Potentiality of ultraspectral sensor in biophysical and biochemical vegetation parameter inversion

Abstract

ABSTRACT Biophysical and biochemical vegetation parameters are important indicators for quantitatively monitoring vegetation growth status and ecosystem. Compared to hyperspectral data, the emergence of ultraspectral remote sensing technology supports the acquisition of thousands of more narrow bands in near-infrared spectrum. It is helpful to capture more detailed vegetation spectral features, thereby providing more accurate quantitative inversion. However, there are still many unknowns in utilizing ultraspectral sensors for vegetation biochemical and biophysical parameters inversion, particularly in extracting meaningful inversion information from thousands of spectral bands. Here, we discuss the potentiality, efficiency and accuracy of the ultraspectral sensor AisaIBIS in this field. Firstly, we conducted sensitivity analysis based on simulated sensor dataset and the PROSAIL model to determine the vegetation parameters suitable for inversion. Secondly, in order to enhance the inversion efficiency and reduce the effects of multicollinearity in ultraspectral data, we proposed the stepped asymptotic optimization strategy that combined random forest (RF) and Gaussian process regression (GPR-BAT) methods for dimensionality reduction. Lastly, using experimental data, we compared the inversion accuracy between traditional band selection methods and our proposed method, thereby validating the application potential of ultraspectral sensors. The experimental results indicate that leaf area index (LAI), chlorophyll content, and brown pigment are the most sensitive parameters to the spectral information and are suitable for inversion. Based on the stepped asymptotic optimization strategy, we effectively avoid incomplete information extraction caused by high correlation and spectral redundancy, achieving optimal bands covering the entire spectrum of the sensor. The LAI inversion shows a correlation of 0.691 and an RMSE of 1.955 with the original data, outperforming the accuracy of the traditional band selection methods RF and principal component analysis (PCA). Moreover, the inversion time has decreased by approximately 66% after the dimensionality reduction. This study illustrates the application value of monitoring vegetation parameters by ultraspectral sensor.