Estimating Monthly Surface Air Temperature Using MODIS LST Data and an Artificial Neural Network in the Loess Plateau, China

Air temperature (Ta) datasets with high spatial and temporal resolutions are needed in a wide range of applications, such as hydrology, ecology, agriculture, and climate change studies. Nonetheless, the density of weather station networks is insufficient, especially in sparsely populated regions, greatly limiting the accuracy of estimates of spatially distributed Ta. Due to their continuous spatial coverage, remotely sensed land surface temperature (LST) data provide the possibility of exploring spatial estimates of Ta. However, because of the complex interaction of land and climate, retrieval of Ta from the LST is still far from straightforward. The estimation accuracy varies greatly depending on the model, particularly for maximum Ta. This study estimated monthly average daily minimum temperature (Tmin), average daily maximum temperature (Tmax) and average daily mean temperature (Tmean) over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (MYD11A2) and some auxiliary data using an artificial neural network (ANN) model. The data from 2003 to 2010 were used to train the ANN models, while 2011 to 2012 weather station temperatures were used to test the trained model. The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean, with root mean square errors (RMSEs) of 1.04 & DEG;C and 1.01 & DEG;C, respectively. Moreover, the best RMSE of Tmax estimation was 1.27 & DEG;C. Compared with the other two published Ta gridded datasets, the produced 1 km x 1 km dataset accurately captured both the temporal and spatial patterns of Ta. The RMSE of Tmin estimation was more sensitive to elevation, while that of Tmax was more sensitive to month. Except for land cover type as the input variable, which reduced the RMSE by approximately 0.01 & DEG;C, the other vegetation-related variables did not improve the performance of the model. The results of this study indicated that ANN, a type of machine learning method, is effective for long-term and large-scale Ta estimation.