Using hyperspectral reflectance to explore the responses of rice canopy chlorophyll fluorescence to water stress
Xu Jialong
Li Yawei
Xu Junzeng
Chen Shengyu
Yin Aojie
Liu Xiaoyin
Wei Qi
Zhou Xue
Abstract：[Objective]Chlorophyll fluorescence is a physiological indicator reflecting crop photosynthesis and water stress.Non-destructively monitoring the changes in chlorophyll fluorescence under water stress is critical for improving irrigation management.This paper explores the applicability of canopy hyperspectral reflectance for elucidating the response of rice canopy chlorophyll fluorescence to water stress.[Method]The experiment was conducted in pots and the measurements were taken during the booting stage of rice.Three water treatments were set,including continuous flooding irrigation(CK),mild drought(MS)and severe drought(HS).Canopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform,from which we analysed the responses of chlorophyll fluorescence traits to soil water change.Prediction models were developed to estimate chlorophyll fluorescence traits using partial least squares regression(PLSR)and backpropagation neural network(BPNN),based on characteristic spectral bands.[Result]①The chlorophyll fluorescence traits Fv/Fm,Y(II),qL and Y(NPQ)varied with water stress,with significant changes observed 3-4 days after cessation of irrigation,and detectable variation identified up to day 6 after terminating irrigation.On day 6 after irrigation cessation,the HS treatment reduced Fv/Fm,Y(II)and qL by 41.3%,46.9%and 53.1%,respectively,whereas increased Y(NPQ)by 117.5%compared with CK.②Savitzky-Golay smoothing and multiplicative scatter correction(MSC)preprocessing effectively reduced the scattering effects on canopy hyperspectral data induced by structural variation.The characteristic spectral bands selected from the hyperspectral data were mainly distributed in the blue(400-500 nm),red and near-infrared regions.③Compared with PLSR,the BPNN was more effective in capturing the nonlinear relationships between hyperspectral data and chlorophyll fluorescence traits.The BPNN was most accurate for estimating Y(NPQ)and qL,with the associated R2 values being 0.867 and 0.845,respectively,and less accurate for estimating Fv/Fm.[Conclusion]Canopy hyperspectral data can be used to estimate rice chlorophyll fluorescence traits.This approach provides a rapid,cost-effective,and non-destructive method for monitoring crop physiological responses to water stress.
Keywords：water stressricehyperspectral reflectancechlorophyll fluorescence parametersprediction models
Publication Date：2026-08-31
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 17-24 )