Obtaining high temporal and spatial resolution spectral data is the key to revealing the influencing factors, effects, and mechanisms of land-atmosphere interactions in deserts. This study, we used MODIS and Sentinel-2 data as data sources to calculate daily reflectance and Normalized Difference Vegetation Index (NDVI) data with a spatial resolution of 10 m, based on the Spatiotemporal Fusion Incorporating Spectral Autocorrelation (FIRST) model, across different climatic zones in the Hobq Desert, northern China, in March. Then, we evaluated the adaptability of the FIRST model in the Hobq Desert based on spatial and textural characteristics, as well as spatial-temporal distribution characteristics, using qualitative analysis, quantitative analysis, and geographic detectors. The results show that the correlation coefficients of First fused data and Sentinel-2 data in red, green, blue, near-infrared bands, and NDVI were 0.574 (p < 0.01), 0.448 (p < 0.01), 0.485 (p < 0.01), 0.573 (p < 0.01), and 0.625 (p < 0.01), and the scatter points were evenly distributed on both sides of y = x. Meanwhile, FIRST NDVI and Sentinel-2 NDVI maintained consistency in spatial texture and hue changes, with similar value ranges. The daily scale coefficient of variation (CV) of FIRST NDVI in different desert types were less than that of MODIS NDVI. Among them, the variability of FIRST NDVI in fixed dunes was significantly smaller than that of MODIS NDVI, with the former’s CV being 0.034 smaller than the latter’s. Besides, it was found that there were significant differences in First NDVI among different desert types based on risk detection, while MODIS NDVI showed insignificant differences between fixed dunes and semi-fixed dunes. This suggests that First model integrated effectively various types of remote sensing data and had strong applicability in the eastern part of Hobq Desert, which could distinguish between fixed dunes and semi-fixed dunes, providing a more accurate monitoring tool for environmental zoning management in desert areas.
