
作者机构： 1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
2.Xinjiang Acad Agr & Reclamat Sci, Res Inst, Shihezi 832003, Peoples R China
3.Shihezi Univ, Oasis Key Lab Ecol Agr Xinjiang, Shihezi 832003, Peoples R China
4.Lewis Univ, Coll Arts & Sci, Romeoville, IL 60446 USA
关键词： spectral vegetation index; cotton canopy information; estimation model
期刊名称：LIFE-BASEL （ 影响因子：3.4； 五年影响因子：3.4 ）
ISSN：
年卷期： 2025 年 15 卷 1 期
页码：
收录情况： SCI
摘要： The spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were collected at various growth stages using an ASD FieldSpec Pro VNIR 2500 spectrometer. Six prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI), to estimate the Leaf Area Index (LAI) and above-ground biomass. For LAI estimation using the NDVI, the power function model (y = 10.083x11.298) demonstrated higher precision, with a multiple correlation coefficient of R2 = 0.8184 and the smallest root mean square error (RMSE = 0.3613). These results confirm the strong predictive capacity of NDVI for LAI, with the power function model offering the best estimation accuracy. In estimating above-ground biomass using RVI, the power function model of y = 6.5218x1.33917 achieved the higher correlation (R2 = 0.8851) for fresh biomass with an RMSE of 0.1033, making it the most accurate. For dry biomass, the exponential function model (y = 9.1565 x 10-5 center dot exp(1.1146x)) was the most precise, achieving an R2 value of 0.8456 and the lowest RMSE value of 0.0076. These findings highlight the potential of spectral remote sensing for accurately predicting cotton canopy structural parameters and biomass weights. By integrating spectral analysis techniques with remote sensing, this research offers valuable insights for precision cotton planting and field management, enabling optimized agricultural practices and enhanced vegetation health monitoring.
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