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• Based on UAV images, the effectiveness and noise susceptibility of 18 vegetation indices in estimating wetland plant species diversity were evaluated. • Revealed that MTCI and NDREI are the most powerful vegetation indices in predicting wetland plant species diversity. • It demonstrates the complementarity of the mean and standard deviation of vegetation indices in predicting plant species diversity. Prior research on vegetation indices (VIs) to estimate species diversity in forest and grassland ecosystems has shown limitations when applied to wetland ecosystems due to their complex structure. Consequently, the predictive capacity of various VIs for wetland plant species diversity and their susceptibility to image noise remain largely unknown. To address these gaps, we utilized high-resolution multispectral images from Unmanned Aerial Vehicles (UAV) and field survey data in marsh wetlands. Various VIs and species diversity indices were computed, and univariate and multiple linear regression models were employed to assess predictive ability of the mean, standard deviation, and coefficient of variation of VIs for wetland plant species diversity. Results revealed that MTCI and NDREI exhibited the highest predictive ability for plant species diversity both before and after masking image noise. While most VIs generally improved in predictive ability after masking image noise, their susceptibility to it varied. MTCI, NDREI, SAVI, MSAVI, and MSAVI2 were less affected by image noise, with minimal changes in predictive ability before and after masking image noise. Conversely, CTVI, NDVI, and other VIs showed high susceptibility to image noise, with significant improvement in predictive ability after masking. ANOVA results indicated that integrating the mean and standard deviation of VIs into models significantly enhanced estimates of species diversity, highlighting their complementarity in predicting species diversity. These findings provide valuable insights into the predictive ability of different VIs for wetland plant species diversity, guiding selection of optimal VIs for predicting plant species diversity at broader scales in the future. [ABSTRACT FROM AUTHOR]

• The combined use of NDVI MEAN and NDVI SD significantly improved the predictive ability for plant species diversity than NDVI MEAN alone. • Previous study had underestimated the potential of predicting plant species diversity based on NDVI-related indices. • Hill numbers showed its high capacity in spectral-species diversity research. The development of near-Earth remote sensing platforms, such as unmanned aerial vehicles (UAV), have provided new opportunities for wetland plant diversity monitoring. Most previous studies on the relationship between spectral and species diversity have focused on the development and use of spectral indices. However, commonly used species diversity indices may not be the best choice for spectral-species diversity research. In this paper, high spatial resolution multispectral images of freshwater marsh were obtained based on UAV in Sanjiang National Nature Reserve, Northeast China, and the commonly used species diversity indices (Richness, Shannon, and Gini-Simpson) and Hill numbers were calculated based on 135 quadrats information obtained from field surveys. The mean value of NDVI (NDVI MEAN), its standard deviation (NDVI SD) and coefficient of variation (NDVI CV) of each quadrat were calculated based on multispectral imagery as a proxy for the spectral indices of the quadrats. The univariate and multivariate linear models were employed to test the predictive ability of NDVI-related indices for commonly used species diversity indices and Hill numbers. The results showed that the predictive ability of NDVI MEAN for species diversity indices was limited, and the combined use of NDVI MEAN and NDVI SD significantly improved the predictive ability of species diversity. The predictive ability of NDVI-related indices to Hill numbers is better than that of commonly used species diversity indices. Commonly used species diversity indices can only represent one or several "point" of the community species diversity, the Hill numbers provide a continuous measure of community species diversity, which can balance the inconsistency between the abundance and coverage of species in the community. Previous spectral-species diversity studies might not have shown the real predictive ability of species diversity based on NDVI-related indices. Our study provides innovative ideas for the selection of species diversity indices in future spectral-species diversity studies. [ABSTRACT FROM AUTHOR]