1.Landsurface temperature data

  Daily MODIS LST/emissivity products (MOD11A1 and MYD11A1 version 6) and the NDVI product (MOD13A2) are provided by NASA and available at https://www.earthdata.nasa.gov/.

  2.DEM data

  The Shuttle Radar Topography Mission 90m digital elevation database (SRTM/DEM, version 4) is available at https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/.

  3.Precipitation data

  The 1-km monthly precipitation dataset for China (Peng et al., 2019) is available at https://doi.org/10.5281/zenodo.3114194.

  4.Snow cover data

  The 500 m Daily Fractional Snow Cover Dataset Over High Asia (Qiu et al., 2017) is available at https://doi.org/10.11888/GlaciolGeocryol.tpe.0000016.file.

  5.Soil properties data

  The China Data Set of Soil Properties for Land Surface Modeling (Shangguan et al., 2013) is available at http://globalchange.bnu.edu.cn/research/soil2.

  6.Weather station data

  The China national surface weather stations (version 3.0) is provided by China National Meteorological Information Center and available at https://data.tpdc.ac.cn/en/data/52c77e9c-df4a-4e27-8e97-d363fdfce10a/.

  7.Borehole data

  The borehole ground temperature data provided by (Zhao et al., 2021) is available at https://doi.org/10.11888/Geocry.tpdc.271107.

  Computation of Ground Surface Freeze and Thaw Indices:

  The computation of ground surface freeze and thaw indices relied on Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data. To address gaps in LST data, we developed a specialized methodology. Initially, we employed a Bayesian approach to estimate clear-sky equivalent LST values (i.e., assuming cloud-free LST) [1]. Subsequently, cloudy LST values were derived by removing cloud effects from the clear-sky equivalent LST, utilizing a solar-cloud-satellite geometry (SCSG)-based method [2]. The remaining missing data comprised less than 10%. Daily mean LST was calculated, and conventional temporal and spatial interpolation techniques were applied to fill in the missing daily mean LST values, yielding annual daily mean LST data.

  Thermal Offset Correction:

  Due to the influence of seasonal snow cover or vegetation on ground surface temperature (GST) in permafrost regions, a typical thermal offset exists between GST and remotely sensed LST data products. Consequently, LST required correction to obtain GST, which was then employed to compute the ground surface freeze-thaw index for permafrost simulations. A regression relationship was established, utilizing site observations, among GST, LST, normalized vegetation index (NDVI), and latitude. This facilitated the estimation of daily mean GST from daily mean LST, subsequently leading to the computation of surface freeze and thaw indices based on the daily mean GST data.

  Mapping Permafrost Distribution on the Qinghai-Tibet Plateau Using Subregion Survey Maps:

  The FROSTNUM/COP method for permafrost mapping [3] employs subregion survey maps as constraints. Spatial clustering and parameter optimization were employed to estimate soil parameters capable of characterizing spatial heterogeneity in soil conditions relevant to permafrost distribution. This approach accounted for the influence of local factors on permafrost distribution, thereby enhancing simulation accuracy. Soil spatial clustering was achieved using the k-prototypes method, which can handle both numerical and categorical variables. Parameter optimization was carried out using the particle swarm algorithm. To address the challenge of parameter equifinality, effective constraints were introduced to ensure efficiency. Ultimately, utilizing previously acquired surface freeze-thaw indices as driving data and 2010 regional permafrost survey maps as optimization targets, we generated a map depicting the distribution of frozen ground types on the Qinghai-Tibet Plateau for the year 2010.

  Verification:

  A comparative analysis was conducted, contrasting the results with borehole data and existing permafrost distribution maps to validate accuracy.

  References

  [1] Chen Y, Nan Z, Zhao S, et al. "A Bayesian Approach for Interpolating Clear-Sky MODIS Land Surface Temperatures on Areas With Extensive Missing Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 14: 515-528.

  [2] Chen Y, Nan Z, Cao Z, et al. "A Stepwise Framework for Interpolating Land Surface Temperature under Cloudy Conditions Based on the Solar-Cloud-Satellite Geometry." ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 197: 292-308.

  [3] Hu J, Zhao S, Nan Z, et al. "An Effective Approach for Mapping Permafrost in a Large Area Using Subregion Maps and Satellite Data." Permafrost and Periglacial Processes, 2020, 31(4): 548-560.