Identification of Non-Photosynthetic Vegetation Fractional Cover via Spectral Data Constrained Unmixing Algorithm Optimization

Non-photosynthetic vegetation fractional cover (fNPV) is a key indicator of vegetation decline and ecological health. Traditional inversion models assume identical spectral signatures for the same vegetation cover class across entire study areas. Spectral variations occur among regions due to divergent soil properties and vegetation types. To address this limitation, extensive ground sampling was conducted; ground observation data from multiple regions were utilized to establish localized spectral libraries, thereby enhancing spectral variability representation within the study area while concurrently optimizing vegetation indices across different sensor systems. The results reveal that, within the optimized spectral mixture analysis model, the coefficient of determination (R2) for fNPV using the NPV soil separation index (NSSI) for Sentinel sensor is 0.6258, and that of fPV using the modified soil adjusted vegetation index (MSAVI) is 0.8055. The MSAVI-NSSI achieved an R2 of 0.7825 for fNPV and 0.8725 for photosynthetic vegetation fractional cover (fPV). Optimized vegetation indices also yielded favorable validation results. Landsat’s theoretical predictions improved by 0.1725, with validated results up by 0.1635. MODIS showed improvements of 0.1365 and 0.1923, respectively. This enhancement significantly improves the accuracy of NPV fractional cover identification, providing critical insights for vegetation ecological health assessment in arid and semi-arid regions under global warming. Furthermore, by optimizing the spectral constraint weights in remote sensing images, a solution is provided for the long-term monitoring of vegetation health status.