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

Quantitative inversion is a major topic in remote sensing science. The development of visible light-based hyperspectral reconstruction techniques has opened novel prospects for low-cost, high-precision remote sensing inversion in agriculture. The aim of this study was to assess 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 correlations 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 the measured physiological parameters, and the accuracy was improved for both the single feature and combined feature inversion modes. However, compared to multispectral sensors, hyperspectral reconstruction provided limited improvement of the inversion model accuracy. The results suggest that for physiological parameters that are not easy to observe directly, deep mining of features in visible light data through hyperspectral reconstruction technology can improve the accuracy of the inversion model. However, 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 scenarios.