Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia

MODIS land surface temperature data (MODIS T-s) products are quantified from the earth surface's reflected thermal infrared signal via sensors onboard the Terra and Aqua satellites. MODIS T-s products are a great value to many environmental applications but often subject to discrepancies when compared to the air temperature (T-a) data that represent the temperature measured at 2 m above the ground surface. Although they are different in their nature, the relationship between T-s and T-a has been established by many researchers. Further validation and correction on the relationship between these two has enabled the estimation of T-a from MODIS T-s products in order to overcome the limitation of T-a that can only provide data in a point form with a very limited area coverage. Therefore, this study was conducted with the objective to assess the accuracy of MODIS T-s products, i.e., MOD11A1, MOD11A2, MYD11A1, and MYD11A2 against T-a and to identify the performance of a modified Linear Scaling using a constant and monthly correction factor (LS-MBC), and Quantile Mapping Mean Bias Correction (QM-MBC) methods for lowland area of Peninsular Malaysia. Furthermore, the correction factor (CF) values for each MBC were adjusted according to the condition set depending on the different bias levels. Then, the performance of the pre- and post-MBC correction for by stations and regions analysis were evaluated through root mean square error (RMSE), percentage bias (PBIAS), mean absolute error (MAE), and correlation coefficient (r). The region dataset is obtained by stacking the air temperature (T-a_r) and surface temperature (T-s_r) data corresponding to the number of stations within the identified regions. The assessment of pre-MBC data for both 36 stations and 5 regions demonstrated poor correspondence with high average errors and percentage biases, i.e., RMSE = 3.33-5.42 degrees C, PBIAS = 1.36-12.07%, MAE = 2.88-4.89 degrees C, and r = 0.16-0.29. The application of the MBCs has successfully reduced the errors and bias percentages, and slightly increased the r values for all MODIS T-s products. All post-MBC depicted good average accuracies (RMSE and MAE < 3 degrees C and PBIAS between +/- 5%) and r between 0.18 and 0.31. In detail, for the station analysis, the LS-MBC using monthly CF recorded better performance than the LS-MBC using constant CF or the QM-MBC. For the regional study, the QM-MBC outperformed the others. This study illustrated that the proposed LS-MBC, in spite of its simplicity, managed to perform well in reducing the error and bias terms of MODIS T-s as much as the performance of the more complex QM-MBC method.

Automated summary

MODIS land surface temperature data are measured from the earth's surface via satellites and have discrepancies when compared to air temperature data, but a relationship between the two has been established. By applying different correction methods, such as Linear Scaling and Quantile Mapping Mean Bias Correction, the accuracy of MODIS T-s products can be improved, with different methods performing better in station vs. regional analysis.