National Earth System Science Data Center GLASS Land Surface Temperature (LST) Download:
1.GLASS LST （AVHRR 0.05° instantaneous（INT） ）
2.GLASS LST （AVHRR 0.05° orbital drift corrected (ODC)）
3.GLASS LST （AVHRR 0.05° monthly ）
Introduction:
The GLASS LST product uses a multi-algorithm ensemble approach based on the combination of nine split-window algorithms (SWA) with the Bayesian Model Averaging (BMA) model (Zhou et al. 2019). The purpose of the multi-algorithm ensemble approach is to obtain a stable estimate of LST. The individual SWAs were determined through global training, sensitivity analysis, and the global test of 17 candidate SWAs that are widely accepted by the scientific communities(Huang et al. 2016). We found that 11 of the 17 candidate SWAs have good accuracy in training; 9 of the 11 SWAs have low sensitivity to the uncertainties of the inputted land surface emissivity (LSE) and atmospheric column water vapor content (CWV)
References:
[1]Huang, F., Zhou, J., Tao, J., Tan, X., Liang, S., & Cheng, J. (2016). PMODTRAN: A parallel implementation based on MODTRAN for massive remote sensing data processing. International Journal of Digital Earth, 9, 819-834
[2]Zhou, J., Liang, S., Cheng, J., Wang, Y., & Ma, J. (2019). The GLASS Land Surface Temperature Product. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 12, 10.1109/JSTARS.2018.2870130
[3] Ma, J., Zhou, J., Gottsche, F.-M., Liang, S., Wang, S., and Li, M. (2020). A global long-term (1981–2000) land surface temperature product for NOAA AVHRR, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-143, in review, 2020.