## 3 Results  
This section is divided into three sub-sections, the first and second of which detail the relationship between the LST from Landsat and MODIS, and monthly temperature of the Malaysia Meteorology Department. The third subsection is on the distribution of LST Landsat and MODIS data during ENSO. Relationship between ONI with LST Landsat, LST MODIS, and Monthly Temperature of the Malaysia Meteorology Department  
The correlation coefficient (R\({}^{2}\)) between the ONI and Landsat is 0.71, which is slightly higher than the correlation of 0.68 between the ONI and MODIS surface temperature (Figure 4). However, both correlations indicate a significant relationship between the ONI and the temperature data obtained from both satellite sources. The R\({}^{2}\) values exceeding 0.50 suggest that changes in the ONI correspond to temperature variations captured by both the MODIS and Landsat satellites. Figure 4: The correlation between land surface temperature (LST) derived from Landsat, LST from MODIS, and the Oceanic Niño Index (ONI). The higher R\({}^{2}\) value between the temperatures from the ONI and MODIS satellites compared to that of the Landsat satellites can be attributed to the larger amount of MODIS data available, allowing for a more accurate representation of both variables [5]. The correlation graph in Figure 4 visually demonstrates the positive relationship between the ONI and surface temperature for both the MODIS and Landsat satellites. This indicates that an increase in the ONI is associated with higher surface temperatures recorded by both satellite systems. These findings provide evidence that ENSO, as indicated by the ONI parameters, influences the surface temperature captured by both the MODIS and Landsat satellites. This study utilizes a correlation analysis to accomplish our fourth objective. The validation of data resulting from satellite remote sensing and ground observations is crucial to assess the accuracy and reliability of the remote sensing data in studying the impact of El Nino-Southern Oscillation (ENSO) on the temperature in Kuching City, Sarawak. The purpose of the correlation analysis is to establish a benchmark and evaluate the effectiveness of remote sensing (RS) data in this context. The correlation between the Oceanic Nino Index (ONI), which serves as an indicator of ENSO, and the air temperature for three months is shown in Figure 4. The analysis utilizes 385 monthly average temperature data from the Malaysian Meteorological Department (MMD) and corresponding ONI values from 1988 to 2019. Our expectation is to observe a positive relationship between the RS temperature and the ONI, with a correlation coefficient (R\({}^{2}\)) exceeding 0.5 (R\({}^{2}\) > 0.5). The results presented in Figure 4 demonstrate a strong positive correlation between the ONI and air temperature from the MMD, with an R\({}^{2}\) value of 0.75. This finding indicates that the occurrence of ENSO, as indicated by the ONI, directly influences the air temperature in Kuching City. Specifically, an increase in the ONI is associated with a rise in air temperature, while a decrease in the ONI corresponds to a decrease in air temperature. It is important to note that an ONI value exceeding one signifies the presence of El Nino, which is often accompanied by elevated temperatures and the onset of drought conditions. Therefore, the higher-than-usual temperatures observed in Kuching City, Sarawak, can be attributed to the occurrence of El Nino events. By validating the correlation between the RS temperature and the ONI, this analysis confirms the impact of ENSO on the air temperature in the study area. These findings enhance our understanding of the relationship between ENSO and temperature dynamics, providing valuable insights for climate studies, environmental monitoring, and informing measures to mitigate the impact of El Nino events in Kuching City, Sarawak.