Investigating the impact of hyperspectral reconstruction techniques on the quantitative inversion of rice physiological parameters: A case study using the MST++ model

Abstract

Quantitative inversion is a significant topic in remote sensing science. The development of visible light-based hyperspectral reconstruction techniques has opened up novel prospects for low-cost, high-precision remote sensing inversion in agriculture. The aim of this study was to assesses the effectiveness of hyperspectral reconstruction technology in agricultural remote sensing applications. Hyperspectral images were reconstructed using the MST++ hyperspectral reconstruction model and compared with the original visible light images in terms of their correlation with physiological parameters, the accuracy of single-feature modeling, and the accuracy of combined feature modeling. The results showed that compared to the visible light image, the reconstructed data exhibited a stronger correlation with physiological parameters, and the accuracy was improved in both the single-feature and the combined feature inversion mode. However, compared to multispectral sensors, hyperspectral reconstruction provided limited improvement on the inversion model accuracy. It was concluded that for physiological parameters that are not easy to be directly observed, deep mining of features in visible light. data through hyperspectral reconstruction technology can improve the accuracy of the inversion model. Appropriate feature selection and simple models are more suitable for the remote sensing inversion task of traditional agronomic plot experiments. To strengthen the application of hyperspectral reconstruction technology in agricultural remote sensing, further development is necessary with broader wavelength ranges and more diverse agricultural scenes.