Estimation Model for Cotton Canopy Structure Parameters Based on Spectral Vegetation Index.

Abstract

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 = 10.083x 11.298 ) demonstrated higher precision, with a multiple correlation coefficient of R 2 = 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 = 6.5218x 1.33917 achieved the higher correlation ( R 2 = 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 × 10 -5 ∙exp(1.1146x) ) was the most precise, achieving an R 2 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.

Plant phenotyping relevance

綿花のLAI・地上部バイオマスという植物形質を分光反射データと植生指数から推定するモデルを開発・精度評価しており、形質取得手法が中心である。

Code and data availability

The paper reports cotton canopy spectral measurements (ASD FieldSpec Pro, LAI, biomass) and regression models, but provides no public dataset, image/sensor data deposit, or author analysis code. The Data Availability Statement only offers contact with corresponding authors, and the only URLs are the CC-BY license, a Bб

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