Applicability of optimized hyperspectral indices for estimating Betalain content in Suaeda salsa

Abstract

ABSTRACT Betalains (Bt) are a collective group of natural, edible, red-yellow plant pigments, and are an important biochemical parameter in vegetable growth. Betalains are believed to have fungicidal activity and their presence in plants is a key to explaining the physiological response and resistance of plants caused by different environmental stress factors or seasonal fluctuations. Hyperspectral remote sensing has proven potential to understand many biochemical processes widely used in investigating vegetation growth condition. To better understand vegetation containing betalain pigments, for growth monitoring and agronomic decision-making, a novel betalain source, Suaeda salsa (S. salsa) was studied by extracting betalain at different phenological periods, and registering the results with time-course measurements of canopy spectral reflectance via hyperspectral remote imaging. Partial least squares regression (PLSR) analysis was undertaken with three spectral transformation methods (the raw hyperspectral reflectance (R), first derivative reflectance (FDR) and second derivative reflectance (SDR). The variable importance in projection (VIP) score resulting from PLSR model was used to determine the key spectral wavelengths and reduce the dimensionality of the hyperspectral reflectance data matrix. The study results demonstrated that Bt content had a significant correlation with the effective wavelength (FD-R 436nm, R 847nm, R 848nm, R 860nm, R 871nm, R 971nm and SD-R 437nm, R 496nm, R 499nm, R 508nm, R 740nm, R 772nm, R 774nm, R 780nm) and optimized spectral indices (simple normalized difference spectral indices (NDSI(802nm,408nm), NDSI(781nm,776nm)), normalized polarization indices (NPDI(781nm,775nm), NPDI(781nm,776nm)), ratio spectral indices (RSI(781nm,776nm), RSI(781nm,775nm)), chlorophyll indices(CI(781nm,776nm), CI(781nm, 775nm)) derived from first derivative spectral reflectance. The PLSR-5 (constructed with the spectral indices) model resulted in an R 2 val (determination coefficient) of 0.79 and root mean square error (RMSEval) was 0.81 μg cm−2. Compared to PLSR-3 model built by sensitive bands, the PLSR-5 model estimation accuracy increased 15%. These results suggest the possibility of estimating Bt content using hyperspectral indices, which contain sufficient information for a successful estimation. Study findings will help researchers in deciding suitable Bt indices for S. salsa growth status and stress assessment, and will facilitate detection growth condition of the vegetables with betalain pigments.