# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 6 Conclusions  
During MERSI-II's operation in orbit, China's satellite remote sensing observation capabilities were increasing. This sensor is the first imaging instrument in the world that can acquire thermal infrared data with a spatial resolution of 250 m and 1000 m. The MERSI-II data can be used to precisely recover the land surface temperature using the recently developed TFSW algorithm. The Moderate Spectral Resolution Atmospheric Transmittance Model simulation dataset served as the source for the SW algorithm coefficients (MODTRAN). In a comparison of the retrieved LST and the MODIS MYD11A1 and MYD21A1 LST product, it was found that the MERSI-II LST is more reliable and achieves a better accuracy in a comparison with MYD11A1.  
In order to study the spatiotemporal changes in LSTs, the FY-3D MERSI-II LST data have never before been employed in Pakistan on this scale. MK trend analysis and Sen's slope estimator were applied to identify significant changes. Due to the limited availability of the MERSI-II data (i.e., 4 years), the MODIS MYD11 AI product was utilized to understand the interannual variations of LSTs in Pakistan. Considering both satellites, these conclusions were noticed: (1) The annual mean LST in Pakistan often follows a pattern with high values in the southern parts and low values in the northern regions, which is consistent with the rule that solar radiation intensity decreases with increasing latitude. However, the spatial distribution of LST is also influenced by elevation and the types of land cover. (2) The spatial distribution of the annual mean LST was almost similar during the study period. The trend analysis shows that the northern areas (region v) have increasing LST trends. The LST changed on average by 0.019 \({}^{\circ}\)C/year, increasing in the northern parts and decreasing by 0.013 \({}^{\circ}\)C/Year in the southern regions. With regard to the sub-regions, a maximum LST were observed in region II, and a greater warming trend were also observed in this region,with a rapidly increasing rate of 0.0046 \({}^{\circ}\)C/year when utilizing long-term data, with the \(p\)-value also being consistent with the slope.  
Due to the influence of the stripe noise in the MERSI-II TIR pictures, the validation of the recovered LST utilized may be a little inaccurate in comparison. Even yet, some in-depth and in situ confirmations are still needed. In order to fully utilize the MERSI-II TIR photos, further work should concentrate on stripe noise removal, as well as confirmation with ground-based stations, particularly in the American regions. Additionally, LST variations are crucial elements that affect and that restrain climate change in various ways. Governmental institutions and groups that are concerned with the environment must therefore pay close attention to changes in the LST, in order to define climate change.  
Conceptualization, B.A. and Z.Q.; methodology, B.A.; software, J.F.; validation, B.A., W.D. and S.L.; formal analysis, B.A.; investigation, Z.Q.; resources, C.Z.; data curation, W.D.; writing--original draft preparation, B.A. and Z.Q.; writing--review and editing, W.D., Z.Q. and C.Z.; visualization, B.A.; supervision, Z.Q. and J.F.; project administration, Z.Q.; funding acquisition, Z.Q. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Key Research and Development Program of China (Grant No.: 2019 YFE0127600), and the National Natural Science Foundation of China (Grant No.: 41771406, 41921001).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.