Dynamic estimation of rice aboveground biomass based on spectral and spatial information extracted from hyperspectral remote sensing images at different combinations of growth stages

Abstract

Aboveground biomass (AGB) is an essential factor in rice ecological research. Optical variables (e.g. reflectivity and vegetation index (VI)) are widely adopted in monitoring AGB while spatial information (e.g. texture) is less introduced into research. Limited research is available regarding the impact on the monitoring accuracy caused by the utility of VI and texture in AGB estimation at different combinations of growth stages. To compare the improvements caused by the involvement of texture at different combinations of growth stages, a hyperspectral camera was mounted on an unmanned aerial vehicle (UAV) to obtain images of rice field during the early growth stages (including the tillering, jointing and booting stage) under five nitrogen levels over two years. Spectral and spatial information derived from images was utilized to compute VIs and textures at different combinations of growth stages. After analyzing their correlations with AGB, multiple stepwise regression (MSR) and multiple linear regression (MLR) techniques were employed to establish rice AGB models using vegetation index (VI), vegetation index combined with the corresponding-band texture (VI-CBT) and vegetation index combined with the full-band texture (VI-FBT). It was found that the models using VI and texture enhanced the capability of the conventional VI to estimate AGB at different combinations of growth stages. The combination of VI and FBT achieved the most accurate estimation, followed by the VI-CBT. Different combinations of growth stages had diverse responses to textures. Overall, the tillering stage had the maximum response to the involvement of textures, followed by the booting stage. Models using VI-CBT and VI-FBT significantly improved the AGB estimation at the booting and tillering stage, respectively. The monitoring accuracy of the jointing stage showed a slight response to the involvement of texture. At the multiple growth stages, the monitoring effect of the tillering-booting stages was significantly improved, followed by the jointing-booting stages either using the model based on VI-CBT or VI-FBT. Models using VI and texture tended to yield larger improvements on the error values at the extreme AGB levels. Near-infrared and red-edge bands were the sensitive bands to estimate rice AGB, and MEA, COR were favorable textures to monitor the AGB. This study quantified the model improvements of using VI and texture in rice AGB estimation at different combinations of growth stages, delivering guidance for timely and accurate rice management in the field.