For this study, the temperature win

For this study, the temperature window in the Threshold Window filtering algorithm was set at±3.5 ◦C to account for the MODIS LST accuracy of ~1 ◦C (1 σ), any variations in emissivity of different types of rocks, and effects of local topographic features that cannot be resolved at the spatial resolution of MODIS LST data. Many but not all mixed pixels of soil and snow will be filtered out with this setting. Adjusting the size of the Threshold Window decreases or increases the number of data points to be counted as part of a zero curtain, decreasing or increasing false negatives or false positives, but a better approach might be to reduce the pixel size. For example, horizontal changes in the extent of permafrost in Tibet Plateau can be estimated to ~460–920 m in 30 years after accounting for the maximum vertical change of permafrost base during the same period (80 m [56,57]) and the average slope of the interiorof the plateau (~5–10◦ [58]). The threshold window algorithm cannot capture changes at such a 100 m scale due to the relatively low resolution of the MODIS LST data. This limitation might be overcome by using ASTER data, with its higher spatial resolution than MODIS, but repeat acquisitions of ASTER images are too infrequent. A compromise may be possible with data fusion: Surface compositions can be mapped using the 15-m pixel−1 visible and 30-m pixel−1 near-infrared data while variations in the surface emissivity can be resolved from the 90-m pixel−1 ASTER products (Figure 11). With this information and the daily LST, it may be possible to generate the higher 90-m spatial resolution of the ASTER surface temperature product using the STARFM algorithm [59]. Data fusion might also be used with the high spatial but irregular and temporal resolution data of ECOSTRESS (Table 2). However, to monitor subtle changes in permafrost extent in the future, we likely will need data with both high spatial and high temporal resolution