Applicability evaluation and correction of CLDAS surface temperature products in permafrost region of Qinghai-Tibet Plateau

doi:10.12006/j.issn.1673-1719.2023.033

Abstract

Abstract:

Land surface temperature (LST) is a crucial parameter to characterize the surface thermal state and conduct research on the surface hydrothermal and ecological processes. The available LST data in the permafrost region of the Qinghai-Tibet Plateau mainly include the observed data of land surface and shallow ground temperature, remote sensing derive data, model simulation data and reanalysis data. CLDAS dataset has a good performance in most regions of China. However, due to the lack of measured data in permafrost regions of the Qinghai-Tibet Plateau and insufficient consideration of the underlying surface of permafrost, the applicability of CLDAS in the Qinghai-Tibet Plateau needs to be further evaluated. Based on the measured LST data of seven stations in the permafrost region, the CLDAS LST data from 2008 to 2018 were evaluated in different freeze-thaw periods and underlying surface types. The results showed significant errors between CLDAS and the measured values (bias=2.09℃, MAE=3.64℃, RMSE=4.67℃, R²=0.83), mainly performed that CLDAS overestimated the measured LST. The applicability of CLDAS LST is good in the thawing period, but poor in the freeze-thaw alternating period (MAE=3.78℃) and freezing period. And it’s better in the alpine desert and alpine desert steppe than that in alpine meadow. Therefore, a multiple stepwise regression correction model was established based on the influences of NDVI, NDSI, snow depth, elevation, slope, aspect and soil texture factors on LST. The correction model took the differences of underlying surface conditions into account and improved the simulation accuracy of CLDAS LST. The results show that the correction models constructed by freezing period, thawing period and alternating period separately performed better than a single model. The accuracy of corrected CLDAS LST by three-period models was significantly improved (bias=-0.11℃, MAE=2.42℃, RMSE=3.23℃, R²=0.89).

Key words: Land surface temperature, CLDAS, Qinghai-Tibetan Plateau, Applicability evaluation, Multiple regression correction

HU Jia-Yi, ZHAO Lin, WANG Chong, HU Guo-Jie, ZOU De-Fu, XING Zan-Pin, JIAO Meng-Di, QIAO Yong-Ping, LIU Guang-Yue, Du Er-Ji. Applicability evaluation and correction of CLDAS surface temperature products in permafrost region of Qinghai-Tibet Plateau[J]. Climate Change Research, 2024, 20(1): 10-25.

Figures/Tables 15

Fig. 1 Distribution of stations in study area

Table 1 Information of stations in study area

Fig. 2 Measured land surface temperature at different stations in different freeze-thaw periods during 2008-2018

Fig. 3 Measured land surface temperature for different vegetation cover types in 2008-2018. (a) All periods, (b) freezing period, (c) alternate period, (d) melting period

Table 2 Accuracy evaluation of CLDAS land surface temperature and measured land surface temperature in different freeze-thaw periods during 2008-2018

Table 3 Assessment of CLDAS land surface temperature for different vegetation types during 2008-2018

Table 4 Correlation between measured land surface temperature and various influencing factors in 2008-2018

Table 5 Correction models of CLDAS land surface temperature

Table 6 Accuracy evaluation and improvement degree of verification samples of single model and 3 models before and after correction (2008-2018)

Fig. 4 Evaluation indicators of verification samples before and after correction for single model and 3 models at different stations in 2008-2018

Fig. 5 Spatial distribution of evaluation indicators before and after correction of verification samples at different stations in 2008-2018

Table 7 Accuracy evaluation and improvement degree of verified samples before and after correction in different freeze-thaw periods during 2008-2018

Fig. 6 Evaluation indicators before and after correction of verification samples at different stations in freeze-thaw periods during 2008-2018

Table 8 Accuracy evaluation of different vegetation cover types of verification samples before and after correction in 2008-2018

Fig. 7 Time series of land surface temperature at different stations in 2008-2018

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