# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 4 Results
### Long-Term Interannual Variations of LST  
Long-term interannual variations of LST are crucial for the investigation of the LST warming and cooling trends. From the above analyses, we observed that the FY-3D MERSI-II data and the TFSW algorithm performed well for understanding the LST variations throughout Pakistan in 2018 and 2021; the validations results also supported their usefulness. However, the FY-3D MERSI-II is a recently launched satellite; to understand the spatial-temporal variations of LSTs to a broad extent requires the crucial contribution of historical data (i.e., over 10 years). The MODIS MYD11A1 product is fully mature and is a widely used LST product at global scales. To understand the interannual LST variations in Pakistan, we used MODIS LST data from 2005 to 2018. The long-term spatial patterns of LST (2005 and 2018) are like the above mentioned annual mean LST trends of MERSI-II. The long-term annual mean LST varies from \(-29\) to \(55\)\({}^{\circ}\)C across Pakistan (Figure 11). The spatial-temporal variation between 14 annual LST raster datasets were performed to demonstrate the change that occurred in Pakistan through MK trend analysis and Sen's slope estimator, and a line chart was introduced to show a mean LST and histogram for an individual year. Figure 11 shows how the mean annual LST  
Figure 10: The monthly mean variations of LST over different sub-regions of Pakistan during 2018 to 2021. Figure 9: Spatial distribution of mean LSTs of 2018 and 2021 at monthly scales in Pakistan. (\({}^{\circ}\)C) varied across the country over this time. The coldest year (34.16 \({}^{\circ}\)C) was 2012, and the warmest year (35.53 \({}^{\circ}\)C) was 2016. The coldest and warmest years were separated by about 1.37 \({}^{\circ}\)C. Similar to the MERSI-II annual mean LST, with increasing latitude, the MODIS annual LST likewise decreased; it had low values in the northern areas and high values in the southern areas. The MODIS annual LST also decreased with increasing latitude: it had low values in the northern regions and high values in the southern regions. The LST trend for every year (from 2005 to 2018) followed this increasing and decreasing trend throughout Pakistan. The MK trend results show almost similar trends, as we have seen for MERSI-II, but the slopes per year of the annual mean LST are very small, as displayed in Figure 11c,d. The slopes were found to be between \(-\)0.01 and 0.01 \({}^{\circ}\)C/year throughout Pakistan. In regions II and V, there was a considerable increasing tendency at a 5% level of significance, with a slope of 0.0046 and 0.0047 \({}^{\circ}\)C/year. Region I exhibited a considerable decreasing trend of roughly \(-\)0.0024 \({}^{\circ}\)C/year. The various increasing and decreasing trends in other regions of the study are insignificant. The results of the long-term interannual variations of MODIS are consistent with an annual mean LST of the FY-3D MERSI-II data. The positive slope and the positive correlation of the LSTs in regions I and II are directly related to their geographical land cover types, which have already been discussed in the above sections. The greatest interannual differences were found in regions I, II, and V. In contrast, there was less interannual variability in region IV. The greatest interannual disparities may be due to changes in the surfaces' vegetation cover. This is because the thermal characteristics of the Earth's surfaces with high vegetation cover differ from those of the Earth's surface with low vegetation cover. The increase in vegetation cover helps to increase the soil moisture content, which helps to prevent soil erosion and desertification. Surface temperatures in the equatorial and temperate regions are often higher in areas with low vegetation cover than in areas with high vegetation cover. Furthermore, Figure 12 shows the mean LST of individual year and histogram of years between 2005 and 2018, and it was observed that the LST trend was almost same,  
Figure 11: The spatial distribution of the mean LSTs and trend variables.