## 5 Conclusions  
The reliable estimation of spatially distributed Land Surface Temperature (LST) is particularly crucial for monitoring regional surface heat fluxes. For the first time, a feasible single-channel algorithm has been developed to derive spatially distributed LST from the infrared sensor (IRS) onboard the Chinese \"Environmental (HuanJing in Chinese) and disaster monitoring and forecasting with a small satellite constellation\" (HJ-1B) with different ancillary data, _i.e._, the MODIS water vapor content (WVC) product and _in situ_ automatic sun tracking photometer CE318 data. The results show that the retrieved HJ-1B LST data, through use of the MOD05 WVC product, can achieve an accuracy comparable to that of the CE318 data for WVC. The largest LST difference between the MOD05-based LST and CE318-based LST was 0.84 K (associated with a WVC difference of 0.38 cm).  
Additionally, the Standard Deviation (STD), Root Mean Square Error (RMSE), and correlation coefficient (R) of HJ-1B/IRS _vs._ the _in situ_ measurements were 2.45 K, 2.78 K, and 0.67, whereas those for the MODIS 1 km LST _vs._ the _in situ_ measurements were 4.07 K, 2.98 K, and 0.79, respectively.  
The spatial pattern of the HJ-1B/LST over the study area in the Heihe River Basin generally agreed well with the MODIS 1 km LST product and contained more detailed spatial textures. In this study, although the view zenith angle (VZA) difference between HJ-1B and MODIS are expected to be small  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**View Zenith Angle (VZA, Degrees)**} \\ \cline{2-3}
**(Month/Day)** & **HJ-1B/IRS** & **MODIS** \\ \hline
05/16 & 33 & 47 \\
06/19 & 32 & 30 \\
08/22 & 31 & 30 \\
09/02 & 31 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the HJ-1B/IRS and MODIS view zenith angles (VZAs) in the solid square from Figure 6 and Figure 8 in 2012.  
Figure 8: Similar to Figure 7, but for different days: (**a**) 16 May 2012; (**b**) 19 June 2012; and (**c**) 2 September 2012.  
for fully covered croplands, these differences are still important source of bias in LST retrievals and impede the validation between different sensors.  
Low spectral information impedes the accurate atmospheric correction of satellite data because single-channel sensors observes much less information on atmospheric conditions than multi-channel sensors. The proposed single-channel method in this paper is mainly suitable for semi-arid regions with low atmospheric WVC.  
To improve the validation process, future work should be dedicated to conducting careful and quantitative validations between different remotely sensed estimations and should eliminate the sensor attribute differences. Additionally, more surface sensors should be deployed to obtain _in situ_ measurements during a field campaign.  
This work was jointly supported by the National Natural Science Foundation of China under grants 41101326 and 91025004 and project CAS/SAFEA Creative Research Teams (Grant nr. KZZD-EW-TZ-09). The authors would like to sincerely thank the China Center for Resources Satellite Data and Applications for providing the HJ-1B satellite data and the HiWATER program for providing the _in situ_ measurements of the AMS and CE318 data.  
[PERSON] was responsible for the study and the write up of the manuscript. The co-authors provided supervision on manuscript construction and revision, and results analysis throughout the process.