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A downscaling method for ERA5 reanalysis land surface temperature over urban and mountain areas

  • role： First author第一作者
  • Affiliation：

    School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

  • Email：zjh3smet@163.com
  • Introduction：朱佳恒，1997年生，男，硕士研究生， 研究方向为热红外遥感与气象应用。E-mail： zjh3smet@163.com
ZHU Jiaheng1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

  • Email：zsyzgx@163.com
  • Introduction：祝善友，1977年生，男，教授，研究方向为热红外遥感基础理论与应用、生态遥感。E-mail： zsyzgx@163.com
ZHU Shanyou1*,  
  • Affiliation：

    School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

YU Fachuan1,  
  • Affiliation：

    School of Geographical Science, Nanjing University of Information Science & Technology, Nanjing 210044, China

ZHANG Guixin2,  
  • Affiliation：

    School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

XU Yongming1

реферат

Land Surface Temperature (LST) is an important parameter in many research fields such as dynamic simulation of land surface processes, regional and global change analysis. It has always been a hot research topic on how to obtain land surface temperature with higher spatio-temporal resolution. Due to the limitation in the availability of satellite imagery data with high spatial and temporal resolution simultaneously, LST downscaling from coarse spatial resolution data is an effective method. Besides LST retrieved from microwave channels or thermal infrared bands, reanalysis dataset can provide long time series of hourly land surface temperature. If the reanalysis LST can be downscaled to produce reliable products with higher spatial resolution or not needs to be further studied.To compare downscaling results at various resolutions from raw reanalysis LST, two different regions in Zhangjiakou, Hebei province were selected as the test areas that represents the urban-rural and the mountainous characteristics respectively. LST at 100 m spatial resolution was retrieved by using Landsat 8 OLI/TIRS data through Mono-Window (MW) algorithm, which was then upscaled to different resolutions of 200 m, 500 m, 1000 m, 2000 m, 5000 m and 10000 m respectively. ERA5 LST data at the resolution of 10000 m is corrected based on Landsat 8 LST, which is downscaled to 6 resolutions by constructing and applying the random forest model. Elevation and six remotely sensed indices including NDVI, MASVI, MNDWI, NMDI, NDBI, NDBSI calculating from the corresponding OLI spectral reflectance were taken as the random forest model parameters. LST downscaling precisions at different spatial resolution within various landcover regions were then evaluated and discussed by using the derived Landsat 8 LST as the reference, and the feature importance of seven land surface parameters in random forest models changed with scales were also analyzed by comparing Gini index.The maximum, minimum and average values of the corrected ERA5 LST and the reference Landsat 8 LST are close at the resolution of 10000 m, but the standard deviation is lower than that of reference LST. The downscaling results of different resolutions can accurately express the LST spatial distribution characteristics for the two experiment areas. As the spatial resolution changes from 5000 m to 100 m, the downscaled LST texture accuracy is significantly improved, however Root Mean Square Error (RMSE) gradually increases and the correlation between the downscaled and the reference LST decreases. RMSE grows from 1.16 to 1.79 ℃ and from 1.61 to 2.49 ℃ for the urban-rural area and the mountains area respectively. For the random forest downscaling model, features importance have no significant change at different resolutions, which to some degree indicates that the random forest model has a relatively stable scale invariant characteristic. NDVI shows higher importance affecting LST distribution in two test areas, and elevation is the most important parameter in mountainous area.The research results show that ERA5 LST and Landsat 8 LST have good consistency in the spatial distribution, which means ERA5 LST has the potentiality to be downscaled to express detailed land surface temperature at higher resolutions. While downscaling ERA5 LST to higher spatial resolution, larger underestimation and overestimation errors will occur in the high temperature area and low temperature area respectively.

ключеви́че слова́

ERA5 reanalysis datasets;land surface temperature;Downscale;random forest;spatial scale

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