Results of these paired-sample t-tests.

Monitoring grassland productivity dynamics is essential for understanding the impacts of climate variation and human activities. Solar-induced chlorophyll fluorescence (SIF) has been validated as an effective indicator of gross primary productivity. Satellite-derived vegetation indices (VIs) have long been used as key proxies for vegetation productivity. However, the ability of different VIs to represent grassland productivity in relation to SIF, as well as their spatiotemporal consistency with SIF at various scales, remains unclear. In this study, we systematically compared the performance of the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Near-Infrared Reflectance of Vegetation (NIRv), using SIF as a benchmark in grassland areas of China. Utilizing TROPOMI SIF and MODIS VI datasets from 2018 to 2021, we analyzed the spatial and temporal consistency between VIs and SIF at a monthly scale and 0.05-degree resolution, employing Pearson correlation coefficients, paired-sample t-tests, and two-way Analysis of Variance (ANOVA). The results indicate that NIRv consistently demonstrates a higher capacity to capture variations in SIF compared to EVI and NDVI. In low-elevation areas with high-productivity grasslands, all three vegetation indices exhibit a stronger ability to represent vegetation productivity than in high-elevation areas with low-productivity vegetation types. These findings suggest that, at a monthly and regional spatiotemporal scale, NIRv can serve as a robust complement to SIF in monitoring vegetation productivity dynamics, particularly given the challenges in acquiring high-quality, long-term SIF data.