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Snow cover is a sensitive indicator of climate change. Normalized Difference Snow Index (NDSI) acquired from optical remote sensing data is usually used for monitoring snow cover, but the existing data are limited in spatiotemporal resolution. Here, we compared two blending strategies, Blend-then-Index (BI) and Index-then-Blend (IB), for generating high spatiotemporal resolution NDSI (daily, 20 m), and designed two groups of experiments (simulated and real) under three different snow cover periods over the Tibetan Plateau (TP). The Flexible Spatiotemporal DAta Fusion (FSDAF) model was used as the fusion model. MODIS (daily, 500 m) and Sentinel-2A&#x002F;B (2&#x2013;5 days, 20 m) data were used as the inputs. The accuracy of the fused NDSI was evaluated from both spectral (root mean square error &#x005B;RMSE&#x005D;, correlation coefficient &#x005B; R &#x005D;, and average difference &#x005B;AD&#x005D;) and spatial (Robert&#x0027;s edge &#x005B;Edge&#x005D;, and local binary pattern &#x005B;LBP&#x005D;) dimensions. Our results showed that the IB strategy produced more accurate NDSI results, with lower RMSE, higher  R , and AD closer to zero compared to the BI strategy. In addition, there was no obvious difference in terms of texture between the two fusion strategies. Generally, the IB strategy is a better choice for generating a high spatiotemporal resolution NDSI through the FSDAF model under different snow cover periods on the TP. This study can provide effective guidelines for producing better high-resolution NDSI time series on the TP.

BI or IB: Which Better Generates High Spatiotemporal Resolution NDSI by Fusing Sentinel-2A/B and MODIS Data?