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
.