Image-to-Image Training for Spatially Seamless Air Temperature Estimation With Satellite Images and Station Data

Air temperature at approximately 2 m above the ground ($T_{a}$) is one of the most important environmental and biophysical parameters to study various earth surface processes. $T_{a}$ measured from meteorological stations is inadequate to study its spatio-temporal patterns since the stations are unevenly and sparsely distributed. Satellite-derived land surface temperature (LST) provides global coverage, and is generally utilized to estimate $T_{a}$ due to the close relationship between LST and $T_{a}$. However, LST products are sensitive to cloud contamination, resulting in missing values in LST and leading to the estimated $T_{a}$ being spatially incomplete. To solve the missing data problem, we propose a deep learning method to estimate spatially seamless $T_{a}$ from LST that contains missing values. Experimental results on 5-year data of mainland China illustrate that the image-to-image training strategy alleviates the missing data problem and fills the gaps in LST implicitly. Plus, the strong linear relationships between observed daily mean $T_{a}$ ($T_{\rm{mean}}$), daily minimum $T_{a}$ ($T_{\min}$), and daily maximum $T_{a}$ ($T_{\max}$) make the estimation of $T_{\rm{mean}}$, $T_{\min}$, and $T_{\max}$ simultaneously possible. For mainland China, the proposed method achieves results with $R^{2}$ of 0.962, 0.953, 0.944, mean absolute error (MAE) of 1.793 $^{\circ }$C, 2.143 $^{\circ }$C, and 2.125 $^{\circ }$C, and root-mean-square error (RMSE) of 2.376 $^{\circ }$C, 2.808 $^{\circ }$C, and 2.823 $^{\circ }$C for $T_{\rm{\rm{mean}}}$, $T_{\min}$, and $T_{\max}$, respectively. Our study provides a new paradigm for estimating spatially seamless ground-level parameters from satellite products. Code and more results are available at https://github.com/cvvsu/LSTa.