Beyond vegetation indices: A new spectrum–texture composite index for improved estimation of rice aboveground biomass

Precise estimation of rice aboveground biomass (AGB) is essential for assessing rice straw resources and their potential utilization as animal feed and lignocellulosic materials. Vegetation indices (VIs) often perform poorly at extreme AGB levels due to spectral saturation and canopy structure complexity. Texture characterizes the spatial distribution and structural traits of vegetation and could compensate for the limitations of spectral information in monitoring AGB, particularly under complex canopies and soil backgrounds. In this study, hyperspectral images covering the entire rice growth cycle were used to derive VIs and texture indices (TIs). Based on the mechanistic linkage between spectral and textural information, two spectrum-texture composite indices (STCIs) were developed: spectrum and corresponding-band texture composite index (SCBTCI) and spectrum and full-band texture composite index (SFBTCI). The optimal VIs, SCBTCIs, and SFBTCIs with the highest correlations with AGB were selected through correlation analysis and used to build models using linear regression. The results showed that STCI-based models consistently outperformed VI-based models. The three optimal SCBTCI models achieved an overall validation rRMSE of 0.271, while the three optimal SFBTCI models performed better with an overall validation rRMSE of 0.240. Compared with the VI 1[872,784] , SCBTCIs improved the estimation at low AGB level (overall=15.656%) and pre-heading stages (overall=14.706%), whereas SFBTCIs showed greater improvements at high AGB level (overall=27.082%) and post-heading stages (overall=23.424%). STCIs showed a stronger linear relationship with AGB and achieved accuracy comparable to or higher than machine learning methods. The great performance of STCIs under extreme AGB conditions demonstrated their capability to mitigate spectral saturation and reduce background interferences. STCIs provide an efficient framework for AGB monitoring, supporting the assessment of rice straw resources and their sustainable utilization as lignocellulosic biomass.